diff --git a/2017/03-classificacao-scikit-learn/hands-on-knn-Patrick.ipynb b/2017/03-classificacao-scikit-learn/hands-on-knn-Patrick.ipynb new file mode 100644 index 0000000..870d6f0 --- /dev/null +++ b/2017/03-classificacao-scikit-learn/hands-on-knn-Patrick.ipynb @@ -0,0 +1,522 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Garimpagem de Dados\n", + "\n", + "## Aula 3 - k-Nearest Neighbors\n", + "\n", + "11/10/2017" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn import datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class KNNClassifier(object):\n", + " def __init__(self):\n", + " self.X_train = None\n", + " self.y_train = None\n", + "\n", + " def euc_distance(self, a, b):\n", + " return np.linalg.norm(a-b)\n", + "\n", + " def closest(self, row):\n", + " \"\"\"\n", + " Retorna a classe respondente ao ponto mais próximo do dataset de treino.\\\n", + " É um exemplo de implementação do kNN com k=1.\n", + " \"\"\"\n", + " dists = [self.euc_distance(row, item) for item in self.X_train]\n", + " nei = dists.index(min(dists))\n", + " return self.y_train[nei]\n", + "\n", + " def fit(self, training_data, training_labels):\n", + " self.X_train = training_data\n", + " self.y_train = training_labels\n", + "\n", + " def predict(self, to_classify):\n", + " predictions = []\n", + " for row in to_classify:\n", + " label = self.closest(row)\n", + " predictions.append(label)\n", + " return predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "iris = datasets.load_iris()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['setosa' 'versicolor' 'virginica']\n" + ] + } + ], + "source": [ + "print(iris.target_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X = iris.data\n", + "y = iris.target" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 5.1 3.5 1.4 0.2]\n", + " [ 4.9 3. 1.4 0.2]\n", + " [ 4.7 3.2 1.3 0.2]\n", + " [ 4.6 3.1 1.5 0.2]\n", + " [ 5. 3.6 1.4 0.2]\n", + " [ 5.4 3.9 1.7 0.4]\n", + " [ 4.6 3.4 1.4 0.3]\n", + " [ 5. 3.4 1.5 0.2]\n", + " [ 4.4 2.9 1.4 0.2]\n", + " [ 4.9 3.1 1.5 0.1]\n", + " [ 5.4 3.7 1.5 0.2]\n", + " [ 4.8 3.4 1.6 0.2]\n", + " [ 4.8 3. 1.4 0.1]\n", + " [ 4.3 3. 1.1 0.1]\n", + " [ 5.8 4. 1.2 0.2]\n", + " [ 5.7 4.4 1.5 0.4]\n", + " [ 5.4 3.9 1.3 0.4]\n", + " [ 5.1 3.5 1.4 0.3]\n", + " [ 5.7 3.8 1.7 0.3]\n", + " [ 5.1 3.8 1.5 0.3]\n", + " [ 5.4 3.4 1.7 0.2]\n", + " [ 5.1 3.7 1.5 0.4]\n", + " [ 4.6 3.6 1. 0.2]\n", + " [ 5.1 3.3 1.7 0.5]\n", + " [ 4.8 3.4 1.9 0.2]\n", + " [ 5. 3. 1.6 0.2]\n", + " [ 5. 3.4 1.6 0.4]\n", + " [ 5.2 3.5 1.5 0.2]\n", + " [ 5.2 3.4 1.4 0.2]\n", + " [ 4.7 3.2 1.6 0.2]\n", + " [ 4.8 3.1 1.6 0.2]\n", + " [ 5.4 3.4 1.5 0.4]\n", + " [ 5.2 4.1 1.5 0.1]\n", + " [ 5.5 4.2 1.4 0.2]\n", + " [ 4.9 3.1 1.5 0.1]\n", + " [ 5. 3.2 1.2 0.2]\n", + " [ 5.5 3.5 1.3 0.2]\n", + " [ 4.9 3.1 1.5 0.1]\n", + " [ 4.4 3. 1.3 0.2]\n", + " [ 5.1 3.4 1.5 0.2]\n", + " [ 5. 3.5 1.3 0.3]\n", + " [ 4.5 2.3 1.3 0.3]\n", + " [ 4.4 3.2 1.3 0.2]\n", + " [ 5. 3.5 1.6 0.6]\n", + " [ 5.1 3.8 1.9 0.4]\n", + " [ 4.8 3. 1.4 0.3]\n", + " [ 5.1 3.8 1.6 0.2]\n", + " [ 4.6 3.2 1.4 0.2]\n", + " [ 5.3 3.7 1.5 0.2]\n", + " [ 5. 3.3 1.4 0.2]\n", + " [ 7. 3.2 4.7 1.4]\n", + " [ 6.4 3.2 4.5 1.5]\n", + " [ 6.9 3.1 4.9 1.5]\n", + " [ 5.5 2.3 4. 1.3]\n", + " [ 6.5 2.8 4.6 1.5]\n", + " [ 5.7 2.8 4.5 1.3]\n", + " [ 6.3 3.3 4.7 1.6]\n", + " [ 4.9 2.4 3.3 1. ]\n", + " [ 6.6 2.9 4.6 1.3]\n", + " [ 5.2 2.7 3.9 1.4]\n", + " [ 5. 2. 3.5 1. ]\n", + " [ 5.9 3. 4.2 1.5]\n", + " [ 6. 2.2 4. 1. ]\n", + " [ 6.1 2.9 4.7 1.4]\n", + " [ 5.6 2.9 3.6 1.3]\n", + " [ 6.7 3.1 4.4 1.4]\n", + " [ 5.6 3. 4.5 1.5]\n", + " [ 5.8 2.7 4.1 1. ]\n", + " [ 6.2 2.2 4.5 1.5]\n", + " [ 5.6 2.5 3.9 1.1]\n", + " [ 5.9 3.2 4.8 1.8]\n", + " [ 6.1 2.8 4. 1.3]\n", + " [ 6.3 2.5 4.9 1.5]\n", + " [ 6.1 2.8 4.7 1.2]\n", + " [ 6.4 2.9 4.3 1.3]\n", + " [ 6.6 3. 4.4 1.4]\n", + " [ 6.8 2.8 4.8 1.4]\n", + " [ 6.7 3. 5. 1.7]\n", + " [ 6. 2.9 4.5 1.5]\n", + " [ 5.7 2.6 3.5 1. ]\n", + " [ 5.5 2.4 3.8 1.1]\n", + " [ 5.5 2.4 3.7 1. ]\n", + " [ 5.8 2.7 3.9 1.2]\n", + " [ 6. 2.7 5.1 1.6]\n", + " [ 5.4 3. 4.5 1.5]\n", + " [ 6. 3.4 4.5 1.6]\n", + " [ 6.7 3.1 4.7 1.5]\n", + " [ 6.3 2.3 4.4 1.3]\n", + " [ 5.6 3. 4.1 1.3]\n", + " [ 5.5 2.5 4. 1.3]\n", + " [ 5.5 2.6 4.4 1.2]\n", + " [ 6.1 3. 4.6 1.4]\n", + " [ 5.8 2.6 4. 1.2]\n", + " [ 5. 2.3 3.3 1. ]\n", + " [ 5.6 2.7 4.2 1.3]\n", + " [ 5.7 3. 4.2 1.2]\n", + " [ 5.7 2.9 4.2 1.3]\n", + " [ 6.2 2.9 4.3 1.3]\n", + " [ 5.1 2.5 3. 1.1]\n", + " [ 5.7 2.8 4.1 1.3]\n", + " [ 6.3 3.3 6. 2.5]\n", + " [ 5.8 2.7 5.1 1.9]\n", + " [ 7.1 3. 5.9 2.1]\n", + " [ 6.3 2.9 5.6 1.8]\n", + " [ 6.5 3. 5.8 2.2]\n", + " [ 7.6 3. 6.6 2.1]\n", + " [ 4.9 2.5 4.5 1.7]\n", + " [ 7.3 2.9 6.3 1.8]\n", + " [ 6.7 2.5 5.8 1.8]\n", + " [ 7.2 3.6 6.1 2.5]\n", + " [ 6.5 3.2 5.1 2. ]\n", + " [ 6.4 2.7 5.3 1.9]\n", + " [ 6.8 3. 5.5 2.1]\n", + " [ 5.7 2.5 5. 2. ]\n", + " [ 5.8 2.8 5.1 2.4]\n", + " [ 6.4 3.2 5.3 2.3]\n", + " [ 6.5 3. 5.5 1.8]\n", + " [ 7.7 3.8 6.7 2.2]\n", + " [ 7.7 2.6 6.9 2.3]\n", + " [ 6. 2.2 5. 1.5]\n", + " [ 6.9 3.2 5.7 2.3]\n", + " [ 5.6 2.8 4.9 2. ]\n", + " [ 7.7 2.8 6.7 2. ]\n", + " [ 6.3 2.7 4.9 1.8]\n", + " [ 6.7 3.3 5.7 2.1]\n", + " [ 7.2 3.2 6. 1.8]\n", + " [ 6.2 2.8 4.8 1.8]\n", + " [ 6.1 3. 4.9 1.8]\n", + " [ 6.4 2.8 5.6 2.1]\n", + " [ 7.2 3. 5.8 1.6]\n", + " [ 7.4 2.8 6.1 1.9]\n", + " [ 7.9 3.8 6.4 2. ]\n", + " [ 6.4 2.8 5.6 2.2]\n", + " [ 6.3 2.8 5.1 1.5]\n", + " [ 6.1 2.6 5.6 1.4]\n", + " [ 7.7 3. 6.1 2.3]\n", + " [ 6.3 3.4 5.6 2.4]\n", + " [ 6.4 3.1 5.5 1.8]\n", + " [ 6. 3. 4.8 1.8]\n", + " [ 6.9 3.1 5.4 2.1]\n", + " [ 6.7 3.1 5.6 2.4]\n", + " [ 6.9 3.1 5.1 2.3]\n", + " [ 5.8 2.7 5.1 1.9]\n", + " [ 6.8 3.2 5.9 2.3]\n", + " [ 6.7 3.3 5.7 2.5]\n", + " [ 6.7 3. 5.2 2.3]\n", + " [ 6.3 2.5 5. 1.9]\n", + " [ 6.5 3. 5.2 2. ]\n", + " [ 6.2 3.4 5.4 2.3]\n", + " [ 5.9 3. 5.1 1.8]]\n", + "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", + " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2]\n", + "600\n" + ] + } + ], + "source": [ + "print(X)\n", + "print(y)\n", + "print(X.size)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "knn = KNNClassifier()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "knn.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "result = knn.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, 1, 2, 2, 0, 0, 2, 2, 1, 1, 1, 2, 1, 2, 0, 2, 1, 2, 1, 1, 1, 1, 2, 0, 1, 1, 1, 0, 2, 2, 2, 2, 1, 0, 2, 0, 0, 1, 0, 1, 0, 1, 2, 2]\n" + ] + } + ], + "source": [ + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 1 1 2 2 0 0 2 2 1 1 1 2 1 2 0 2 1 2 2 1 1 1 2 0 1 2 1 0 2 2 2 2 1 0 2 0\n", + " 0 1 0 1 0 1 2 2]\n" + ] + } + ], + "source": [ + "print(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "score = metrics.accuracy_score(y_pred=result, y_true=y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.955556\n" + ] + } + ], + "source": [ + "print('{0:f}'.format(score))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "knn1 = KNeighborsClassifier(n_neighbors=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", + " metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n", + " weights='uniform')" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn1.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "result1 = knn1.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "score1 = metrics.accuracy_score(result1, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.955556\n" + ] + } + ], + "source": [ + "print('{0:f}'.format(score1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2017/04-knn-exercicio/knn_Patrick.ipynb b/2017/04-knn-exercicio/knn_Patrick.ipynb new file mode 100644 index 0000000..48f3ae9 --- /dev/null +++ b/2017/04-knn-exercicio/knn_Patrick.ipynb @@ -0,0 +1,2249 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Garimpagem de Dados\n", + "\n", + "## Aula 4 - Exercício de Classificação com kNN\n", + "\n", + "13/10/2017\n", + "\n", + "Patrick Anderson Moreira Marcelino" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Dataset:** Titanic: Machine Learning from Disaster\n", + "\n", + "https://www.kaggle.com/c/titanic/data\n", + "\n", + "Partindo da aula passada:\n", + "\n", + "1. Atualizar a função que mede a distância euclidiana para o pacote do scikit-learn \n", + "\n", + "2. Implementar uma função que selecione os k vizinhos mais próximos (k > 1)\n", + "\n", + "3. Implementar uma função que recebe os k vizinhos mais próximos e determinar a classe correta\n", + "\n", + "4. Transformar as features categoricas em numéricas (tip: pandas ou scikit-learn)\n", + "\n", + "5. Analisar a necessidade de normalizar as features numéricas (tip: pandas ou scikit-learn)\n", + "\n", + "6. Selecionar as features baseada na correlação (tip: pandas)\n", + "\n", + "7. Separar o dataset em treino (75%) / teste (25%) / validação (10% do treino)\n", + "\n", + "4. Execute o classificador para 30 k's pulando de 4 em 4 e apresente todas as acurácias utilizando o dataset de validação (Qual o melhor k?) [plotar um gráfico com os resultados]\n", + "\n", + "5. Executar o classificador para o melhor k encontrado utilizando o dataset de teste e apresentar um relatório da precisão (tip: scikit-learn) [plotar um gráfico com os resultados]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets\n", + "from sklearn.neighbors import DistanceMetric\n", + "from collections import Counter\n", + "from operator import itemgetter\n", + "from sklearn import metrics\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class KNNClassifier(object):\n", + " def __init__(self):\n", + " self.train_data = None\n", + " self.train_labels = None\n", + " \n", + " \n", + " def fit(self, train_d, train_l):\n", + " self.train_data = train_d\n", + " self.train_labels = train_l\n", + " \n", + " \n", + " def euc_distance(self, a, b):\n", + " dist = DistanceMetric.get_metric('euclidean') # utilizando a distância euclidiana do scikit\n", + " return dist.pairwise([a], [b])\n", + "\n", + " # Encontrando os k vizinhos mais próximos (k > 1)\n", + " def closests(self, k, test):\n", + " distances = []\n", + " for i in range(len(self.train_data)):\n", + " dist = self.euc_distance(test, self.train_data[i])\n", + " distances.append((self.train_data[i], dist, self.train_labels[i]))\n", + " distances.sort(key = itemgetter(1))\n", + " neighbors = distances[:k]\n", + " return neighbors\n", + " \n", + " # Faz a eleição dos k vizinhos mais próximos, determinando a classe \n", + " def election(self, neighbors):\n", + " class_counter = Counter()\n", + " for neighbor in neighbors:\n", + " class_counter[neighbor[2]] += 1\n", + " return class_counter.most_common(1)[0][0]\n", + " \n", + " def predict(self, k, test):\n", + " votes = []\n", + " for i in range(len(test)):\n", + " neighbors = self.closests(k, test[i])\n", + " v = self.election(neighbors)\n", + " votes.append(v)\n", + " return votes" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
101113Sandstrom, Miss. Marguerite Rutfemale4.011PP 954916.7000G6S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
121303Saundercock, Mr. William Henrymale20.000A/5. 21518.0500NaNS
131403Andersson, Mr. Anders Johanmale39.01534708231.2750NaNS
141503Vestrom, Miss. Hulda Amanda Adolfinafemale14.0003504067.8542NaNS
151612Hewlett, Mrs. (Mary D Kingcome)female55.00024870616.0000NaNS
161703Rice, Master. Eugenemale2.04138265229.1250NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.0000NaNS
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...female31.01034576318.0000NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.2250NaNC
202102Fynney, Mr. Joseph Jmale35.00023986526.0000NaNS
212212Beesley, Mr. Lawrencemale34.00024869813.0000D56S
222313McGowan, Miss. Anna \"Annie\"female15.0003309238.0292NaNQ
232411Sloper, Mr. William Thompsonmale28.00011378835.5000A6S
242503Palsson, Miss. Torborg Danirafemale8.03134990921.0750NaNS
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female38.01534707731.3875NaNS
262703Emir, Mr. Farred ChehabmaleNaN0026317.2250NaNC
272801Fortune, Mr. Charles Alexandermale19.03219950263.0000C23 C25 C27S
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.8792NaNQ
293003Todoroff, Mr. LaliomaleNaN003492167.8958NaNS
.......................................
86186202Giles, Mr. Frederick Edwardmale21.0102813411.5000NaNS
86286311Swift, Mrs. Frederick Joel (Margaret Welles Ba...female48.0001746625.9292D17S
86386403Sage, Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
86486502Gill, Mr. John Williammale24.00023386613.0000NaNS
86586612Bystrom, Mrs. (Karolina)female42.00023685213.0000NaNS
86686712Duran y More, Miss. Asuncionfemale27.010SC/PARIS 214913.8583NaNC
86786801Roebling, Mr. Washington Augustus IImale31.000PC 1759050.4958A24S
86886903van Melkebeke, Mr. PhilemonmaleNaN003457779.5000NaNS
86987013Johnson, Master. Harold Theodormale4.01134774211.1333NaNS
87087103Balkic, Mr. Cerinmale26.0003492487.8958NaNS
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47.0111175152.5542D35S
87287301Carlsson, Mr. Frans Olofmale33.0006955.0000B51 B53 B55S
87387403Vander Cruyssen, Mr. Victormale47.0003457659.0000NaNS
87487512Abelson, Mrs. Samuel (Hannah Wizosky)female28.010P/PP 338124.0000NaNC
87587613Najib, Miss. Adele Kiamie \"Jane\"female15.00026677.2250NaNC
87687703Gustafsson, Mr. Alfred Ossianmale20.00075349.8458NaNS
87787803Petroff, Mr. Nedeliomale19.0003492127.8958NaNS
87887903Laleff, Mr. KristomaleNaN003492177.8958NaNS
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)female56.0011176783.1583C50C
88088112Shelley, Mrs. William (Imanita Parrish Hall)female25.00123043326.0000NaNS
88188203Markun, Mr. Johannmale33.0003492577.8958NaNS
88288303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167NaNS
88388402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000NaNS
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500NaNS
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
\n", + "

891 rows × 12 columns

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Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "5 Moran, Mr. James male NaN 0 \n", + "6 McCarthy, Mr. Timothy J male 54.0 0 \n", + "7 Palsson, Master. Gosta Leonard male 2.0 3 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n", + "10 Sandstrom, Miss. Marguerite Rut female 4.0 1 \n", + "11 Bonnell, Miss. Elizabeth female 58.0 0 \n", + "12 Saundercock, Mr. William Henry male 20.0 0 \n", + "13 Andersson, Mr. Anders Johan male 39.0 1 \n", + "14 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 \n", + "15 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 \n", + "16 Rice, Master. Eugene male 2.0 4 \n", + "17 Williams, Mr. Charles Eugene male NaN 0 \n", + "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1 \n", + "19 Masselmani, Mrs. Fatima female NaN 0 \n", + "20 Fynney, Mr. Joseph J male 35.0 0 \n", + "21 Beesley, Mr. Lawrence male 34.0 0 \n", + "22 McGowan, Miss. Anna \"Annie\" female 15.0 0 \n", + "23 Sloper, Mr. William Thompson male 28.0 0 \n", + "24 Palsson, Miss. Torborg Danira female 8.0 3 \n", + "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1 \n", + "26 Emir, Mr. Farred Chehab male NaN 0 \n", + "27 Fortune, Mr. Charles Alexander male 19.0 3 \n", + "28 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 \n", + "29 Todoroff, Mr. Lalio male NaN 0 \n", + ".. ... ... ... ... \n", + "861 Giles, Mr. Frederick Edward male 21.0 1 \n", + "862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0 \n", + "863 Sage, Miss. Dorothy Edith \"Dolly\" female NaN 8 \n", + "864 Gill, Mr. John William male 24.0 0 \n", + "865 Bystrom, Mrs. (Karolina) female 42.0 0 \n", + "866 Duran y More, Miss. Asuncion female 27.0 1 \n", + "867 Roebling, Mr. Washington Augustus II male 31.0 0 \n", + "868 van Melkebeke, Mr. Philemon male NaN 0 \n", + "869 Johnson, Master. Harold Theodor male 4.0 1 \n", + "870 Balkic, Mr. Cerin male 26.0 0 \n", + "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 \n", + "872 Carlsson, Mr. Frans Olof male 33.0 0 \n", + "873 Vander Cruyssen, Mr. Victor male 47.0 0 \n", + "874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1 \n", + "875 Najib, Miss. Adele Kiamie \"Jane\" female 15.0 0 \n", + "876 Gustafsson, Mr. Alfred Ossian male 20.0 0 \n", + "877 Petroff, Mr. Nedelio male 19.0 0 \n", + "878 Laleff, Mr. Kristo male NaN 0 \n", + "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 \n", + "880 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 \n", + "881 Markun, Mr. Johann male 33.0 0 \n", + "882 Dahlberg, Miss. Gerda Ulrika female 22.0 0 \n", + "883 Banfield, Mr. Frederick James male 28.0 0 \n", + "884 Sutehall, Mr. Henry Jr male 25.0 0 \n", + "885 Rice, Mrs. William (Margaret Norton) female 39.0 0 \n", + "886 Montvila, Rev. Juozas male 27.0 0 \n", + "887 Graham, Miss. Margaret Edith female 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", + "889 Behr, Mr. Karl Howell male 26.0 0 \n", + "890 Dooley, Mr. Patrick male 32.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n", + "5 0 330877 8.4583 NaN Q \n", + "6 0 17463 51.8625 E46 S \n", + "7 1 349909 21.0750 NaN S \n", + "8 2 347742 11.1333 NaN S \n", + "9 0 237736 30.0708 NaN C \n", + "10 1 PP 9549 16.7000 G6 S \n", + "11 0 113783 26.5500 C103 S \n", + "12 0 A/5. 2151 8.0500 NaN S \n", + "13 5 347082 31.2750 NaN S \n", + "14 0 350406 7.8542 NaN S \n", + "15 0 248706 16.0000 NaN S \n", + "16 1 382652 29.1250 NaN Q \n", + "17 0 244373 13.0000 NaN S \n", + "18 0 345763 18.0000 NaN S \n", + "19 0 2649 7.2250 NaN C \n", + "20 0 239865 26.0000 NaN S \n", + "21 0 248698 13.0000 D56 S \n", + "22 0 330923 8.0292 NaN Q \n", + "23 0 113788 35.5000 A6 S \n", + "24 1 349909 21.0750 NaN S \n", + "25 5 347077 31.3875 NaN S \n", + "26 0 2631 7.2250 NaN C \n", + "27 2 19950 263.0000 C23 C25 C27 S \n", + "28 0 330959 7.8792 NaN Q \n", + "29 0 349216 7.8958 NaN S \n", + ".. ... ... ... ... ... \n", + "861 0 28134 11.5000 NaN S \n", + "862 0 17466 25.9292 D17 S \n", + "863 2 CA. 2343 69.5500 NaN S \n", + "864 0 233866 13.0000 NaN S \n", + "865 0 236852 13.0000 NaN S \n", + "866 0 SC/PARIS 2149 13.8583 NaN C \n", + "867 0 PC 17590 50.4958 A24 S \n", + "868 0 345777 9.5000 NaN S \n", + "869 1 347742 11.1333 NaN S \n", + "870 0 349248 7.8958 NaN S \n", + "871 1 11751 52.5542 D35 S \n", + "872 0 695 5.0000 B51 B53 B55 S \n", + "873 0 345765 9.0000 NaN S \n", + "874 0 P/PP 3381 24.0000 NaN C \n", + "875 0 2667 7.2250 NaN C \n", + "876 0 7534 9.8458 NaN S \n", + "877 0 349212 7.8958 NaN S \n", + "878 0 349217 7.8958 NaN S \n", + "879 1 11767 83.1583 C50 C \n", + "880 1 230433 26.0000 NaN S \n", + "881 0 349257 7.8958 NaN S \n", + "882 0 7552 10.5167 NaN S \n", + "883 0 C.A./SOTON 34068 10.5000 NaN S \n", + "884 0 SOTON/OQ 392076 7.0500 NaN S \n", + "885 5 382652 29.1250 NaN Q \n", + "886 0 211536 13.0000 NaN S \n", + "887 0 112053 30.0000 B42 S \n", + "888 2 W./C. 6607 23.4500 NaN S \n", + "889 0 111369 30.0000 C148 C \n", + "890 0 370376 7.7500 NaN Q \n", + "\n", + "[891 rows x 12 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Carrega o dataset\n", + "dataset = pd.read_csv(\"train.csv\")\n", + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500SCS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250SCS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500SCS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 SC S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 SC S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 SC S " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Dados faltantes (NaN)\n", + "\n", + "dataset.Age = dataset.Age.fillna(dataset.Age.mean()) # utilizada a média das idades\n", + "dataset.Cabin = dataset.Cabin.fillna('SC')\n", + "\n", + "dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare \\\n", + "0 1 0 3 108 1 22.0 1 0 523 18 \n", + "1 2 1 1 190 0 38.0 1 0 596 207 \n", + "2 3 1 3 353 0 26.0 0 0 669 41 \n", + "3 4 1 1 272 0 35.0 1 0 49 189 \n", + "4 5 0 3 15 1 35.0 0 0 472 43 \n", + "\n", + " Cabin Embarked \n", + "0 146 2 \n", + "1 81 0 \n", + "2 146 2 \n", + "3 55 2 \n", + "4 146 2 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Transformando dados categóricos em numéricos\n", + "columns = ['Name','Sex','SibSp','Parch','Ticket','Fare','Cabin','Embarked']\n", + "datasetc = dataset\n", + "for c in columns:\n", + " datasetc[c] = dataset[c].astype('category')\n", + " datasetc[c] = datasetc[c].cat.codes\n", + "datasetc.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
00.0000000.01.00.1213481.00.2711740.1666670.00.7691180.0728740.9931971.000000
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name Sex Age SibSp Parch \\\n", + "0 0.000000 0.0 1.0 0.121348 1.0 0.271174 0.166667 0.0 \n", + "1 0.001124 1.0 0.0 0.213483 0.0 0.472229 0.166667 0.0 \n", + "2 0.002247 1.0 1.0 0.396629 0.0 0.321438 0.000000 0.0 \n", + "3 0.003371 1.0 0.0 0.305618 0.0 0.434531 0.166667 0.0 \n", + "4 0.004494 0.0 1.0 0.016854 1.0 0.434531 0.000000 0.0 \n", + "\n", + " Ticket Fare Cabin Embarked \n", + "0 0.769118 0.072874 0.993197 1.000000 \n", + "1 0.876471 0.838057 0.551020 0.333333 \n", + "2 0.983824 0.165992 0.993197 1.000000 \n", + "3 0.072059 0.765182 0.374150 1.000000 \n", + "4 0.694118 0.174089 0.993197 1.000000 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Normalizando os atributos numéricos\n", + "from sklearn import preprocessing\n", + "min_max_scaler = preprocessing.MinMaxScaler()\n", + "dataset_scaled = min_max_scaler.fit_transform(dataset)\n", + "datasetn = pd.DataFrame(dataset_scaled)\n", + "datasetn.columns = ['PassengerId','Survived','Pclass','Name','Sex','Age','SibSp','Parch','Ticket','Fare','Cabin','Embarked']\n", + "datasetn.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId1.000000-0.005007-0.035144-0.0385590.0429390.033207-0.065229-0.001652-0.056554-0.006390-0.0351970.012985
Survived-0.0050071.000000-0.338481-0.057343-0.543351-0.069809-0.0263850.081629-0.1645490.333943-0.253658-0.176509
Pclass-0.035144-0.3384811.0000000.0528310.131900-0.3313390.0781410.0184430.319869-0.7241190.6821760.173511
Name-0.038559-0.0573430.0528311.0000000.0203140.057466-0.035535-0.0491050.047348-0.0538460.062119-0.010633
Sex0.042939-0.5433510.1319000.0203141.0000000.084153-0.123164-0.2454890.059372-0.2653890.0959910.118492
Age0.033207-0.069809-0.3313390.0574660.0841531.000000-0.254997-0.179191-0.0688480.110296-0.234912-0.039610
SibSp-0.065229-0.0263850.078141-0.035535-0.123164-0.2549971.0000000.4233380.0692380.3686880.0406870.069165
Parch-0.0016520.0816290.018443-0.049105-0.245489-0.1791910.4233381.0000000.0200030.361243-0.0281790.043351
Ticket-0.056554-0.1645490.3198690.0473480.059372-0.0688480.0692380.0200031.000000-0.1681530.2430820.011146
Fare-0.0063900.333943-0.724119-0.053846-0.2653890.1102960.3686880.361243-0.1681531.000000-0.538549-0.169849
Cabin-0.035197-0.2536580.6821760.0621190.095991-0.2349120.040687-0.0281790.243082-0.5385491.0000000.226137
Embarked0.012985-0.1765090.173511-0.0106330.118492-0.0396100.0691650.0433510.011146-0.1698490.2261371.000000
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name Sex Age \\\n", + "PassengerId 1.000000 -0.005007 -0.035144 -0.038559 0.042939 0.033207 \n", + "Survived -0.005007 1.000000 -0.338481 -0.057343 -0.543351 -0.069809 \n", + "Pclass -0.035144 -0.338481 1.000000 0.052831 0.131900 -0.331339 \n", + "Name -0.038559 -0.057343 0.052831 1.000000 0.020314 0.057466 \n", + "Sex 0.042939 -0.543351 0.131900 0.020314 1.000000 0.084153 \n", + "Age 0.033207 -0.069809 -0.331339 0.057466 0.084153 1.000000 \n", + "SibSp -0.065229 -0.026385 0.078141 -0.035535 -0.123164 -0.254997 \n", + "Parch -0.001652 0.081629 0.018443 -0.049105 -0.245489 -0.179191 \n", + "Ticket -0.056554 -0.164549 0.319869 0.047348 0.059372 -0.068848 \n", + "Fare -0.006390 0.333943 -0.724119 -0.053846 -0.265389 0.110296 \n", + "Cabin -0.035197 -0.253658 0.682176 0.062119 0.095991 -0.234912 \n", + "Embarked 0.012985 -0.176509 0.173511 -0.010633 0.118492 -0.039610 \n", + "\n", + " SibSp Parch Ticket Fare Cabin Embarked \n", + "PassengerId -0.065229 -0.001652 -0.056554 -0.006390 -0.035197 0.012985 \n", + "Survived -0.026385 0.081629 -0.164549 0.333943 -0.253658 -0.176509 \n", + "Pclass 0.078141 0.018443 0.319869 -0.724119 0.682176 0.173511 \n", + "Name -0.035535 -0.049105 0.047348 -0.053846 0.062119 -0.010633 \n", + "Sex -0.123164 -0.245489 0.059372 -0.265389 0.095991 0.118492 \n", + "Age -0.254997 -0.179191 -0.068848 0.110296 -0.234912 -0.039610 \n", + "SibSp 1.000000 0.423338 0.069238 0.368688 0.040687 0.069165 \n", + "Parch 0.423338 1.000000 0.020003 0.361243 -0.028179 0.043351 \n", + "Ticket 0.069238 0.020003 1.000000 -0.168153 0.243082 0.011146 \n", + "Fare 0.368688 0.361243 -0.168153 1.000000 -0.538549 -0.169849 \n", + "Cabin 0.040687 -0.028179 0.243082 -0.538549 1.000000 0.226137 \n", + "Embarked 0.069165 0.043351 0.011146 -0.169849 0.226137 1.000000 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Selecionando as features baseado na correlação \n", + "datasetn.corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Survived Pclass Sex Fare\n", + "0 0.0 1.0 1.0 0.072874\n", + "1 1.0 0.0 0.0 0.838057\n", + "2 1.0 1.0 0.0 0.165992\n", + "3 1.0 0.0 0.0 0.765182\n", + "4 0.0 1.0 1.0 0.174089" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Descartando as features que não servem pra predição\n", + "datasetd = datasetn.drop(['PassengerId','SibSp','Embarked','Age','Name','Parch','Ticket','Cabin'], axis=1)\n", + "datasetd.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Split do dataset em treino (75%) / teste (25%) / validação (10% do treino)\n", + "\n", + "y = datasetd.Survived.values\n", + "X = datasetd.drop('Survived', axis=1).values\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)\n", + "X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a_accuracy = []\n", + "ks = []\n", + "\n", + "for k in range(3,30,4): # k variando de 3 a 30, de 4 em 4\n", + " knn = KNNClassifier()\n", + " knn.fit(X_train, y_train) # treinamento do modelo\n", + " result = knn.predict(k, X_test) # resultado\n", + " accuracy = metrics.accuracy_score(result,y_test)\n", + " a_accuracy.append(accuracy)\n", + " ks.append(k)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mxtMhMc7Ok9RA9u4jXPb0l/xpdhZDOiYw957hXDWoDRG2FxL0xqWlsHnvUZZ+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\n+N/evpjMfSf4+w1nE+Hi1PV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(ks, a_accuracy);\n", + "plt.grid()\n", + "plt.title('acurácia x nº de k\\'s')\n", + "plt.xlabel('k')\n", + "plt.ylabel(u'Acurácia')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Acurácia: 0.80269058296 \n", + "Índice: 6 \n", + "Melhor k: 27\n" + ] + } + ], + "source": [ + "# Encontrar melhor acurácia\n", + "index, best_accuracy = max(enumerate(a_accuracy))\n", + "print('Acurácia:', best_accuracy,\n", + " '\\nÍndice:',index,\n", + " '\\nMelhor k:',ks[index])" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.776119\n" + ] + } + ], + "source": [ + "knn_best = KNNClassifier()\n", + "knn_best.fit(X_train, y_train)\n", + "result = knn.predict(ks[index], X_validation)\n", + "accuracy = metrics.accuracy_score(result,y_validation)\n", + "print('{0:f}'.format(accuracy))" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " Survived 0.77 0.93 0.84 43\n", + "Not Survived 0.80 0.50 0.62 24\n", + "\n", + " avg / total 0.78 0.78 0.76 67\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report\n", + "print(classification_report(y_validation, result, target_names=['Survived', 'Not Survived']))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2017/05-naive-bayes/Naive_Bayes_Trabalho_Patrick.ipynb b/2017/05-naive-bayes/Naive_Bayes_Trabalho_Patrick.ipynb new file mode 100644 index 0000000..6eddee9 --- /dev/null +++ b/2017/05-naive-bayes/Naive_Bayes_Trabalho_Patrick.ipynb @@ -0,0 +1,410 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Naive Bayes - Trabalho\n", + "\n", + "## Questão 1\n", + "\n", + "Implemente um classifador Naive Bayes para o problema de predizer a qualidade de um carro. Para este fim, utilizaremos um conjunto de dados referente a qualidade de carros, disponível no [UCI](https://archive.ics.uci.edu/ml/datasets/car+evaluation). Este dataset de carros possui as seguintes features e classe:\n", + "\n", + "** Attributos **\n", + "1. buying: vhigh, high, med, low\n", + "2. maint: vhigh, high, med, low\n", + "3. doors: 2, 3, 4, 5, more\n", + "4. persons: 2, 4, more\n", + "5. lug_boot: small, med, big\n", + "6. safety: low, med, high\n", + "\n", + "** Classes **\n", + "1. unacc, acc, good, vgood\n", + "\n", + "## Questão 2\n", + "Crie uma versão de sua implementação usando as funções disponíveis na biblioteca SciKitLearn para o Naive Bayes ([veja aqui](http://scikit-learn.org/stable/modules/naive_bayes.html)) \n", + "\n", + "## Questão 3\n", + "\n", + "Analise a acurácia dos dois algoritmos e discuta a sua solução." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.naive_bayes import MultinomialNB, GaussianNB\n", + "from sklearn.cross_validation import train_test_split\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.metrics import accuracy_score, classification_report" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class NaiveBayes:\n", + " def __init__(self):\n", + " self.lEncoder = LabelEncoder()\n", + " self.X = None; self.y = None\n", + " self.classProb = None; self.likeTable = {}\n", + " \n", + " # Separa o dataset pelas classes\n", + " def separateByClass(self):\n", + " separated = {}\n", + " for i in range(len(self.y)):\n", + " if (self.y[i] not in separated):\n", + " separated[self.y[i]] = []\n", + " separated[self.y[i]].append(self.X[i])\n", + " return separated\n", + " \n", + " # Contrói a tabela de verossimilhança\n", + " def makeLikeTable(self):\n", + " separateClass = self.separateByClass()\n", + " classSizes = [len(separateClass[i]) for i in separateClass.keys()] \n", + " self.classProb = np.array(classSizes) / sum(classSizes)\n", + " \n", + " self.likeTable = {}\n", + " for label in separateClass.keys():\n", + " auxiliar = np.column_stack(separateClass[label])\n", + " for attribute,idx in zip(auxiliar, range(len(auxiliar))):\n", + " counts = np.asarray(np.unique(attribute, return_counts=True)).T\n", + " for i in range(4):\n", + " self.likeTable[(label, idx, i)] = 0\n", + " for count_it in counts:\n", + " self.likeTable[(label, idx, count_it[0])] = count_it[1] / len(separateClass[label])\n", + "\n", + " # Calcula a probabilidade de cada entrada, através da MLE\n", + " def calculateProbability(self, inputVector):\n", + " separateClass = self.separateByClass()\n", + " \n", + " probabilities = {}\n", + " for label,_ in separateClass.items():\n", + " probabilities[label] = self.classProb[label]\n", + " for i in range(len(inputVector)):\n", + " probabilities[label] *= self.likeTable[label, i, inputVector[i]]\n", + " \n", + " return probabilities\n", + " \n", + " # Treinamento, com criação da tabela de verossimilhança\n", + " def fit(self, X_train, y_train):\n", + " self.X = X_train\n", + " self.y = y_train\n", + " \n", + " self.makeLikeTable()\n", + " \n", + " # Calcula as probabilidades preditas\n", + " def predict(self, inputArray):\n", + " ''' Return a list of predictions for each row in inputArray correspondent to\n", + " the label of the class with the maximum probability '''\n", + " predictions = []\n", + " for row in inputArray:\n", + " probabilities = self.calculateProbability(row)\n", + " predictions.append(max(probabilities, key=probabilities.get))\n", + " return predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " vhigh vhigh.1 2 2.1 small low unacc\n", + "0 vhigh vhigh 2 2 small med unacc\n", + "1 vhigh vhigh 2 2 small high unacc\n", + "2 vhigh vhigh 2 2 med low unacc\n", + "3 vhigh vhigh 2 2 med med unacc\n", + "4 vhigh vhigh 2 2 med high unacc" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = pd.read_csv(\"carData.csv\")\n", + "dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1727\n", + "1381\n", + "346\n" + ] + } + ], + "source": [ + "for i in range(0, dataset.shape[1]):\n", + " dataset.iloc[:,i] = LabelEncoder().fit_transform(dataset.iloc[:,i])\n", + "\n", + "# Split do dataset em 80% para treino e 20% para teste\n", + "X_train, X_test, y_train, y_test = train_test_split(dataset.iloc[:,:-1], dataset.iloc[:,-1], test_size=0.2)\n", + "\n", + "print(len(dataset))\n", + "print(len(X_train))\n", + "print(len(X_test))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "#Implementação do sklearn\n", + "mult = MultinomialNB()\n", + "mult.fit(X_train.values,y_train.values)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2]\n" + ] + } + ], + "source": [ + "prediction = mult.predict(X_test.values)\n", + "print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score, classification_report" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.658959537572\n" + ] + } + ], + "source": [ + "accuracy_mult = accuracy_score(y_true = y_test,y_pred = prediction)\n", + "print(accuracy_mult)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Naive Bayes:\n", + "Total Accuracy: 0.7369942196531792%\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " unacc 0.49 0.93 0.64 85\n", + " acc 0.47 0.53 0.50 17\n", + " good 1.00 0.73 0.85 228\n", + " vgood 0.00 0.00 0.00 16\n", + "\n", + "avg / total 0.80 0.74 0.74 346\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/patrick/miniconda3/envs/datascience/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n", + " 'precision', 'predicted', average, warn_for)\n" + ] + } + ], + "source": [ + "# Utilização da classe Naive Bayes criada\n", + "myNaive = NaiveBayes()\n", + "myNaive.fit(X_train.values, y_train.values)\n", + "y_pred = myNaive.predict(X_test.values)\n", + "\n", + "print(\"Naive Bayes:\")\n", + "print(\"Total Accuracy: {}%\".format(accuracy_score(y_true=y_test, y_pred=y_pred)))\n", + "\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_true=y_test, y_pred=y_pred, target_names=[\"unacc\", \"acc\", \"good\", \"vgood\"]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2017/06-linear-regression/Linear_Regression_Trabalho-Patrick.ipynb b/2017/06-linear-regression/Linear_Regression_Trabalho-Patrick.ipynb new file mode 100644 index 0000000..203c89d --- /dev/null +++ b/2017/06-linear-regression/Linear_Regression_Trabalho-Patrick.ipynb @@ -0,0 +1,347 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Regressão Linear Simples - Trabalho\n", + "\n", + "## Estudo de caso: Seguro de automóvel sueco\n", + "\n", + "Agora, sabemos como implementar um modelo de regressão linear simples. Vamos aplicá-lo ao conjunto de dados do seguro de automóveis sueco. Esta seção assume que você baixou o conjunto de dados para o arquivo insurance.csv, o qual está disponível no notebook respectivo.\n", + "\n", + "O conjunto de dados envolve a previsão do pagamento total de todas as reclamações em milhares de Kronor sueco, dado o número total de reclamações. É um dataset composto por 63 observações com 1 variável de entrada e 1 variável de saída. Os nomes das variáveis são os seguintes:\n", + "\n", + "1. Número de reivindicações.\n", + "2. Pagamento total para todas as reclamações em milhares de Kronor sueco.\n", + "\n", + "Voce deve adicionar algumas funções acessórias à regressão linear simples. Especificamente, uma função para carregar o arquivo CSV chamado *load_csv ()*, uma função para converter um conjunto de dados carregado para números chamado *str_column_to_float ()*, uma função para avaliar um algoritmo usando um conjunto de treino e teste chamado *split_train_split ()*, a função para calcular RMSE chamado *rmse_metric ()* e uma função para avaliar um algoritmo chamado *evaluate_algorithm()*.\n", + "\n", + "Utilize um conjunto de dados de treinamento de 60% dos dados para preparar o modelo. As previsões devem ser feitas nos restantes 40%. \n", + "\n", + "Compare a performabce do seu algoritmo com o algoritmo baseline, o qual utiliza a média dos pagamentos realizados para realizar a predição ( a média é 72,251 mil Kronor).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.metrics import mean_squared_error\n", + "from random import randrange\n", + "from csv import reader\n", + "from math import sqrt" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the mean value of a list of numbers\n", + "def mean(values):\n", + " return sum(values) / float(len(values))\n", + "\n", + "# Calculate the variance of a list of numbers\n", + "def variance(values, mean):\n", + " return sum([(x-mean)**2 for x in values])\n", + "\n", + "# Calculate covariance between x and y\n", + "def covariance(x, mean_x, y, mean_y):\n", + " covar = 0.0\n", + " for i in range(len(x)):\n", + " covar += (x[i] - mean_x) * (y[i] - mean_y)\n", + " return covar\n", + "\n", + "# Calculate coefficients\n", + "def coefficients(dataset):\n", + " x = [row[0] for row in dataset]\n", + " y = [row[1] for row in dataset]\n", + " x_mean, y_mean = mean(x), mean(y)\n", + " b1 = covariance(x, x_mean, y, y_mean) / variance(x, x_mean)\n", + " b0 = y_mean - b1 * x_mean\n", + " return [b0, b1]\n", + "\n", + "def str_column_to_float(dataset):\n", + " for row in range(len(dataset)):\n", + " for column in range(len(dataset[row])):\n", + " dataset[row][column] = float(dataset[row][column])\n", + " \n", + "def load_csv(filename):\n", + " dataset = list()\n", + " with open(filename, 'r') as file:\n", + " csv_reader = reader(file)\n", + " for row in csv_reader:\n", + " if not row:\n", + " continue\n", + " dataset.append(row)\n", + " return dataset\n", + "\n", + "\n", + "def train_test_split(dataset, splitRatio):\n", + " train = list()\n", + " train_size = splitRatio * len(dataset)\n", + " dataset_copy = list(dataset)\n", + " while len(train) < train_size:\n", + " index = randrange(len(dataset_copy))\n", + " train.append(dataset_copy.pop(index))\n", + " return train, dataset_copy\n", + "\n", + "def rmse_metric(actual, predicted):\n", + " sum_error = 0.0\n", + " for i in range(len(actual)):\n", + " prediction_error = predicted[i] - actual[i]\n", + " sum_error += (prediction_error ** 2)\n", + " mean_error = sum_error / float(len(actual))\n", + " return sqrt(mean_error)\n", + "\n", + "def simple_linear_regression(train, test):\n", + " predictions = list()\n", + " b1, b0 = coefficients(train)\n", + " for row in test:\n", + " yhat = b1 + b0 * row[0]\n", + " predictions.append(yhat)\n", + " return predictions\n", + "\n", + "def evaluate_algorithm(dataset, algorithm, splitRatio, *args):\n", + " train, test = train_test_split(dataset, splitRatio)\n", + " test_set = list()\n", + " for row in test:\n", + " row_copy = list(row)\n", + " row_copy[-1] = None\n", + " test_set.append(row_copy)\n", + " predicted = algorithm(train, test_set, *args)\n", + " actual = [row[-1] for row in test]\n", + " rmse = rmse_metric(actual, predicted)\n", + " return rmse" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[['108', '392.5'],\n", + " ['19', '46.2'],\n", + " ['13', '15.7'],\n", + " ['124', '422.2'],\n", + " ['40', '119.4'],\n", + " ['57', '170.9'],\n", + " ['23', '56.9'],\n", + " ['14', '77.5'],\n", + " ['45', '214'],\n", + " ['10', '65.3'],\n", + " ['5', '20.9'],\n", + " ['48', '248.1'],\n", + " ['11', '23.5'],\n", + " ['23', '39.6'],\n", + " ['7', '48.8'],\n", + " ['2', '6.6'],\n", + " ['24', '134.9'],\n", + " ['6', '50.9'],\n", + " ['3', '4.4'],\n", + " ['23', '113'],\n", + " ['6', '14.8'],\n", + " ['9', '48.7'],\n", + " ['9', '52.1'],\n", + " ['3', '13.2'],\n", + " ['29', '103.9'],\n", + " ['7', '77.5'],\n", + " ['4', '11.8'],\n", + " ['20', '98.1'],\n", + " ['7', '27.9'],\n", + " ['4', '38.1'],\n", + " ['0', '0'],\n", + " ['25', '69.2'],\n", + " ['6', '14.6'],\n", + " ['5', '40.3'],\n", + " ['22', '161.5'],\n", + " ['11', '57.2'],\n", + " ['61', '217.6'],\n", + " ['12', '58.1'],\n", + " ['4', '12.6'],\n", + " ['16', '59.6'],\n", + " ['13', '89.9'],\n", + " ['60', '202.4'],\n", + " ['41', '181.3'],\n", + " ['37', '152.8'],\n", + " ['55', '162.8'],\n", + " ['41', '73.4'],\n", + " ['11', '21.3'],\n", + " ['27', '92.6'],\n", + " ['8', '76.1'],\n", + " ['3', '39.9'],\n", + " ['17', '142.1'],\n", + " ['13', '93'],\n", + " ['13', '31.9'],\n", + " ['15', '32.1'],\n", + " ['8', '55.6'],\n", + " ['29', '133.3'],\n", + " ['30', '194.5'],\n", + " ['24', '137.9'],\n", + " ['9', '87.4'],\n", + " ['31', '209.8'],\n", + " ['14', '95.5'],\n", + " ['53', '244.6'],\n", + " ['26', '187.5']]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = load_csv('insurance.csv')\n", + "dataset \n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "str_column_to_float(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[108.0, 392.5],\n", + " [19.0, 46.2],\n", + " [13.0, 15.7],\n", + " [124.0, 422.2],\n", + " [40.0, 119.4],\n", + " [57.0, 170.9],\n", + " [23.0, 56.9],\n", + " [14.0, 77.5],\n", + " [45.0, 214.0],\n", + " [10.0, 65.3],\n", + " [5.0, 20.9],\n", + " [48.0, 248.1],\n", + " [11.0, 23.5],\n", + " [23.0, 39.6],\n", + " [7.0, 48.8],\n", + " [2.0, 6.6],\n", + " [24.0, 134.9],\n", + " [6.0, 50.9],\n", + " [3.0, 4.4],\n", + " [23.0, 113.0],\n", + " [6.0, 14.8],\n", + " [9.0, 48.7],\n", + " [9.0, 52.1],\n", + " [3.0, 13.2],\n", + " [29.0, 103.9],\n", + " [7.0, 77.5],\n", + " [4.0, 11.8],\n", + " [20.0, 98.1],\n", + " [7.0, 27.9],\n", + " [4.0, 38.1],\n", + " [0.0, 0.0],\n", + " [25.0, 69.2],\n", + " [6.0, 14.6],\n", + " [5.0, 40.3],\n", + " [22.0, 161.5],\n", + " [11.0, 57.2],\n", + " [61.0, 217.6],\n", + " [12.0, 58.1],\n", + " [4.0, 12.6],\n", + " [16.0, 59.6],\n", + " [13.0, 89.9],\n", + " [60.0, 202.4],\n", + " [41.0, 181.3],\n", + " [37.0, 152.8],\n", + " [55.0, 162.8],\n", + " [41.0, 73.4],\n", + " [11.0, 21.3],\n", + " [27.0, 92.6],\n", + " [8.0, 76.1],\n", + " [3.0, 39.9],\n", + " [17.0, 142.1],\n", + " [13.0, 93.0],\n", + " [13.0, 31.9],\n", + " [15.0, 32.1],\n", + " [8.0, 55.6],\n", + " [29.0, 133.3],\n", + " [30.0, 194.5],\n", + " [24.0, 137.9],\n", + " [9.0, 87.4],\n", + " [31.0, 209.8],\n", + " [14.0, 95.5],\n", + " [53.0, 244.6],\n", + " [26.0, 187.5]]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 28.024\n" + ] + } + ], + "source": [ + "rmse = evaluate_algorithm(dataset, simple_linear_regression, 0.6)\n", + "print('RMSE: %.3f' % (rmse))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2017/06-linear-regression/Multivariate_Regression_Trabalho-Patrick.ipynb b/2017/06-linear-regression/Multivariate_Regression_Trabalho-Patrick.ipynb new file mode 100644 index 0000000..5f93c69 --- /dev/null +++ b/2017/06-linear-regression/Multivariate_Regression_Trabalho-Patrick.ipynb @@ -0,0 +1,14685 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Regressão Linear Multivariada - Trabalho\n", + "\n", + "## Estudo de caso: Qualidade de Vinhos\n", + "\n", + "Nesta trabalho, treinaremos um modelo de regressão linear usando descendência de gradiente estocástico no conjunto de dados da Qualidade do Vinho. O exemplo pressupõe que uma cópia CSV do conjunto de dados está no diretório de trabalho atual com o nome do arquivo *winequality-white.csv*.\n", + "\n", + "O conjunto de dados de qualidade do vinho envolve a previsão da qualidade dos vinhos brancos em uma escala, com medidas químicas de cada vinho. É um problema de classificação multiclasse, mas também pode ser enquadrado como um problema de regressão. O número de observações para cada classe não é equilibrado. Existem 4.898 observações com 11 variáveis de entrada e 1 variável de saída. Os nomes das variáveis são os seguintes:\n", + "\n", + "1. Fixed acidity.\n", + "2. Volatile acidity.\n", + "3. Citric acid.\n", + "4. Residual sugar.\n", + "5. Chlorides.\n", + "6. Free sulfur dioxide. \n", + "7. Total sulfur dioxide. \n", + "8. Density.\n", + "9. pH.\n", + "10. Sulphates.\n", + "11. Alcohol.\n", + "12. Quality (score between 0 and 10).\n", + "\n", + "O desempenho de referencia de predição do valor médio é um RMSE de aproximadamente 0.148 pontos de qualidade.\n", + "\n", + "Utilize o exemplo apresentado no tutorial e altere-o de forma a carregar os dados e analisar a acurácia de sua solução. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from math import sqrt\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import preprocessing\n", + "from random import randrange" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import csv\n", + " \n", + "def loadCsv(filename):\n", + " lines = csv.reader(open(filename, \"r\"), delimiter=';')\n", + " dataset = list(lines)[1:]\n", + " for i in range(len(dataset)):\n", + " dataset[i] = [float(x) for x in dataset[i]]\n", + " return dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "winequality-white.csv\n", + "Arquivo carregado winequality-white.csv com 4898 linhas\n" + ] + }, + { + "data": { + "text/plain": [ + "[[7.0, 0.27, 0.36, 20.7, 0.045, 45.0, 170.0, 1.001, 3.0, 0.45, 8.8, 6.0],\n", + " [6.3, 0.3, 0.34, 1.6, 0.049, 14.0, 132.0, 0.994, 3.3, 0.49, 9.5, 6.0],\n", + " [8.1, 0.28, 0.4, 6.9, 0.05, 30.0, 97.0, 0.9951, 3.26, 0.44, 10.1, 6.0],\n", + " [7.2, 0.23, 0.32, 8.5, 0.058, 47.0, 186.0, 0.9956, 3.19, 0.4, 9.9, 6.0],\n", + " [7.2, 0.23, 0.32, 8.5, 0.058, 47.0, 186.0, 0.9956, 3.19, 0.4, 9.9, 6.0],\n", + " [8.1, 0.28, 0.4, 6.9, 0.05, 30.0, 97.0, 0.9951, 3.26, 0.44, 10.1, 6.0],\n", + " [6.2, 0.32, 0.16, 7.0, 0.045, 30.0, 136.0, 0.9949, 3.18, 0.47, 9.6, 6.0],\n", + " [7.0, 0.27, 0.36, 20.7, 0.045, 45.0, 170.0, 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32.0, 155.0, 0.9951, 3.39, 0.62, 10.7, 7.0],\n", + " [5.1, 0.39, 0.21, 1.7, 0.027, 15.0, 72.0, 0.9894, 3.5, 0.45, 12.5, 6.0],\n", + " [5.7, 0.36, 0.34, 4.2, 0.026, 21.0, 77.0, 0.9907, 3.41, 0.45, 11.9, 6.0],\n", + " [6.9, 0.19, 0.33, 1.6, 0.043, 63.0, 149.0, 0.9925, 3.44, 0.52, 10.8, 5.0],\n", + " [6.0, 0.41, 0.21, 1.9, 0.05, 29.0, 122.0, 0.9928, 3.42, 0.52, 10.5, 6.0],\n", + " [7.4, 0.28, 0.3, 5.3, 0.054, 44.0, 161.0, 0.9941, 3.12, 0.48, 10.3, 6.0],\n", + " [7.4, 0.3, 0.3, 5.2, 0.053, 45.0, 163.0, 0.9941, 3.12, 0.45, 10.3, 6.0],\n", + " [6.9, 0.19, 0.33, 1.6, 0.043, 63.0, 149.0, 0.9925, 3.44, 0.52, 10.8, 5.0],\n", + " [7.7, 0.28, 0.39, 8.9, 0.036, 8.0, 117.0, 0.9935, 3.06, 0.38, 12.0, 7.0],\n", + " [8.6, 0.16, 0.38, 3.4, 0.04, 41.0, 143.0, 0.9932, 2.95, 0.39, 10.2, 6.0],\n", + " [8.2, 0.26, 0.44, 1.3, 0.046, 7.0, 69.0, 0.9944, 3.14, 0.62, 10.2, 4.0],\n", + " [6.5, 0.25, 0.27, 15.2, 0.049, 75.0, 217.0, 0.9972, 3.19, 0.39, 9.9, 5.0],\n", + " [7.0, 0.24, 0.18, 1.3, 0.046, 9.0, 62.0, 0.994, 3.38, 0.47, 10.1, 4.0],\n", + " [8.6, 0.18, 0.36, 1.8, 0.04, 24.0, 187.0, 0.9956, 3.25, 0.55, 9.5, 6.0],\n", + " [7.8, 0.27, 0.34, 1.6, 0.046, 27.0, 154.0, 0.9927, 3.05, 0.45, 10.5, 6.0],\n", + " [6.0, 0.26, 0.34, 1.3, 0.046, 6.0, 29.0, 0.9924, 3.29, 0.63, 10.4, 5.0],\n", + " [6.1, 0.24, 0.27, 9.8, 0.062, 33.0, 152.0, 0.9966, 3.31, 0.47, 9.5, 6.0],\n", + " [8.0, 0.24, 0.3, 17.45, 0.056, 43.0, 184.0, 0.9997, 3.05, 0.5, 9.2, 6.0],\n", + " [7.6, 0.21, 0.6, 2.1, 0.046, 47.0, 165.0, 0.9936, 3.05, 0.54, 10.1, 7.0],\n", + " ...]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filename = 'winequality-white.csv'\n", + "print(filename)\n", + "dataset = loadCsv(filename)\n", + "print('Arquivo carregado', filename, 'com', len(dataset), 'linhas')\n", + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 \\\n", + "0 0.307692 0.186275 0.216867 0.308282 0.106825 0.149826 0.373550 \n", + "1 0.240385 0.215686 0.204819 0.015337 0.118694 0.041812 0.285383 \n", + "2 0.413462 0.196078 0.240964 0.096626 0.121662 0.097561 0.204176 \n", + "3 0.326923 0.147059 0.192771 0.121166 0.145401 0.156794 0.410673 \n", + "4 0.326923 0.147059 0.192771 0.121166 0.145401 0.156794 0.410673 \n", + "\n", + " 7 8 9 10 11 \n", + "0 0.267785 0.254545 0.267442 0.129032 0.5 \n", + "1 0.132832 0.527273 0.313953 0.241935 0.5 \n", + "2 0.154039 0.490909 0.255814 0.338710 0.5 \n", + "3 0.163678 0.427273 0.209302 0.306452 0.5 \n", + "4 0.163678 0.427273 0.209302 0.306452 0.5 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Normalização\n", + "min_max_scaler = preprocessing.MinMaxScaler()\n", + "dataset_scaled = min_max_scaler.fit_transform(dataset)\n", + "data = pd.DataFrame(dataset_scaled)\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Esperado=0.500, Predito=1.243, Erro=0.743\n", + "Esperado=0.500, Predito=1.332, Erro=0.832\n", + "Esperado=0.500, Predito=1.332, Erro=0.832\n", + "Esperado=0.500, Predito=1.201, Erro=0.701\n", + "Esperado=0.500, Predito=1.201, Erro=0.701\n", + "Esperado=0.500, Predito=1.332, Erro=0.832\n", + "Esperado=0.500, Predito=1.363, Erro=0.863\n", + "Esperado=0.500, Predito=1.243, Erro=0.743\n", + "Esperado=0.500, Predito=1.332, Erro=0.832\n", + "Esperado=0.500, Predito=1.287, Erro=0.787\n", + "Esperado=0.333, Predito=1.481, Erro=1.147\n", + "Esperado=0.333, Predito=1.254, Erro=0.920\n", + "Esperado=0.333, Predito=1.302, Erro=0.969\n", + "Esperado=0.667, Predito=1.351, Erro=0.685\n", + "Esperado=0.333, Predito=1.730, Erro=1.397\n", + "Esperado=0.667, Predito=1.275, Erro=0.608\n", + "Esperado=0.500, Predito=1.554, Erro=1.054\n", + "Esperado=0.833, Predito=2.129, Erro=1.296\n", + "Esperado=0.500, Predito=1.545, Erro=1.045\n", + "Esperado=0.333, Predito=1.369, Erro=1.035\n", + "Esperado=0.833, Predito=2.129, Erro=1.296\n", + "Esperado=0.667, Predito=1.340, Erro=0.673\n", + "Esperado=0.833, Predito=1.354, Erro=0.521\n", + "Esperado=0.333, Predito=1.972, Erro=1.639\n", + "Esperado=0.500, Predito=1.328, Erro=0.828\n", + "Esperado=0.500, Predito=1.350, Erro=0.850\n", + "Esperado=0.500, Predito=1.248, Erro=0.748\n", + "Esperado=0.500, Predito=1.442, Erro=0.942\n", + "Esperado=0.500, Predito=1.425, Erro=0.925\n", + "Esperado=0.667, Predito=1.710, Erro=1.044\n", + "Esperado=0.500, Predito=1.327, Erro=0.827\n", + "Esperado=0.500, Predito=1.059, Erro=0.559\n", + "Esperado=0.500, Predito=1.299, Erro=0.799\n", + "Esperado=0.500, Predito=1.006, Erro=0.506\n", + "Esperado=0.333, Predito=1.280, Erro=0.946\n", + "Esperado=0.333, Predito=1.492, Erro=1.159\n", + "Esperado=0.333, Predito=1.430, Erro=1.096\n", + "Esperado=0.500, Predito=1.346, Erro=0.846\n", + "Esperado=0.333, Predito=1.125, Erro=0.791\n", + "Esperado=0.333, Predito=1.125, Erro=0.791\n", + "Esperado=0.500, Predito=1.138, Erro=0.638\n", + "Esperado=0.500, Predito=1.141, Erro=0.641\n", + "Esperado=0.500, Predito=1.363, Erro=0.863\n", + "Esperado=0.500, Predito=1.272, Erro=0.772\n", + "Esperado=0.500, Predito=1.257, Erro=0.757\n", + "Esperado=0.667, Predito=1.184, Erro=0.517\n", + "Esperado=0.167, Predito=1.659, Erro=1.492\n", + "Esperado=0.333, Predito=1.674, Erro=1.341\n", + "Esperado=0.500, Predito=1.363, Erro=0.863\n", + "Esperado=0.333, Predito=1.183, Erro=0.850\n", + "Esperado=0.500, Predito=1.280, Erro=0.780\n", + "Esperado=0.667, Predito=1.424, Erro=0.758\n", + "Esperado=0.667, Predito=1.159, Erro=0.492\n", + "Esperado=0.500, Predito=1.121, Erro=0.621\n", + "Esperado=0.500, Predito=1.051, Erro=0.551\n", + "Esperado=0.500, Predito=1.351, Erro=0.851\n", + "Esperado=0.500, Predito=1.202, Erro=0.702\n", + "Esperado=0.500, Predito=1.038, Erro=0.538\n", + "Esperado=0.500, Predito=1.432, Erro=0.932\n", + "Esperado=0.500, Predito=1.090, Erro=0.590\n", + "Esperado=0.500, Predito=1.100, Erro=0.600\n", + "Esperado=0.500, Predito=1.038, Erro=0.538\n", + "Esperado=0.333, Predito=1.559, Erro=1.225\n", + "Esperado=0.500, Predito=1.432, Erro=0.932\n", + "Esperado=0.500, Predito=1.253, Erro=0.753\n", + "Esperado=0.333, Predito=1.417, Erro=1.083\n", + "Esperado=0.667, Predito=1.480, Erro=0.813\n", + "Esperado=0.333, Predito=1.209, Erro=0.876\n", + "Esperado=0.833, Predito=1.408, Erro=0.575\n", + "Esperado=0.333, Predito=1.141, Erro=0.808\n", + "Esperado=0.500, Predito=1.263, Erro=0.763\n", + "Esperado=0.333, Predito=1.443, Erro=1.109\n", + "Esperado=0.333, Predito=1.536, Erro=1.202\n", + "Esperado=0.500, Predito=1.156, Erro=0.656\n", + "Esperado=0.833, Predito=1.408, Erro=0.575\n", + "Esperado=0.333, Predito=1.141, Erro=0.808\n", + "Esperado=0.667, Predito=1.266, Erro=0.599\n", + "Esperado=0.667, Predito=1.663, Erro=0.996\n", + "Esperado=0.333, Predito=1.152, Erro=0.819\n", + "Esperado=0.333, Predito=1.644, Erro=1.310\n", + "Esperado=0.500, Predito=1.484, Erro=0.984\n", + "Esperado=0.500, Predito=1.257, Erro=0.757\n", + "Esperado=0.333, Predito=1.406, Erro=1.073\n", + "Esperado=0.500, Predito=1.209, Erro=0.709\n", + "Esperado=0.333, Predito=1.500, Erro=1.167\n", + "Esperado=0.500, Predito=1.525, Erro=1.025\n", + "Esperado=0.500, Predito=1.421, Erro=0.921\n", + "Esperado=0.500, Predito=1.296, Erro=0.796\n", + "Esperado=0.333, Predito=1.500, Erro=1.167\n", + "Esperado=0.500, Predito=1.525, Erro=1.025\n", + "Esperado=0.500, Predito=1.421, Erro=0.921\n", + "Esperado=0.333, Predito=1.241, Erro=0.907\n", + "Esperado=0.667, Predito=1.389, Erro=0.722\n", + "Esperado=0.667, Predito=1.341, Erro=0.675\n", + "Esperado=0.667, Predito=1.218, Erro=0.551\n", + "Esperado=0.500, Predito=1.210, Erro=0.710\n", + "Esperado=0.500, Predito=1.379, Erro=0.879\n", + "Esperado=0.667, Predito=1.288, Erro=0.621\n", + "Esperado=0.167, Predito=1.344, Erro=1.178\n", + "Esperado=0.500, Predito=1.379, Erro=0.879\n", + "Esperado=0.333, Predito=1.202, Erro=0.869\n", + "Esperado=0.333, Predito=1.001, Erro=0.668\n", + "Esperado=0.333, Predito=1.138, Erro=0.805\n", + "Esperado=0.333, Predito=1.324, Erro=0.991\n", + "Esperado=0.333, Predito=1.202, Erro=0.869\n", + "Esperado=0.500, Predito=0.945, Erro=0.445\n", + "Esperado=0.333, Predito=1.001, Erro=0.668\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.333, Predito=1.524, Erro=1.191\n", + "Esperado=0.500, Predito=0.983, Erro=0.483\n", + "Esperado=0.333, Predito=1.299, Erro=0.965\n", + "Esperado=0.333, Predito=1.347, Erro=1.014\n", + "Esperado=0.333, Predito=1.532, Erro=1.199\n", + "Esperado=0.333, Predito=1.532, Erro=1.199\n", + "Esperado=0.167, Predito=1.638, Erro=1.471\n", + "Esperado=0.500, Predito=1.441, Erro=0.941\n", + "Esperado=0.500, Predito=1.155, Erro=0.655\n", + "Esperado=0.333, Predito=1.347, Erro=1.014\n", + "Esperado=0.333, Predito=1.386, Erro=1.053\n", + "Esperado=0.333, Predito=1.496, Erro=1.162\n", + "Esperado=0.333, Predito=1.234, Erro=0.901\n", + "Esperado=0.333, Predito=1.020, Erro=0.687\n", + "Esperado=0.500, Predito=1.030, Erro=0.530\n", + "Esperado=0.500, Predito=0.968, Erro=0.468\n", + "Esperado=0.500, Predito=1.239, Erro=0.739\n", + "Esperado=0.333, Predito=1.516, Erro=1.183\n", + "Esperado=0.667, Predito=1.317, Erro=0.650\n", + "Esperado=0.667, Predito=1.499, Erro=0.832\n", + "Esperado=0.500, Predito=1.500, Erro=1.000\n", + "Esperado=0.333, Predito=1.266, Erro=0.932\n", + "Esperado=0.667, Predito=1.317, Erro=0.650\n", + "Esperado=0.333, Predito=1.611, Erro=1.277\n", + "Esperado=0.333, Predito=1.258, Erro=0.924\n", + "Esperado=0.333, Predito=1.272, Erro=0.939\n", + "Esperado=0.333, Predito=1.377, Erro=1.044\n", + "Esperado=0.500, Predito=1.208, Erro=0.708\n", + "Esperado=0.333, Predito=1.391, Erro=1.057\n", + "Esperado=0.667, Predito=1.250, Erro=0.584\n", + "Esperado=0.500, Predito=1.489, Erro=0.989\n", + "Esperado=0.333, Predito=1.355, Erro=1.022\n", + "Esperado=0.333, Predito=1.391, Erro=1.057\n", + "Esperado=0.500, Predito=1.302, Erro=0.802\n", + "Esperado=0.500, Predito=1.224, Erro=0.724\n", + "Esperado=0.500, Predito=1.304, Erro=0.804\n", + "Esperado=0.500, Predito=1.246, Erro=0.746\n", + "Esperado=0.500, Predito=1.038, Erro=0.538\n", + "Esperado=0.167, Predito=1.741, Erro=1.574\n", + "Esperado=0.667, Predito=1.656, Erro=0.989\n", + "Esperado=0.500, Predito=1.139, Erro=0.639\n", + "Esperado=0.667, Predito=1.232, Erro=0.566\n", + "Esperado=0.500, Predito=1.144, Erro=0.644\n", + "Esperado=0.500, Predito=1.139, Erro=0.639\n", + "Esperado=0.333, Predito=1.147, Erro=0.813\n", + "Esperado=0.500, Predito=1.478, Erro=0.978\n", + "Esperado=0.500, Predito=1.148, Erro=0.648\n", + "Esperado=0.500, Predito=1.148, Erro=0.648\n", + "Esperado=0.667, Predito=1.522, Erro=0.855\n", + "Esperado=0.833, Predito=1.646, Erro=0.813\n", + "Esperado=0.833, Predito=1.646, Erro=0.813\n", + "Esperado=0.667, Predito=1.522, Erro=0.855\n", + "Esperado=0.333, Predito=1.111, Erro=0.778\n", + "Esperado=0.333, Predito=1.450, Erro=1.116\n", + "Esperado=0.500, Predito=1.148, Erro=0.648\n", + "Esperado=0.333, Predito=1.266, Erro=0.933\n", + "Esperado=0.333, Predito=1.272, Erro=0.939\n", + "Esperado=0.500, Predito=1.391, Erro=0.891\n", + "Esperado=0.667, Predito=1.777, Erro=1.110\n", + "Esperado=0.333, Predito=1.347, Erro=1.014\n", + "Esperado=0.333, Predito=1.434, Erro=1.101\n", + "Esperado=0.500, Predito=1.596, Erro=1.096\n", + "Esperado=0.500, Predito=1.164, Erro=0.664\n", + "Esperado=0.167, Predito=1.679, Erro=1.513\n", + "Esperado=0.667, Predito=1.233, Erro=0.566\n", + "Esperado=0.333, Predito=1.479, Erro=1.146\n", + "Esperado=0.500, Predito=1.196, Erro=0.696\n", + "Esperado=0.167, Predito=1.521, Erro=1.354\n", + "Esperado=0.333, Predito=1.172, Erro=0.838\n", + "Esperado=0.167, Predito=1.796, Erro=1.630\n", + "Esperado=0.500, Predito=1.361, Erro=0.861\n", + "Esperado=0.500, Predito=1.497, Erro=0.997\n", + "Esperado=0.333, Predito=1.333, Erro=1.000\n", + "Esperado=0.333, Predito=1.282, Erro=0.949\n", + "Esperado=0.500, Predito=1.395, Erro=0.895\n", + "Esperado=0.333, Predito=1.364, Erro=1.031\n", + "Esperado=0.333, Predito=1.419, Erro=1.086\n", + "Esperado=0.500, Predito=1.352, Erro=0.852\n", + "Esperado=0.333, Predito=1.439, Erro=1.105\n", + "Esperado=0.833, Predito=2.026, Erro=1.192\n", + "Esperado=0.167, Predito=1.298, Erro=1.131\n", + "Esperado=0.500, Predito=1.307, Erro=0.807\n", + "Esperado=0.333, Predito=1.282, Erro=0.949\n", + "Esperado=0.500, Predito=1.340, Erro=0.840\n", + "Esperado=0.333, Predito=1.044, Erro=0.711\n", + "Esperado=0.333, Predito=1.260, Erro=0.926\n", + "Esperado=0.500, Predito=1.263, Erro=0.763\n", + "Esperado=0.333, Predito=1.237, Erro=0.904\n", + "Esperado=0.333, Predito=1.218, Erro=0.885\n", + "Esperado=0.333, Predito=1.345, Erro=1.011\n", + "Esperado=0.333, Predito=1.218, Erro=0.885\n", + "Esperado=0.333, Predito=1.312, Erro=0.979\n", + "Esperado=0.333, Predito=1.345, Erro=1.011\n", + "Esperado=0.333, Predito=1.808, Erro=1.475\n", + "Esperado=0.500, Predito=1.513, Erro=1.013\n", + "Esperado=0.167, Predito=1.352, Erro=1.185\n", + "Esperado=0.333, Predito=1.100, Erro=0.767\n", + "Esperado=0.333, Predito=0.975, Erro=0.641\n", + "Esperado=0.167, Predito=1.645, Erro=1.478\n", + "Esperado=0.333, Predito=2.040, Erro=1.707\n", + "Esperado=0.500, Predito=1.690, Erro=1.190\n", + "Esperado=0.333, Predito=1.207, Erro=0.873\n", + "Esperado=0.667, Predito=1.318, Erro=0.652\n", + "Esperado=0.333, Predito=0.975, Erro=0.641\n", + "Esperado=0.500, Predito=0.989, Erro=0.489\n", + "Esperado=0.667, Predito=1.317, Erro=0.651\n", + "Esperado=0.333, Predito=1.206, Erro=0.873\n", + "Esperado=0.333, Predito=1.278, Erro=0.945\n", + "Esperado=0.333, Predito=1.206, Erro=0.873\n", + "Esperado=0.333, Predito=1.310, Erro=0.977\n", + "Esperado=0.333, Predito=1.200, Erro=0.867\n", + "Esperado=0.333, Predito=1.278, Erro=0.945\n", + "Esperado=0.500, Predito=2.036, Erro=1.536\n", + "Esperado=0.667, Predito=1.317, Erro=0.651\n", + "Esperado=0.500, Predito=1.049, Erro=0.549\n", + "Esperado=0.500, Predito=1.787, Erro=1.287\n", + "Esperado=0.333, Predito=1.157, Erro=0.824\n", + "Esperado=0.500, Predito=1.152, Erro=0.652\n", + "Esperado=0.500, Predito=1.266, Erro=0.766\n", + "Esperado=0.500, Predito=1.152, Erro=0.652\n", + "Esperado=0.333, Predito=1.157, Erro=0.824\n", + "Esperado=0.167, Predito=1.905, Erro=1.739\n", + "Esperado=0.500, Predito=1.369, Erro=0.869\n", + "Esperado=0.500, Predito=1.343, Erro=0.843\n", + "Esperado=0.500, Predito=1.170, Erro=0.670\n", + "Esperado=0.500, Predito=1.170, Erro=0.670\n", + "Esperado=0.500, Predito=1.170, Erro=0.670\n", + "Esperado=0.500, Predito=1.170, Erro=0.670\n", + "Esperado=0.500, Predito=1.777, Erro=1.277\n", + "Esperado=0.667, Predito=1.331, Erro=0.665\n", + "Esperado=0.500, Predito=1.413, Erro=0.913\n", + "Esperado=0.333, Predito=1.329, Erro=0.996\n", + "Esperado=0.333, Predito=1.424, Erro=1.091\n", + "Esperado=0.667, Predito=1.209, Erro=0.542\n", + "Esperado=0.500, Predito=1.319, Erro=0.819\n", + "Esperado=0.333, Predito=1.451, Erro=1.118\n", + "Esperado=0.500, Predito=1.467, Erro=0.967\n", + "Esperado=0.667, Predito=1.465, Erro=0.798\n", + "Esperado=0.667, Predito=1.138, Erro=0.471\n", + "Esperado=0.667, Predito=1.126, Erro=0.459\n", + "Esperado=0.333, Predito=1.562, Erro=1.228\n", + "Esperado=0.167, Predito=1.425, Erro=1.259\n", + "Esperado=0.000, Predito=1.329, Erro=1.329\n", + "Esperado=0.333, Predito=1.451, Erro=1.118\n", + "Esperado=0.000, Predito=1.353, Erro=1.353\n", + "Esperado=0.500, Predito=1.223, Erro=0.723\n", + "Esperado=0.833, Predito=1.340, Erro=0.507\n", + "Esperado=0.667, Predito=1.469, Erro=0.802\n", + "Esperado=0.667, Predito=1.469, Erro=0.802\n", + "Esperado=0.500, Predito=1.381, Erro=0.881\n", + "Esperado=0.167, Predito=1.448, Erro=1.281\n", + "Esperado=0.500, Predito=1.025, Erro=0.525\n", + "Esperado=0.333, Predito=1.279, Erro=0.945\n", + "Esperado=0.333, Predito=1.236, Erro=0.903\n", + "Esperado=0.500, Predito=1.427, Erro=0.927\n", + "Esperado=0.500, Predito=1.268, Erro=0.768\n", + "Esperado=0.333, Predito=1.279, Erro=0.945\n", + "Esperado=0.500, Predito=1.384, Erro=0.884\n", + "Esperado=0.333, Predito=1.906, Erro=1.572\n", + "Esperado=0.500, Predito=1.916, Erro=1.416\n", + "Esperado=0.500, Predito=1.925, Erro=1.425\n", + "Esperado=0.500, Predito=1.384, Erro=0.884\n", + "Esperado=0.333, Predito=1.947, Erro=1.613\n", + "Esperado=0.333, Predito=1.187, Erro=0.854\n", + "Esperado=0.333, Predito=1.720, Erro=1.387\n", + "Esperado=0.333, Predito=1.418, Erro=1.085\n", + "Esperado=0.500, Predito=1.231, Erro=0.731\n", + "Esperado=0.500, Predito=1.255, Erro=0.755\n", + "Esperado=0.333, Predito=1.304, Erro=0.970\n", + "Esperado=0.167, Predito=1.404, Erro=1.237\n", + "Esperado=0.667, Predito=1.680, Erro=1.013\n", + "Esperado=0.833, Predito=1.361, Erro=0.528\n", + "Esperado=0.833, Predito=1.635, Erro=0.801\n", + "Esperado=0.167, Predito=1.764, Erro=1.597\n", + "Esperado=0.333, Predito=1.430, Erro=1.097\n", + "Esperado=0.333, Predito=1.406, Erro=1.073\n", + "Esperado=0.333, Predito=1.312, Erro=0.979\n", + "Esperado=0.500, Predito=1.369, Erro=0.869\n", + "Esperado=0.667, Predito=1.186, Erro=0.520\n", + "Esperado=0.667, Predito=1.186, Erro=0.520\n", + "Esperado=0.667, Predito=1.186, Erro=0.520\n", + "Esperado=0.667, Predito=1.186, Erro=0.520\n", + "Esperado=0.500, Predito=1.161, Erro=0.661\n", + "Esperado=0.333, Predito=1.276, Erro=0.943\n", + "Esperado=0.667, Predito=1.455, Erro=0.788\n", + "Esperado=0.000, Predito=1.726, Erro=1.726\n", + "Esperado=0.500, Predito=1.419, Erro=0.919\n", + "Esperado=0.333, Predito=1.569, Erro=1.235\n", + "Esperado=0.667, Predito=1.362, Erro=0.695\n", + "Esperado=0.500, Predito=1.153, Erro=0.653\n", + "Esperado=0.500, Predito=1.118, Erro=0.618\n", + "Esperado=0.500, Predito=1.741, Erro=1.241\n", + "Esperado=0.500, Predito=1.137, Erro=0.637\n", + "Esperado=0.500, Predito=1.741, Erro=1.241\n", + "Esperado=0.500, Predito=1.391, Erro=0.891\n", + "Esperado=0.500, Predito=1.518, Erro=1.018\n", + "Esperado=0.500, Predito=1.518, Erro=1.018\n", + "Esperado=0.333, Predito=1.553, Erro=1.220\n", + "Esperado=0.500, Predito=1.501, Erro=1.001\n", + "Esperado=0.667, Predito=1.392, Erro=0.726\n", + "Esperado=0.500, Predito=1.354, Erro=0.854\n", + "Esperado=0.667, Predito=1.740, Erro=1.073\n", + "Esperado=0.833, Predito=2.031, Erro=1.198\n", + "Esperado=0.500, Predito=1.287, Erro=0.787\n", + "Esperado=0.500, Predito=1.584, Erro=1.084\n", + "Esperado=0.333, Predito=1.542, Erro=1.209\n", + "Esperado=0.500, Predito=1.238, Erro=0.738\n", + "Esperado=0.500, Predito=1.243, Erro=0.743\n", + "Esperado=0.333, Predito=1.219, Erro=0.885\n", + "Esperado=0.667, Predito=1.465, Erro=0.798\n", + "Esperado=0.500, Predito=1.154, Erro=0.654\n", + "Esperado=0.667, Predito=1.141, Erro=0.475\n", + "Esperado=0.333, Predito=1.219, Erro=0.885\n", + "Esperado=0.500, Predito=1.243, Erro=0.743\n", + "Esperado=0.500, Predito=1.279, Erro=0.779\n", + "Esperado=0.333, Predito=1.698, Erro=1.364\n", + "Esperado=0.333, Predito=1.471, Erro=1.137\n", + "Esperado=0.500, Predito=1.396, Erro=0.896\n", + "Esperado=0.500, Predito=1.555, Erro=1.055\n", + "Esperado=0.500, Predito=1.354, Erro=0.854\n", + "Esperado=0.333, Predito=1.240, Erro=0.906\n", + "Esperado=0.833, Predito=1.673, Erro=0.840\n", + "Esperado=0.333, Predito=1.233, Erro=0.900\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.333, Predito=1.287, Erro=0.953\n", + "Esperado=0.333, Predito=1.265, Erro=0.931\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.500, Predito=1.272, Erro=0.772\n", + "Esperado=0.500, Predito=1.251, Erro=0.751\n", + "Esperado=0.333, Predito=1.377, Erro=1.043\n", + "Esperado=0.667, Predito=1.448, Erro=0.781\n", + "Esperado=0.667, Predito=1.178, Erro=0.511\n", + "Esperado=0.500, Predito=1.307, Erro=0.807\n", + "Esperado=0.500, Predito=1.230, Erro=0.730\n", + "Esperado=0.333, Predito=1.579, Erro=1.245\n", + "Esperado=0.500, Predito=1.536, Erro=1.036\n", + "Esperado=0.500, Predito=1.015, Erro=0.515\n", + "Esperado=0.667, Predito=1.525, Erro=0.858\n", + "Esperado=0.500, Predito=1.110, Erro=0.610\n", + "Esperado=0.500, Predito=1.206, Erro=0.706\n", + "Esperado=0.333, Predito=1.164, Erro=0.831\n", + "Esperado=0.667, Predito=1.329, Erro=0.662\n", + "Esperado=0.667, Predito=1.417, Erro=0.750\n", + "Esperado=0.500, Predito=1.395, Erro=0.895\n", + "Esperado=0.667, Predito=1.417, Erro=0.750\n", + "Esperado=0.500, Predito=1.573, Erro=1.073\n", + "Esperado=0.500, Predito=1.091, Erro=0.591\n", + "Esperado=0.500, Predito=1.091, Erro=0.591\n", + "Esperado=0.500, Predito=1.619, Erro=1.119\n", + "Esperado=0.500, Predito=1.172, Erro=0.672\n", + "Esperado=0.500, Predito=1.383, Erro=0.883\n", + "Esperado=0.500, Predito=1.328, Erro=0.828\n", + "Esperado=0.333, Predito=1.350, Erro=1.017\n", + "Esperado=0.500, Predito=1.037, Erro=0.537\n", + "Esperado=0.500, Predito=1.091, Erro=0.591\n", + "Esperado=0.667, Predito=1.195, Erro=0.528\n", + "Esperado=0.667, Predito=1.627, Erro=0.961\n", + "Esperado=0.500, Predito=1.336, Erro=0.836\n", + "Esperado=0.500, Predito=1.333, Erro=0.833\n", + "Esperado=0.500, Predito=1.504, Erro=1.004\n", + "Esperado=0.333, Predito=1.521, Erro=1.187\n", + "Esperado=0.500, Predito=1.333, Erro=0.833\n", + "Esperado=0.500, Predito=1.504, Erro=1.004\n", + "Esperado=0.333, Predito=2.489, Erro=2.156\n", + "Esperado=0.500, Predito=1.257, Erro=0.757\n", + "Esperado=0.667, Predito=1.522, Erro=0.856\n", + "Esperado=0.667, Predito=1.522, Erro=0.856\n", + "Esperado=0.667, Predito=1.418, Erro=0.752\n", + "Esperado=0.667, Predito=1.155, Erro=0.489\n", + "Esperado=0.500, Predito=1.495, Erro=0.995\n", + "Esperado=0.667, Predito=1.418, Erro=0.752\n", + "Esperado=0.667, Predito=1.155, Erro=0.489\n", + "Esperado=0.500, Predito=1.305, Erro=0.805\n", + "Esperado=0.500, Predito=1.444, Erro=0.944\n", + "Esperado=0.500, Predito=1.257, Erro=0.757\n", + "Esperado=0.667, Predito=1.494, Erro=0.827\n", + "Esperado=0.667, Predito=1.522, Erro=0.856\n", + "Esperado=0.667, Predito=1.120, Erro=0.454\n", + "Esperado=0.333, Predito=1.665, Erro=1.331\n", + "Esperado=0.500, Predito=1.192, Erro=0.692\n", + "Esperado=0.667, Predito=1.442, Erro=0.776\n", + "Esperado=0.667, Predito=1.442, Erro=0.776\n", + "Esperado=0.333, Predito=1.465, Erro=1.131\n", + "Esperado=0.500, Predito=1.833, Erro=1.333\n", + "Esperado=0.500, Predito=1.442, Erro=0.942\n", + "Esperado=0.333, Predito=1.404, Erro=1.071\n", + "Esperado=0.333, Predito=1.259, Erro=0.926\n", + "Esperado=0.333, Predito=1.354, Erro=1.020\n", + "Esperado=0.500, Predito=1.316, Erro=0.816\n", + "Esperado=0.333, Predito=1.281, Erro=0.948\n", + "Esperado=0.500, Predito=1.238, Erro=0.738\n", + "Esperado=0.500, Predito=1.316, Erro=0.816\n", + "Esperado=0.333, Predito=1.404, Erro=1.071\n", + "Esperado=0.333, Predito=1.410, Erro=1.077\n", + "Esperado=0.333, Predito=1.313, Erro=0.980\n", + "Esperado=0.500, Predito=1.544, Erro=1.044\n", + "Esperado=0.333, Predito=1.259, Erro=0.926\n", + "Esperado=0.667, Predito=1.470, Erro=0.803\n", + "Esperado=0.333, Predito=1.657, Erro=1.324\n", + "Esperado=0.500, Predito=1.126, Erro=0.626\n", + "Esperado=0.333, Predito=1.354, Erro=1.020\n", + "Esperado=0.500, Predito=1.224, Erro=0.724\n", + "Esperado=0.500, Predito=1.560, Erro=1.060\n", + "Esperado=0.333, Predito=1.203, Erro=0.869\n", + "Esperado=0.333, Predito=1.203, Erro=0.869\n", + "Esperado=0.500, Predito=1.224, Erro=0.724\n", + "Esperado=0.500, Predito=1.560, Erro=1.060\n", + "Esperado=0.500, Predito=1.072, Erro=0.572\n", + "Esperado=0.333, Predito=1.222, Erro=0.888\n", + "Esperado=0.500, Predito=1.072, Erro=0.572\n", + "Esperado=0.500, Predito=1.483, Erro=0.983\n", + "Esperado=0.667, Predito=1.328, Erro=0.661\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.667, Predito=1.341, Erro=0.674\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.333, Predito=1.306, Erro=0.973\n", + "Esperado=0.333, Predito=1.104, Erro=0.770\n", + "Esperado=0.333, Predito=1.370, Erro=1.036\n", + "Esperado=0.333, Predito=1.306, Erro=0.973\n", + "Esperado=0.333, Predito=1.332, Erro=0.998\n", + "Esperado=0.667, Predito=1.201, Erro=0.534\n", + "Esperado=0.167, Predito=1.800, Erro=1.633\n", + "Esperado=0.833, Predito=1.401, Erro=0.568\n", + "Esperado=0.667, Predito=1.368, Erro=0.701\n", + "Esperado=0.333, Predito=1.392, Erro=1.059\n", + "Esperado=0.833, Predito=1.411, Erro=0.578\n", + "Esperado=0.667, Predito=1.201, Erro=0.534\n", + "Esperado=0.333, Predito=1.426, Erro=1.092\n", + "Esperado=0.667, Predito=1.408, Erro=0.742\n", + "Esperado=0.500, Predito=1.684, Erro=1.184\n", + "Esperado=0.833, Predito=1.615, Erro=0.781\n", + "Esperado=0.500, Predito=1.782, Erro=1.282\n", + "Esperado=0.500, Predito=1.269, Erro=0.769\n", + "Esperado=0.000, Predito=1.464, Erro=1.464\n", + "Esperado=0.333, Predito=1.487, Erro=1.153\n", + "Esperado=0.500, Predito=1.632, Erro=1.132\n", + "Esperado=0.500, Predito=1.632, Erro=1.132\n", + "Esperado=0.667, Predito=1.441, Erro=0.775\n", + "Esperado=0.333, Predito=1.878, Erro=1.545\n", + "Esperado=0.333, Predito=1.151, Erro=0.818\n", + "Esperado=0.667, Predito=1.725, Erro=1.058\n", + "Esperado=0.667, Predito=1.374, Erro=0.708\n", + "Esperado=0.667, Predito=1.441, Erro=0.775\n", + "Esperado=0.500, Predito=1.632, Erro=1.132\n", + "Esperado=0.667, Predito=1.375, Erro=0.708\n", + "Esperado=0.333, Predito=1.187, Erro=0.853\n", + "Esperado=0.500, Predito=1.632, Erro=1.132\n", + "Esperado=0.333, Predito=1.545, Erro=1.212\n", + "Esperado=0.333, Predito=1.187, Erro=0.853\n", + "Esperado=0.333, Predito=1.242, Erro=0.909\n", + "Esperado=0.333, Predito=1.210, Erro=0.877\n", + "Esperado=0.500, Predito=1.272, Erro=0.772\n", + "Esperado=0.333, Predito=1.475, Erro=1.142\n", + "Esperado=0.333, Predito=1.108, Erro=0.775\n", + "Esperado=0.500, Predito=1.022, Erro=0.522\n", + "Esperado=0.500, Predito=1.272, Erro=0.772\n", + "Esperado=0.333, Predito=1.475, Erro=1.142\n", + "Esperado=0.333, Predito=1.392, Erro=1.058\n", + "Esperado=0.500, Predito=1.631, Erro=1.131\n", + "Esperado=0.333, Predito=1.691, Erro=1.357\n", + "Esperado=0.500, Predito=1.401, Erro=0.901\n", + "Esperado=0.667, Predito=1.120, Erro=0.453\n", + "Esperado=0.500, Predito=0.976, Erro=0.476\n", + "Esperado=0.333, Predito=1.322, Erro=0.989\n", + "Esperado=0.667, Predito=1.405, Erro=0.739\n", + "Esperado=0.500, Predito=1.291, Erro=0.791\n", + "Esperado=0.500, Predito=1.793, Erro=1.293\n", + "Esperado=0.500, Predito=1.320, Erro=0.820\n", + "Esperado=0.333, Predito=1.705, Erro=1.372\n", + "Esperado=0.500, Predito=1.533, Erro=1.033\n", + "Esperado=0.333, Predito=1.233, Erro=0.900\n", + "Esperado=0.333, Predito=1.705, Erro=1.372\n", + "Esperado=0.333, Predito=1.629, Erro=1.296\n", + "Esperado=0.500, Predito=1.181, Erro=0.681\n", + "Esperado=0.500, Predito=1.122, Erro=0.622\n", + "Esperado=0.500, Predito=1.417, Erro=0.917\n", + "Esperado=0.500, Predito=1.545, Erro=1.045\n", + "Esperado=0.500, Predito=1.206, Erro=0.706\n", + "Esperado=0.500, Predito=1.432, Erro=0.932\n", + "Esperado=0.667, Predito=1.366, Erro=0.699\n", + "Esperado=0.500, Predito=1.171, Erro=0.671\n", + "Esperado=0.500, Predito=1.359, Erro=0.859\n", + "Esperado=0.500, Predito=1.327, Erro=0.827\n", + "Esperado=0.500, Predito=1.793, Erro=1.293\n", + "Esperado=0.167, Predito=1.470, Erro=1.303\n", + "Esperado=0.500, Predito=1.463, Erro=0.963\n", + "Esperado=0.333, Predito=1.529, Erro=1.196\n", + "Esperado=0.167, Predito=1.470, Erro=1.303\n", + "Esperado=0.500, Predito=1.340, Erro=0.840\n", + "Esperado=0.500, Predito=1.169, Erro=0.669\n", + "Esperado=0.500, Predito=1.440, Erro=0.940\n", + "Esperado=0.333, Predito=1.439, Erro=1.106\n", + "Esperado=0.500, Predito=1.132, Erro=0.632\n", + "Esperado=0.500, Predito=1.132, Erro=0.632\n", + "Esperado=0.333, Predito=1.657, Erro=1.323\n", + "Esperado=0.667, Predito=1.337, Erro=0.671\n", + "Esperado=0.500, Predito=2.060, Erro=1.560\n", + "Esperado=0.667, Predito=1.337, Erro=0.671\n", + "Esperado=0.500, Predito=1.261, Erro=0.761\n", + "Esperado=0.500, Predito=1.519, Erro=1.019\n", + "Esperado=0.500, Predito=1.442, Erro=0.942\n", + "Esperado=0.500, Predito=1.439, Erro=0.939\n", + "Esperado=0.667, Predito=1.303, Erro=0.636\n", + "Esperado=0.500, Predito=1.408, Erro=0.908\n", + "Esperado=0.333, Predito=1.050, Erro=0.717\n", + "Esperado=0.500, Predito=1.190, Erro=0.690\n", + "Esperado=0.500, Predito=1.236, Erro=0.736\n", + "Esperado=0.333, Predito=1.515, Erro=1.182\n", + "Esperado=0.333, Predito=1.319, Erro=0.985\n", + "Esperado=0.333, Predito=1.319, Erro=0.985\n", + "Esperado=0.500, Predito=1.275, Erro=0.775\n", + "Esperado=0.500, Predito=1.437, Erro=0.937\n", + "Esperado=0.333, Predito=1.437, Erro=1.104\n", + "Esperado=0.500, Predito=1.309, Erro=0.809\n", + "Esperado=0.167, Predito=1.570, Erro=1.403\n", + "Esperado=0.500, Predito=1.485, Erro=0.985\n", + "Esperado=0.500, Predito=1.528, Erro=1.028\n", + "Esperado=0.500, Predito=1.253, Erro=0.753\n", + "Esperado=0.500, Predito=1.229, Erro=0.729\n", + "Esperado=0.333, Predito=1.074, Erro=0.741\n", + "Esperado=0.500, Predito=1.226, Erro=0.726\n", + "Esperado=0.500, Predito=1.226, Erro=0.726\n", + "Esperado=0.500, Predito=1.226, Erro=0.726\n", + "Esperado=0.333, Predito=1.438, Erro=1.104\n", + "Esperado=0.333, Predito=1.438, Erro=1.104\n", + "Esperado=0.500, Predito=1.226, Erro=0.726\n", + "Esperado=0.333, Predito=1.319, Erro=0.986\n", + "Esperado=0.500, Predito=1.400, Erro=0.900\n", + "Esperado=0.167, Predito=1.572, Erro=1.405\n", + "Esperado=0.333, Predito=1.546, Erro=1.213\n", + "Esperado=0.500, Predito=1.569, Erro=1.069\n", + "Esperado=0.500, Predito=1.358, Erro=0.858\n", + "Esperado=0.500, Predito=1.313, Erro=0.813\n", + "Esperado=0.500, Predito=1.206, Erro=0.706\n", + "Esperado=0.500, Predito=1.421, Erro=0.921\n", + "Esperado=0.500, Predito=1.369, Erro=0.869\n", + "Esperado=0.667, Predito=1.196, Erro=0.530\n", + "Esperado=0.333, Predito=1.546, Erro=1.213\n", + "Esperado=0.667, Predito=1.386, Erro=0.720\n", + "Esperado=0.667, Predito=1.266, Erro=0.600\n", + "Esperado=0.667, Predito=1.380, Erro=0.713\n", + "Esperado=0.667, Predito=1.386, Erro=0.720\n", + "Esperado=0.667, Predito=1.380, Erro=0.713\n", + "Esperado=0.667, Predito=1.266, Erro=0.600\n", + "Esperado=0.333, Predito=1.546, Erro=1.213\n", + "Esperado=0.500, Predito=1.599, Erro=1.099\n", + "Esperado=0.333, Predito=1.135, Erro=0.802\n", + "Esperado=0.500, Predito=1.785, Erro=1.285\n", + "Esperado=0.667, Predito=1.385, Erro=0.718\n", + "Esperado=0.333, Predito=1.486, Erro=1.152\n", + "Esperado=0.500, Predito=1.501, Erro=1.001\n", + "Esperado=0.667, Predito=1.575, Erro=0.908\n", + "Esperado=0.333, Predito=1.631, Erro=1.298\n", + "Esperado=0.500, Predito=1.240, Erro=0.740\n", + "Esperado=0.500, Predito=1.506, Erro=1.006\n", + "Esperado=0.333, Predito=1.354, Erro=1.021\n", + "Esperado=0.500, Predito=1.349, Erro=0.849\n", + "Esperado=0.500, Predito=1.434, Erro=0.934\n", + "Esperado=0.333, Predito=1.478, Erro=1.145\n", + "Esperado=0.667, Predito=1.393, Erro=0.726\n", + "Esperado=0.333, Predito=1.427, Erro=1.093\n", + "Esperado=0.667, Predito=1.312, Erro=0.646\n", + "Esperado=0.667, Predito=1.273, Erro=0.606\n", + "Esperado=0.500, Predito=1.253, Erro=0.753\n", + "Esperado=0.500, Predito=1.270, Erro=0.770\n", + "Esperado=0.667, Predito=1.338, Erro=0.672\n", + "Esperado=0.667, Predito=1.393, Erro=0.726\n", + "Esperado=0.667, Predito=1.613, Erro=0.947\n", + "Esperado=0.333, Predito=1.478, Erro=1.145\n", + "Esperado=0.333, Predito=1.046, Erro=0.713\n", + "Esperado=0.500, Predito=1.290, Erro=0.790\n", + "Esperado=0.500, Predito=1.133, Erro=0.633\n", + "Esperado=0.667, Predito=1.316, Erro=0.649\n", + "Esperado=0.500, Predito=1.133, Erro=0.633\n", + "Esperado=0.500, Predito=1.224, Erro=0.724\n", + "Esperado=0.667, Predito=1.481, Erro=0.815\n", + "Esperado=0.667, Predito=1.410, Erro=0.743\n", + "Esperado=0.500, Predito=1.276, Erro=0.776\n", + "Esperado=0.333, Predito=1.294, Erro=0.960\n", + "Esperado=0.500, Predito=1.267, Erro=0.767\n", + "Esperado=0.333, Predito=1.561, Erro=1.228\n", + "Esperado=0.333, Predito=1.491, Erro=1.158\n", + "Esperado=0.333, Predito=1.541, Erro=1.208\n", + "Esperado=0.667, Predito=1.368, Erro=0.701\n", + "Esperado=0.333, Predito=1.561, Erro=1.228\n", + "Esperado=0.500, Predito=1.369, Erro=0.869\n", + "Esperado=0.833, Predito=1.369, Erro=0.535\n", + "Esperado=0.667, Predito=1.387, Erro=0.721\n", + "Esperado=0.500, Predito=1.379, Erro=0.879\n", + "Esperado=0.500, Predito=1.205, Erro=0.705\n", + "Esperado=0.333, Predito=1.491, Erro=1.158\n", + "Esperado=0.333, Predito=1.541, Erro=1.208\n", + "Esperado=0.500, Predito=1.337, Erro=0.837\n", + "Esperado=0.500, Predito=1.337, Erro=0.837\n", + "Esperado=0.333, Predito=1.221, Erro=0.888\n", + "Esperado=0.333, Predito=1.221, Erro=0.888\n", + "Esperado=0.333, Predito=1.350, Erro=1.017\n", + "Esperado=0.500, Predito=1.737, Erro=1.237\n", + "Esperado=0.833, Predito=1.757, Erro=0.924\n", + "Esperado=0.500, Predito=1.252, Erro=0.752\n", + "Esperado=0.333, Predito=1.095, Erro=0.762\n", + "Esperado=0.333, Predito=1.039, Erro=0.705\n", + "Esperado=0.333, Predito=1.039, Erro=0.705\n", + "Esperado=0.333, Predito=1.095, Erro=0.762\n", + "Esperado=0.667, Predito=1.632, Erro=0.966\n", + "Esperado=0.500, Predito=1.386, Erro=0.886\n", + "Esperado=0.500, Predito=1.342, Erro=0.842\n", + "Esperado=0.500, Predito=1.142, Erro=0.642\n", + "Esperado=0.333, Predito=1.698, Erro=1.365\n", + "Esperado=0.333, Predito=1.231, Erro=0.898\n", + "Esperado=0.500, Predito=1.517, Erro=1.017\n", + "Esperado=0.333, Predito=1.365, Erro=1.032\n", + "Esperado=0.333, Predito=1.460, Erro=1.127\n", + "Esperado=0.833, Predito=1.351, Erro=0.518\n", + "Esperado=0.167, Predito=1.789, Erro=1.622\n", + "Esperado=0.500, Predito=1.467, Erro=0.967\n", + "Esperado=0.500, Predito=1.467, Erro=0.967\n", + "Esperado=0.500, Predito=1.475, Erro=0.975\n", + "Esperado=0.333, Predito=1.160, Erro=0.826\n", + "Esperado=0.333, Predito=1.160, Erro=0.826\n", + "Esperado=0.500, Predito=1.424, Erro=0.924\n", + "Esperado=0.333, Predito=1.305, Erro=0.972\n", + "Esperado=0.500, Predito=1.228, Erro=0.728\n", + "Esperado=0.500, Predito=1.181, Erro=0.681\n", + "Esperado=0.667, Predito=1.195, Erro=0.529\n", + "Esperado=0.333, Predito=1.387, Erro=1.054\n", + "Esperado=0.333, Predito=1.643, Erro=1.310\n", + "Esperado=0.333, Predito=1.619, Erro=1.285\n", + "Esperado=0.667, Predito=1.327, Erro=0.661\n", + "Esperado=0.167, Predito=1.252, Erro=1.085\n", + "Esperado=0.500, Predito=1.304, Erro=0.804\n", + "Esperado=0.333, Predito=1.233, Erro=0.900\n", + "Esperado=0.333, Predito=1.232, Erro=0.899\n", + "Esperado=0.333, Predito=1.190, Erro=0.857\n", + "Esperado=0.167, Predito=1.418, Erro=1.251\n", + "Esperado=0.500, Predito=0.901, Erro=0.401\n", + "Esperado=0.333, Predito=1.563, Erro=1.229\n", + "Esperado=0.667, Predito=1.293, Erro=0.626\n", + "Esperado=0.667, Predito=1.293, Erro=0.626\n", + "Esperado=0.667, Predito=1.293, Erro=0.626\n", + "Esperado=0.667, Predito=1.293, Erro=0.626\n", + "Esperado=0.500, Predito=1.236, Erro=0.736\n", + "Esperado=0.667, Predito=1.512, Erro=0.845\n", + "Esperado=0.500, Predito=1.424, Erro=0.924\n", + "Esperado=0.500, Predito=1.345, Erro=0.845\n", + "Esperado=0.333, Predito=1.422, Erro=1.088\n", + "Esperado=0.333, Predito=1.348, Erro=1.015\n", + "Esperado=0.167, Predito=1.347, Erro=1.180\n", + "Esperado=0.333, Predito=1.196, Erro=0.863\n", + "Esperado=0.667, Predito=1.499, Erro=0.832\n", + "Esperado=0.167, Predito=1.823, Erro=1.656\n", + "Esperado=0.333, Predito=1.196, Erro=0.863\n", + "Esperado=0.500, Predito=1.169, Erro=0.669\n", + "Esperado=0.333, Predito=1.302, Erro=0.969\n", + "Esperado=0.500, Predito=1.581, Erro=1.081\n", + "Esperado=0.500, Predito=1.169, Erro=0.669\n", + "Esperado=0.500, Predito=1.067, Erro=0.567\n", + "Esperado=0.333, Predito=1.296, Erro=0.962\n", + "Esperado=0.500, Predito=1.599, Erro=1.099\n", + "Esperado=0.500, Predito=1.501, Erro=1.001\n", + "Esperado=0.833, Predito=1.649, Erro=0.816\n", + "Esperado=0.500, Predito=1.533, Erro=1.033\n", + "Esperado=0.333, Predito=1.457, Erro=1.123\n", + "Esperado=0.500, Predito=1.216, Erro=0.716\n", + "Esperado=0.500, Predito=1.259, Erro=0.759\n", + "Esperado=0.667, Predito=1.490, Erro=0.823\n", + "Esperado=0.667, Predito=1.490, Erro=0.823\n", + "Esperado=0.667, Predito=1.489, Erro=0.823\n", + "Esperado=0.333, Predito=1.255, Erro=0.922\n", + "Esperado=0.333, Predito=1.382, Erro=1.049\n", + "Esperado=0.500, Predito=1.235, Erro=0.735\n", + "Esperado=0.333, Predito=1.316, Erro=0.983\n", + "Esperado=0.333, Predito=1.686, Erro=1.353\n", + "Esperado=0.333, Predito=1.418, Erro=1.084\n", + "Esperado=0.667, Predito=1.319, Erro=0.652\n", + "Esperado=0.167, Predito=2.152, Erro=1.985\n", + "Esperado=0.500, Predito=1.304, Erro=0.804\n", + "Esperado=0.667, Predito=1.273, Erro=0.606\n", + "Esperado=0.167, Predito=1.283, Erro=1.116\n", + "Esperado=0.500, Predito=1.557, Erro=1.057\n", + "Esperado=0.333, Predito=1.546, Erro=1.213\n", + "Esperado=0.333, Predito=1.571, Erro=1.238\n", + "Esperado=0.500, Predito=1.490, Erro=0.990\n", + "Esperado=0.333, Predito=1.421, Erro=1.087\n", + "Esperado=0.500, Predito=1.292, Erro=0.792\n", + "Esperado=0.333, Predito=1.463, Erro=1.130\n", + "Esperado=0.333, Predito=1.463, Erro=1.130\n", + "Esperado=0.333, Predito=1.449, Erro=1.115\n", + "Esperado=0.333, Predito=1.659, Erro=1.326\n", + "Esperado=0.667, Predito=1.929, Erro=1.262\n", + "Esperado=0.167, Predito=1.477, Erro=1.310\n", + "Esperado=0.500, Predito=1.385, Erro=0.885\n", + "Esperado=0.500, Predito=1.479, Erro=0.979\n", + "Esperado=0.333, Predito=1.251, Erro=0.918\n", + "Esperado=0.333, Predito=1.465, Erro=1.131\n", + "Esperado=0.500, Predito=1.354, Erro=0.854\n", + "Esperado=0.500, Predito=1.229, Erro=0.729\n", + "Esperado=0.333, Predito=1.565, Erro=1.232\n", + "Esperado=0.333, Predito=1.266, Erro=0.932\n", + "Esperado=0.500, Predito=1.487, Erro=0.987\n", + "Esperado=0.500, Predito=1.487, Erro=0.987\n", + "Esperado=0.333, Predito=1.371, Erro=1.038\n", + "Esperado=0.500, Predito=1.611, Erro=1.111\n", + "Esperado=0.667, Predito=1.424, Erro=0.758\n", + "Esperado=0.500, Predito=1.617, Erro=1.117\n", + "Esperado=0.333, Predito=1.287, Erro=0.954\n", + "Esperado=0.667, Predito=1.424, Erro=0.758\n", + "Esperado=0.667, Predito=1.400, Erro=0.734\n", + "Esperado=0.333, Predito=1.256, Erro=0.923\n", + "Esperado=0.333, Predito=1.447, Erro=1.114\n", + "Esperado=0.500, Predito=1.503, Erro=1.003\n", + "Esperado=0.833, Predito=1.551, Erro=0.718\n", + "Esperado=0.667, Predito=1.346, Erro=0.679\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Esperado=0.333, Predito=1.504, Erro=1.170\n", + "Esperado=0.667, Predito=1.516, Erro=0.850\n", + 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Predito=1.339, Erro=0.839\n", + "Esperado=0.500, Predito=1.209, Erro=0.709\n", + "Esperado=0.500, Predito=1.348, Erro=0.848\n", + "Esperado=0.500, Predito=1.339, Erro=0.839\n", + "Esperado=0.500, Predito=1.284, Erro=0.784\n", + "Esperado=0.500, Predito=1.205, Erro=0.705\n", + "Esperado=0.500, Predito=1.314, Erro=0.814\n", + "Esperado=0.333, Predito=1.383, Erro=1.050\n", + "Esperado=0.667, Predito=1.484, Erro=0.817\n", + "Esperado=0.333, Predito=1.254, Erro=0.920\n", + "Esperado=0.500, Predito=1.503, Erro=1.003\n", + "Esperado=0.667, Predito=1.413, Erro=0.746\n", + "Esperado=0.667, Predito=1.401, Erro=0.734\n", + "Esperado=0.500, Predito=1.209, Erro=0.709\n", + "Esperado=0.500, Predito=1.245, Erro=0.745\n", + "Esperado=0.333, Predito=1.261, Erro=0.927\n", + "Esperado=0.500, Predito=1.737, Erro=1.237\n", + "Esperado=0.500, Predito=1.479, Erro=0.979\n", + "Esperado=0.500, Predito=1.343, Erro=0.843\n", + "Esperado=0.333, Predito=1.681, Erro=1.347\n", + "Esperado=0.500, Predito=1.314, Erro=0.814\n", + "Esperado=0.667, Predito=0.958, Erro=0.291\n", + "Esperado=0.667, Predito=0.958, Erro=0.291\n", + "Esperado=0.667, Predito=1.037, Erro=0.370\n", + "Esperado=0.667, Predito=1.008, Erro=0.342\n", + "Esperado=0.667, Predito=1.099, Erro=0.432\n", + "Esperado=0.333, Predito=1.312, Erro=0.979\n", + "Esperado=1.000, Predito=1.364, Erro=0.364\n", + "Esperado=0.500, Predito=1.472, Erro=0.972\n", + "Esperado=0.500, Predito=1.189, Erro=0.689\n", + "Esperado=0.333, Predito=1.236, Erro=0.903\n", + "Esperado=0.667, Predito=1.492, Erro=0.826\n", + "Esperado=0.833, Predito=1.302, Erro=0.468\n", + "Esperado=0.167, Predito=1.538, Erro=1.371\n", + "Esperado=0.500, Predito=1.408, Erro=0.908\n", + "Esperado=0.667, Predito=1.492, Erro=0.826\n", + "Esperado=0.833, Predito=1.302, Erro=0.468\n", + "Esperado=0.333, Predito=1.236, Erro=0.903\n", + "Esperado=0.500, Predito=1.226, Erro=0.726\n", + "Esperado=0.500, Predito=1.282, Erro=0.782\n", + "Esperado=0.500, Predito=1.282, Erro=0.782\n", + "Esperado=0.667, Predito=1.436, Erro=0.770\n", + "Esperado=0.500, Predito=1.226, Erro=0.726\n", + "Esperado=0.500, Predito=1.282, Erro=0.782\n", + "Esperado=0.667, Predito=1.472, Erro=0.805\n", + "Esperado=0.333, Predito=1.695, Erro=1.362\n", + "Esperado=0.667, Predito=1.472, Erro=0.805\n", + "Esperado=0.333, Predito=1.355, Erro=1.022\n", + "Esperado=0.333, Predito=1.280, Erro=0.947\n", + "Esperado=0.500, Predito=1.601, Erro=1.101\n", + "Esperado=0.500, Predito=1.479, Erro=0.979\n", + "Esperado=0.500, Predito=1.063, Erro=0.563\n", + "Esperado=0.833, Predito=1.286, Erro=0.453\n", + "Esperado=0.500, Predito=1.201, Erro=0.701\n", + "Esperado=0.333, Predito=1.284, Erro=0.950\n", + "Esperado=0.333, Predito=1.531, Erro=1.197\n", + "Esperado=0.667, Predito=1.217, Erro=0.550\n", + "Esperado=0.500, Predito=1.378, Erro=0.878\n", + "Esperado=0.500, Predito=1.201, Erro=0.701\n", + "Esperado=0.333, Predito=1.336, Erro=1.002\n", + "Esperado=0.500, Predito=1.298, Erro=0.798\n", + "Esperado=0.500, Predito=1.406, Erro=0.906\n", + "Esperado=0.500, Predito=1.325, Erro=0.825\n", + "Esperado=0.333, Predito=1.284, Erro=0.950\n", + "Esperado=0.500, Predito=1.537, Erro=1.037\n", + "Esperado=0.667, Predito=1.406, Erro=0.740\n", + "Esperado=0.500, Predito=1.310, Erro=0.810\n", + "Esperado=0.500, Predito=1.605, Erro=1.105\n", + "Esperado=0.333, Predito=1.244, Erro=0.910\n", + "Esperado=0.333, Predito=1.312, Erro=0.978\n", + "Esperado=0.333, Predito=1.789, Erro=1.456\n", + "Esperado=0.333, Predito=1.244, Erro=0.910\n", + "Esperado=0.333, Predito=1.789, Erro=1.456\n", + "Esperado=1.000, Predito=1.729, Erro=0.729\n", + "Esperado=0.500, Predito=2.008, Erro=1.508\n", + "Esperado=0.333, Predito=1.084, Erro=0.751\n", + "Esperado=0.500, Predito=1.121, Erro=0.621\n", + "Esperado=0.333, Predito=1.312, Erro=0.978\n", + "Esperado=0.500, Predito=1.268, Erro=0.768\n", + "Esperado=0.500, Predito=1.393, Erro=0.893\n", + "Esperado=1.000, Predito=1.438, Erro=0.438\n", + "Esperado=0.667, Predito=1.174, Erro=0.507\n", + "Esperado=0.667, Predito=1.137, Erro=0.470\n", + "Esperado=0.500, Predito=1.454, Erro=0.954\n", + "Esperado=0.167, Predito=1.339, Erro=1.172\n", + "Esperado=0.833, Predito=1.619, Erro=0.786\n", + "Esperado=0.500, Predito=1.392, Erro=0.892\n", + "Esperado=0.500, Predito=1.454, Erro=0.954\n", + "Esperado=0.833, Predito=1.760, Erro=0.926\n", + "Esperado=0.833, Predito=1.353, Erro=0.519\n", + "Esperado=0.833, Predito=1.353, Erro=0.519\n", + "Esperado=0.833, Predito=1.353, Erro=0.519\n", + "Esperado=0.667, Predito=1.513, Erro=0.846\n", + "Esperado=0.667, Predito=1.497, Erro=0.830\n", + "Esperado=0.667, Predito=1.189, Erro=0.522\n", + "Esperado=0.667, Predito=1.189, Erro=0.522\n", + "Esperado=0.667, Predito=1.269, Erro=0.602\n", + "Esperado=0.833, Predito=1.289, Erro=0.456\n", + "Esperado=0.833, Predito=1.289, Erro=0.456\n", + "Esperado=0.333, Predito=1.141, Erro=0.807\n", + "Esperado=0.333, Predito=1.411, Erro=1.078\n", + "Esperado=0.667, Predito=1.189, Erro=0.522\n", + "Esperado=0.500, Predito=1.278, Erro=0.778\n", + "Esperado=0.667, Predito=1.269, Erro=0.602\n", + "Esperado=0.333, Predito=1.545, Erro=1.212\n", + "Esperado=0.667, Predito=1.442, Erro=0.775\n", + "Esperado=0.333, Predito=1.356, Erro=1.023\n", + "Esperado=0.667, Predito=1.442, Erro=0.775\n", + "Esperado=0.667, Predito=1.448, Erro=0.781\n", + "Esperado=0.333, Predito=1.545, Erro=1.212\n", + "Esperado=0.333, Predito=1.567, Erro=1.234\n", + "Esperado=0.667, Predito=1.297, Erro=0.631\n", + "Esperado=0.333, Predito=1.350, Erro=1.017\n", + "Esperado=0.833, Predito=1.443, Erro=0.609\n", + "Esperado=0.667, Predito=1.458, Erro=0.792\n", + "Esperado=0.500, Predito=1.518, Erro=1.018\n", + "Esperado=0.500, Predito=1.475, Erro=0.975\n", + "Esperado=0.333, Predito=1.498, Erro=1.164\n", + "Esperado=0.500, Predito=1.327, Erro=0.827\n", + "Esperado=0.667, Predito=1.442, Erro=0.775\n", + "Esperado=0.833, Predito=1.289, Erro=0.455\n", + "Esperado=0.667, Predito=1.547, Erro=0.880\n", + "Esperado=0.500, Predito=1.409, Erro=0.909\n", + "Esperado=0.333, Predito=1.581, Erro=1.248\n", + "Esperado=0.333, Predito=1.356, Erro=1.023\n", + "Esperado=0.500, Predito=1.211, Erro=0.711\n", + "Esperado=0.000, Predito=0.940, Erro=0.940\n", + "Esperado=0.333, Predito=1.044, Erro=0.711\n", + "Esperado=0.667, Predito=1.258, Erro=0.592\n", + "Esperado=1.000, Predito=1.581, Erro=0.581\n", + "Esperado=0.500, Predito=1.273, Erro=0.773\n", + "Esperado=0.500, Predito=0.966, Erro=0.466\n", + "Esperado=0.833, Predito=1.434, Erro=0.600\n", + "Esperado=0.667, Predito=1.258, Erro=0.592\n", + "Esperado=0.500, Predito=1.472, Erro=0.972\n", + "Esperado=0.500, Predito=1.444, Erro=0.944\n", + "Esperado=0.500, Predito=1.444, Erro=0.944\n", + "Esperado=0.500, Predito=1.298, Erro=0.798\n", + "Esperado=0.667, Predito=1.724, Erro=1.057\n", + "Esperado=0.500, Predito=1.412, Erro=0.912\n", + "Esperado=0.667, Predito=1.581, Erro=0.915\n", + "Esperado=0.500, Predito=1.412, Erro=0.912\n", + "Esperado=0.667, Predito=1.581, Erro=0.915\n", + "Esperado=0.500, Predito=1.482, Erro=0.982\n", + "Esperado=0.667, Predito=1.306, Erro=0.639\n", + "Esperado=0.333, Predito=1.265, Erro=0.931\n", + "Esperado=0.667, Predito=1.433, Erro=0.766\n", + "Esperado=0.667, Predito=1.306, Erro=0.639\n", + "Esperado=0.500, Predito=1.203, Erro=0.703\n", + "Esperado=0.500, Predito=1.248, Erro=0.748\n", + "Esperado=0.500, Predito=1.342, Erro=0.842\n", + "Esperado=0.500, Predito=1.330, Erro=0.830\n", + "Esperado=0.500, Predito=1.248, Erro=0.748\n", + "Esperado=0.667, Predito=1.343, Erro=0.676\n", + "Esperado=0.500, Predito=1.292, Erro=0.792\n", + "Esperado=0.333, Predito=1.213, Erro=0.879\n", + "Esperado=0.500, Predito=1.302, Erro=0.802\n", + "Esperado=0.833, Predito=1.096, Erro=0.263\n", + "Esperado=0.167, Predito=1.365, Erro=1.198\n", + "Esperado=0.167, Predito=1.418, Erro=1.251\n", + "Esperado=0.833, Predito=1.399, Erro=0.566\n", + "Esperado=0.167, Predito=1.267, Erro=1.101\n", + "Esperado=0.333, Predito=1.234, Erro=0.900\n", + "Esperado=0.333, Predito=1.505, Erro=1.172\n", + "Esperado=0.333, Predito=1.505, Erro=1.171\n", + "Esperado=0.333, Predito=1.523, Erro=1.190\n", + "Esperado=0.333, Predito=1.177, Erro=0.844\n", + "Esperado=0.167, Predito=1.289, Erro=1.123\n", + "Esperado=0.333, Predito=1.176, Erro=0.843\n", + "Esperado=0.667, Predito=1.171, Erro=0.504\n", + "Esperado=0.500, Predito=1.298, Erro=0.798\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.667, Predito=1.023, Erro=0.356\n", + "Esperado=0.667, Predito=1.010, Erro=0.343\n", + "Esperado=0.500, Predito=1.321, Erro=0.821\n", + "Esperado=0.500, Predito=1.321, Erro=0.821\n", + "Esperado=0.500, Predito=1.291, Erro=0.791\n", + "Esperado=0.833, Predito=1.421, Erro=0.588\n", + "Esperado=0.333, Predito=1.255, Erro=0.922\n", + "Esperado=0.333, Predito=1.756, Erro=1.422\n", + "Esperado=0.667, Predito=1.223, Erro=0.557\n", + "Esperado=0.333, Predito=1.344, Erro=1.011\n", + "Esperado=0.333, Predito=1.441, Erro=1.108\n", + "Esperado=0.667, Predito=1.214, Erro=0.548\n", + "Esperado=0.333, Predito=1.161, Erro=0.828\n", + "Esperado=0.500, Predito=1.283, Erro=0.783\n", + "Esperado=0.333, Predito=1.283, Erro=0.949\n", + "Esperado=0.333, Predito=1.198, Erro=0.864\n", + "Esperado=0.333, Predito=1.175, Erro=0.842\n", + "Esperado=0.333, Predito=1.622, Erro=1.288\n", + "Esperado=0.333, Predito=1.441, Erro=1.108\n", + "Esperado=0.500, Predito=1.535, Erro=1.035\n", + "Esperado=0.667, Predito=1.251, Erro=0.584\n", + "Esperado=0.333, Predito=1.494, Erro=1.161\n", + "Esperado=0.500, Predito=1.344, Erro=0.844\n", + "Esperado=0.667, Predito=1.527, Erro=0.860\n", + "Esperado=0.667, Predito=1.753, Erro=1.086\n", + "Esperado=0.667, Predito=1.043, Erro=0.377\n", + "Esperado=0.667, Predito=1.123, Erro=0.456\n", + "Esperado=0.333, Predito=1.424, Erro=1.091\n", + "Esperado=0.333, Predito=1.011, Erro=0.678\n", + "Esperado=0.167, Predito=2.089, Erro=1.923\n", + "Esperado=0.333, Predito=1.376, Erro=1.043\n", + "Esperado=0.333, Predito=1.316, Erro=0.983\n", + "Esperado=0.500, Predito=1.225, Erro=0.725\n", + "Esperado=0.500, Predito=1.173, Erro=0.673\n", + "Esperado=0.333, Predito=1.136, Erro=0.803\n", + "Esperado=0.500, Predito=1.225, Erro=0.725\n", + "Esperado=0.333, Predito=1.316, Erro=0.983\n", + "Esperado=0.333, Predito=1.272, Erro=0.938\n", + "Esperado=0.667, Predito=1.299, Erro=0.633\n", + "Esperado=0.667, Predito=1.246, Erro=0.579\n", + "Esperado=0.500, Predito=1.165, Erro=0.665\n", + "Esperado=0.500, Predito=1.165, Erro=0.665\n", + "Esperado=0.667, Predito=1.808, Erro=1.141\n", + "Esperado=0.500, Predito=1.165, Erro=0.665\n", + "Esperado=0.500, Predito=1.311, Erro=0.811\n", + "Esperado=0.500, Predito=1.248, Erro=0.748\n", + "Esperado=0.333, Predito=1.264, Erro=0.930\n", + "Esperado=0.500, Predito=1.311, Erro=0.811\n", + "Esperado=0.500, Predito=1.153, Erro=0.653\n", + "Esperado=0.667, Predito=1.271, Erro=0.604\n", + "Esperado=0.667, Predito=1.676, Erro=1.009\n", + "Esperado=0.667, Predito=1.639, Erro=0.972\n", + "Esperado=0.667, Predito=1.478, Erro=0.812\n", + "Esperado=0.500, Predito=1.153, Erro=0.653\n", + "Esperado=0.667, Predito=1.271, Erro=0.604\n", + "Esperado=0.500, Predito=1.489, Erro=0.989\n", + "Esperado=0.333, Predito=1.314, Erro=0.981\n", + "Esperado=0.500, Predito=1.108, Erro=0.608\n", + "Esperado=0.333, Predito=1.009, Erro=0.676\n", + "Esperado=0.667, Predito=1.513, Erro=0.846\n", + "Esperado=0.333, Predito=1.162, Erro=0.829\n", + "Esperado=0.500, Predito=1.138, Erro=0.638\n", + "Esperado=0.667, Predito=1.513, Erro=0.846\n", + "Esperado=0.500, Predito=1.676, Erro=1.176\n", + "Esperado=0.500, Predito=1.583, Erro=1.083\n", + "Esperado=0.333, Predito=1.282, Erro=0.948\n", + "Esperado=0.500, Predito=1.632, Erro=1.132\n", + "Esperado=0.500, Predito=1.356, Erro=0.856\n", + "Esperado=0.500, Predito=1.371, Erro=0.871\n", + "Esperado=0.333, Predito=1.282, Erro=0.948\n", + "Esperado=0.667, Predito=1.401, Erro=0.735\n", + "Esperado=0.500, Predito=1.052, Erro=0.552\n", + "Esperado=0.167, Predito=1.403, Erro=1.237\n", + "Esperado=0.333, Predito=1.248, Erro=0.914\n", + "Esperado=0.167, Predito=1.267, Erro=1.101\n", + "Esperado=0.500, Predito=1.183, Erro=0.683\n", + "Esperado=0.500, Predito=1.310, Erro=0.810\n", + "Esperado=0.333, Predito=1.413, Erro=1.080\n", + "Esperado=0.500, Predito=1.245, Erro=0.745\n", + "Esperado=0.500, Predito=1.257, Erro=0.757\n", + "Esperado=0.667, Predito=1.249, Erro=0.582\n", + "Esperado=0.667, Predito=1.126, Erro=0.460\n", + "Esperado=0.333, Predito=1.276, Erro=0.942\n", + "Esperado=0.500, Predito=1.269, Erro=0.769\n", + "Esperado=0.667, Predito=1.291, Erro=0.624\n", + "Esperado=0.500, Predito=1.610, Erro=1.110\n", + "Esperado=0.500, Predito=1.335, Erro=0.835\n", + "Esperado=0.500, Predito=1.610, Erro=1.110\n", + "Esperado=0.333, Predito=1.656, Erro=1.323\n", + "Esperado=0.667, Predito=1.310, Erro=0.643\n", + "Esperado=0.500, Predito=1.067, Erro=0.567\n", + "Esperado=0.500, Predito=1.316, Erro=0.816\n", + "Esperado=0.667, Predito=1.351, Erro=0.684\n", + "Esperado=0.500, Predito=1.100, Erro=0.600\n", + "Esperado=0.333, Predito=1.283, Erro=0.949\n", + "Esperado=0.333, Predito=1.092, Erro=0.759\n", + "Esperado=0.333, Predito=1.377, Erro=1.044\n", + "Esperado=0.333, Predito=1.718, Erro=1.385\n", + "Esperado=0.500, Predito=1.316, Erro=0.816\n", + "Esperado=0.500, Predito=1.039, Erro=0.539\n", + "Esperado=0.500, Predito=1.160, Erro=0.660\n", + "Esperado=0.667, Predito=1.296, Erro=0.629\n", + "Esperado=0.667, Predito=1.296, Erro=0.629\n", + "Esperado=0.833, Predito=1.185, Erro=0.352\n", + "Esperado=0.500, Predito=1.168, Erro=0.668\n", + "Esperado=0.500, Predito=1.778, Erro=1.278\n", + "Esperado=0.500, Predito=1.102, Erro=0.602\n", + "Esperado=0.500, Predito=1.125, Erro=0.625\n", + "Esperado=0.167, Predito=1.641, Erro=1.475\n", + "Esperado=0.667, Predito=1.395, Erro=0.728\n", + "Esperado=0.167, Predito=1.933, Erro=1.766\n", + "Esperado=0.500, Predito=1.359, Erro=0.859\n", + "Esperado=0.500, Predito=1.562, Erro=1.062\n", + "Esperado=0.500, Predito=1.553, Erro=1.053\n", + "Esperado=0.500, Predito=1.553, Erro=1.053\n", + "Esperado=0.000, Predito=2.134, Erro=2.134\n", + "Esperado=0.500, Predito=1.292, Erro=0.792\n", + "Esperado=0.333, Predito=1.500, Erro=1.166\n", + "Esperado=0.333, Predito=1.704, Erro=1.371\n", + "Esperado=0.667, Predito=1.096, Erro=0.430\n", + "Esperado=0.333, Predito=1.360, Erro=1.027\n", + "Esperado=0.167, Predito=2.271, Erro=2.104\n", + "Esperado=0.333, Predito=1.397, Erro=1.063\n", + "Esperado=0.167, Predito=1.746, Erro=1.579\n", + "Esperado=0.333, Predito=1.316, Erro=0.983\n", + "Esperado=0.667, Predito=1.261, Erro=0.595\n", + "Esperado=0.333, Predito=1.108, Erro=0.775\n", + "Esperado=0.333, Predito=1.148, Erro=0.815\n", + "Esperado=0.333, Predito=1.339, Erro=1.006\n", + "Esperado=0.333, Predito=1.420, Erro=1.087\n", + "Esperado=0.500, Predito=1.778, Erro=1.278\n", + "Esperado=0.333, Predito=1.232, Erro=0.898\n", + "Esperado=0.500, Predito=1.087, Erro=0.587\n", + "Esperado=0.333, Predito=1.341, Erro=1.008\n", + "Esperado=0.167, Predito=1.537, Erro=1.370\n", + "Esperado=0.333, Predito=1.339, Erro=1.006\n", + "Esperado=0.333, Predito=1.253, Erro=0.920\n", + "Esperado=0.500, Predito=1.280, Erro=0.780\n", + "Esperado=0.333, Predito=1.715, Erro=1.382\n", + "Esperado=0.500, Predito=1.280, Erro=0.780\n", + "Esperado=0.167, Predito=1.317, Erro=1.151\n", + "Esperado=0.667, Predito=1.571, Erro=0.905\n", + "Esperado=0.333, Predito=1.252, Erro=0.918\n", + "Esperado=0.333, Predito=1.424, Erro=1.091\n", + "Esperado=0.333, Predito=1.415, Erro=1.082\n", + "Esperado=0.500, Predito=1.559, Erro=1.059\n", + "Esperado=0.333, Predito=1.277, Erro=0.944\n", + "Esperado=0.500, Predito=1.272, Erro=0.772\n", + "Esperado=0.667, Predito=1.245, Erro=0.578\n", + "Esperado=0.667, Predito=1.531, Erro=0.864\n", + "Esperado=0.500, Predito=1.329, Erro=0.829\n", + "Esperado=0.667, Predito=1.612, Erro=0.945\n", + "Esperado=0.333, Predito=1.277, Erro=0.944\n", + "Esperado=0.667, Predito=1.357, Erro=0.691\n", + "Esperado=0.333, Predito=1.493, Erro=1.160\n", + "Esperado=0.500, Predito=1.788, Erro=1.288\n", + "Esperado=0.667, Predito=1.767, Erro=1.101\n", + "Esperado=0.500, Predito=1.465, Erro=0.965\n", + "Esperado=0.333, Predito=1.420, Erro=1.086\n", + "Esperado=0.333, Predito=1.325, Erro=0.992\n", + "Esperado=0.500, Predito=1.494, Erro=0.994\n", + "Esperado=0.667, Predito=0.993, Erro=0.326\n", + "Esperado=0.500, Predito=1.211, Erro=0.711\n", + "Esperado=0.500, Predito=1.211, Erro=0.711\n", + "Esperado=0.500, Predito=1.122, Erro=0.622\n", + "Esperado=0.667, Predito=1.155, Erro=0.488\n", + "Esperado=0.333, Predito=1.279, Erro=0.946\n", + "Esperado=0.833, Predito=1.364, Erro=0.531\n", + "Esperado=0.833, Predito=1.295, Erro=0.462\n", + "Esperado=0.500, Predito=1.122, Erro=0.622\n", + "Esperado=0.667, Predito=1.109, Erro=0.442\n", + "Esperado=0.500, Predito=1.229, Erro=0.729\n", + "Esperado=0.500, Predito=1.138, Erro=0.638\n", + "Esperado=0.500, Predito=1.234, Erro=0.734\n", + "Esperado=0.667, Predito=1.073, Erro=0.406\n", + "Esperado=0.333, Predito=1.069, Erro=0.736\n", + "Esperado=0.833, Predito=1.265, Erro=0.432\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.667, Predito=1.155, Erro=0.488\n", + "Esperado=0.500, Predito=1.284, Erro=0.784\n", + "Esperado=0.667, Predito=1.850, Erro=1.184\n", + "Esperado=0.500, Predito=1.211, Erro=0.711\n", + "Esperado=0.500, Predito=1.224, Erro=0.724\n", + "Esperado=0.333, Predito=1.270, Erro=0.936\n", + "Esperado=0.333, Predito=1.349, Erro=1.016\n", + "Esperado=0.333, Predito=1.279, Erro=0.946\n", + "Esperado=0.667, Predito=1.531, Erro=0.864\n", + "Esperado=0.833, Predito=1.364, Erro=0.531\n", + "Esperado=0.667, Predito=1.749, Erro=1.083\n", + "Esperado=0.667, Predito=1.526, Erro=0.859\n", + "Esperado=0.167, Predito=1.157, Erro=0.990\n", + "Esperado=0.667, Predito=1.749, Erro=1.083\n", + "Esperado=0.500, Predito=1.376, Erro=0.876\n", + "Esperado=0.500, Predito=1.261, Erro=0.761\n", + "Esperado=0.333, Predito=1.186, Erro=0.852\n", + "Esperado=0.167, Predito=1.628, Erro=1.461\n", + "Esperado=0.833, Predito=1.055, Erro=0.221\n", + "Esperado=0.333, Predito=1.615, Erro=1.282\n", + "Esperado=0.333, Predito=1.762, Erro=1.428\n", + "Esperado=0.333, Predito=1.614, Erro=1.281\n", + "Esperado=0.333, Predito=1.564, Erro=1.231\n", + "Esperado=0.500, Predito=1.002, Erro=0.502\n", + "Esperado=0.500, Predito=1.534, Erro=1.034\n", + "Esperado=0.667, Predito=1.324, Erro=0.657\n", + "Esperado=0.333, Predito=1.506, Erro=1.173\n", + "Esperado=0.333, Predito=1.305, Erro=0.971\n", + "Esperado=0.500, Predito=1.757, Erro=1.257\n", + "Esperado=0.667, Predito=1.074, Erro=0.408\n", + "Esperado=0.667, Predito=1.088, Erro=0.421\n", + "Esperado=0.333, Predito=0.944, Erro=0.611\n", + "Esperado=0.667, Predito=1.283, Erro=0.616\n", + "Esperado=0.500, Predito=1.078, Erro=0.578\n", + "Esperado=0.500, Predito=1.246, Erro=0.746\n", + "Esperado=0.333, Predito=1.530, Erro=1.197\n", + "Esperado=0.333, Predito=1.178, Erro=0.845\n", + "Esperado=0.333, Predito=1.562, Erro=1.229\n", + "Esperado=0.500, Predito=1.290, Erro=0.790\n", + "Esperado=0.833, Predito=1.427, Erro=0.594\n", + "Esperado=0.833, Predito=1.487, Erro=0.653\n", + "Esperado=0.333, Predito=1.386, Erro=1.053\n", + "Esperado=0.333, Predito=1.392, Erro=1.059\n", + "Esperado=0.500, Predito=1.141, Erro=0.641\n", + "Esperado=0.333, Predito=1.586, Erro=1.252\n", + "Esperado=0.333, Predito=1.386, Erro=1.053\n", + "Esperado=0.333, Predito=1.277, Erro=0.944\n", + "Esperado=0.333, Predito=1.288, Erro=0.955\n", + "Esperado=0.333, Predito=1.284, Erro=0.951\n", + "Esperado=0.333, Predito=1.294, Erro=0.960\n", + "Esperado=0.333, Predito=1.411, Erro=1.078\n", + "Esperado=0.500, Predito=1.201, Erro=0.701\n", + "Esperado=0.333, Predito=1.157, Erro=0.824\n", + "Esperado=0.333, Predito=1.276, Erro=0.943\n", + "Esperado=0.333, Predito=1.284, Erro=0.951\n", + "Esperado=0.167, Predito=1.773, Erro=1.607\n", + "Esperado=0.500, Predito=1.487, Erro=0.987\n", + "Esperado=0.167, Predito=1.060, Erro=0.893\n", + "Esperado=0.167, Predito=1.480, Erro=1.313\n", + "Esperado=0.500, Predito=1.265, Erro=0.765\n", + "Esperado=0.500, Predito=1.265, Erro=0.765\n", + "Esperado=0.500, Predito=1.228, Erro=0.728\n", + "Esperado=0.500, Predito=1.265, Erro=0.765\n", + "Esperado=0.500, Predito=1.296, Erro=0.796\n", + "Esperado=0.500, Predito=1.265, Erro=0.765\n", + "Esperado=0.667, Predito=1.376, Erro=0.709\n", + "Esperado=0.500, Predito=1.228, Erro=0.728\n", + "Esperado=0.500, Predito=1.186, Erro=0.686\n", + "Esperado=0.333, Predito=1.270, Erro=0.937\n", + "Esperado=0.333, Predito=1.457, Erro=1.124\n", + "Esperado=0.500, Predito=1.386, Erro=0.886\n", + "Esperado=0.333, Predito=1.171, Erro=0.838\n", + "Esperado=0.500, Predito=1.296, Erro=0.796\n", + "Esperado=0.500, Predito=1.324, Erro=0.824\n", + "Esperado=0.333, Predito=1.786, Erro=1.453\n", + "Esperado=0.500, Predito=1.541, Erro=1.041\n", + "Esperado=0.333, Predito=1.400, Erro=1.066\n", + "Esperado=0.500, Predito=1.372, Erro=0.872\n", + "Esperado=0.333, Predito=1.185, Erro=0.852\n", + "Esperado=0.667, Predito=1.413, Erro=0.746\n", + "Esperado=0.500, Predito=1.362, Erro=0.862\n", + "Esperado=0.333, Predito=1.489, Erro=1.156\n", + "Esperado=0.333, Predito=1.067, Erro=0.733\n", + "Esperado=0.333, Predito=1.786, Erro=1.453\n", + "Esperado=0.500, Predito=1.508, Erro=1.008\n", + "Esperado=0.333, Predito=1.400, Erro=1.066\n", + "Esperado=0.500, Predito=1.313, Erro=0.813\n", + "Esperado=0.667, Predito=1.826, Erro=1.160\n", + "Esperado=0.333, Predito=1.533, Erro=1.199\n", + "Esperado=0.333, Predito=1.297, Erro=0.964\n", + "Esperado=0.833, Predito=1.219, Erro=0.386\n", + "Esperado=0.500, Predito=1.083, Erro=0.583\n", + "Esperado=0.333, Predito=1.235, Erro=0.901\n", + "Esperado=0.500, Predito=1.541, Erro=1.041\n", + "Esperado=0.667, Predito=1.500, Erro=0.833\n", + "Esperado=0.500, Predito=1.208, Erro=0.708\n", + "Esperado=0.667, Predito=1.172, Erro=0.505\n", + "Esperado=0.500, Predito=1.565, Erro=1.065\n", + "Esperado=0.500, Predito=1.185, Erro=0.685\n", + "Esperado=0.667, Predito=1.500, Erro=0.833\n", + "Esperado=0.667, Predito=1.172, Erro=0.505\n", + "Esperado=0.500, Predito=1.208, Erro=0.708\n", + "Esperado=0.667, Predito=1.306, Erro=0.640\n", + "Esperado=0.500, Predito=1.186, Erro=0.686\n", + "Esperado=0.667, Predito=1.175, Erro=0.508\n", + "Esperado=0.333, Predito=1.211, Erro=0.878\n", + "Esperado=0.500, Predito=1.437, Erro=0.937\n", + "Esperado=0.500, Predito=1.437, Erro=0.937\n", + "Esperado=0.333, Predito=1.368, Erro=1.035\n", + "Esperado=0.500, Predito=1.133, Erro=0.633\n", + "Esperado=0.333, Predito=1.211, Erro=0.878\n", + "Esperado=0.500, Predito=1.335, Erro=0.835\n", + "Esperado=0.500, Predito=1.414, Erro=0.914\n", + "Esperado=0.500, Predito=1.177, Erro=0.677\n", + "Esperado=0.333, Predito=1.191, Erro=0.858\n", + "Esperado=0.500, Predito=1.529, Erro=1.029\n", + "Esperado=0.500, Predito=1.437, Erro=0.937\n", + "Esperado=0.500, Predito=1.267, Erro=0.767\n", + "Esperado=0.333, Predito=1.365, Erro=1.032\n", + "Esperado=0.833, Predito=1.471, Erro=0.637\n", + "Esperado=0.333, Predito=1.933, Erro=1.600\n", + "Esperado=0.833, Predito=1.258, Erro=0.425\n", + "Esperado=0.833, Predito=1.471, Erro=0.637\n", + "Esperado=0.500, Predito=1.282, Erro=0.782\n", + "Esperado=0.667, Predito=1.825, Erro=1.158\n", + "Esperado=0.500, Predito=1.456, Erro=0.956\n", + "Esperado=0.333, Predito=1.365, Erro=1.032\n", + "Esperado=0.667, Predito=1.447, Erro=0.780\n", + "Esperado=0.500, Predito=1.608, Erro=1.108\n", + "Esperado=0.667, Predito=1.447, Erro=0.780\n", + "Esperado=0.333, Predito=1.141, Erro=0.808\n", + "Esperado=0.500, Predito=1.355, Erro=0.855\n", + "Esperado=0.000, Predito=1.476, Erro=1.476\n", + "Esperado=0.500, Predito=1.227, Erro=0.727\n", + "Esperado=0.667, Predito=1.780, Erro=1.114\n", + "Esperado=0.667, Predito=1.846, Erro=1.179\n", + "Esperado=0.500, Predito=1.597, Erro=1.097\n", + "Esperado=0.500, Predito=1.341, Erro=0.841\n", + "Esperado=0.333, Predito=1.423, Erro=1.090\n", + "Esperado=0.500, Predito=1.608, Erro=1.108\n", + "Esperado=0.333, Predito=1.339, Erro=1.005\n", + "Esperado=0.667, Predito=1.814, Erro=1.148\n", + "Esperado=0.333, Predito=1.374, Erro=1.041\n", + "Esperado=0.500, Predito=1.023, Erro=0.523\n", + "Esperado=0.667, Predito=1.170, Erro=0.503\n", + "Esperado=0.667, Predito=1.767, Erro=1.100\n", + "Esperado=0.667, Predito=1.814, Erro=1.148\n", + "Esperado=0.333, Predito=1.195, Erro=0.861\n", + "Esperado=0.167, Predito=2.135, Erro=1.968\n", + "Esperado=0.667, Predito=1.201, Erro=0.534\n", + "Esperado=0.500, Predito=1.242, Erro=0.742\n", + "Esperado=0.667, Predito=1.157, Erro=0.490\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Esperado=0.333, Predito=1.939, Erro=1.606\n", + "Esperado=0.667, Predito=1.416, Erro=0.749\n", + "Esperado=0.333, Predito=1.146, Erro=0.813\n", + "Esperado=0.500, Predito=1.143, Erro=0.643\n", + "Esperado=0.667, Predito=1.383, Erro=0.716\n", + 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Predito=1.525, Erro=1.025\n", + "Esperado=0.500, Predito=1.141, Erro=0.641\n", + "Esperado=0.667, Predito=1.322, Erro=0.655\n", + "Esperado=0.333, Predito=1.251, Erro=0.917\n", + "Esperado=0.500, Predito=1.263, Erro=0.763\n", + "Esperado=0.500, Predito=1.436, Erro=0.936\n", + "Esperado=0.500, Predito=1.488, Erro=0.988\n", + "Esperado=0.667, Predito=1.437, Erro=0.770\n", + "Esperado=0.333, Predito=1.268, Erro=0.934\n", + "Esperado=0.833, Predito=1.674, Erro=0.841\n", + "Esperado=0.667, Predito=1.437, Erro=0.770\n", + "Esperado=0.500, Predito=1.488, Erro=0.988\n", + "Esperado=0.667, Predito=1.170, Erro=0.503\n", + "Esperado=0.667, Predito=1.486, Erro=0.819\n", + "Esperado=0.667, Predito=1.608, Erro=0.942\n", + "Esperado=0.500, Predito=1.177, Erro=0.677\n", + "Esperado=0.500, Predito=1.044, Erro=0.544\n", + "Esperado=0.500, Predito=1.289, Erro=0.789\n", + "Esperado=0.500, Predito=1.208, Erro=0.708\n", + "Esperado=0.167, Predito=1.888, Erro=1.721\n", + "Esperado=0.167, Predito=1.916, Erro=1.750\n", + "Esperado=0.500, Predito=1.289, Erro=0.789\n", + "Esperado=0.500, Predito=1.566, Erro=1.066\n", + "Esperado=0.667, Predito=1.700, Erro=1.033\n", + "Esperado=0.500, Predito=1.626, Erro=1.126\n", + "Esperado=0.333, Predito=1.283, Erro=0.950\n", + "Esperado=0.500, Predito=1.194, Erro=0.694\n", + "Esperado=0.333, Predito=1.161, Erro=0.828\n", + "Esperado=0.500, Predito=1.069, Erro=0.569\n", + "Esperado=0.500, Predito=1.098, Erro=0.598\n", + "Esperado=0.333, Predito=2.054, Erro=1.721\n", + "Esperado=0.667, Predito=1.165, Erro=0.498\n", + "Esperado=0.833, Predito=1.454, Erro=0.621\n", + "Esperado=0.333, Predito=1.146, Erro=0.813\n", + "Esperado=0.500, Predito=1.111, Erro=0.611\n", + "Esperado=0.500, Predito=1.248, Erro=0.748\n", + "Esperado=0.500, Predito=1.451, Erro=0.951\n", + "Esperado=0.500, Predito=1.301, Erro=0.801\n", + "Esperado=0.333, Predito=1.264, Erro=0.931\n", + "Esperado=0.333, Predito=1.369, Erro=1.036\n", + "Esperado=0.500, Predito=1.320, Erro=0.820\n", + "Esperado=0.500, Predito=1.207, Erro=0.707\n", + "Esperado=0.500, Predito=1.301, Erro=0.801\n", + "Esperado=0.333, Predito=1.258, Erro=0.925\n", + "Esperado=0.500, Predito=1.164, Erro=0.664\n", + "Esperado=0.333, Predito=1.142, Erro=0.808\n", + "Esperado=0.500, Predito=1.538, Erro=1.038\n", + "Esperado=0.500, Predito=1.791, Erro=1.291\n", + "Esperado=0.500, Predito=1.449, Erro=0.949\n", + "Esperado=0.333, Predito=1.413, Erro=1.080\n", + "Esperado=0.500, Predito=1.270, Erro=0.770\n", + "Esperado=0.667, Predito=1.633, Erro=0.966\n", + "Esperado=0.500, Predito=1.084, Erro=0.584\n", + "Esperado=0.500, Predito=1.293, Erro=0.793\n", + "Esperado=0.500, Predito=1.522, Erro=1.022\n", + "Esperado=0.333, Predito=1.089, Erro=0.756\n", + "Esperado=0.333, Predito=1.118, Erro=0.785\n", + "Esperado=0.500, Predito=0.993, Erro=0.493\n", + "Esperado=0.667, Predito=1.153, Erro=0.487\n", + "Esperado=0.833, Predito=1.582, Erro=0.749\n", + "Esperado=0.500, Predito=1.347, Erro=0.847\n", + "Esperado=0.500, Predito=1.433, Erro=0.933\n", + "Esperado=0.833, Predito=1.381, Erro=0.547\n", + "Esperado=0.333, Predito=1.206, Erro=0.872\n", + "Esperado=0.333, Predito=1.206, Erro=0.872\n", + "Esperado=0.500, Predito=1.866, Erro=1.366\n", + "Esperado=0.500, Predito=1.010, Erro=0.510\n", + "Esperado=0.333, Predito=1.687, Erro=1.353\n", + "Esperado=0.500, Predito=1.866, Erro=1.366\n", + "Esperado=0.500, Predito=1.328, Erro=0.828\n", + "Esperado=0.833, Predito=1.350, Erro=0.517\n", + "Esperado=0.833, Predito=1.737, Erro=0.904\n", + "Esperado=0.667, Predito=1.731, Erro=1.064\n", + "Esperado=0.667, Predito=1.509, Erro=0.842\n", + "Esperado=0.833, Predito=1.381, Erro=0.547\n", + "Esperado=0.167, Predito=1.315, Erro=1.149\n", + "Esperado=0.667, Predito=2.190, Erro=1.523\n", + "Esperado=0.667, Predito=1.621, Erro=0.954\n", + "Esperado=0.500, Predito=1.433, Erro=0.933\n", + "Esperado=0.333, Predito=1.206, Erro=0.872\n", + "Esperado=0.333, Predito=0.974, Erro=0.640\n", + "Esperado=0.333, Predito=1.194, Erro=0.861\n", + "Esperado=0.500, Predito=1.285, Erro=0.785\n", + "Esperado=0.500, Predito=1.269, Erro=0.769\n", + "Esperado=0.833, Predito=1.586, Erro=0.752\n", + "Esperado=0.667, Predito=1.273, Erro=0.607\n", + "Esperado=0.500, Predito=1.053, Erro=0.553\n", + "Esperado=0.667, Predito=1.123, Erro=0.456\n", + "Esperado=0.667, Predito=1.353, Erro=0.686\n", + "Esperado=0.167, Predito=1.286, Erro=1.119\n", + "Esperado=0.333, Predito=1.194, Erro=0.861\n", + "Esperado=0.667, Predito=0.969, Erro=0.302\n", + "Esperado=0.500, Predito=1.099, Erro=0.599\n", + "Esperado=0.333, Predito=1.213, Erro=0.880\n", + "Esperado=0.500, Predito=1.285, Erro=0.785\n", + "Esperado=0.333, Predito=1.963, Erro=1.629\n", + "Esperado=0.500, Predito=1.313, Erro=0.813\n", + "Esperado=0.667, Predito=1.392, Erro=0.725\n", + "Esperado=0.500, Predito=1.347, Erro=0.847\n", + "Esperado=0.500, Predito=1.347, Erro=0.847\n", + "Esperado=0.667, Predito=1.324, Erro=0.658\n", + "Esperado=0.667, Predito=1.390, Erro=0.723\n", + "Esperado=0.500, Predito=1.550, Erro=1.050\n", + "Esperado=0.500, Predito=1.585, Erro=1.085\n", + "Esperado=0.667, Predito=1.349, Erro=0.682\n", + "Esperado=0.500, Predito=1.605, Erro=1.105\n", + "Esperado=0.667, Predito=1.590, Erro=0.923\n", + "Esperado=0.667, Predito=1.877, Erro=1.210\n", + "Esperado=0.500, Predito=1.649, Erro=1.149\n", + "Esperado=0.500, Predito=1.396, Erro=0.896\n", + "Esperado=0.500, Predito=1.494, Erro=0.994\n", + "Esperado=0.333, Predito=1.546, Erro=1.213\n", + "Esperado=0.667, Predito=2.273, Erro=1.606\n", + "Esperado=0.500, Predito=1.838, Erro=1.338\n", + "Esperado=0.667, Predito=1.235, Erro=0.568\n", + "Esperado=0.500, Predito=1.066, Erro=0.566\n", + "Esperado=0.500, Predito=1.165, Erro=0.665\n", + "Esperado=0.500, Predito=1.217, Erro=0.717\n", + "Esperado=0.500, Predito=1.838, Erro=1.338\n", + "Esperado=0.500, Predito=0.979, Erro=0.479\n", + "Esperado=0.667, Predito=2.273, Erro=1.606\n", + "Esperado=0.667, Predito=1.296, Erro=0.629\n", + "Esperado=0.667, Predito=1.235, Erro=0.568\n", + "Esperado=0.667, Predito=1.204, Erro=0.537\n", + "Esperado=0.667, Predito=1.204, Erro=0.537\n", + "Esperado=0.500, Predito=1.284, Erro=0.784\n", + "Esperado=0.667, Predito=1.204, Erro=0.537\n", + "Esperado=0.500, Predito=2.029, Erro=1.529\n", + "Esperado=0.833, Predito=1.655, Erro=0.822\n", + "Esperado=0.833, Predito=1.488, Erro=0.654\n", + "Esperado=0.333, Predito=1.418, Erro=1.084\n", + "Esperado=0.167, Predito=1.442, Erro=1.276\n", + "Esperado=0.833, Predito=1.179, Erro=0.345\n", + "Esperado=0.500, Predito=1.451, Erro=0.951\n", + "Esperado=0.667, Predito=1.204, Erro=0.537\n", + "Esperado=0.500, Predito=1.178, Erro=0.678\n", + "Esperado=0.500, Predito=1.375, Erro=0.875\n", + "Esperado=0.500, Predito=1.284, Erro=0.784\n", + "Esperado=0.833, Predito=1.756, Erro=0.922\n", + "Esperado=0.500, Predito=1.365, Erro=0.865\n", + "Esperado=0.500, Predito=1.154, Erro=0.654\n", + "Esperado=0.333, Predito=1.423, Erro=1.089\n", + "Esperado=0.500, Predito=1.327, Erro=0.827\n", + "Esperado=0.000, Predito=2.043, Erro=2.043\n", + "Esperado=0.333, Predito=1.493, Erro=1.159\n", + "Esperado=0.667, Predito=1.063, Erro=0.396\n", + "Esperado=0.167, Predito=1.276, Erro=1.109\n", + "Esperado=0.500, Predito=1.189, Erro=0.689\n", + "Esperado=0.333, Predito=1.036, Erro=0.703\n", + "Esperado=0.167, Predito=1.281, Erro=1.115\n", + "Esperado=0.500, Predito=1.365, Erro=0.865\n", + "Esperado=0.500, Predito=1.226, Erro=0.726\n", + "Esperado=0.500, Predito=1.301, Erro=0.801\n", + "Esperado=0.333, Predito=1.409, Erro=1.075\n", + "Esperado=0.667, Predito=1.295, Erro=0.628\n", + "Esperado=0.333, Predito=1.170, Erro=0.837\n", + "Esperado=0.167, Predito=1.374, Erro=1.207\n", + "Esperado=0.333, Predito=1.170, Erro=0.837\n", + "Esperado=0.667, Predito=1.598, Erro=0.932\n", + "Esperado=0.500, Predito=1.369, Erro=0.869\n", + "Esperado=0.333, Predito=1.409, Erro=1.075\n", + "Esperado=0.333, Predito=1.254, Erro=0.921\n", + "Esperado=0.333, Predito=1.968, Erro=1.635\n", + "Esperado=0.667, Predito=1.295, Erro=0.628\n", + "Esperado=0.333, Predito=1.127, Erro=0.794\n", + "Esperado=0.333, Predito=1.202, Erro=0.868\n", + "Esperado=0.333, Predito=1.202, Erro=0.869\n", + "Esperado=0.333, Predito=1.317, Erro=0.984\n", + "Esperado=0.333, Predito=1.125, Erro=0.792\n", + "Esperado=0.500, Predito=1.260, Erro=0.760\n", + "Esperado=0.500, Predito=1.623, Erro=1.123\n", + "Esperado=0.500, Predito=1.326, Erro=0.826\n", + "Esperado=0.500, Predito=1.443, Erro=0.943\n", + "Esperado=0.500, Predito=1.193, Erro=0.693\n", + "Esperado=0.500, Predito=1.193, Erro=0.693\n", + "Esperado=0.500, Predito=1.145, Erro=0.645\n", + "Esperado=0.500, Predito=1.623, Erro=1.123\n", + "Esperado=0.333, Predito=1.413, Erro=1.079\n", + "Esperado=0.500, Predito=1.623, Erro=1.123\n", + "Esperado=0.667, Predito=1.845, Erro=1.178\n", + "Esperado=0.333, Predito=1.243, Erro=0.910\n", + "Esperado=0.333, Predito=1.620, Erro=1.287\n", + "Esperado=0.500, Predito=1.443, Erro=0.943\n", + "Esperado=0.500, Predito=1.326, Erro=0.826\n", + "Esperado=0.500, Predito=1.345, Erro=0.845\n", + "Esperado=0.500, Predito=1.050, Erro=0.550\n", + "Esperado=0.500, Predito=1.281, Erro=0.781\n", + "Esperado=0.500, Predito=1.172, Erro=0.672\n", + "Esperado=0.500, Predito=1.193, Erro=0.693\n", + "Esperado=0.500, Predito=1.209, Erro=0.709\n", + "Esperado=0.833, Predito=1.679, Erro=0.845\n", + "Esperado=0.333, Predito=1.254, Erro=0.920\n", + "Esperado=0.500, Predito=1.145, Erro=0.645\n", + "Esperado=0.667, Predito=1.362, Erro=0.695\n", + "Esperado=0.333, Predito=1.161, Erro=0.827\n", + "Esperado=0.333, Predito=1.413, Erro=1.079\n", + "Esperado=0.667, Predito=1.845, Erro=1.178\n", + "Esperado=0.500, Predito=1.623, Erro=1.123\n", + "Esperado=0.333, Predito=1.150, Erro=0.816\n", + "Esperado=0.500, Predito=1.255, Erro=0.755\n", + "Esperado=0.167, Predito=1.215, Erro=1.048\n", + "Esperado=0.500, Predito=0.991, Erro=0.491\n", + "Esperado=0.333, Predito=2.330, Erro=1.997\n", + "Esperado=0.500, Predito=1.632, Erro=1.132\n", + "Esperado=0.500, Predito=1.183, Erro=0.683\n", + "Esperado=0.500, Predito=1.201, Erro=0.701\n", + "Esperado=0.500, Predito=0.991, Erro=0.491\n", + "Esperado=0.500, Predito=1.381, Erro=0.881\n", + "Esperado=0.500, Predito=1.436, Erro=0.936\n", + "Esperado=0.167, Predito=1.215, Erro=1.048\n", + "Esperado=0.000, Predito=1.347, Erro=1.347\n", + "Esperado=0.500, Predito=1.274, Erro=0.774\n", + "Esperado=0.500, Predito=1.196, Erro=0.696\n", + "Esperado=0.500, Predito=1.332, Erro=0.832\n", + "Esperado=0.500, Predito=1.335, Erro=0.835\n", + "Esperado=0.333, Predito=1.057, Erro=0.723\n", + "Esperado=0.500, Predito=1.169, Erro=0.669\n", + "Esperado=0.333, Predito=1.337, Erro=1.003\n", + "Esperado=0.333, Predito=1.392, Erro=1.059\n", + "Esperado=0.833, Predito=1.550, Erro=0.717\n", + "Esperado=0.833, Predito=1.572, Erro=0.739\n", + "Esperado=0.667, Predito=1.463, Erro=0.796\n", + "Esperado=0.333, Predito=1.852, Erro=1.519\n", + "Esperado=0.667, Predito=1.444, Erro=0.777\n", + "Esperado=0.500, Predito=1.448, Erro=0.948\n", + "Esperado=0.500, Predito=2.027, Erro=1.527\n", + "Esperado=0.667, Predito=1.121, Erro=0.454\n", + "Esperado=0.333, Predito=1.111, Erro=0.778\n", + "Esperado=0.333, Predito=1.194, Erro=0.861\n", + "Esperado=0.667, Predito=1.561, Erro=0.894\n", + "Esperado=0.833, Predito=1.255, Erro=0.421\n", + "Esperado=0.667, Predito=1.145, Erro=0.479\n", + "Esperado=0.500, Predito=1.315, Erro=0.815\n", + "Esperado=0.500, Predito=1.440, Erro=0.940\n", + "Esperado=0.500, Predito=1.248, Erro=0.748\n", + "Esperado=0.333, Predito=1.212, Erro=0.878\n", + "Esperado=0.333, Predito=1.160, Erro=0.826\n", + "Esperado=0.500, Predito=1.343, Erro=0.843\n", + "Esperado=0.667, Predito=1.175, Erro=0.508\n", + "Esperado=0.500, Predito=1.169, Erro=0.669\n", + "Esperado=0.667, Predito=1.121, Erro=0.454\n", + "Esperado=0.500, Predito=1.336, Erro=0.836\n", + "Esperado=0.500, Predito=1.174, Erro=0.674\n", + "Esperado=0.500, Predito=1.210, Erro=0.710\n", + "Esperado=0.500, Predito=1.291, Erro=0.791\n", + "Esperado=0.333, Predito=1.111, Erro=0.778\n", + "Esperado=0.500, Predito=1.112, Erro=0.612\n", + "Esperado=0.333, Predito=1.194, Erro=0.861\n", + "Esperado=0.333, Predito=1.380, Erro=1.046\n", + "Esperado=0.500, Predito=1.336, Erro=0.836\n", + "Esperado=0.500, Predito=1.098, Erro=0.598\n", + "Esperado=0.333, Predito=1.207, Erro=0.874\n", + "Esperado=0.500, Predito=1.456, Erro=0.956\n", + "Esperado=0.500, Predito=1.468, Erro=0.968\n", + "Esperado=0.500, Predito=1.423, Erro=0.923\n", + "Esperado=0.500, Predito=1.098, Erro=0.598\n", + "Esperado=0.333, Predito=1.207, Erro=0.874\n", + "Esperado=0.667, Predito=1.335, Erro=0.668\n", + "Esperado=0.667, Predito=1.216, Erro=0.549\n", + "Esperado=0.500, Predito=1.400, Erro=0.900\n", + "Esperado=0.500, Predito=1.277, Erro=0.777\n", + "Esperado=0.500, Predito=1.046, Erro=0.546\n", + "Esperado=0.333, Predito=0.904, Erro=0.571\n", + "Esperado=0.500, Predito=1.276, Erro=0.776\n", + "Esperado=0.500, Predito=1.054, Erro=0.554\n", + "Esperado=0.500, Predito=1.400, Erro=0.900\n", + "Esperado=0.500, Predito=1.277, Erro=0.777\n", + "Esperado=0.167, Predito=1.569, Erro=1.403\n", + "Esperado=0.500, Predito=1.217, Erro=0.717\n", + "Esperado=0.500, Predito=1.288, Erro=0.788\n", + "Esperado=0.333, Predito=0.904, Erro=0.571\n", + "Esperado=0.500, Predito=1.104, Erro=0.604\n", + "Esperado=0.500, Predito=1.315, Erro=0.815\n", + "Esperado=0.500, Predito=1.332, Erro=0.832\n", + "Esperado=0.500, Predito=1.752, Erro=1.252\n", + "Esperado=0.500, Predito=1.046, Erro=0.546\n", + "Esperado=0.500, Predito=1.219, Erro=0.719\n", + "Esperado=0.500, Predito=0.958, Erro=0.458\n", + "Esperado=0.667, Predito=1.381, Erro=0.715\n", + "Esperado=0.667, Predito=1.592, Erro=0.925\n", + "Esperado=0.667, Predito=1.795, Erro=1.129\n", + "Esperado=0.667, Predito=1.393, Erro=0.726\n", + "Esperado=0.500, Predito=1.298, Erro=0.798\n", + "Esperado=0.500, Predito=1.219, Erro=0.719\n", + "Esperado=0.167, Predito=1.593, Erro=1.426\n", + "Esperado=0.167, Predito=1.352, Erro=1.185\n", + "Esperado=0.500, Predito=1.607, Erro=1.107\n", + "Esperado=0.333, Predito=1.340, Erro=1.007\n", + "Esperado=0.667, Predito=1.481, Erro=0.814\n", + "Esperado=0.500, Predito=1.607, Erro=1.107\n", + "Esperado=0.333, Predito=1.340, Erro=1.007\n", + "Esperado=0.500, Predito=1.200, Erro=0.700\n", + "Esperado=0.333, Predito=1.083, Erro=0.749\n", + "Esperado=0.333, Predito=1.152, Erro=0.819\n", + "Esperado=0.500, Predito=1.479, Erro=0.979\n", + "Esperado=0.333, Predito=1.349, Erro=1.016\n", + "Esperado=0.333, Predito=1.343, Erro=1.010\n", + "Esperado=0.500, Predito=1.479, Erro=0.979\n", + "Esperado=0.500, Predito=1.318, Erro=0.818\n", + "Esperado=0.333, Predito=1.219, Erro=0.885\n", + "Esperado=0.167, Predito=1.063, Erro=0.896\n", + "Esperado=0.500, Predito=1.433, Erro=0.933\n", + "Esperado=0.500, Predito=1.265, Erro=0.765\n", + "Esperado=0.167, Predito=2.095, Erro=1.928\n", + "Esperado=0.333, Predito=1.376, Erro=1.042\n", + "Esperado=0.167, Predito=1.395, Erro=1.229\n", + "Esperado=0.333, Predito=1.383, Erro=1.050\n", + "Esperado=0.500, Predito=1.360, Erro=0.860\n", + "Esperado=0.667, Predito=1.300, Erro=0.633\n", + "Esperado=0.333, Predito=1.376, Erro=1.042\n", + "Esperado=0.500, Predito=1.192, Erro=0.692\n", + "Esperado=0.333, Predito=1.232, Erro=0.899\n", + "Esperado=0.333, Predito=1.362, Erro=1.028\n", + "Esperado=0.500, Predito=1.192, Erro=0.692\n", + "Esperado=0.333, Predito=1.232, Erro=0.899\n", + "Esperado=0.500, Predito=1.410, Erro=0.910\n", + "Esperado=0.500, Predito=1.473, Erro=0.973\n", + "Esperado=0.500, Predito=1.073, Erro=0.573\n", + "Esperado=0.500, Predito=1.188, Erro=0.688\n", + "Esperado=0.500, Predito=1.241, Erro=0.741\n", + "Esperado=0.500, Predito=1.222, Erro=0.722\n", + "Esperado=0.500, Predito=1.236, Erro=0.736\n", + "Esperado=0.333, Predito=1.804, Erro=1.471\n", + "Esperado=0.500, Predito=1.236, Erro=0.736\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.500, Predito=1.540, Erro=1.040\n", + "Esperado=0.500, Predito=1.222, Erro=0.722\n", + "Esperado=0.500, Predito=1.542, Erro=1.042\n", + "Esperado=0.333, Predito=1.628, Erro=1.294\n", + "Esperado=0.667, Predito=1.803, Erro=1.137\n", + "Esperado=0.333, Predito=1.433, Erro=1.099\n", + "Esperado=1.000, Predito=1.471, Erro=0.471\n", + "Esperado=0.500, Predito=1.553, Erro=1.053\n", + "Esperado=0.667, Predito=1.068, Erro=0.402\n", + "Esperado=0.333, Predito=1.259, Erro=0.926\n", + "Esperado=0.667, Predito=1.202, Erro=0.535\n", + "Esperado=0.500, Predito=1.553, Erro=1.053\n", + "Esperado=0.500, Predito=1.201, Erro=0.701\n", + "Esperado=0.667, Predito=1.255, Erro=0.588\n", + "Esperado=0.667, Predito=1.077, Erro=0.410\n", + "Esperado=0.333, Predito=1.333, Erro=0.999\n", + "Esperado=0.500, Predito=1.239, Erro=0.739\n", + "Esperado=0.500, Predito=1.314, Erro=0.814\n", + "Esperado=0.500, Predito=1.148, Erro=0.648\n", + "Esperado=0.500, Predito=1.405, Erro=0.905\n", + "Esperado=0.833, Predito=1.249, Erro=0.416\n", + "Esperado=0.667, Predito=1.255, Erro=0.588\n", + "Esperado=0.333, Predito=1.266, Erro=0.933\n", + "Esperado=0.500, Predito=1.431, Erro=0.931\n", + "Esperado=0.500, Predito=0.977, Erro=0.477\n", + "Esperado=0.667, Predito=1.192, Erro=0.525\n", + "Esperado=0.500, Predito=1.332, Erro=0.832\n", + "Esperado=0.500, Predito=1.280, Erro=0.780\n", + "Esperado=0.333, Predito=1.266, Erro=0.933\n", + "Esperado=0.500, Predito=1.213, Erro=0.713\n", + "Esperado=0.500, Predito=0.937, Erro=0.437\n", + "Esperado=0.333, Predito=1.530, Erro=1.197\n", + "Esperado=0.500, Predito=1.213, Erro=0.713\n", + "Esperado=0.833, Predito=1.586, Erro=0.753\n", + "Esperado=0.667, Predito=1.177, Erro=0.511\n", + "Esperado=0.500, Predito=1.292, Erro=0.792\n", + "Esperado=0.500, Predito=1.076, Erro=0.576\n", + "Esperado=0.333, Predito=1.611, Erro=1.278\n", + "Esperado=0.500, Predito=1.021, Erro=0.521\n", + "Esperado=0.333, Predito=1.695, Erro=1.362\n", + "Esperado=0.333, Predito=1.187, Erro=0.854\n", + "Esperado=0.333, Predito=1.611, Erro=1.278\n", + "Esperado=0.500, Predito=1.278, Erro=0.778\n", + "Esperado=0.667, Predito=1.096, Erro=0.430\n", + "Esperado=0.667, Predito=1.475, Erro=0.808\n", + "Esperado=0.667, Predito=1.096, Erro=0.430\n", + "Esperado=0.667, Predito=1.403, Erro=0.737\n", + "Esperado=0.500, Predito=1.278, Erro=0.778\n", + "Esperado=0.667, Predito=1.189, Erro=0.523\n", + "Esperado=0.333, Predito=1.478, Erro=1.145\n", + "Esperado=0.167, Predito=1.339, Erro=1.173\n", + "Esperado=0.667, Predito=1.711, Erro=1.044\n", + "Esperado=0.500, Predito=1.605, Erro=1.105\n", + "Esperado=0.167, Predito=1.335, Erro=1.168\n", + "Esperado=0.500, Predito=1.393, Erro=0.893\n", + "Esperado=0.333, Predito=1.500, Erro=1.167\n", + "Esperado=0.333, Predito=1.412, Erro=1.079\n", + "Esperado=0.333, Predito=1.018, Erro=0.684\n", + "Esperado=0.500, Predito=1.753, Erro=1.253\n", + "Esperado=0.333, Predito=1.285, Erro=0.952\n", + "Esperado=0.333, Predito=1.318, Erro=0.985\n", + "Esperado=0.500, Predito=1.267, Erro=0.767\n", + "Esperado=0.500, Predito=1.286, Erro=0.786\n", + "Esperado=0.667, Predito=1.182, Erro=0.515\n", + "Esperado=0.500, Predito=1.393, Erro=0.893\n", + "Esperado=0.167, Predito=1.390, Erro=1.223\n", + "Esperado=0.833, Predito=1.203, Erro=0.370\n", + "Esperado=0.333, Predito=1.649, Erro=1.315\n", + "Esperado=0.500, Predito=1.670, Erro=1.170\n", + "Esperado=0.667, Predito=1.099, Erro=0.432\n", + "Esperado=0.500, Predito=1.320, Erro=0.820\n", + "Esperado=0.500, Predito=1.322, Erro=0.822\n", + "Esperado=0.667, Predito=1.535, Erro=0.868\n", + "Esperado=0.333, Predito=1.201, Erro=0.867\n", + "Esperado=0.333, Predito=1.216, Erro=0.883\n", + "Esperado=0.500, Predito=1.259, Erro=0.759\n", + "Esperado=0.333, Predito=1.240, Erro=0.907\n", + "Esperado=0.667, Predito=1.535, Erro=0.868\n", + "Esperado=0.500, Predito=1.223, Erro=0.723\n", + "Esperado=0.500, Predito=1.394, Erro=0.894\n", + "Esperado=0.333, Predito=1.432, Erro=1.098\n", + "Esperado=0.333, Predito=1.439, Erro=1.106\n", + "Esperado=0.500, Predito=1.657, Erro=1.157\n", + "Esperado=0.667, Predito=1.171, Erro=0.504\n", + "Esperado=0.667, Predito=1.171, Erro=0.504\n", + "Esperado=0.667, Predito=1.240, Erro=0.573\n", + "Esperado=0.667, Predito=1.171, Erro=0.504\n", + "Esperado=0.333, Predito=1.527, Erro=1.193\n", + "Esperado=0.667, Predito=1.240, Erro=0.573\n", + "Esperado=0.000, Predito=1.396, Erro=1.396\n", + "Esperado=0.500, Predito=1.262, Erro=0.762\n", + "Esperado=0.167, Predito=1.552, Erro=1.385\n", + "Esperado=0.667, Predito=1.171, Erro=0.504\n", + "Esperado=0.500, Predito=1.411, Erro=0.911\n", + "Esperado=0.333, Predito=1.295, Erro=0.962\n", + "Esperado=0.500, Predito=0.977, Erro=0.477\n", + "Esperado=0.500, Predito=1.226, Erro=0.726\n", + "Esperado=0.500, Predito=1.320, Erro=0.820\n", + "Esperado=0.500, Predito=1.245, Erro=0.745\n", + "Esperado=0.500, Predito=1.411, Erro=0.911\n", + "Esperado=0.500, Predito=1.385, Erro=0.885\n", + "Esperado=0.500, Predito=1.421, Erro=0.921\n", + "Esperado=0.333, Predito=1.384, Erro=1.051\n", + "Esperado=0.167, Predito=1.114, Erro=0.947\n", + "Esperado=0.333, Predito=1.367, Erro=1.034\n", + "Esperado=0.333, Predito=1.339, Erro=1.005\n", + "Esperado=0.500, Predito=1.374, Erro=0.874\n", + "Esperado=0.500, Predito=1.310, Erro=0.810\n", + "Esperado=0.333, Predito=1.287, Erro=0.954\n", + "Esperado=0.167, Predito=2.062, Erro=1.895\n", + "Esperado=0.333, Predito=1.213, Erro=0.879\n", + "Esperado=0.333, Predito=1.386, Erro=1.053\n", + "Esperado=0.333, Predito=1.276, Erro=0.942\n", + "Esperado=0.500, Predito=1.190, Erro=0.690\n", + "Esperado=0.500, Predito=1.473, Erro=0.973\n", + "Esperado=0.333, Predito=1.334, Erro=1.001\n", + "Esperado=0.833, Predito=1.340, Erro=0.506\n", + "Esperado=0.500, Predito=1.290, Erro=0.790\n", + "Esperado=0.500, Predito=1.239, Erro=0.739\n", + "Esperado=0.167, Predito=1.091, Erro=0.924\n", + "Esperado=0.500, Predito=1.075, Erro=0.575\n", + "Esperado=0.667, Predito=1.252, Erro=0.585\n", + "Esperado=0.667, Predito=1.193, Erro=0.526\n", + "Esperado=0.500, Predito=1.679, Erro=1.179\n", + "Esperado=0.833, Predito=1.340, Erro=0.506\n", + "Esperado=0.500, Predito=1.095, Erro=0.595\n", + "Esperado=0.500, Predito=1.031, Erro=0.531\n", + "Esperado=0.500, Predito=1.470, Erro=0.970\n", + "Esperado=0.500, Predito=1.619, Erro=1.119\n", + "Esperado=0.333, Predito=1.334, Erro=1.001\n", + "Esperado=0.500, Predito=1.482, Erro=0.982\n", + "Esperado=0.500, Predito=1.124, Erro=0.624\n", + "Esperado=0.500, Predito=1.398, Erro=0.898\n", + "Esperado=0.333, Predito=1.805, Erro=1.472\n", + "Esperado=0.500, Predito=1.402, Erro=0.902\n", + "Esperado=0.500, Predito=1.074, Erro=0.574\n", + "Esperado=0.500, Predito=1.566, Erro=1.066\n", + "Esperado=0.333, Predito=1.069, Erro=0.736\n", + "Esperado=0.500, Predito=1.074, Erro=0.574\n", + "Esperado=0.333, Predito=1.081, Erro=0.747\n", + "Esperado=0.167, Predito=1.291, Erro=1.124\n", + "Esperado=0.500, Predito=1.442, Erro=0.942\n", + "Esperado=0.500, Predito=1.601, Erro=1.101\n", + "Esperado=0.500, Predito=1.185, Erro=0.685\n", + "Esperado=0.500, Predito=1.374, Erro=0.874\n", + "Esperado=0.333, Predito=1.036, Erro=0.703\n", + "Esperado=0.333, Predito=1.354, Erro=1.021\n", + "Esperado=0.333, Predito=1.173, Erro=0.839\n", + "Esperado=0.500, Predito=1.374, Erro=0.874\n", + "Esperado=0.333, Predito=1.341, Erro=1.007\n", + "Esperado=0.500, Predito=1.106, Erro=0.606\n", + "Esperado=0.500, Predito=1.213, Erro=0.713\n", + "Esperado=0.667, Predito=1.254, Erro=0.588\n", + "Esperado=0.500, Predito=1.319, Erro=0.819\n", + "Esperado=0.667, Predito=1.320, Erro=0.653\n", + "Esperado=0.500, Predito=1.157, Erro=0.657\n", + "Esperado=0.500, Predito=1.185, Erro=0.685\n", + "Esperado=0.333, Predito=1.248, Erro=0.915\n", + "Esperado=0.333, Predito=1.253, Erro=0.920\n", + "Esperado=0.333, Predito=1.196, Erro=0.863\n", + "Esperado=0.333, Predito=1.875, Erro=1.541\n", + "Esperado=0.500, Predito=1.276, Erro=0.776\n", + "Esperado=0.500, Predito=1.410, Erro=0.910\n", + "Esperado=0.667, Predito=1.170, Erro=0.503\n", + "Esperado=0.500, Predito=1.228, Erro=0.728\n", + "Esperado=0.333, Predito=1.248, Erro=0.915\n", + "Esperado=0.333, Predito=1.253, Erro=0.920\n", + "Esperado=0.333, Predito=1.306, Erro=0.973\n", + "Esperado=0.333, Predito=1.485, Erro=1.152\n", + "Esperado=0.333, Predito=1.407, Erro=1.074\n", + "Esperado=0.667, Predito=1.811, Erro=1.144\n", + "Esperado=0.500, Predito=1.562, Erro=1.062\n", + "Esperado=0.500, Predito=1.292, Erro=0.792\n", + "Esperado=0.500, Predito=1.586, Erro=1.086\n", + "Esperado=0.500, Predito=1.562, Erro=1.062\n", + "Esperado=0.500, Predito=1.165, Erro=0.665\n", + "Esperado=0.500, Predito=1.845, Erro=1.345\n", + "Esperado=0.333, Predito=1.337, Erro=1.004\n", + "Esperado=0.500, Predito=1.292, Erro=0.792\n", + "Esperado=0.833, Predito=1.331, Erro=0.498\n", + "Esperado=0.833, Predito=1.331, Erro=0.498\n", + "Esperado=0.333, Predito=1.078, Erro=0.744\n", + "Esperado=0.167, Predito=1.434, Erro=1.268\n", + "Esperado=0.500, Predito=1.253, Erro=0.753\n", + "Esperado=0.500, Predito=1.728, Erro=1.228\n", + "Esperado=0.667, Predito=1.385, Erro=0.718\n", + "Esperado=0.500, Predito=1.253, Erro=0.753\n", + "Esperado=0.667, Predito=1.557, Erro=0.891\n", + "Esperado=0.667, Predito=1.420, Erro=0.753\n", + "Esperado=0.333, Predito=1.537, Erro=1.204\n", + "Esperado=0.667, Predito=1.502, Erro=0.835\n", + "Esperado=0.333, Predito=1.046, Erro=0.713\n", + "Esperado=0.333, Predito=1.388, Erro=1.055\n", + "Esperado=0.500, Predito=1.286, Erro=0.786\n", + "Esperado=0.333, Predito=1.563, Erro=1.229\n", + "Esperado=0.333, Predito=1.496, Erro=1.163\n", + "Esperado=0.500, Predito=1.457, Erro=0.957\n", + "Esperado=0.333, Predito=1.726, Erro=1.392\n", + "Esperado=0.833, Predito=1.289, Erro=0.456\n", + "Esperado=0.500, Predito=1.128, Erro=0.628\n", + "Esperado=0.500, Predito=1.384, Erro=0.884\n", + "Esperado=0.500, Predito=1.277, Erro=0.777\n", + "Esperado=0.333, Predito=0.959, Erro=0.626\n", + "Esperado=0.500, Predito=1.128, Erro=0.628\n", + "Esperado=0.500, Predito=1.384, Erro=0.884\n", + "Esperado=0.500, Predito=1.277, Erro=0.777\n", + "Esperado=0.333, Predito=1.340, Erro=1.007\n", + "Esperado=0.333, Predito=1.403, Erro=1.069\n", + "Esperado=0.500, Predito=1.394, Erro=0.894\n", + "Esperado=0.333, Predito=1.340, Erro=1.007\n", + "Esperado=0.500, Predito=1.394, Erro=0.894\n", + "Esperado=0.500, Predito=1.403, Erro=0.903\n", + "Esperado=0.333, Predito=1.403, Erro=1.069\n", + "Esperado=0.500, Predito=1.754, Erro=1.254\n", + "Esperado=0.500, Predito=1.130, Erro=0.630\n", + "Esperado=0.667, Predito=1.652, Erro=0.986\n", + "Esperado=0.500, Predito=1.520, Erro=1.020\n", + "Esperado=0.667, Predito=1.512, Erro=0.845\n", + "Esperado=0.167, Predito=1.548, Erro=1.382\n", + "Esperado=0.500, Predito=1.424, Erro=0.924\n", + "Esperado=0.500, Predito=1.567, Erro=1.067\n", + "Esperado=0.500, Predito=1.520, Erro=1.020\n", + "Esperado=0.333, Predito=1.323, Erro=0.990\n", + "Esperado=0.667, Predito=1.652, Erro=0.986\n", + "Esperado=0.500, Predito=1.252, Erro=0.752\n", + "Esperado=0.333, Predito=1.235, Erro=0.901\n", + "Esperado=0.333, Predito=1.531, Erro=1.197\n", + "Esperado=0.333, Predito=1.235, Erro=0.901\n", + "Esperado=0.500, Predito=1.252, Erro=0.752\n", + "Esperado=0.500, Predito=1.215, Erro=0.715\n", + "Esperado=0.667, Predito=1.687, Erro=1.021\n", + "Esperado=0.667, Predito=1.687, Erro=1.021\n", + "Esperado=0.667, Predito=1.890, Erro=1.224\n", + "Esperado=0.667, Predito=1.687, Erro=1.021\n", + "Esperado=0.667, Predito=1.890, Erro=1.224\n", + "Esperado=0.500, Predito=1.251, Erro=0.751\n", + "Esperado=0.333, Predito=1.875, Erro=1.541\n", + "Esperado=0.500, Predito=1.178, Erro=0.678\n", + "Esperado=0.667, Predito=1.442, Erro=0.775\n", + "Esperado=0.500, Predito=1.494, Erro=0.994\n", + "Esperado=0.500, Predito=1.178, Erro=0.678\n", + "Esperado=0.333, Predito=1.222, Erro=0.889\n", + "Esperado=0.667, Predito=1.442, Erro=0.775\n", + "Esperado=0.500, Predito=1.186, Erro=0.686\n", + "Esperado=0.500, Predito=1.678, Erro=1.178\n", + "Esperado=0.500, Predito=1.693, Erro=1.193\n", + "Esperado=0.333, Predito=1.551, Erro=1.217\n", + "Esperado=0.333, Predito=1.168, Erro=0.834\n", + "Esperado=0.333, Predito=1.551, Erro=1.217\n", + "Esperado=0.333, Predito=1.908, Erro=1.575\n", + "Esperado=0.500, Predito=1.323, Erro=0.823\n", + "Esperado=0.667, Predito=1.310, Erro=0.643\n", + "Esperado=0.500, Predito=1.693, Erro=1.193\n", + "Esperado=0.667, Predito=1.348, Erro=0.682\n", + "Esperado=0.333, Predito=1.436, Erro=1.103\n", + "Esperado=0.667, Predito=1.365, Erro=0.698\n", + "Esperado=0.500, Predito=1.241, Erro=0.741\n", + "Esperado=0.167, Predito=2.459, Erro=2.292\n", + "Esperado=0.333, Predito=1.409, Erro=1.076\n", + "Esperado=0.333, Predito=1.391, Erro=1.057\n", + "Esperado=0.500, Predito=1.241, Erro=0.741\n", + "Esperado=0.500, Predito=1.341, Erro=0.841\n", + "Esperado=0.500, Predito=1.216, Erro=0.716\n", + "Esperado=0.667, Predito=1.484, Erro=0.817\n", + "Esperado=0.333, Predito=1.253, Erro=0.920\n", + "Esperado=0.500, Predito=1.426, Erro=0.926\n", + "Esperado=0.500, Predito=1.513, Erro=1.013\n", + "Esperado=0.500, Predito=1.370, Erro=0.870\n", + "Esperado=0.500, Predito=1.539, Erro=1.039\n", + "Esperado=0.667, Predito=1.274, Erro=0.607\n", + "Esperado=0.667, Predito=1.088, Erro=0.421\n", + "Esperado=0.500, Predito=1.204, Erro=0.704\n", + "Esperado=0.500, Predito=1.265, Erro=0.765\n", + "Esperado=0.333, Predito=1.331, Erro=0.998\n", + "Esperado=0.333, Predito=1.331, Erro=0.998\n", + "Esperado=0.333, Predito=1.284, Erro=0.951\n", + "Esperado=0.333, Predito=1.465, Erro=1.131\n", + "Esperado=0.500, Predito=1.155, Erro=0.655\n", + "Esperado=0.500, Predito=1.116, Erro=0.616\n", + "Esperado=0.500, Predito=1.267, Erro=0.767\n", + "Esperado=0.500, Predito=1.066, Erro=0.566\n", + "Esperado=0.333, Predito=1.456, Erro=1.123\n", + "Esperado=0.500, Predito=1.066, Erro=0.566\n", + "Esperado=0.333, Predito=1.456, Erro=1.123\n", + "Esperado=0.333, Predito=1.456, Erro=1.123\n", + "Esperado=0.333, Predito=1.198, Erro=0.864\n", + "Esperado=0.500, Predito=1.066, Erro=0.566\n", + "Esperado=0.333, Predito=1.568, Erro=1.235\n", + "Esperado=0.333, Predito=1.264, Erro=0.930\n", + "Esperado=0.333, Predito=1.259, Erro=0.926\n", + "Esperado=0.333, Predito=1.282, Erro=0.948\n", + "Esperado=0.333, Predito=1.456, Erro=1.123\n", + "Esperado=0.333, Predito=1.505, Erro=1.172\n", + "Esperado=0.333, Predito=1.170, Erro=0.836\n", + "Esperado=0.333, Predito=1.170, Erro=0.836\n", + "Esperado=0.333, Predito=1.393, Erro=1.059\n", + "Esperado=0.500, Predito=1.279, Erro=0.779\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.500, Predito=1.522, Erro=1.022\n", + "Esperado=0.667, Predito=1.894, Erro=1.227\n", + "Esperado=0.500, Predito=1.196, Erro=0.696\n", + "Esperado=0.333, Predito=1.117, Erro=0.784\n", + "Esperado=0.500, Predito=1.195, Erro=0.695\n", + "Esperado=0.333, Predito=1.492, Erro=1.159\n", + "Esperado=0.500, Predito=1.279, Erro=0.779\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.333, Predito=1.503, Erro=1.170\n", + "Esperado=0.667, Predito=1.711, Erro=1.045\n", + "Esperado=0.667, Predito=1.703, Erro=1.037\n", + "Esperado=0.333, Predito=1.003, Erro=0.670\n", + "Esperado=0.333, Predito=1.181, Erro=0.848\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.500, Predito=1.466, Erro=0.966\n", + "Esperado=0.500, Predito=1.226, Erro=0.726\n", + "Esperado=0.500, Predito=1.629, Erro=1.129\n", + "Esperado=0.667, Predito=1.703, Erro=1.037\n", + "Esperado=0.333, Predito=1.181, Erro=0.848\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.500, Predito=1.481, Erro=0.981\n", + "Esperado=0.500, Predito=1.330, Erro=0.830\n", + "Esperado=0.667, Predito=1.347, Erro=0.680\n", + "Esperado=0.333, Predito=1.003, Erro=0.670\n", + "Esperado=0.333, Predito=0.969, Erro=0.636\n", + "Esperado=0.333, Predito=1.480, Erro=1.147\n", + "Esperado=0.167, Predito=1.250, Erro=1.083\n", + "Esperado=0.500, Predito=1.173, Erro=0.673\n", + "Esperado=0.500, Predito=1.209, Erro=0.709\n", + "Esperado=0.333, Predito=1.250, Erro=0.916\n", + "Esperado=0.500, Predito=1.371, Erro=0.871\n", + "Esperado=0.333, Predito=1.419, Erro=1.086\n", + "Esperado=0.500, Predito=1.327, Erro=0.827\n", + "Esperado=0.000, Predito=1.732, Erro=1.732\n", + "Esperado=0.500, Predito=1.982, Erro=1.482\n", + "Esperado=0.333, Predito=1.523, Erro=1.190\n", + "Esperado=0.500, Predito=1.465, Erro=0.965\n", + "Esperado=0.333, Predito=1.419, Erro=1.086\n", + "Esperado=0.500, Predito=1.327, Erro=0.827\n", + "Esperado=0.667, Predito=1.103, Erro=0.436\n", + "Esperado=0.333, Predito=1.010, Erro=0.676\n", + "Esperado=0.333, Predito=1.245, Erro=0.912\n", + "Esperado=0.333, Predito=1.509, Erro=1.175\n", + "Esperado=0.333, Predito=1.245, Erro=0.912\n", + "Esperado=0.333, Predito=1.509, Erro=1.175\n", + "Esperado=0.333, Predito=1.290, Erro=0.957\n", + "Esperado=0.333, Predito=1.477, Erro=1.143\n", + "Esperado=0.500, Predito=1.366, Erro=0.866\n", + "Esperado=0.500, Predito=1.199, Erro=0.699\n", + "Esperado=0.333, Predito=1.475, Erro=1.142\n", + "Esperado=0.667, Predito=1.264, Erro=0.597\n", + "Esperado=0.333, Predito=1.319, Erro=0.986\n", + "Esperado=0.333, Predito=1.274, Erro=0.941\n", + "Esperado=0.167, Predito=2.651, Erro=2.484\n", + "Esperado=0.667, Predito=1.501, Erro=0.834\n", + "Esperado=0.500, Predito=1.547, Erro=1.047\n", + "Esperado=0.333, Predito=1.274, Erro=0.941\n", + "Esperado=0.333, Predito=1.342, Erro=1.009\n", + "Esperado=0.333, Predito=1.040, Erro=0.707\n", + "Esperado=0.500, Predito=0.943, Erro=0.443\n", + "Esperado=0.500, Predito=1.422, Erro=0.922\n", + "Esperado=0.333, Predito=1.017, Erro=0.684\n", + "Esperado=0.333, Predito=1.017, Erro=0.684\n", + "Esperado=0.333, Predito=1.272, Erro=0.939\n", + "Esperado=0.333, Predito=1.270, Erro=0.937\n", + "Esperado=0.500, Predito=1.422, Erro=0.922\n", + "Esperado=0.333, Predito=1.309, Erro=0.975\n", + "Esperado=0.500, Predito=1.161, Erro=0.661\n", + "Esperado=0.500, Predito=1.148, Erro=0.648\n", + "Esperado=0.667, Predito=1.285, Erro=0.618\n", + "Esperado=0.500, Predito=1.253, Erro=0.753\n", + "Esperado=0.667, Predito=1.931, Erro=1.264\n", + "Esperado=0.500, Predito=1.760, Erro=1.260\n", + "Esperado=0.667, Predito=1.931, Erro=1.264\n", + "Esperado=0.333, Predito=1.110, Erro=0.776\n", + "Esperado=0.333, Predito=1.206, Erro=0.872\n", + "Esperado=0.333, Predito=1.206, Erro=0.872\n", + "Esperado=0.500, Predito=1.271, Erro=0.771\n", + "Esperado=0.333, Predito=1.459, Erro=1.126\n", + "Esperado=0.500, Predito=1.163, Erro=0.663\n", + "Esperado=0.500, Predito=1.493, Erro=0.993\n", + "Esperado=0.500, Predito=1.451, Erro=0.951\n", + "Esperado=0.833, Predito=1.137, Erro=0.303\n", + "Esperado=0.833, Predito=1.137, Erro=0.303\n", + "Esperado=0.833, Predito=1.137, Erro=0.303\n", + "Esperado=0.833, Predito=1.137, Erro=0.303\n", + "Esperado=0.833, Predito=1.137, Erro=0.303\n", + "Esperado=0.500, Predito=1.266, Erro=0.766\n", + "Esperado=0.500, Predito=1.160, Erro=0.660\n", + "Esperado=0.333, Predito=1.291, Erro=0.958\n", + "Esperado=0.500, Predito=1.251, Erro=0.751\n", + "Esperado=0.667, Predito=1.449, Erro=0.783\n", + "Esperado=0.167, Predito=1.542, Erro=1.375\n", + "Esperado=0.833, Predito=1.137, Erro=0.303\n", + "Esperado=0.333, Predito=1.027, Erro=0.693\n", + "Esperado=0.500, Predito=1.461, Erro=0.961\n", + "Esperado=0.500, Predito=1.430, Erro=0.930\n", + "Esperado=0.500, Predito=1.419, Erro=0.919\n", + "Esperado=0.500, Predito=1.430, Erro=0.930\n", + "Esperado=0.500, Predito=1.419, Erro=0.919\n", + "Esperado=0.500, Predito=1.419, Erro=0.919\n", + "Esperado=0.500, Predito=1.111, Erro=0.611\n", + "Esperado=0.333, Predito=1.357, Erro=1.024\n", + "Esperado=0.333, Predito=1.356, Erro=1.023\n", + "Esperado=0.500, Predito=1.387, Erro=0.887\n", + "Esperado=0.500, Predito=1.461, Erro=0.961\n", + "Esperado=0.500, Predito=1.513, Erro=1.013\n", + "Esperado=0.500, Predito=1.430, Erro=0.930\n", + "Esperado=0.500, Predito=1.419, Erro=0.919\n", + "Esperado=0.500, Predito=1.217, Erro=0.717\n", + "Esperado=0.333, Predito=1.126, Erro=0.792\n", + "Esperado=0.667, Predito=1.667, Erro=1.000\n", + "Esperado=0.500, Predito=1.373, Erro=0.873\n", + "Esperado=0.333, Predito=1.126, Erro=0.792\n", + "Esperado=0.333, Predito=1.229, Erro=0.896\n", + "Esperado=0.500, Predito=1.603, Erro=1.103\n", + "Esperado=0.333, Predito=1.837, Erro=1.504\n", + "Esperado=0.500, Predito=1.347, Erro=0.847\n", + "Esperado=0.667, Predito=1.668, Erro=1.001\n", + "Esperado=0.333, Predito=1.837, Erro=1.504\n", + "Esperado=0.667, Predito=1.289, Erro=0.622\n", + "Esperado=0.500, Predito=1.603, Erro=1.103\n", + "Esperado=0.500, Predito=1.573, Erro=1.073\n", + "Esperado=0.500, Predito=1.167, Erro=0.667\n", + "Esperado=0.500, Predito=1.426, Erro=0.926\n", + "Esperado=0.500, Predito=1.167, Erro=0.667\n", + "Esperado=0.333, Predito=1.707, Erro=1.374\n", + "Esperado=0.333, Predito=1.265, Erro=0.931\n", + "Esperado=0.333, Predito=1.343, Erro=1.009\n", + "Esperado=0.333, Predito=1.305, Erro=0.972\n", + "Esperado=0.500, Predito=1.179, Erro=0.679\n", + "Esperado=0.500, Predito=1.426, Erro=0.926\n", + "Esperado=0.500, Predito=1.688, Erro=1.188\n", + "Esperado=0.333, Predito=1.398, Erro=1.065\n", + "Esperado=0.500, Predito=1.358, Erro=0.858\n", + "Esperado=0.333, Predito=1.237, Erro=0.904\n", + "Esperado=0.333, Predito=1.525, Erro=1.191\n", + "Esperado=0.333, Predito=1.095, Erro=0.761\n", + "Esperado=0.500, Predito=1.319, Erro=0.819\n", + "Esperado=0.333, Predito=1.398, Erro=1.065\n", + "Esperado=0.333, Predito=0.980, Erro=0.646\n", + "Esperado=0.333, Predito=1.215, Erro=0.882\n", + "Esperado=0.500, Predito=1.434, Erro=0.934\n", + "Esperado=0.333, Predito=0.980, Erro=0.646\n", + "Esperado=0.500, Predito=0.985, Erro=0.485\n", + "Esperado=0.500, Predito=1.230, Erro=0.730\n", + "Esperado=0.500, Predito=1.628, Erro=1.128\n", + "Esperado=0.333, Predito=1.215, Erro=0.882\n", + "Esperado=0.333, Predito=1.410, Erro=1.077\n", + "Esperado=0.333, Predito=1.363, Erro=1.029\n", + "Esperado=0.333, Predito=1.344, Erro=1.010\n", + "Esperado=0.667, Predito=1.395, Erro=0.728\n", + "Esperado=0.000, Predito=1.305, Erro=1.305\n", + "Esperado=0.333, Predito=1.082, Erro=0.748\n", + "Esperado=0.333, Predito=1.082, Erro=0.748\n", + "Esperado=0.333, Predito=1.780, Erro=1.447\n", + "Esperado=0.333, Predito=1.780, Erro=1.447\n", + "Esperado=0.500, Predito=1.220, Erro=0.720\n", + "Esperado=0.333, Predito=1.082, Erro=0.748\n", + "Esperado=0.667, Predito=1.777, Erro=1.111\n", + "Esperado=0.333, Predito=1.281, Erro=0.948\n", + "Esperado=0.333, Predito=1.524, Erro=1.190\n", + "Esperado=0.333, Predito=1.381, Erro=1.047\n", + "Esperado=0.500, Predito=1.242, Erro=0.742\n", + "Esperado=0.333, Predito=1.224, Erro=0.891\n", + "Esperado=0.333, Predito=1.221, Erro=0.887\n", + "Esperado=0.500, Predito=1.394, Erro=0.894\n", + "Esperado=0.333, Predito=1.268, Erro=0.934\n", + "Esperado=0.333, Predito=1.282, Erro=0.948\n", + "Esperado=0.333, Predito=1.299, Erro=0.966\n", + "Esperado=0.500, Predito=1.386, Erro=0.886\n", + "Esperado=0.667, Predito=1.455, Erro=0.788\n", + "Esperado=0.500, Predito=1.397, Erro=0.897\n", + "Esperado=0.500, Predito=1.280, Erro=0.780\n", + "Esperado=0.333, Predito=1.415, Erro=1.082\n", + "Esperado=0.333, Predito=1.539, Erro=1.206\n", + "Esperado=0.500, Predito=1.394, Erro=0.894\n", + "Esperado=0.333, Predito=1.221, Erro=0.887\n", + "Esperado=0.333, Predito=1.576, Erro=1.243\n", + "Esperado=0.500, Predito=1.324, Erro=0.824\n", + "Esperado=0.500, Predito=1.371, Erro=0.871\n", + "Esperado=0.167, Predito=1.640, Erro=1.473\n", + "Esperado=0.333, Predito=1.576, Erro=1.243\n", + "Esperado=0.333, Predito=1.545, Erro=1.212\n", + "Esperado=0.500, Predito=1.375, Erro=0.875\n", + "Esperado=0.333, Predito=1.359, Erro=1.026\n", + "Esperado=0.500, Predito=1.323, Erro=0.823\n", + "Esperado=0.500, Predito=1.323, Erro=0.823\n", + "Esperado=0.333, Predito=1.161, Erro=0.828\n", + "Esperado=0.500, Predito=1.163, Erro=0.663\n", + "Esperado=0.500, Predito=1.024, Erro=0.524\n", + "Esperado=0.500, Predito=1.323, Erro=0.823\n", + "Esperado=0.333, Predito=1.208, Erro=0.874\n", + "Esperado=0.333, Predito=1.169, Erro=0.836\n", + "Esperado=0.333, Predito=2.037, Erro=1.703\n", + "Esperado=0.333, Predito=1.100, Erro=0.767\n", + "Esperado=0.333, Predito=1.100, Erro=0.767\n", + "Esperado=0.333, Predito=1.325, Erro=0.992\n", + "Esperado=0.333, Predito=1.274, Erro=0.941\n", + "Esperado=0.333, Predito=1.214, Erro=0.880\n", + "Esperado=0.500, Predito=1.078, Erro=0.578\n", + "Esperado=0.333, Predito=1.200, Erro=0.866\n", + "Esperado=0.333, Predito=1.214, Erro=0.880\n", + "Esperado=0.500, Predito=1.545, Erro=1.045\n", + "Esperado=0.333, Predito=1.367, Erro=1.034\n", + "Esperado=0.333, Predito=1.181, Erro=0.848\n", + "Esperado=0.333, Predito=1.200, Erro=0.866\n", + "Esperado=0.333, Predito=1.274, Erro=0.941\n", + "Esperado=0.333, Predito=1.358, Erro=1.024\n", + "Esperado=0.500, Predito=1.318, Erro=0.818\n", + "Esperado=0.500, Predito=1.078, Erro=0.578\n", + "Esperado=0.500, Predito=1.279, Erro=0.779\n", + "Esperado=0.333, Predito=1.115, Erro=0.782\n", + "Esperado=0.333, Predito=1.115, Erro=0.782\n", + "Esperado=0.333, Predito=1.115, Erro=0.782\n", + "Esperado=0.333, Predito=1.509, Erro=1.175\n", + "Esperado=0.500, Predito=1.279, Erro=0.779\n", + "Esperado=0.333, Predito=1.115, Erro=0.782\n", + "Esperado=0.167, Predito=1.205, Erro=1.038\n", + "Esperado=0.333, Predito=1.407, Erro=1.074\n", + "Esperado=0.500, Predito=1.301, Erro=0.801\n", + "Esperado=0.167, Predito=1.076, Erro=0.909\n", + "Esperado=0.333, Predito=1.148, Erro=0.815\n", + "Esperado=0.667, Predito=1.756, Erro=1.089\n", + "Esperado=0.333, Predito=1.279, Erro=0.945\n", + "Esperado=0.333, Predito=1.148, Erro=0.815\n", + "Esperado=0.667, Predito=1.756, Erro=1.089\n", + "Esperado=0.333, Predito=1.340, Erro=1.006\n", + "Esperado=0.333, Predito=1.634, Erro=1.300\n", + "Esperado=0.333, Predito=1.557, Erro=1.224\n", + "Esperado=0.500, Predito=1.691, Erro=1.191\n", + "Esperado=0.333, Predito=1.615, Erro=1.281\n", + "Esperado=0.333, Predito=1.459, Erro=1.126\n", + "Esperado=0.500, Predito=1.197, Erro=0.697\n", + "Esperado=0.500, Predito=1.197, Erro=0.697\n", + "Esperado=0.500, Predito=1.512, Erro=1.012\n", + "Esperado=0.500, Predito=1.392, Erro=0.892\n", + "Esperado=0.333, Predito=1.219, Erro=0.886\n", + "Esperado=0.500, Predito=1.275, Erro=0.775\n", + "Esperado=0.333, Predito=1.219, Erro=0.886\n", + "Esperado=0.333, Predito=1.174, Erro=0.840\n", + "Esperado=0.333, Predito=1.305, Erro=0.972\n", + "Esperado=0.333, Predito=1.374, Erro=1.040\n", + "Esperado=0.333, Predito=1.374, Erro=1.040\n", + "Esperado=0.333, Predito=1.242, Erro=0.909\n", + "Esperado=0.667, Predito=1.382, Erro=0.715\n", + "Esperado=0.500, Predito=1.262, Erro=0.762\n", + "Esperado=0.500, Predito=1.192, Erro=0.692\n", + "Esperado=0.500, Predito=0.986, Erro=0.486\n", + "Esperado=0.333, Predito=1.443, Erro=1.109\n", + "Esperado=0.333, Predito=1.305, Erro=0.972\n", + "Esperado=0.500, Predito=1.377, Erro=0.877\n", + "Esperado=0.500, Predito=1.372, Erro=0.872\n", + "Esperado=0.333, Predito=1.374, Erro=1.040\n", + "Esperado=0.500, Predito=1.211, Erro=0.711\n", + "Esperado=0.500, Predito=1.165, Erro=0.665\n", + "Esperado=0.167, Predito=2.623, Erro=2.457\n", + "Esperado=0.333, Predito=1.403, Erro=1.070\n", + "Esperado=0.167, Predito=1.314, Erro=1.148\n", + "Esperado=0.500, Predito=1.306, Erro=0.806\n", + "Esperado=0.500, Predito=1.356, Erro=0.856\n", + "Esperado=0.167, Predito=1.299, Erro=1.132\n", + "Esperado=0.500, Predito=1.530, Erro=1.030\n", + "Esperado=0.667, Predito=1.634, Erro=0.968\n", + "Esperado=0.500, Predito=1.610, Erro=1.110\n", + "Esperado=0.500, Predito=1.306, Erro=0.806\n", + "Esperado=0.333, Predito=1.288, Erro=0.955\n", + "Esperado=0.333, Predito=1.399, Erro=1.066\n", + "Esperado=0.333, Predito=1.399, Erro=1.066\n", + "Esperado=0.667, Predito=1.149, Erro=0.483\n", + "Esperado=0.667, Predito=1.149, Erro=0.483\n", + "Esperado=0.667, Predito=1.149, Erro=0.483\n", + "Esperado=0.667, Predito=1.149, Erro=0.483\n", + "Esperado=0.667, Predito=1.149, Erro=0.483\n", + "Esperado=0.333, Predito=1.270, Erro=0.937\n", + "Esperado=0.333, Predito=1.434, Erro=1.101\n", + "Esperado=0.667, Predito=1.149, Erro=0.483\n", + "Esperado=0.667, Predito=1.149, Erro=0.482\n", + "Esperado=0.333, Predito=1.270, Erro=0.937\n", + "Esperado=0.667, Predito=1.336, Erro=0.670\n", + "Esperado=0.333, Predito=1.434, Erro=1.101\n", + "Esperado=0.500, Predito=1.370, Erro=0.870\n", + "Esperado=0.500, Predito=1.477, Erro=0.977\n", + "Esperado=0.500, Predito=1.349, Erro=0.849\n", + "Esperado=0.333, Predito=1.417, Erro=1.083\n", + "Esperado=0.333, Predito=1.326, Erro=0.992\n", + "Esperado=0.500, Predito=1.437, Erro=0.937\n", + "Esperado=0.667, Predito=1.112, Erro=0.445\n", + "Esperado=0.333, Predito=1.539, Erro=1.205\n", + "Esperado=0.333, Predito=1.177, Erro=0.843\n", + "Esperado=0.500, Predito=1.142, Erro=0.642\n", + "Esperado=0.500, Predito=1.149, Erro=0.649\n", + "Esperado=0.500, Predito=1.541, Erro=1.041\n", + "Esperado=0.333, Predito=1.140, Erro=0.807\n", + "Esperado=0.500, Predito=1.361, Erro=0.861\n", + "Esperado=0.500, Predito=1.149, Erro=0.649\n", + "Esperado=0.500, Predito=1.142, Erro=0.642\n", + "Esperado=0.333, Predito=1.177, Erro=0.843\n", + "Esperado=0.500, Predito=1.245, Erro=0.745\n", + "Esperado=0.500, Predito=1.169, Erro=0.669\n", + "Esperado=0.333, Predito=1.320, Erro=0.987\n", + "Esperado=0.500, Predito=1.306, Erro=0.806\n", + "Esperado=0.333, Predito=1.247, Erro=0.914\n", + "Esperado=0.500, Predito=1.482, Erro=0.982\n", + "Esperado=0.333, Predito=1.320, Erro=0.987\n", + "Esperado=0.333, Predito=1.302, Erro=0.969\n", + "Esperado=0.333, Predito=1.367, Erro=1.033\n", + "Esperado=0.333, Predito=1.666, Erro=1.333\n", + "Esperado=0.500, Predito=1.357, Erro=0.857\n", + "Esperado=0.667, Predito=1.439, Erro=0.772\n", + "Esperado=0.333, Predito=1.386, Erro=1.053\n", + "Esperado=0.500, Predito=1.665, Erro=1.165\n", + "Esperado=0.667, Predito=1.271, Erro=0.604\n", + "Esperado=0.500, Predito=1.480, Erro=0.980\n", + "Esperado=0.500, Predito=1.665, Erro=1.165\n", + "Esperado=0.500, Predito=1.313, Erro=0.813\n", + "Esperado=0.333, Predito=1.386, Erro=1.053\n", + "Esperado=0.500, Predito=1.303, Erro=0.803\n", + "Esperado=0.333, Predito=1.474, Erro=1.140\n", + "Esperado=0.500, Predito=1.323, Erro=0.823\n", + "Esperado=0.500, Predito=1.488, Erro=0.988\n", + "Esperado=0.667, Predito=1.346, Erro=0.680\n", + "Esperado=0.500, Predito=1.198, Erro=0.698\n", + "Esperado=0.500, Predito=1.303, Erro=0.803\n", + "Esperado=0.667, Predito=1.743, Erro=1.077\n", + "Esperado=0.500, Predito=1.245, Erro=0.745\n", + "Esperado=0.667, Predito=1.743, Erro=1.077\n", + "Esperado=0.167, Predito=1.183, Erro=1.016\n", + "Esperado=0.333, Predito=1.268, Erro=0.934\n", + "Esperado=0.333, Predito=1.250, Erro=0.916\n", + "Esperado=0.667, Predito=1.231, Erro=0.564\n", + "Esperado=0.500, Predito=1.323, Erro=0.823\n", + "Esperado=0.667, Predito=1.231, Erro=0.564\n", + "Esperado=0.500, Predito=1.323, Erro=0.823\n", + "Esperado=0.500, Predito=1.110, Erro=0.610\n", + "Esperado=0.333, Predito=1.534, Erro=1.201\n", + "Esperado=0.333, Predito=1.442, Erro=1.109\n", + "Esperado=0.500, Predito=1.591, Erro=1.091\n", + "Esperado=0.333, Predito=1.311, Erro=0.978\n", + "Esperado=0.167, Predito=1.368, Erro=1.201\n", + "Esperado=0.500, Predito=1.162, Erro=0.662\n", + "Esperado=0.500, Predito=1.591, Erro=1.091\n", + "Esperado=0.333, Predito=1.302, Erro=0.968\n", + "Esperado=0.333, Predito=1.311, Erro=0.978\n", + "Esperado=0.333, Predito=1.213, Erro=0.880\n", + "Esperado=0.333, Predito=1.524, Erro=1.190\n", + "Esperado=0.333, Predito=1.429, Erro=1.096\n", + "Esperado=0.667, Predito=1.357, Erro=0.690\n", + "Esperado=0.167, Predito=1.368, Erro=1.201\n", + "Esperado=0.500, Predito=1.162, Erro=0.662\n", + "Esperado=0.500, Predito=1.207, Erro=0.707\n", + "Esperado=0.333, Predito=1.247, Erro=0.914\n", + "Esperado=0.500, Predito=1.506, Erro=1.006\n", + "Esperado=0.667, Predito=1.308, Erro=0.641\n", + "Esperado=0.333, Predito=1.354, Erro=1.021\n", + "Esperado=0.333, Predito=1.247, Erro=0.914\n", + "Esperado=0.500, Predito=1.275, Erro=0.775\n", + "Esperado=0.500, Predito=1.215, Erro=0.715\n", + "Esperado=0.333, Predito=1.215, Erro=0.882\n", + "Esperado=0.500, Predito=1.502, Erro=1.002\n", + "Esperado=0.333, Predito=1.651, Erro=1.318\n", + "Esperado=0.500, Predito=1.502, Erro=1.002\n", + "Esperado=0.333, Predito=1.651, Erro=1.318\n", + "Esperado=0.500, Predito=1.371, Erro=0.871\n", + "Esperado=0.500, Predito=1.338, Erro=0.838\n", + "Esperado=0.333, Predito=1.322, Erro=0.989\n", + "Esperado=0.500, Predito=1.471, Erro=0.971\n", + "Esperado=0.333, Predito=1.322, Erro=0.989\n", + "Esperado=0.333, Predito=1.591, Erro=1.257\n", + "Esperado=0.500, Predito=1.471, Erro=0.971\n", + "Esperado=0.500, Predito=1.051, Erro=0.551\n", + "Esperado=0.500, Predito=1.308, Erro=0.808\n", + "Esperado=0.667, Predito=1.370, Erro=0.703\n", + "Esperado=0.500, Predito=1.314, Erro=0.814\n", + "Esperado=0.500, Predito=1.287, Erro=0.787\n", + "Esperado=0.333, Predito=1.664, Erro=1.331\n", + "Esperado=0.667, Predito=1.370, Erro=0.703\n", + "Esperado=0.167, Predito=1.327, Erro=1.160\n", + "Esperado=0.500, Predito=1.324, Erro=0.824\n", + "Esperado=0.500, Predito=1.084, Erro=0.584\n", + "Esperado=0.500, Predito=1.324, Erro=0.824\n", + "Esperado=0.333, Predito=1.516, Erro=1.183\n", + "Esperado=0.500, Predito=1.200, Erro=0.700\n", + "Esperado=0.500, Predito=1.212, Erro=0.712\n", + "Esperado=0.333, Predito=1.563, Erro=1.230\n", + "Esperado=0.333, Predito=1.563, Erro=1.230\n", + "Esperado=0.333, Predito=1.563, Erro=1.230\n", + "Esperado=0.333, Predito=1.563, Erro=1.230\n", + "Esperado=0.333, Predito=1.550, Erro=1.217\n", + "Esperado=0.333, Predito=1.587, Erro=1.253\n", + "Esperado=0.333, Predito=1.563, Erro=1.230\n", + "Esperado=0.667, Predito=1.797, Erro=1.130\n", + "Esperado=0.667, Predito=1.330, Erro=0.664\n", + "Esperado=0.500, Predito=1.254, Erro=0.754\n", + "Esperado=0.500, Predito=1.368, Erro=0.868\n", + "Esperado=0.667, Predito=1.797, Erro=1.130\n", + "Esperado=0.667, Predito=1.651, Erro=0.984\n", + "Esperado=0.500, Predito=1.523, Erro=1.023\n", + "Esperado=0.667, Predito=1.459, Erro=0.792\n", + "Esperado=0.500, Predito=1.379, Erro=0.879\n", + "Esperado=0.833, Predito=1.799, Erro=0.965\n", + "Esperado=0.667, Predito=1.651, Erro=0.984\n", + "Esperado=0.667, Predito=1.534, Erro=0.868\n", + "Esperado=0.333, Predito=1.215, Erro=0.882\n", + "Esperado=0.333, Predito=1.199, Erro=0.866\n", + "Esperado=0.333, Predito=1.571, Erro=1.238\n", + "Esperado=0.500, Predito=1.462, Erro=0.962\n", + "Esperado=0.667, Predito=1.603, Erro=0.936\n", + "Esperado=0.333, Predito=1.484, Erro=1.150\n", + "Esperado=0.333, Predito=1.129, Erro=0.795\n", + "Esperado=0.333, Predito=1.294, Erro=0.961\n", + "Esperado=0.500, Predito=1.305, Erro=0.805\n", + "Esperado=0.333, Predito=1.129, Erro=0.795\n", + "Esperado=0.667, Predito=1.325, Erro=0.659\n", + "Esperado=0.333, Predito=1.294, Erro=0.961\n", + "Esperado=0.667, Predito=1.690, Erro=1.023\n", + "Esperado=0.500, Predito=1.273, Erro=0.773\n", + "Esperado=0.500, Predito=1.781, Erro=1.281\n", + "Esperado=0.667, Predito=1.270, Erro=0.603\n", + "Esperado=0.333, Predito=1.174, Erro=0.840\n", + "Esperado=0.167, Predito=1.238, Erro=1.071\n", + "Esperado=0.667, Predito=1.768, Erro=1.101\n", + "Esperado=0.500, Predito=1.464, Erro=0.964\n", + "Esperado=0.333, Predito=1.723, Erro=1.390\n", + "Esperado=0.500, Predito=1.620, Erro=1.120\n", + "Esperado=0.500, Predito=1.416, Erro=0.916\n", + "Esperado=0.333, Predito=1.341, Erro=1.008\n", + "Esperado=0.500, Predito=1.322, Erro=0.822\n", + "Esperado=0.333, Predito=1.310, Erro=0.977\n", + "Esperado=0.500, Predito=1.322, Erro=0.822\n", + "Esperado=0.333, Predito=1.310, Erro=0.977\n", + "Esperado=0.333, Predito=1.391, Erro=1.057\n", + "Esperado=0.500, Predito=1.573, Erro=1.073\n", + "Esperado=0.333, Predito=1.320, Erro=0.987\n", + "Esperado=0.500, Predito=1.621, Erro=1.121\n", + "Esperado=0.833, Predito=1.776, Erro=0.943\n", + "Esperado=0.333, Predito=1.248, Erro=0.914\n", + "Esperado=0.333, Predito=1.396, Erro=1.062\n", + "Esperado=0.333, Predito=1.510, Erro=1.176\n", + "Esperado=0.167, Predito=1.347, Erro=1.181\n", + "Esperado=0.333, Predito=1.569, Erro=1.236\n", + "Esperado=0.500, Predito=1.257, Erro=0.757\n", + "Esperado=0.500, Predito=1.494, Erro=0.994\n", + "Esperado=0.333, Predito=1.569, Erro=1.236\n", + "Esperado=0.833, Predito=1.786, Erro=0.953\n", + "Esperado=0.333, Predito=1.207, Erro=0.874\n", + "Esperado=0.500, Predito=1.257, Erro=0.757\n", + "Esperado=0.500, Predito=1.291, Erro=0.791\n", + "Esperado=0.167, Predito=1.240, Erro=1.073\n", + "Esperado=0.500, Predito=1.318, Erro=0.818\n", + "Esperado=0.500, Predito=1.623, Erro=1.123\n", + "Esperado=0.333, Predito=1.755, Erro=1.422\n", + "Esperado=0.333, Predito=1.540, Erro=1.206\n", + "Esperado=0.500, Predito=1.337, Erro=0.837\n", + "Esperado=0.500, Predito=1.200, Erro=0.700\n", + "Esperado=0.667, Predito=1.219, Erro=0.552\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.667, Predito=1.845, Erro=1.178\n", + "Esperado=0.500, Predito=1.488, Erro=0.988\n", + "Esperado=0.333, Predito=1.239, Erro=0.905\n", + "Esperado=0.333, Predito=1.511, Erro=1.178\n", + "Esperado=0.333, Predito=1.571, Erro=1.237\n", + "Esperado=0.500, Predito=1.323, Erro=0.823\n", + "Esperado=0.333, Predito=1.341, Erro=1.008\n", + "Esperado=0.500, Predito=1.220, Erro=0.720\n", + "Esperado=0.333, Predito=1.253, Erro=0.919\n", + "Esperado=0.333, Predito=1.405, Erro=1.072\n", + "Esperado=0.333, Predito=1.369, Erro=1.036\n", + "Esperado=0.333, Predito=1.388, Erro=1.055\n", + "Esperado=0.500, Predito=1.321, Erro=0.821\n", + "Esperado=0.500, Predito=1.436, Erro=0.936\n", + "Esperado=0.500, Predito=1.202, Erro=0.702\n", + "Esperado=0.667, Predito=1.097, Erro=0.430\n", + "Esperado=0.333, Predito=1.564, Erro=1.230\n", + "Esperado=0.167, Predito=1.452, Erro=1.286\n", + "Esperado=0.000, Predito=1.723, Erro=1.723\n", + "Esperado=0.500, Predito=1.290, Erro=0.790\n", + "Esperado=0.500, Predito=1.543, Erro=1.043\n", + "Esperado=0.500, Predito=1.290, Erro=0.790\n", + "Esperado=0.500, Predito=1.215, Erro=0.715\n", + "Esperado=0.333, Predito=1.282, Erro=0.948\n", + "Esperado=0.167, Predito=1.422, Erro=1.255\n", + "Esperado=0.167, Predito=1.421, Erro=1.254\n", + "Esperado=0.500, Predito=1.373, Erro=0.873\n", + "Esperado=0.833, Predito=1.108, Erro=0.275\n", + "Esperado=0.500, Predito=1.401, Erro=0.901\n", + "Esperado=0.833, Predito=1.108, Erro=0.275\n", + "Esperado=0.333, Predito=1.072, Erro=0.738\n", + "Esperado=0.167, Predito=1.402, Erro=1.236\n", + "Esperado=0.167, Predito=1.402, Erro=1.236\n", + "Esperado=0.167, Predito=1.250, Erro=1.084\n", + "Esperado=0.833, Predito=1.820, Erro=0.987\n", + "Esperado=0.833, Predito=1.408, Erro=0.575\n", + "Esperado=0.500, Predito=1.418, Erro=0.918\n", + "Esperado=0.667, Predito=1.124, Erro=0.457\n", + "Esperado=0.500, Predito=1.125, Erro=0.625\n", + "Esperado=0.333, Predito=2.018, Erro=1.684\n", + "Esperado=0.333, Predito=1.463, Erro=1.129\n", + "Esperado=0.333, Predito=1.457, Erro=1.124\n", + "Esperado=0.500, Predito=1.236, Erro=0.736\n", + "Esperado=0.500, Predito=1.602, Erro=1.102\n", + "Esperado=0.500, Predito=1.230, Erro=0.730\n", + "Esperado=0.167, Predito=1.301, Erro=1.135\n", + "Esperado=0.167, Predito=1.175, Erro=1.008\n", + "Esperado=0.500, Predito=1.374, Erro=0.874\n", + "Esperado=0.500, Predito=1.946, Erro=1.446\n", + "Esperado=0.333, Predito=1.229, Erro=0.896\n", + "Esperado=0.333, Predito=1.225, Erro=0.892\n", + "Esperado=0.500, Predito=1.832, Erro=1.332\n", + "Esperado=0.500, Predito=1.516, Erro=1.016\n", + "Esperado=0.333, Predito=2.048, Erro=1.715\n", + "Esperado=0.167, Predito=1.342, Erro=1.176\n", + "Esperado=0.500, Predito=1.231, Erro=0.731\n", + "Esperado=0.500, Predito=1.316, Erro=0.816\n", + "Esperado=0.167, Predito=1.652, Erro=1.485\n", + "Esperado=0.167, Predito=1.342, Erro=1.176\n", + "Esperado=0.167, Predito=1.669, Erro=1.503\n", + "Esperado=0.333, Predito=1.256, Erro=0.923\n", + "Esperado=0.500, Predito=1.231, Erro=0.731\n", + "Esperado=0.333, Predito=2.207, Erro=1.874\n", + "Esperado=0.333, Predito=1.453, Erro=1.119\n", + "Esperado=0.333, Predito=1.527, Erro=1.194\n", + "Esperado=0.667, Predito=1.764, Erro=1.097\n", + "Esperado=0.333, Predito=1.341, Erro=1.008\n", + "Esperado=0.333, Predito=1.785, Erro=1.452\n", + "Esperado=0.500, Predito=1.416, Erro=0.916\n", + "Esperado=0.333, Predito=1.785, Erro=1.452\n", + "Esperado=0.500, Predito=1.416, Erro=0.916\n", + "Esperado=0.500, Predito=1.310, Erro=0.810\n", + "Esperado=0.500, Predito=1.493, Erro=0.993\n", + "Esperado=0.500, Predito=1.310, Erro=0.810\n", + "Esperado=0.500, Predito=1.493, Erro=0.993\n", + "Esperado=0.333, Predito=1.290, Erro=0.957\n", + "Esperado=0.333, Predito=1.244, Erro=0.910\n", + "Esperado=0.333, Predito=1.279, Erro=0.946\n", + "Esperado=0.500, Predito=1.427, Erro=0.927\n", + "Esperado=0.500, Predito=1.362, Erro=0.862\n", + "Esperado=0.167, Predito=1.440, Erro=1.274\n", + "Esperado=0.333, Predito=1.528, Erro=1.194\n", + "Esperado=0.500, Predito=1.312, Erro=0.812\n", + "Esperado=0.333, Predito=1.528, Erro=1.194\n", + "Esperado=0.500, Predito=1.553, Erro=1.053\n", + "Esperado=0.333, Predito=1.667, Erro=1.334\n", + "Esperado=0.500, Predito=1.686, Erro=1.186\n", + "Esperado=0.333, Predito=1.279, Erro=0.945\n", + "Esperado=0.333, Predito=1.476, Erro=1.142\n", + "Esperado=0.333, Predito=1.279, Erro=0.945\n", + "Esperado=0.333, Predito=1.518, Erro=1.184\n", + "Esperado=0.333, Predito=1.476, Erro=1.142\n", + "Esperado=0.500, Predito=1.440, Erro=0.940\n", + "Esperado=0.500, Predito=1.430, Erro=0.930\n", + "Esperado=0.500, Predito=1.494, Erro=0.994\n", + "Esperado=0.333, Predito=1.830, Erro=1.496\n", + "Esperado=0.333, Predito=1.830, Erro=1.496\n", + "Esperado=0.667, Predito=1.295, Erro=0.628\n", + "Esperado=0.500, Predito=1.321, Erro=0.821\n", + "Esperado=0.333, Predito=1.464, Erro=1.131\n", + "Esperado=0.500, Predito=1.458, Erro=0.958\n", + "Esperado=0.500, Predito=1.529, Erro=1.029\n", + "Esperado=0.500, Predito=1.529, Erro=1.029\n", + "Esperado=0.500, Predito=1.458, Erro=0.958\n", + "Esperado=0.333, Predito=1.406, Erro=1.073\n", + "Esperado=0.333, Predito=1.406, Erro=1.073\n", + "Esperado=0.333, Predito=1.233, Erro=0.900\n", + "Esperado=0.333, Predito=1.210, Erro=0.876\n", + "Esperado=0.667, Predito=1.446, Erro=0.780\n", + "Esperado=0.333, Predito=1.165, Erro=0.831\n", + "Esperado=0.333, Predito=1.226, Erro=0.893\n", + "Esperado=0.333, Predito=1.207, Erro=0.874\n", + "Esperado=0.500, Predito=1.438, Erro=0.938\n", + "Esperado=0.500, Predito=1.117, Erro=0.617\n", + "Esperado=0.333, Predito=1.341, Erro=1.008\n", + "Esperado=0.333, Predito=1.257, Erro=0.923\n", + "Esperado=0.667, Predito=1.600, Erro=0.934\n", + "Esperado=0.500, Predito=1.268, Erro=0.768\n", + "Esperado=0.500, Predito=1.405, Erro=0.905\n", + "Esperado=0.667, Predito=1.346, Erro=0.680\n", + "Esperado=0.333, Predito=1.854, Erro=1.520\n", + "Esperado=0.500, Predito=1.289, Erro=0.789\n", + "Esperado=0.667, Predito=1.346, Erro=0.680\n", + "Esperado=0.500, Predito=1.290, Erro=0.790\n", + "Esperado=0.500, Predito=1.266, Erro=0.766\n", + "Esperado=0.333, Predito=1.498, Erro=1.164\n", + "Esperado=0.500, Predito=1.480, Erro=0.980\n", + "Esperado=0.500, Predito=1.269, Erro=0.769\n", + "Esperado=0.500, Predito=1.290, Erro=0.790\n", + "Esperado=0.333, Predito=1.310, Erro=0.977\n", + "Esperado=0.500, Predito=1.405, Erro=0.905\n", + "Esperado=0.333, Predito=1.157, Erro=0.823\n", + "Esperado=0.500, Predito=1.440, Erro=0.940\n", + "Esperado=0.500, Predito=1.440, Erro=0.940\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.500, Predito=1.749, Erro=1.249\n", + "Esperado=0.333, Predito=1.128, Erro=0.795\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.167, Predito=1.408, Erro=1.241\n", + "Esperado=0.167, Predito=1.408, Erro=1.241\n", + "Esperado=0.333, Predito=1.155, Erro=0.822\n", + "Esperado=0.333, Predito=1.262, Erro=0.929\n", + "Esperado=0.333, Predito=1.168, Erro=0.835\n", + "Esperado=0.333, Predito=1.168, Erro=0.835\n", + "Esperado=0.500, Predito=1.425, Erro=0.925\n", + "Esperado=0.333, Predito=1.168, Erro=0.835\n", + "Esperado=0.333, Predito=1.398, Erro=1.065\n", + "Esperado=0.167, Predito=1.390, Erro=1.223\n", + "Esperado=0.167, Predito=1.390, Erro=1.223\n", + "Esperado=0.500, Predito=1.310, Erro=0.810\n", + "Esperado=0.500, Predito=1.647, Erro=1.147\n", + "Esperado=0.500, Predito=1.398, Erro=0.898\n", + "Esperado=0.667, Predito=1.609, Erro=0.943\n", + "Esperado=0.500, Predito=1.284, Erro=0.784\n", + "Esperado=0.333, Predito=1.396, Erro=1.063\n", + "Esperado=0.500, Predito=1.274, Erro=0.774\n", + "Esperado=0.500, Predito=1.435, Erro=0.935\n", + "Esperado=0.500, Predito=1.109, Erro=0.609\n", + "Esperado=0.333, Predito=1.411, Erro=1.078\n", + "Esperado=0.667, Predito=1.622, Erro=0.955\n", + "Esperado=0.667, Predito=1.170, Erro=0.503\n", + "Esperado=0.500, Predito=1.539, Erro=1.039\n", + "Esperado=0.500, Predito=1.780, Erro=1.280\n", + "Esperado=0.667, Predito=1.170, Erro=0.503\n", + "Esperado=0.333, Predito=1.238, Erro=0.905\n", + "Esperado=0.333, Predito=1.587, Erro=1.254\n", + "Esperado=0.667, Predito=1.344, Erro=0.678\n", + "Esperado=0.833, Predito=1.125, Erro=0.292\n", + "Esperado=0.333, Predito=1.275, Erro=0.941\n", + "Esperado=0.333, Predito=1.587, Erro=1.254\n", + "Esperado=0.833, Predito=1.125, Erro=0.292\n", + "Esperado=0.667, Predito=1.344, Erro=0.678\n", + "Esperado=0.500, Predito=1.154, Erro=0.654\n", + "Esperado=0.500, Predito=1.121, Erro=0.621\n", + "Esperado=0.333, Predito=1.521, Erro=1.188\n", + "Esperado=0.500, Predito=1.484, Erro=0.984\n", + "Esperado=0.167, Predito=1.668, Erro=1.502\n", + "Esperado=0.167, Predito=1.668, Erro=1.502\n", + "Esperado=0.333, Predito=1.205, Erro=0.872\n", + "Esperado=0.667, Predito=1.260, Erro=0.593\n", + "Esperado=0.500, Predito=1.358, Erro=0.858\n", + "Esperado=0.500, Predito=1.162, Erro=0.662\n", + "Esperado=0.500, Predito=1.272, Erro=0.772\n", + "Esperado=0.500, Predito=1.272, Erro=0.772\n", + "Esperado=0.333, Predito=1.318, Erro=0.985\n", + "Esperado=0.333, Predito=1.417, Erro=1.084\n", + "Esperado=0.500, Predito=1.358, Erro=0.858\n", + "Esperado=0.333, Predito=1.380, Erro=1.047\n", + "Esperado=0.500, Predito=1.162, Erro=0.662\n", + "Esperado=0.500, Predito=1.470, Erro=0.970\n", + "Esperado=0.500, Predito=1.338, Erro=0.838\n", + "Esperado=0.333, Predito=1.758, Erro=1.425\n", + "Esperado=0.333, Predito=1.372, Erro=1.039\n", + "Esperado=0.500, Predito=1.309, Erro=0.809\n", + "Esperado=0.333, Predito=1.279, Erro=0.946\n", + "Esperado=0.333, Predito=1.372, Erro=1.039\n", + "Esperado=0.500, Predito=1.309, Erro=0.809\n", + "Esperado=0.333, Predito=1.412, Erro=1.079\n", + "Esperado=0.667, Predito=1.303, Erro=0.636\n", + "Esperado=0.667, Predito=1.348, Erro=0.681\n", + "Esperado=0.667, Predito=1.303, Erro=0.636\n", + "Esperado=0.333, Predito=1.206, Erro=0.873\n", + "Esperado=0.667, Predito=1.348, Erro=0.681\n", + "Esperado=0.667, Predito=1.303, Erro=0.636\n", + "Esperado=0.333, Predito=1.186, Erro=0.853\n", + "Esperado=0.500, Predito=1.400, Erro=0.900\n", + "Esperado=0.333, Predito=1.300, Erro=0.967\n", + "Esperado=0.500, Predito=1.400, Erro=0.900\n", + "Esperado=0.333, Predito=1.186, Erro=0.853\n", + "Esperado=0.500, Predito=1.539, Erro=1.039\n", + "Esperado=0.333, Predito=1.427, Erro=1.094\n", + "Esperado=0.667, Predito=1.329, Erro=0.663\n", + "Esperado=0.333, Predito=1.349, Erro=1.016\n", + "Esperado=0.500, Predito=1.323, Erro=0.823\n", + "Esperado=0.500, Predito=1.381, Erro=0.881\n", + "Esperado=0.333, Predito=1.349, Erro=1.016\n", + "Esperado=0.500, Predito=1.168, Erro=0.668\n", + "Esperado=0.333, Predito=1.061, Erro=0.727\n", + "Esperado=0.333, Predito=1.414, Erro=1.080\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Esperado=0.333, Predito=1.361, Erro=1.027\n", + "Esperado=0.500, Predito=1.040, Erro=0.540\n", + "Esperado=0.333, Predito=1.303, Erro=0.970\n", + "Esperado=0.333, Predito=1.278, Erro=0.945\n", + "Esperado=0.667, Predito=1.601, Erro=0.934\n", + "Esperado=0.500, Predito=1.205, Erro=0.705\n", + "Esperado=0.333, Predito=1.469, Erro=1.136\n", + "Esperado=0.667, Predito=1.117, Erro=0.451\n", + "Esperado=0.667, Predito=1.117, Erro=0.451\n", + "Esperado=0.667, Predito=1.117, Erro=0.451\n", + "Esperado=0.667, Predito=1.117, Erro=0.451\n", + "Esperado=0.667, Predito=1.117, Erro=0.451\n", + "Esperado=0.667, Predito=1.117, Erro=0.451\n", + "Esperado=0.500, Predito=0.951, Erro=0.451\n", + "Esperado=0.667, Predito=1.117, Erro=0.451\n", + "Esperado=0.167, Predito=1.907, Erro=1.740\n", + "Esperado=0.500, Predito=1.073, Erro=0.573\n", + "Esperado=0.667, Predito=1.670, Erro=1.003\n", + "Esperado=0.333, Predito=1.505, Erro=1.171\n", + "Esperado=0.333, Predito=1.216, Erro=0.882\n", + "Esperado=0.667, Predito=2.261, Erro=1.595\n", + "Esperado=0.333, Predito=1.740, Erro=1.407\n", + "Esperado=0.333, Predito=1.433, Erro=1.100\n", + "Esperado=0.500, Predito=1.477, Erro=0.977\n", + "Esperado=0.333, Predito=1.433, Erro=1.100\n", + "Esperado=0.500, Predito=1.469, Erro=0.969\n", + "Esperado=0.667, Predito=1.292, Erro=0.625\n", + "Esperado=0.333, Predito=1.389, Erro=1.056\n", + "Esperado=0.500, Predito=1.477, Erro=0.977\n", + "Esperado=0.667, Predito=1.561, Erro=0.894\n", + "Esperado=0.667, Predito=1.561, Erro=0.894\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.500, Predito=1.262, Erro=0.762\n", + "Esperado=0.500, Predito=1.362, Erro=0.862\n", + "Esperado=0.500, Predito=1.339, Erro=0.839\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.333, Predito=1.424, Erro=1.091\n", + "Esperado=0.333, Predito=1.352, Erro=1.019\n", + "Esperado=0.667, Predito=1.561, Erro=0.894\n", + "Esperado=0.333, Predito=1.375, Erro=1.042\n", + "Esperado=0.333, Predito=1.339, Erro=1.006\n", + "Esperado=0.667, Predito=1.380, Erro=0.713\n", + "Esperado=0.667, Predito=1.380, Erro=0.713\n", + "Esperado=0.667, Predito=1.380, Erro=0.713\n", + "Esperado=0.667, Predito=1.305, Erro=0.638\n", + "Esperado=0.500, Predito=1.403, Erro=0.903\n", + "Esperado=0.333, Predito=1.204, Erro=0.871\n", + "Esperado=0.333, Predito=1.375, Erro=1.042\n", + "Esperado=0.667, Predito=1.305, Erro=0.638\n", + "Esperado=0.667, Predito=1.380, Erro=0.713\n", + "Esperado=0.333, Predito=1.339, Erro=1.006\n", + "Esperado=0.333, Predito=0.977, Erro=0.643\n", + "Esperado=0.667, Predito=1.330, Erro=0.663\n", + "Esperado=0.667, Predito=1.150, Erro=0.483\n", + "Esperado=0.500, Predito=1.306, Erro=0.806\n", + "Esperado=0.333, Predito=1.954, Erro=1.620\n", + "Esperado=0.500, Predito=1.553, Erro=1.053\n", + "Esperado=0.667, Predito=1.375, Erro=0.708\n", + "Esperado=0.333, Predito=1.528, Erro=1.194\n", + "Esperado=0.333, Predito=1.184, Erro=0.851\n", + "Esperado=0.333, Predito=1.597, Erro=1.263\n", + 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Predito=1.339, Erro=1.006\n", + "Esperado=0.167, Predito=1.465, Erro=1.298\n", + "Esperado=0.667, Predito=1.307, Erro=0.640\n", + "Esperado=0.667, Predito=1.320, Erro=0.654\n", + "Esperado=0.500, Predito=1.194, Erro=0.694\n", + "Esperado=0.500, Predito=1.371, Erro=0.871\n", + "Esperado=0.500, Predito=1.509, Erro=1.009\n", + "Esperado=0.500, Predito=1.391, Erro=0.891\n", + "Esperado=0.833, Predito=1.046, Erro=0.213\n", + "Esperado=0.667, Predito=1.373, Erro=0.706\n", + "Esperado=0.333, Predito=1.544, Erro=1.211\n", + "Esperado=0.667, Predito=1.592, Erro=0.925\n", + "Esperado=0.667, Predito=1.093, Erro=0.426\n", + "Esperado=0.500, Predito=2.536, Erro=2.036\n", + "Esperado=0.333, Predito=1.265, Erro=0.932\n", + "Esperado=0.667, Predito=1.592, Erro=0.925\n", + "Esperado=0.667, Predito=1.522, Erro=0.856\n", + "Esperado=0.667, Predito=1.009, Erro=0.343\n", + "Esperado=0.500, Predito=1.528, Erro=1.028\n", + "Esperado=0.667, Predito=1.569, Erro=0.902\n", + "Esperado=0.667, Predito=1.515, Erro=0.849\n", + "Esperado=0.500, Predito=1.211, Erro=0.711\n", + "Esperado=0.500, Predito=1.281, Erro=0.781\n", + "Esperado=0.333, Predito=1.275, Erro=0.942\n", + "Esperado=0.500, Predito=1.069, Erro=0.569\n", + "Esperado=0.500, Predito=1.221, Erro=0.721\n", + "Esperado=0.500, Predito=1.213, Erro=0.713\n", + "Esperado=0.500, Predito=1.527, Erro=1.027\n", + "Esperado=0.333, Predito=1.275, Erro=0.942\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.667, Predito=1.103, Erro=0.437\n", + "Esperado=0.500, Predito=1.221, Erro=0.721\n", + "Esperado=0.333, Predito=1.203, Erro=0.870\n", + "Esperado=0.500, Predito=1.213, Erro=0.713\n", + "Esperado=0.500, Predito=1.069, Erro=0.569\n", + "Esperado=0.500, Predito=1.319, Erro=0.819\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.500, Predito=1.370, Erro=0.870\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.500, Predito=1.262, Erro=0.762\n", + "Esperado=0.500, Predito=1.164, Erro=0.664\n", + "Esperado=0.333, Predito=1.210, Erro=0.876\n", + "Esperado=0.500, Predito=1.370, Erro=0.870\n", + "Esperado=0.500, Predito=1.319, Erro=0.819\n", + "Esperado=0.667, Predito=1.828, Erro=1.161\n", + "Esperado=0.333, Predito=1.147, Erro=0.814\n", + "Esperado=0.333, Predito=1.147, Erro=0.814\n", + "Esperado=0.333, Predito=1.147, Erro=0.814\n", + "Esperado=0.500, Predito=1.229, Erro=0.729\n", + "Esperado=0.500, Predito=1.229, Erro=0.729\n", + "Esperado=0.500, Predito=1.438, Erro=0.938\n", + "Esperado=0.333, Predito=1.147, Erro=0.814\n", + "Esperado=0.500, Predito=1.210, Erro=0.710\n", + "Esperado=0.333, Predito=1.442, Erro=1.109\n", + "Esperado=0.333, Predito=1.442, Erro=1.109\n", + "Esperado=0.333, Predito=1.185, Erro=0.852\n", + "Esperado=0.333, Predito=1.141, Erro=0.808\n", + "Esperado=0.500, Predito=1.210, Erro=0.710\n", + "Esperado=0.500, Predito=1.248, Erro=0.748\n", + "Esperado=0.500, Predito=1.383, Erro=0.883\n", + "Esperado=0.333, Predito=1.442, Erro=1.109\n", + "Esperado=0.500, Predito=1.147, Erro=0.647\n", + "Esperado=0.500, Predito=1.339, Erro=0.839\n", + "Esperado=0.500, Predito=1.138, Erro=0.638\n", + "Esperado=0.667, Predito=1.355, Erro=0.688\n", + "Esperado=0.333, Predito=1.682, Erro=1.349\n", + "Esperado=0.667, Predito=1.809, Erro=1.142\n", + "Esperado=0.500, Predito=1.397, Erro=0.897\n", + "Esperado=0.500, Predito=1.138, Erro=0.638\n", + "Esperado=0.333, Predito=1.282, Erro=0.948\n", + "Esperado=0.500, Predito=1.182, Erro=0.682\n", + "Esperado=0.500, Predito=1.122, Erro=0.622\n", + "Esperado=0.500, Predito=1.500, Erro=1.000\n", + "Esperado=0.667, Predito=1.578, Erro=0.911\n", + "Esperado=0.333, Predito=1.362, Erro=1.028\n", + "Esperado=0.333, Predito=1.777, Erro=1.444\n", + "Esperado=0.333, Predito=1.741, Erro=1.407\n", + "Esperado=0.667, Predito=1.578, Erro=0.911\n", + "Esperado=0.500, Predito=1.389, Erro=0.889\n", + "Esperado=0.500, Predito=1.500, Erro=1.000\n", + "Esperado=0.500, Predito=1.122, Erro=0.622\n", + "Esperado=0.667, Predito=1.176, Erro=0.509\n", + "Esperado=0.333, Predito=1.798, Erro=1.465\n", + "Esperado=0.667, Predito=1.542, Erro=0.875\n", + "Esperado=0.500, Predito=1.115, Erro=0.615\n", + "Esperado=0.333, Predito=1.798, Erro=1.465\n", + "Esperado=0.500, Predito=1.394, Erro=0.894\n", + "Esperado=0.500, Predito=1.254, Erro=0.754\n", + "Esperado=0.500, Predito=1.254, Erro=0.754\n", + "Esperado=0.667, Predito=1.840, Erro=1.173\n", + "Esperado=0.500, Predito=1.443, Erro=0.943\n", + "Esperado=0.500, Predito=1.370, Erro=0.870\n", + "Esperado=0.833, Predito=1.576, Erro=0.743\n", + "Esperado=0.500, Predito=1.370, Erro=0.870\n", + "Esperado=0.833, Predito=1.576, Erro=0.743\n", + "Esperado=0.500, Predito=1.389, Erro=0.889\n", + "Esperado=0.500, Predito=1.759, Erro=1.259\n", + "Esperado=0.833, Predito=1.456, Erro=0.623\n", + "Esperado=0.333, Predito=1.014, Erro=0.681\n", + "Esperado=0.333, Predito=1.428, Erro=1.095\n", + "Esperado=0.500, Predito=1.499, Erro=0.999\n", + "Esperado=0.333, Predito=0.976, Erro=0.643\n", + "Esperado=0.500, Predito=1.011, Erro=0.511\n", + "Esperado=0.500, Predito=1.233, Erro=0.733\n", + "Esperado=0.500, Predito=1.199, Erro=0.699\n", + "Esperado=0.333, Predito=1.204, Erro=0.871\n", + "Esperado=0.333, Predito=1.401, Erro=1.068\n", + "Esperado=0.500, Predito=1.405, Erro=0.905\n", + "Esperado=0.500, Predito=1.026, Erro=0.526\n", + "Esperado=0.500, Predito=1.318, Erro=0.818\n", + "Esperado=0.500, Predito=1.294, Erro=0.794\n", + "Esperado=0.500, Predito=1.026, Erro=0.526\n", + "Esperado=0.500, Predito=1.405, Erro=0.905\n", + "Esperado=0.333, Predito=1.401, Erro=1.068\n", + "Esperado=0.667, Predito=1.609, Erro=0.942\n", + "Esperado=0.500, Predito=1.430, Erro=0.930\n", + "Esperado=0.667, Predito=1.488, Erro=0.821\n", + "Esperado=0.667, Predito=1.776, Erro=1.109\n", + "Esperado=0.833, Predito=1.494, Erro=0.661\n", + "Esperado=0.833, Predito=1.494, Erro=0.661\n", + "Esperado=0.833, Predito=1.486, Erro=0.652\n", + "Esperado=0.500, Predito=1.330, Erro=0.830\n", + "Esperado=0.667, Predito=1.776, Erro=1.109\n", + "Esperado=0.333, Predito=1.240, Erro=0.906\n", + "Esperado=0.333, Predito=1.374, Erro=1.041\n", + "Esperado=0.500, Predito=3.220, Erro=2.720\n", + "Esperado=0.500, Predito=1.327, Erro=0.827\n", + "Esperado=0.500, Predito=1.327, Erro=0.827\n", + "Esperado=0.500, Predito=1.327, Erro=0.827\n", + "Esperado=0.333, Predito=1.257, Erro=0.924\n", + "Esperado=0.500, Predito=1.474, Erro=0.974\n", + "Esperado=0.333, Predito=1.257, Erro=0.924\n", + "Esperado=0.333, Predito=1.549, Erro=1.216\n", + "Esperado=0.333, Predito=1.541, Erro=1.208\n", + "Esperado=0.500, Predito=1.327, Erro=0.827\n", + "Esperado=0.333, Predito=1.248, Erro=0.915\n", + "Esperado=0.333, Predito=1.290, Erro=0.957\n", + "Esperado=0.667, Predito=1.765, Erro=1.098\n", + "Esperado=0.333, Predito=1.362, Erro=1.029\n", + "Esperado=0.833, Predito=1.320, Erro=0.487\n", + "Esperado=0.667, Predito=1.765, Erro=1.098\n", + "Esperado=0.333, Predito=1.362, Erro=1.029\n", + "Esperado=0.667, Predito=1.445, Erro=0.779\n", + "Esperado=0.667, Predito=1.298, Erro=0.631\n", + "Esperado=0.333, Predito=1.248, Erro=0.915\n", + "Esperado=0.333, Predito=1.290, Erro=0.957\n", + "Esperado=0.500, Predito=1.058, Erro=0.558\n", + "Esperado=0.833, Predito=1.149, Erro=0.316\n", + "Esperado=0.833, Predito=1.320, Erro=0.487\n", + "Esperado=0.500, Predito=1.226, Erro=0.726\n", + "Esperado=0.333, Predito=1.062, Erro=0.729\n", + "Esperado=0.333, Predito=1.079, Erro=0.746\n", + "Esperado=0.500, Predito=1.301, Erro=0.801\n", + "Esperado=0.667, Predito=1.741, Erro=1.074\n", + "Esperado=0.667, Predito=1.408, Erro=0.741\n", + "Esperado=0.333, Predito=1.547, Erro=1.213\n", + "Esperado=0.500, Predito=1.196, Erro=0.696\n", + "Esperado=0.667, Predito=1.741, Erro=1.074\n", + "Esperado=0.667, Predito=1.304, Erro=0.638\n", + "Esperado=0.333, Predito=1.455, Erro=1.122\n", + "Esperado=0.333, Predito=1.193, Erro=0.859\n", + "Esperado=0.667, Predito=1.304, Erro=0.638\n", + "Esperado=0.167, Predito=0.876, Erro=0.709\n", + "Esperado=0.500, Predito=1.276, Erro=0.776\n", + "Esperado=0.333, Predito=1.280, Erro=0.947\n", + "Esperado=0.333, Predito=1.673, Erro=1.339\n", + "Esperado=0.333, Predito=1.208, Erro=0.875\n", + "Esperado=0.500, Predito=1.466, Erro=0.966\n", + "Esperado=0.500, Predito=1.310, Erro=0.810\n", + "Esperado=0.500, Predito=1.316, Erro=0.816\n", + "Esperado=0.500, Predito=1.178, Erro=0.678\n", + "Esperado=0.667, Predito=1.105, Erro=0.438\n", + "Esperado=0.667, Predito=1.105, Erro=0.438\n", + "Esperado=0.333, Predito=1.337, Erro=1.003\n", + "Esperado=0.333, Predito=1.256, Erro=0.923\n", + "Esperado=0.333, Predito=1.562, Erro=1.229\n", + "Esperado=0.500, Predito=1.247, Erro=0.747\n", + "Esperado=0.667, Predito=1.623, Erro=0.956\n", + "Esperado=0.500, Predito=1.111, Erro=0.611\n", + "Esperado=0.500, Predito=1.316, Erro=0.816\n", + "Esperado=0.500, Predito=1.178, Erro=0.678\n", + "Esperado=0.500, Predito=1.362, Erro=0.862\n", + "Esperado=0.667, Predito=1.227, Erro=0.561\n", + "Esperado=0.667, Predito=1.178, Erro=0.512\n", + "Esperado=0.500, Predito=1.486, Erro=0.986\n", + "Esperado=0.500, Predito=1.449, Erro=0.949\n", + "Esperado=0.500, Predito=1.146, Erro=0.646\n", + "Esperado=0.500, Predito=1.196, Erro=0.696\n", + "Esperado=0.333, Predito=1.242, Erro=0.908\n", + "Esperado=0.667, Predito=1.331, Erro=0.664\n", + "Esperado=0.667, Predito=1.227, Erro=0.561\n", + "Esperado=0.333, Predito=1.273, Erro=0.939\n", + "Esperado=0.333, Predito=1.323, Erro=0.989\n", + "Esperado=0.333, Predito=1.712, Erro=1.379\n", + "Esperado=0.333, Predito=1.238, Erro=0.904\n", + "Esperado=0.333, Predito=1.298, Erro=0.964\n", + "Esperado=0.333, Predito=1.277, Erro=0.943\n", + "Esperado=0.500, Predito=1.247, Erro=0.747\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.667, Predito=1.472, Erro=0.806\n", + "Esperado=0.667, Predito=1.385, Erro=0.718\n", + "Esperado=0.833, Predito=1.612, Erro=0.779\n", + "Esperado=0.667, Predito=1.472, Erro=0.806\n", + "Esperado=0.500, Predito=1.371, Erro=0.871\n", + "Esperado=0.500, Predito=1.238, Erro=0.738\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.500, Predito=1.218, Erro=0.718\n", + "Esperado=0.500, Predito=1.192, Erro=0.692\n", + "Esperado=0.500, Predito=1.247, Erro=0.747\n", + "Esperado=0.500, Predito=1.198, Erro=0.698\n", + "Esperado=0.667, Predito=1.385, Erro=0.718\n", + "Esperado=0.667, Predito=1.880, Erro=1.214\n", + "Esperado=0.333, Predito=1.103, Erro=0.770\n", + "Esperado=0.500, Predito=1.182, Erro=0.682\n", + "Esperado=0.667, Predito=1.880, Erro=1.214\n", + "Esperado=0.500, Predito=1.203, Erro=0.703\n", + "Esperado=0.667, Predito=1.595, Erro=0.928\n", + "Esperado=0.833, Predito=1.683, Erro=0.850\n", + "Esperado=0.667, Predito=1.739, Erro=1.072\n", + "Esperado=0.500, Predito=1.203, Erro=0.703\n", + "Esperado=0.333, Predito=1.241, Erro=0.908\n", + "Esperado=0.333, Predito=0.987, Erro=0.654\n", + "Esperado=0.500, Predito=1.548, Erro=1.048\n", + "Esperado=0.500, Predito=1.507, Erro=1.007\n", + "Esperado=0.333, Predito=1.314, Erro=0.980\n", + "Esperado=0.667, Predito=1.272, Erro=0.605\n", + "Esperado=0.333, Predito=1.193, Erro=0.859\n", + "Esperado=0.667, Predito=1.948, Erro=1.282\n", + "Esperado=0.667, Predito=1.639, Erro=0.973\n", + "Esperado=0.500, Predito=1.729, Erro=1.229\n", + "Esperado=0.333, Predito=1.520, Erro=1.187\n", + "Esperado=0.333, Predito=1.341, Erro=1.008\n", + "Esperado=0.167, Predito=1.152, Erro=0.985\n", + "Esperado=0.500, Predito=1.625, Erro=1.125\n", + "Esperado=0.833, Predito=1.399, Erro=0.565\n", + "Esperado=0.500, Predito=1.106, Erro=0.606\n", + "Esperado=0.333, Predito=1.564, Erro=1.230\n", + "Esperado=0.667, Predito=1.543, Erro=0.876\n", + "Esperado=0.667, Predito=1.166, Erro=0.499\n", + "Esperado=0.333, Predito=1.714, Erro=1.380\n", + "Esperado=0.333, Predito=1.492, Erro=1.159\n", + "Esperado=0.333, Predito=1.564, Erro=1.230\n", + "Esperado=0.333, Predito=1.131, Erro=0.798\n", + "Esperado=0.333, Predito=1.160, Erro=0.827\n", + "Esperado=0.500, Predito=1.225, Erro=0.725\n", + "Esperado=0.667, Predito=1.429, Erro=0.763\n", + "Esperado=0.333, Predito=1.458, Erro=1.124\n", + "Esperado=0.500, Predito=1.315, Erro=0.815\n", + "Esperado=0.667, Predito=1.395, Erro=0.728\n", + "Esperado=0.333, Predito=1.353, Erro=1.020\n", + "Esperado=0.333, Predito=1.242, Erro=0.909\n", + "Esperado=0.500, Predito=1.315, Erro=0.815\n", + "Esperado=0.500, Predito=1.285, Erro=0.785\n", + "Esperado=0.500, Predito=1.249, Erro=0.749\n", + "Esperado=0.333, Predito=1.461, Erro=1.128\n", + "Esperado=0.333, Predito=1.458, Erro=1.124\n", + "Esperado=0.667, Predito=1.429, Erro=0.763\n", + "Esperado=0.333, Predito=1.021, Erro=0.688\n", + "Esperado=0.500, Predito=1.189, Erro=0.689\n", + "Esperado=0.500, Predito=1.189, Erro=0.689\n", + "Esperado=0.500, Predito=1.741, Erro=1.241\n", + "Esperado=0.667, Predito=1.692, Erro=1.025\n", + "Esperado=0.500, Predito=1.393, Erro=0.893\n", + "Esperado=0.333, Predito=1.128, Erro=0.795\n", + "Esperado=0.167, Predito=1.378, Erro=1.211\n", + "Esperado=0.500, Predito=1.656, Erro=1.156\n", + "Esperado=0.833, Predito=1.700, Erro=0.867\n", + "Esperado=0.500, Predito=1.279, Erro=0.779\n", + "Esperado=0.500, Predito=1.190, Erro=0.690\n", + "Esperado=0.333, Predito=1.317, Erro=0.983\n", + "Esperado=0.833, Predito=1.470, Erro=0.637\n", + "Esperado=0.667, Predito=1.396, Erro=0.729\n", + "Esperado=0.667, Predito=1.396, Erro=0.729\n", + "Esperado=0.500, Predito=1.190, Erro=0.690\n", + "Esperado=0.833, Predito=1.642, Erro=0.809\n", + "Esperado=0.833, Predito=1.470, Erro=0.637\n", + "Esperado=0.500, Predito=1.326, Erro=0.826\n", + "Esperado=0.500, Predito=1.279, Erro=0.779\n", + "Esperado=0.333, Predito=1.108, Erro=0.775\n", + "Esperado=0.167, Predito=1.230, Erro=1.063\n", + "Esperado=0.333, Predito=1.045, Erro=0.712\n", + "Esperado=0.333, Predito=1.317, Erro=0.983\n", + "Esperado=0.833, Predito=0.977, Erro=0.144\n", + "Esperado=0.833, Predito=0.977, Erro=0.144\n", + "Esperado=0.500, Predito=1.355, Erro=0.855\n", + "Esperado=0.333, Predito=1.502, Erro=1.168\n", + "Esperado=0.333, Predito=1.250, Erro=0.916\n", + "Esperado=0.833, Predito=0.977, Erro=0.144\n", + "Esperado=0.500, Predito=1.355, Erro=0.855\n", + "Esperado=0.833, Predito=1.760, Erro=0.927\n", + "Esperado=0.500, Predito=1.345, Erro=0.845\n", + "Esperado=0.500, Predito=1.345, Erro=0.845\n", + "Esperado=0.500, Predito=1.250, Erro=0.750\n", + "Esperado=0.500, Predito=1.168, Erro=0.668\n", + "Esperado=0.333, Predito=1.264, Erro=0.931\n", + "Esperado=0.333, Predito=1.239, Erro=0.905\n", + "Esperado=0.333, Predito=1.240, Erro=0.907\n", + "Esperado=0.333, Predito=1.264, Erro=0.931\n", + "Esperado=0.667, Predito=1.597, Erro=0.930\n", + "Esperado=0.333, Predito=1.518, Erro=1.185\n", + "Esperado=0.500, Predito=1.195, Erro=0.695\n", + "Esperado=0.500, Predito=1.185, Erro=0.685\n", + "Esperado=0.333, Predito=1.239, Erro=0.905\n", + "Esperado=0.667, Predito=1.767, Erro=1.100\n", + "Esperado=0.667, Predito=1.203, Erro=0.537\n", + "Esperado=0.500, Predito=1.361, Erro=0.861\n", + "Esperado=0.333, Predito=1.470, Erro=1.137\n", + "Esperado=0.667, Predito=1.707, Erro=1.041\n", + "Esperado=0.333, Predito=1.346, Erro=1.013\n", + "Esperado=0.833, Predito=1.665, Erro=0.832\n", + "Esperado=0.500, Predito=1.227, Erro=0.727\n", + "Esperado=0.500, Predito=1.227, Erro=0.727\n", + "Esperado=0.333, Predito=1.346, Erro=1.013\n", + "Esperado=0.500, Predito=1.198, Erro=0.698\n", + "Esperado=0.333, Predito=1.346, Erro=1.013\n", + "Esperado=0.333, Predito=1.229, Erro=0.896\n", + "Esperado=0.500, Predito=1.222, Erro=0.722\n", + "Esperado=0.500, Predito=1.227, Erro=0.727\n", + "Esperado=0.500, Predito=1.202, Erro=0.702\n", + "Esperado=0.500, Predito=1.141, Erro=0.641\n", + "Esperado=0.500, Predito=1.467, Erro=0.967\n", + "Esperado=0.500, Predito=1.237, Erro=0.737\n", + "Esperado=0.500, Predito=1.237, Erro=0.737\n", + "Esperado=0.667, Predito=1.482, Erro=0.815\n", + "Esperado=0.667, Predito=1.536, Erro=0.869\n", + "Esperado=0.667, Predito=1.219, Erro=0.552\n", + "Esperado=0.500, Predito=1.309, Erro=0.809\n", + "Esperado=0.500, Predito=1.321, Erro=0.821\n", + "Esperado=0.500, Predito=1.434, Erro=0.934\n", + "Esperado=0.667, Predito=1.201, Erro=0.534\n", + "Esperado=0.667, Predito=1.201, Erro=0.534\n", + "Esperado=0.667, Predito=1.197, Erro=0.530\n", + "Esperado=0.667, Predito=1.295, Erro=0.628\n", + "Esperado=0.500, Predito=1.135, Erro=0.635\n", + "Esperado=0.667, Predito=1.388, Erro=0.721\n", + "Esperado=0.667, Predito=1.597, Erro=0.930\n", + "Esperado=0.667, Predito=1.199, Erro=0.532\n", + "Esperado=0.667, Predito=1.197, Erro=0.530\n", + "Esperado=0.667, Predito=1.197, Erro=0.530\n", + "Esperado=0.500, Predito=1.397, Erro=0.897\n", + "Esperado=0.833, Predito=1.587, Erro=0.754\n", + "Esperado=0.500, Predito=1.408, Erro=0.908\n", + "Esperado=0.667, Predito=1.705, Erro=1.038\n", + "Esperado=0.667, Predito=1.197, Erro=0.530\n", + "Esperado=0.667, Predito=1.201, Erro=0.534\n", + "Esperado=0.667, Predito=1.199, Erro=0.532\n", + "Esperado=0.667, Predito=1.597, Erro=0.930\n", + "Esperado=0.667, Predito=1.295, Erro=0.628\n", + "Esperado=0.500, Predito=1.217, Erro=0.717\n", + "Esperado=0.500, Predito=1.406, Erro=0.906\n", + "Esperado=0.500, Predito=1.135, Erro=0.635\n", + "Esperado=0.667, Predito=1.388, Erro=0.721\n", + "Esperado=0.667, Predito=1.449, Erro=0.783\n", + "Esperado=0.500, Predito=1.235, Erro=0.735\n", + "Esperado=0.333, Predito=1.268, Erro=0.935\n", + "Esperado=0.500, Predito=1.342, Erro=0.842\n", + "Esperado=0.500, Predito=1.393, Erro=0.893\n", + "Esperado=0.500, Predito=1.176, Erro=0.676\n", + "Esperado=0.500, Predito=1.656, Erro=1.156\n", + "Esperado=0.500, Predito=1.455, Erro=0.955\n", + "Esperado=0.500, Predito=1.176, Erro=0.676\n", + "Esperado=0.500, Predito=1.460, Erro=0.960\n", + "Esperado=0.833, Predito=1.280, Erro=0.446\n", + "Esperado=0.500, Predito=1.455, Erro=0.955\n", + "Esperado=0.500, Predito=1.432, Erro=0.932\n", + "Esperado=0.167, Predito=1.369, Erro=1.202\n", + "Esperado=0.333, Predito=2.039, Erro=1.706\n", + "Esperado=0.500, Predito=1.656, Erro=1.156\n", + "Esperado=0.500, Predito=1.176, Erro=0.676\n", + "Esperado=0.667, Predito=1.446, Erro=0.779\n", + "Esperado=0.500, Predito=1.151, Erro=0.651\n", + "Esperado=0.333, Predito=1.423, Erro=1.090\n", + "Esperado=0.500, Predito=1.256, Erro=0.756\n", + "Esperado=0.833, Predito=1.826, Erro=0.992\n", + "Esperado=0.833, Predito=1.826, Erro=0.992\n", + "Esperado=0.500, Predito=1.075, Erro=0.575\n", + "Esperado=0.333, Predito=1.158, Erro=0.825\n", + "Esperado=0.500, Predito=1.250, Erro=0.750\n", + "Esperado=0.500, Predito=1.292, Erro=0.792\n", + "Esperado=0.667, Predito=1.332, Erro=0.665\n", + "Esperado=0.333, Predito=1.476, Erro=1.143\n", + "Esperado=0.500, Predito=1.485, Erro=0.985\n", + "Esperado=0.500, Predito=1.328, Erro=0.828\n", + "Esperado=0.500, Predito=1.193, Erro=0.693\n", + "Esperado=0.333, Predito=1.181, Erro=0.847\n", + "Esperado=0.500, Predito=1.484, Erro=0.984\n", + "Esperado=0.500, Predito=1.485, Erro=0.985\n", + "Esperado=0.500, Predito=1.111, Erro=0.611\n", + "Esperado=0.500, Predito=1.379, Erro=0.879\n", + "Esperado=0.500, Predito=1.074, Erro=0.574\n", + "Esperado=0.500, Predito=1.329, Erro=0.829\n", + "Esperado=0.333, Predito=1.252, Erro=0.919\n", + "Esperado=0.500, Predito=1.290, Erro=0.790\n", + "Esperado=0.333, Predito=1.467, Erro=1.133\n", + "Esperado=0.167, Predito=1.350, Erro=1.183\n", + "Esperado=0.333, Predito=1.467, Erro=1.133\n", + "Esperado=0.667, Predito=1.183, Erro=0.516\n", + "Esperado=0.333, Predito=1.081, Erro=0.748\n", + "Esperado=0.500, Predito=1.185, Erro=0.685\n", + "Esperado=0.500, Predito=1.378, Erro=0.878\n", + "Esperado=0.333, Predito=1.488, Erro=1.154\n", + "Esperado=0.333, Predito=1.753, Erro=1.420\n", + "Esperado=0.333, Predito=1.207, Erro=0.874\n", + "Esperado=0.500, Predito=1.181, Erro=0.681\n", + "Esperado=0.333, Predito=1.474, Erro=1.140\n", + "Esperado=0.333, Predito=1.474, Erro=1.140\n", + "Esperado=0.833, Predito=1.576, Erro=0.743\n", + "Esperado=0.333, Predito=1.474, Erro=1.140\n", + "Esperado=0.333, Predito=1.154, Erro=0.820\n", + "Esperado=0.500, Predito=1.080, Erro=0.580\n", + "Esperado=0.333, Predito=1.154, Erro=0.820\n", + "Esperado=0.167, Predito=1.331, Erro=1.164\n", + "Esperado=0.500, Predito=1.308, Erro=0.808\n", + "Esperado=0.833, Predito=1.943, Erro=1.110\n", + "Esperado=0.833, Predito=1.576, Erro=0.743\n", + "Esperado=0.667, Predito=1.580, Erro=0.913\n", + "Esperado=0.500, Predito=1.439, Erro=0.939\n", + "Esperado=0.333, Predito=1.366, Erro=1.032\n", + "Esperado=0.333, Predito=1.410, Erro=1.077\n", + "Esperado=0.667, Predito=1.216, Erro=0.549\n", + "Esperado=0.333, Predito=1.162, Erro=0.829\n", + "Esperado=0.333, Predito=1.207, Erro=0.874\n", + "Esperado=0.333, Predito=1.341, Erro=1.008\n", + "Esperado=0.333, Predito=1.833, Erro=1.500\n", + "Esperado=0.500, Predito=1.181, Erro=0.681\n", + "Esperado=0.333, Predito=1.474, Erro=1.140\n", + "Esperado=0.500, Predito=1.544, Erro=1.044\n", + "Esperado=0.667, Predito=1.664, Erro=0.997\n", + "Esperado=0.500, Predito=1.384, Erro=0.884\n", + "Esperado=0.500, Predito=1.238, Erro=0.738\n", + "Esperado=0.667, Predito=1.664, Erro=0.997\n", + "Esperado=0.000, Predito=1.171, Erro=1.171\n", + "Esperado=0.500, Predito=1.532, Erro=1.032\n", + "Esperado=0.667, Predito=1.470, Erro=0.803\n", + "Esperado=0.500, Predito=1.384, Erro=0.884\n", + "Esperado=0.500, Predito=1.327, Erro=0.827\n", + "Esperado=0.500, Predito=1.425, Erro=0.925\n", + "Esperado=0.667, Predito=1.403, Erro=0.737\n", + "Esperado=0.500, Predito=1.269, Erro=0.769\n", + "Esperado=0.500, Predito=1.269, Erro=0.769\n", + "Esperado=0.667, Predito=1.227, Erro=0.560\n", + "Esperado=0.333, Predito=2.329, Erro=1.995\n", + "Esperado=0.667, Predito=1.427, Erro=0.760\n", + "Esperado=0.667, Predito=1.552, Erro=0.885\n", + "Esperado=0.667, Predito=1.141, Erro=0.475\n", + "Esperado=0.500, Predito=1.263, Erro=0.763\n", + "Esperado=0.500, Predito=1.282, Erro=0.782\n", + "Esperado=0.667, Predito=1.403, Erro=0.737\n", + "Esperado=0.333, Predito=1.274, Erro=0.940\n", + "Esperado=0.500, Predito=1.368, Erro=0.868\n", + "Esperado=0.500, Predito=1.425, Erro=0.925\n", + "Esperado=0.500, Predito=1.369, Erro=0.869\n", + "Esperado=0.333, Predito=1.531, Erro=1.197\n", + "Esperado=0.167, Predito=1.490, Erro=1.323\n", + "Esperado=0.500, Predito=1.465, Erro=0.965\n", + "Esperado=0.667, Predito=1.326, Erro=0.659\n", + "Esperado=0.333, Predito=1.412, Erro=1.079\n", + "Esperado=0.500, Predito=1.220, Erro=0.720\n", + "Esperado=0.500, Predito=1.190, Erro=0.690\n", + "Esperado=0.500, Predito=1.190, Erro=0.690\n", + "Esperado=0.667, Predito=1.281, Erro=0.614\n", + "Esperado=0.667, Predito=1.326, Erro=0.659\n", + "Esperado=0.667, Predito=1.617, Erro=0.950\n", + "Esperado=0.333, Predito=1.053, Erro=0.720\n", + "Esperado=0.500, Predito=1.215, Erro=0.715\n", + "Esperado=0.333, Predito=1.412, Erro=1.079\n", + "Esperado=0.500, Predito=1.273, Erro=0.773\n", + "Esperado=0.333, Predito=1.292, Erro=0.958\n", + "Esperado=0.500, Predito=1.220, Erro=0.720\n", + "Esperado=0.333, Predito=1.259, Erro=0.925\n", + "Esperado=0.667, Predito=1.196, Erro=0.530\n", + "Esperado=0.333, Predito=1.549, Erro=1.215\n", + "Esperado=0.500, Predito=1.502, Erro=1.002\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.500, Predito=1.562, Erro=1.062\n", + "Esperado=0.333, Predito=1.411, Erro=1.078\n", + "Esperado=0.500, Predito=1.190, Erro=0.690\n", + "Esperado=0.500, Predito=1.413, Erro=0.913\n", + "Esperado=0.500, Predito=1.171, Erro=0.671\n", + "Esperado=0.500, Predito=1.508, Erro=1.008\n", + "Esperado=0.333, Predito=1.200, Erro=0.867\n", + "Esperado=0.500, Predito=1.286, Erro=0.786\n", + "Esperado=0.500, Predito=1.289, Erro=0.789\n", + "Esperado=0.500, Predito=1.087, Erro=0.587\n", + "Esperado=0.500, Predito=1.508, Erro=1.008\n", + "Esperado=0.667, Predito=1.519, Erro=0.852\n", + "Esperado=0.500, Predito=1.215, Erro=0.715\n", + "Esperado=0.333, Predito=1.200, Erro=0.866\n", + "Esperado=0.500, Predito=1.158, Erro=0.658\n", + "Esperado=0.500, Predito=1.158, Erro=0.658\n", + "Esperado=0.500, Predito=1.158, Erro=0.658\n", + "Esperado=0.500, Predito=1.445, Erro=0.945\n", + "Esperado=0.500, Predito=1.158, Erro=0.658\n", + "Esperado=0.500, Predito=1.445, Erro=0.945\n", + "Esperado=0.500, Predito=1.803, Erro=1.303\n", + "Esperado=0.500, Predito=1.158, Erro=0.658\n", + "Esperado=0.500, Predito=1.461, Erro=0.961\n", + "Esperado=0.500, Predito=1.454, Erro=0.954\n", + "Esperado=0.500, Predito=1.718, Erro=1.218\n", + "Esperado=0.667, Predito=1.638, Erro=0.971\n", + "Esperado=0.333, Predito=1.078, Erro=0.744\n", + "Esperado=0.667, Predito=1.638, Erro=0.971\n", + "Esperado=0.667, Predito=1.654, Erro=0.987\n", + "Esperado=0.500, Predito=1.171, Erro=0.671\n", + "Esperado=0.500, Predito=1.649, Erro=1.149\n", + "Esperado=0.333, Predito=1.078, Erro=0.744\n", + "Esperado=0.667, Predito=1.638, Erro=0.971\n", + "Esperado=0.667, Predito=1.654, Erro=0.987\n", + "Esperado=0.500, Predito=1.614, Erro=1.114\n", + "Esperado=0.500, Predito=2.145, Erro=1.645\n", + "Esperado=0.500, Predito=1.561, Erro=1.061\n", + "Esperado=0.667, Predito=1.439, Erro=0.772\n", + "Esperado=0.500, Predito=1.229, Erro=0.729\n", + "Esperado=0.500, Predito=1.157, Erro=0.657\n", + "Esperado=0.500, Predito=1.116, Erro=0.616\n", + "Esperado=0.500, Predito=1.207, Erro=0.707\n", + "Esperado=0.833, Predito=1.435, Erro=0.601\n", + "Esperado=0.833, Predito=1.460, Erro=0.627\n", + "Esperado=0.833, Predito=1.518, Erro=0.685\n", + "Esperado=0.500, Predito=1.124, Erro=0.624\n", + "Esperado=0.500, Predito=1.207, Erro=0.707\n", + "Esperado=0.333, Predito=1.545, Erro=1.212\n", + "Esperado=0.500, Predito=1.027, Erro=0.527\n", + "Esperado=0.167, Predito=1.111, Erro=0.944\n", + "Esperado=0.500, Predito=1.320, Erro=0.820\n", + "Esperado=0.500, Predito=1.168, Erro=0.668\n", + "Esperado=0.333, Predito=0.984, Erro=0.651\n", + "Esperado=0.500, Predito=1.320, Erro=0.820\n", + "Esperado=0.667, Predito=1.133, Erro=0.466\n", + "Esperado=0.500, Predito=1.247, Erro=0.747\n", + "Esperado=0.167, Predito=1.473, Erro=1.306\n", + "Esperado=0.667, Predito=1.267, Erro=0.601\n", + "Esperado=0.833, Predito=1.223, Erro=0.390\n", + "Esperado=0.333, Predito=1.081, Erro=0.748\n", + "Esperado=0.667, Predito=1.580, Erro=0.913\n", + "Esperado=0.500, Predito=1.284, Erro=0.784\n", + "Esperado=0.500, Predito=1.168, Erro=0.668\n", + "Esperado=0.333, Predito=0.984, Erro=0.651\n", + "Esperado=0.333, Predito=1.164, Erro=0.831\n", + "Esperado=0.500, Predito=1.296, Erro=0.796\n", + "Esperado=0.667, Predito=1.153, Erro=0.486\n", + "Esperado=0.500, Predito=1.524, Erro=1.024\n", + "Esperado=0.667, Predito=1.153, Erro=0.486\n", + "Esperado=0.667, Predito=1.386, Erro=0.720\n", + "Esperado=0.667, Predito=1.413, Erro=0.746\n", + "Esperado=0.500, Predito=1.524, Erro=1.024\n", + "Esperado=0.667, Predito=1.685, Erro=1.019\n", + "Esperado=0.667, Predito=1.153, Erro=0.486\n", + "Esperado=0.333, Predito=1.256, Erro=0.923\n", + "Esperado=0.667, Predito=1.732, Erro=1.066\n", + "Esperado=0.667, Predito=1.773, Erro=1.106\n", + "Esperado=0.500, Predito=1.524, Erro=1.024\n", + "Esperado=0.333, Predito=1.441, Erro=1.108\n", + "Esperado=0.333, Predito=1.208, Erro=0.874\n", + "Esperado=0.333, Predito=1.359, Erro=1.025\n", + "Esperado=0.500, Predito=1.428, Erro=0.928\n", + "Esperado=0.333, Predito=1.282, Erro=0.949\n", + "Esperado=0.500, Predito=1.119, Erro=0.619\n", + "Esperado=0.500, Predito=1.576, Erro=1.076\n", + "Esperado=0.500, Predito=1.297, Erro=0.797\n", + "Esperado=0.333, Predito=1.096, Erro=0.763\n", + "Esperado=0.333, Predito=1.359, Erro=1.025\n", + "Esperado=0.167, Predito=1.295, Erro=1.129\n", + "Esperado=0.667, Predito=1.680, Erro=1.013\n", + "Esperado=0.333, Predito=1.767, Erro=1.434\n", + "Esperado=0.500, Predito=1.538, Erro=1.038\n", + "Esperado=0.500, Predito=1.368, Erro=0.868\n", + "Esperado=0.500, Predito=1.428, Erro=0.928\n", + "Esperado=0.500, Predito=1.033, Erro=0.533\n", + "Esperado=0.667, Predito=1.574, Erro=0.907\n", + "Esperado=0.500, Predito=1.193, Erro=0.693\n", + "Esperado=0.500, Predito=1.317, Erro=0.817\n", + "Esperado=0.500, Predito=1.288, Erro=0.788\n", + "Esperado=0.667, Predito=1.358, Erro=0.691\n", + "Esperado=0.500, Predito=0.984, Erro=0.484\n", + "Esperado=0.500, Predito=1.527, Erro=1.027\n", + "Esperado=0.333, Predito=1.476, Erro=1.142\n", + "Esperado=0.500, Predito=1.233, Erro=0.733\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.500, Predito=1.033, Erro=0.533\n", + "Esperado=0.500, Predito=1.294, Erro=0.794\n", + "Esperado=0.667, Predito=1.250, Erro=0.584\n", + "Esperado=0.333, Predito=1.211, Erro=0.877\n", + "Esperado=0.667, Predito=1.522, Erro=0.855\n", + "Esperado=0.500, Predito=1.194, Erro=0.694\n", + "Esperado=0.667, Predito=1.498, Erro=0.832\n", + "Esperado=0.500, Predito=1.563, Erro=1.063\n", + "Esperado=0.667, Predito=1.522, Erro=0.855\n", + "Esperado=0.667, Predito=1.270, Erro=0.603\n", + "Esperado=0.333, Predito=1.211, Erro=0.877\n", + "Esperado=0.500, Predito=1.318, Erro=0.818\n", + "Esperado=0.500, Predito=1.342, Erro=0.842\n", + "Esperado=0.667, Predito=1.293, Erro=0.626\n", + "Esperado=0.667, Predito=1.250, Erro=0.584\n", + "Esperado=0.500, Predito=1.186, Erro=0.686\n", + "Esperado=0.500, Predito=1.305, Erro=0.805\n", + "Esperado=0.833, Predito=1.359, Erro=0.525\n", + "Esperado=0.833, Predito=1.397, Erro=0.564\n", + "Esperado=0.333, Predito=1.608, Erro=1.274\n", + "Esperado=0.500, Predito=1.156, Erro=0.656\n", + "Esperado=0.500, Predito=1.156, Erro=0.656\n", + "Esperado=0.500, Predito=1.156, Erro=0.656\n", + "Esperado=0.500, Predito=1.156, Erro=0.656\n", + "Esperado=0.500, Predito=1.156, Erro=0.656\n", + "Esperado=0.500, Predito=1.156, Erro=0.656\n", + "Esperado=0.333, Predito=1.554, Erro=1.221\n", + "Esperado=0.667, Predito=1.178, Erro=0.511\n", + "Esperado=0.833, Predito=1.570, Erro=0.737\n", + "Esperado=0.500, Predito=1.208, Erro=0.708\n", + "Esperado=0.000, Predito=1.064, Erro=1.064\n", + "Esperado=0.500, Predito=1.360, Erro=0.860\n", + "Esperado=0.500, Predito=1.177, Erro=0.677\n", + "Esperado=0.500, Predito=1.660, Erro=1.160\n", + "Esperado=0.333, Predito=1.560, Erro=1.227\n", + "Esperado=0.333, Predito=1.012, Erro=0.679\n", + "Esperado=0.667, Predito=1.633, Erro=0.966\n", + "Esperado=0.667, Predito=1.382, Erro=0.715\n", + "Esperado=0.667, Predito=1.405, Erro=0.738\n", + "Esperado=0.333, Predito=1.376, Erro=1.042\n", + "Esperado=0.167, Predito=1.635, Erro=1.468\n", + "Esperado=0.833, Predito=1.410, Erro=0.577\n", + "Esperado=0.667, Predito=1.153, Erro=0.486\n", + "Esperado=0.333, Predito=1.560, Erro=1.227\n", + "Esperado=0.500, Predito=1.194, Erro=0.694\n", + "Esperado=0.333, Predito=1.012, Erro=0.679\n", + "Esperado=0.500, Predito=1.609, Erro=1.109\n", + "Esperado=0.667, Predito=1.163, Erro=0.497\n", + "Esperado=0.500, Predito=1.126, Erro=0.626\n", + "Esperado=0.500, Predito=1.398, Erro=0.898\n", + "Esperado=0.667, Predito=1.366, Erro=0.700\n", + "Esperado=0.667, Predito=1.394, Erro=0.727\n", + "Esperado=0.667, Predito=1.366, Erro=0.700\n", + "Esperado=0.500, Predito=1.126, Erro=0.626\n", + "Esperado=0.333, Predito=1.298, Erro=0.965\n", + "Esperado=0.333, Predito=1.283, Erro=0.950\n", + "Esperado=0.833, Predito=1.117, Erro=0.284\n", + "Esperado=0.333, Predito=1.298, Erro=0.964\n", + "Esperado=0.667, Predito=1.296, Erro=0.629\n", + "Esperado=0.500, Predito=1.348, Erro=0.848\n", + "Esperado=0.333, Predito=1.298, Erro=0.964\n", + "Esperado=0.333, Predito=1.298, Erro=0.965\n", + "Esperado=0.333, Predito=1.210, Erro=0.877\n", + "Esperado=0.500, Predito=1.361, Erro=0.861\n", + "Esperado=0.667, Predito=1.448, Erro=0.781\n", + "Esperado=0.333, Predito=1.283, Erro=0.950\n", + "Esperado=0.833, Predito=1.117, Erro=0.284\n", + "Esperado=0.500, Predito=1.303, Erro=0.803\n", + "Esperado=0.667, Predito=1.394, Erro=0.728\n", + "Esperado=0.667, Predito=1.549, Erro=0.883\n", + "Esperado=0.667, Predito=1.376, Erro=0.709\n", + "Esperado=0.667, Predito=1.549, Erro=0.883\n", + "Esperado=0.000, Predito=1.284, Erro=1.284\n", + "Esperado=0.500, Predito=1.473, Erro=0.973\n", + "Esperado=0.667, Predito=1.787, Erro=1.120\n", + "Esperado=0.667, Predito=1.550, Erro=0.883\n", + "Esperado=0.667, Predito=1.354, Erro=0.688\n", + "Esperado=0.667, Predito=1.546, Erro=0.879\n", + "Esperado=0.667, Predito=1.115, Erro=0.448\n", + "Esperado=0.500, Predito=1.624, Erro=1.124\n", + "Esperado=0.667, Predito=1.313, Erro=0.646\n", + "Esperado=0.500, Predito=1.266, Erro=0.766\n", + "Esperado=0.500, Predito=1.478, Erro=0.978\n", + "Esperado=0.667, Predito=1.460, Erro=0.793\n", + "Esperado=0.333, Predito=1.250, Erro=0.917\n", + "Esperado=0.333, Predito=1.439, Erro=1.106\n", + "Esperado=0.500, Predito=1.487, Erro=0.987\n", + "Esperado=0.667, Predito=1.509, Erro=0.842\n", + "Esperado=0.500, Predito=1.275, Erro=0.775\n", + "Esperado=0.500, Predito=1.256, Erro=0.756\n", + "Esperado=0.667, Predito=1.383, Erro=0.716\n", + "Esperado=0.333, Predito=1.096, Erro=0.763\n", + "Esperado=0.667, Predito=1.297, Erro=0.631\n", + "Esperado=0.333, Predito=1.311, Erro=0.978\n", + "Esperado=0.500, Predito=1.261, Erro=0.761\n", + "Esperado=0.500, Predito=1.281, Erro=0.781\n", + "Esperado=0.500, Predito=1.309, Erro=0.809\n", + "Esperado=0.667, Predito=1.330, Erro=0.663\n", + "Esperado=0.500, Predito=1.156, Erro=0.656\n", + "Esperado=0.500, Predito=1.603, Erro=1.103\n", + "Esperado=0.500, Predito=1.573, Erro=1.073\n", + "Esperado=0.500, Predito=1.573, Erro=1.073\n", + "Esperado=0.500, Predito=1.573, Erro=1.073\n", + "Esperado=0.500, Predito=1.337, Erro=0.837\n", + "Esperado=0.500, Predito=1.080, Erro=0.580\n", + "Esperado=0.500, Predito=1.588, Erro=1.088\n", + "Esperado=0.500, Predito=1.080, Erro=0.580\n", + "Esperado=0.333, Predito=1.331, Erro=0.998\n", + "Esperado=0.667, Predito=1.537, Erro=0.870\n", + "Esperado=0.500, Predito=1.573, Erro=1.073\n", + "Esperado=0.500, Predito=1.241, Erro=0.741\n", + "Esperado=0.500, Predito=1.232, Erro=0.732\n", + "Esperado=0.500, Predito=1.210, Erro=0.710\n", + "Esperado=0.500, Predito=1.337, Erro=0.837\n", + "Esperado=0.500, Predito=1.168, Erro=0.668\n", + "Esperado=0.500, Predito=1.400, Erro=0.900\n", + "Esperado=0.500, Predito=1.320, Erro=0.820\n", + "Esperado=0.500, Predito=1.565, Erro=1.065\n", + "Esperado=0.500, Predito=1.685, Erro=1.185\n", + "Esperado=0.667, Predito=1.405, Erro=0.738\n", + "Esperado=0.500, Predito=1.581, Erro=1.081\n", + "Esperado=0.500, Predito=1.190, Erro=0.690\n", + "Esperado=0.667, Predito=1.275, Erro=0.608\n", + "Esperado=0.500, Predito=1.565, Erro=1.065\n", + "Esperado=0.500, Predito=1.685, Erro=1.185\n", + "Esperado=0.667, Predito=1.245, Erro=0.578\n", + "Esperado=0.500, Predito=1.168, Erro=0.668\n", + "Esperado=0.500, Predito=1.400, Erro=0.900\n", + "Esperado=0.500, Predito=1.320, Erro=0.820\n", + "Esperado=0.500, Predito=1.192, Erro=0.692\n", + "Esperado=0.667, Predito=1.334, Erro=0.667\n", + "Esperado=0.500, Predito=1.431, Erro=0.931\n", + "Esperado=0.500, Predito=1.503, Erro=1.003\n", + "Esperado=0.500, Predito=1.130, Erro=0.630\n", + "Esperado=0.667, Predito=1.378, Erro=0.711\n", + "Esperado=0.500, Predito=1.503, Erro=1.003\n", + "Esperado=0.500, Predito=1.431, Erro=0.931\n", + "Esperado=0.500, Predito=1.216, Erro=0.716\n", + "Esperado=0.667, Predito=1.494, Erro=0.827\n", + "Esperado=0.333, Predito=1.110, Erro=0.777\n", + "Esperado=0.500, Predito=1.311, Erro=0.811\n", + "Esperado=0.500, Predito=1.130, Erro=0.630\n", + "Esperado=0.500, Predito=1.674, Erro=1.174\n", + "Esperado=0.833, Predito=1.458, Erro=0.625\n", + "Esperado=0.333, Predito=1.414, Erro=1.081\n", + "Esperado=0.667, Predito=1.480, Erro=0.813\n", + "Esperado=0.667, Predito=1.192, Erro=0.526\n", + "Esperado=0.667, Predito=1.480, Erro=0.813\n", + "Esperado=0.500, Predito=1.383, Erro=0.883\n", + "Esperado=0.500, Predito=1.359, Erro=0.859\n", + "Esperado=0.500, Predito=1.476, Erro=0.976\n", + "Esperado=0.833, Predito=1.458, Erro=0.625\n", + "Esperado=0.333, Predito=1.414, Erro=1.081\n", + "Esperado=0.500, Predito=1.740, Erro=1.240\n", + "Esperado=0.667, Predito=1.516, Erro=0.849\n", + "Esperado=0.500, Predito=1.416, Erro=0.916\n", + "Esperado=0.833, Predito=1.405, Erro=0.572\n", + "Esperado=0.500, Predito=1.586, Erro=1.086\n", + "Esperado=0.500, Predito=1.437, Erro=0.937\n", + "Esperado=0.333, Predito=1.261, Erro=0.927\n", + "Esperado=0.333, Predito=1.284, Erro=0.951\n", + "Esperado=0.500, Predito=1.393, Erro=0.893\n", + "Esperado=0.500, Predito=1.181, Erro=0.681\n", + "Esperado=0.333, Predito=1.539, Erro=1.206\n", + "Esperado=0.500, Predito=1.481, Erro=0.981\n", + "Esperado=0.500, Predito=1.725, Erro=1.225\n", + "Esperado=0.333, Predito=1.231, Erro=0.897\n", + "Esperado=0.500, Predito=1.725, Erro=1.225\n", + "Esperado=0.333, Predito=1.400, Erro=1.067\n", + "Esperado=0.500, Predito=1.238, Erro=0.738\n", + "Esperado=0.500, Predito=1.095, Erro=0.595\n", + "Esperado=0.500, Predito=1.481, Erro=0.981\n", + "Esperado=0.333, Predito=1.539, Erro=1.206\n", + "Esperado=0.500, Predito=1.447, Erro=0.947\n", + "Esperado=0.000, Predito=1.278, Erro=1.278\n", + "Esperado=0.667, Predito=1.472, Erro=0.805\n", + "Esperado=0.500, Predito=1.108, Erro=0.608\n", + "Esperado=0.500, Predito=1.303, Erro=0.803\n", + "Esperado=0.500, Predito=1.323, Erro=0.823\n", + "Esperado=0.667, Predito=1.472, Erro=0.805\n", + "Esperado=0.667, Predito=1.528, Erro=0.861\n", + "Esperado=0.500, Predito=1.505, Erro=1.005\n", + "Esperado=0.167, Predito=2.231, Erro=2.064\n", + "Esperado=0.500, Predito=1.505, Erro=1.005\n", + "Esperado=0.667, Predito=1.528, Erro=0.861\n", + "Esperado=0.333, Predito=1.240, Erro=0.906\n", + "Esperado=0.833, Predito=1.595, Erro=0.762\n", + "Esperado=0.833, Predito=1.621, Erro=0.788\n", + "Esperado=0.333, Predito=1.358, Erro=1.025\n", + "Esperado=0.500, Predito=1.749, Erro=1.249\n", + "Esperado=0.500, Predito=1.339, Erro=0.839\n", + "Esperado=0.500, Predito=1.339, Erro=0.839\n", + "Esperado=0.500, Predito=1.385, Erro=0.885\n", + "Esperado=0.500, Predito=1.749, Erro=1.249\n", + "Esperado=0.333, Predito=1.358, Erro=1.025\n", + "Esperado=0.500, Predito=1.339, Erro=0.839\n", + "Esperado=0.500, Predito=1.349, Erro=0.849\n", + "Esperado=0.333, Predito=1.368, Erro=1.035\n", + "Esperado=0.667, Predito=1.531, Erro=0.864\n", + "Esperado=0.500, Predito=1.541, Erro=1.041\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.500, Predito=1.605, Erro=1.105\n", + "Esperado=0.333, Predito=1.487, Erro=1.154\n", + "Esperado=0.333, Predito=1.382, Erro=1.049\n", + "Esperado=0.333, Predito=1.487, Erro=1.154\n", + "Esperado=0.333, Predito=1.388, Erro=1.054\n", + "Esperado=0.333, Predito=1.379, Erro=1.045\n", + "Esperado=0.667, Predito=1.304, Erro=0.637\n", + "Esperado=0.500, Predito=1.090, Erro=0.590\n", + "Esperado=0.333, Predito=1.382, Erro=1.049\n", + "Esperado=0.833, Predito=1.418, Erro=0.585\n", + "Esperado=0.500, Predito=1.455, Erro=0.955\n", + "Esperado=0.500, Predito=0.988, Erro=0.488\n", + "Esperado=0.667, Predito=1.870, Erro=1.204\n", + "Esperado=0.833, Predito=1.418, Erro=0.585\n", + "Esperado=0.667, Predito=1.244, Erro=0.577\n", + "Esperado=0.667, Predito=1.244, Erro=0.577\n", + "Esperado=0.333, Predito=1.434, Erro=1.101\n", + "Esperado=0.500, Predito=1.516, Erro=1.016\n", + "Esperado=0.333, Predito=1.851, Erro=1.517\n", + "Esperado=0.500, Predito=1.281, Erro=0.781\n", + "Esperado=0.333, Predito=1.567, Erro=1.233\n", + "Esperado=0.667, Predito=1.058, Erro=0.392\n", + "Esperado=0.667, Predito=1.887, Erro=1.220\n", + "Esperado=0.500, Predito=1.068, Erro=0.568\n", + "Esperado=0.500, Predito=1.265, Erro=0.765\n", + "Esperado=0.833, Predito=1.431, Erro=0.598\n", + "Esperado=0.500, Predito=1.320, Erro=0.820\n", + "Esperado=0.667, Predito=1.499, Erro=0.833\n", + "Esperado=0.333, Predito=1.348, Erro=1.015\n", + "Esperado=0.500, Predito=1.358, Erro=0.858\n", + "Esperado=0.500, Predito=1.320, Erro=0.820\n", + "Esperado=0.333, Predito=1.162, Erro=0.829\n", + "Esperado=0.833, Predito=1.233, Erro=0.400\n", + "Esperado=0.500, Predito=1.068, Erro=0.568\n", + "Esperado=0.833, Predito=1.431, Erro=0.598\n", + "Esperado=0.500, Predito=1.265, Erro=0.765\n", + "Esperado=0.500, Predito=1.231, Erro=0.731\n", + "Esperado=0.833, Predito=1.627, Erro=0.794\n", + "Esperado=0.500, Predito=1.178, Erro=0.678\n", + "Esperado=0.500, Predito=1.546, Erro=1.046\n", + "Esperado=0.833, Predito=1.548, Erro=0.715\n", + "Esperado=0.667, Predito=1.468, Erro=0.801\n", + "Esperado=0.500, Predito=1.546, Erro=1.046\n", + "Esperado=0.667, Predito=1.675, Erro=1.009\n", + "Esperado=0.833, Predito=1.627, Erro=0.794\n", + "Esperado=0.333, Predito=1.220, Erro=0.887\n", + "Esperado=0.833, Predito=1.711, Erro=0.877\n", + "Esperado=0.667, Predito=1.514, Erro=0.848\n", + "Esperado=0.833, Predito=1.548, Erro=0.715\n", + "Esperado=0.667, Predito=1.249, Erro=0.583\n", + "Esperado=0.500, Predito=1.356, Erro=0.856\n", + "Esperado=0.500, Predito=1.178, Erro=0.678\n", + "Esperado=0.500, Predito=1.270, Erro=0.770\n", + "Esperado=0.833, Predito=1.401, Erro=0.568\n", + "Esperado=0.667, Predito=1.468, Erro=0.801\n", + "Esperado=0.500, Predito=1.150, Erro=0.650\n", + "Esperado=0.667, Predito=1.542, Erro=0.875\n", + "Esperado=0.667, Predito=1.542, Erro=0.875\n", + "Esperado=0.500, Predito=1.382, Erro=0.882\n", + "Esperado=0.667, Predito=1.237, Erro=0.570\n", + "Esperado=0.667, Predito=1.398, Erro=0.731\n", + "Esperado=0.500, Predito=1.820, Erro=1.320\n", + "Esperado=0.500, Predito=1.441, Erro=0.941\n", + "Esperado=0.667, Predito=1.578, Erro=0.911\n", + "Esperado=0.667, Predito=1.542, Erro=0.875\n", + "Esperado=0.500, Predito=1.382, Erro=0.882\n", + "Esperado=0.333, Predito=1.375, Erro=1.041\n", + "Esperado=0.667, Predito=1.651, Erro=0.985\n", + "Esperado=0.667, Predito=1.332, Erro=0.665\n", + "Esperado=0.667, Predito=1.410, Erro=0.744\n", + "Esperado=0.500, Predito=1.566, Erro=1.066\n", + "Esperado=0.667, Predito=1.687, Erro=1.020\n", + "Esperado=0.333, Predito=1.375, Erro=1.041\n", + "Esperado=0.500, Predito=1.360, Erro=0.860\n", + "Esperado=0.500, Predito=1.324, Erro=0.824\n", + "Esperado=0.667, Predito=1.293, Erro=0.626\n", + "Esperado=0.500, Predito=1.429, Erro=0.929\n", + "Esperado=0.500, Predito=1.634, Erro=1.134\n", + "Esperado=0.500, Predito=1.706, Erro=1.206\n", + "Esperado=0.667, Predito=1.403, Erro=0.736\n", + "Esperado=0.667, Predito=1.698, Erro=1.032\n", + "Esperado=0.667, Predito=1.545, Erro=0.878\n", + "Esperado=0.333, Predito=1.285, Erro=0.952\n", + "Esperado=0.667, Predito=1.646, Erro=0.980\n", + "Esperado=0.333, Predito=1.063, Erro=0.730\n", + "Esperado=0.667, Predito=1.352, Erro=0.686\n", + "Esperado=0.333, Predito=1.357, Erro=1.024\n", + "Esperado=0.333, Predito=1.063, Erro=0.730\n", + "Esperado=0.500, Predito=1.534, Erro=1.034\n", + "Esperado=0.500, Predito=1.534, Erro=1.034\n", + "Esperado=0.500, Predito=1.367, Erro=0.867\n", + "Esperado=0.500, Predito=1.141, Erro=0.641\n", + "Esperado=0.167, Predito=2.064, Erro=1.897\n", + "Esperado=0.667, Predito=1.602, Erro=0.935\n", + "Esperado=0.333, Predito=1.407, Erro=1.074\n", + "Esperado=0.333, Predito=1.277, Erro=0.943\n", + "Esperado=0.500, Predito=1.566, Erro=1.066\n", + "Esperado=0.500, Predito=1.222, Erro=0.722\n", + "Esperado=0.333, Predito=1.407, Erro=1.074\n", + "Esperado=0.333, Predito=1.277, Erro=0.943\n", + "Esperado=0.500, Predito=1.236, Erro=0.736\n", + "Esperado=0.333, Predito=1.349, Erro=1.016\n", + "Esperado=0.667, Predito=1.452, Erro=0.786\n", + "Esperado=0.500, Predito=1.365, Erro=0.865\n", + "Esperado=0.500, Predito=1.438, Erro=0.938\n", + "Esperado=0.500, Predito=1.274, Erro=0.774\n", + "Esperado=0.500, Predito=1.210, Erro=0.710\n", + "Esperado=0.500, Predito=1.532, Erro=1.032\n", + "Esperado=0.500, Predito=1.532, Erro=1.032\n", + "Esperado=0.500, Predito=1.184, Erro=0.684\n", + "Esperado=0.500, Predito=1.210, Erro=0.710\n", + "Esperado=0.500, Predito=1.184, Erro=0.684\n", + "Esperado=0.500, Predito=1.807, Erro=1.307\n", + "Esperado=0.500, Predito=1.532, Erro=1.032\n", + "Esperado=0.500, Predito=1.186, Erro=0.686\n", + "Esperado=0.500, Predito=1.183, Erro=0.683\n", + "Esperado=0.500, Predito=1.183, Erro=0.683\n", + "Esperado=0.500, Predito=1.183, Erro=0.683\n", + "Esperado=0.500, Predito=1.146, Erro=0.646\n", + "Esperado=0.500, Predito=1.135, Erro=0.635\n", + "Esperado=0.667, Predito=1.405, Erro=0.738\n", + "Esperado=0.500, Predito=1.146, Erro=0.646\n", + "Esperado=0.500, Predito=1.183, Erro=0.683\n", + "Esperado=0.167, Predito=1.446, Erro=1.280\n", + "Esperado=0.500, Predito=2.251, Erro=1.751\n", + "Esperado=0.333, Predito=1.478, Erro=1.144\n", + "Esperado=0.500, Predito=1.534, Erro=1.034\n", + "Esperado=0.333, Predito=1.478, Erro=1.144\n", + "Esperado=0.500, Predito=2.251, Erro=1.751\n", + "Esperado=0.500, Predito=1.271, Erro=0.771\n", + "Esperado=0.500, Predito=1.614, Erro=1.114\n", + "Esperado=0.500, Predito=1.314, Erro=0.814\n", + "Esperado=0.667, Predito=1.337, Erro=0.671\n", + "Esperado=0.500, Predito=1.271, Erro=0.771\n", + "Esperado=0.500, Predito=1.314, Erro=0.814\n", + "Esperado=0.167, Predito=2.043, Erro=1.877\n", + "Esperado=0.500, Predito=1.614, Erro=1.114\n", + "Esperado=0.500, Predito=1.504, Erro=1.004\n", + "Esperado=0.500, Predito=1.169, Erro=0.669\n", + "Esperado=0.667, Predito=1.178, Erro=0.511\n", + "Esperado=0.333, Predito=1.459, Erro=1.126\n", + "Esperado=0.667, Predito=1.389, Erro=0.722\n", + "Esperado=0.167, Predito=1.198, Erro=1.032\n", + "Esperado=0.667, Predito=1.278, Erro=0.612\n", + "Esperado=0.333, Predito=1.325, Erro=0.991\n", + "Esperado=0.667, Predito=1.497, Erro=0.831\n", + "Esperado=0.500, Predito=1.537, Erro=1.037\n", + "Esperado=0.500, Predito=1.319, Erro=0.819\n", + "Esperado=0.667, Predito=1.278, Erro=0.612\n", + "Esperado=0.667, Predito=1.283, Erro=0.616\n", + "Esperado=0.667, Predito=1.357, Erro=0.690\n", + "Esperado=0.500, Predito=1.146, Erro=0.646\n", + "Esperado=0.500, Predito=1.146, Erro=0.646\n", + "Esperado=0.500, Predito=1.146, Erro=0.646\n", + "Esperado=0.667, Predito=1.436, Erro=0.770\n", + "Esperado=0.333, Predito=1.366, Erro=1.033\n", + "Esperado=0.667, Predito=1.356, Erro=0.689\n", + "Esperado=0.667, Predito=1.356, Erro=0.690\n", + "Esperado=0.667, Predito=1.190, Erro=0.524\n", + "Esperado=0.667, Predito=1.189, Erro=0.522\n", + "Esperado=0.667, Predito=1.264, Erro=0.598\n", + "Esperado=0.500, Predito=1.172, Erro=0.672\n", + "Esperado=0.667, Predito=1.438, Erro=0.772\n", + "Esperado=0.500, Predito=1.586, Erro=1.086\n", + "Esperado=0.500, Predito=0.972, Erro=0.472\n", + "Esperado=0.667, Predito=1.480, Erro=0.813\n", + "Esperado=0.500, Predito=1.430, Erro=0.930\n", + "Esperado=0.667, Predito=1.670, Erro=1.003\n", + "Esperado=0.500, Predito=1.521, Erro=1.021\n", + "Esperado=0.333, Predito=1.407, Erro=1.073\n", + "Esperado=0.333, Predito=1.535, Erro=1.202\n", + "Esperado=0.500, Predito=1.206, Erro=0.706\n", + "Esperado=0.500, Predito=1.047, Erro=0.547\n", + "Esperado=0.500, Predito=1.047, Erro=0.547\n", + "Esperado=0.333, Predito=1.407, Erro=1.073\n", + "Esperado=0.500, Predito=1.206, Erro=0.706\n", + "Esperado=0.500, Predito=1.222, Erro=0.722\n", + "Esperado=0.500, Predito=1.047, Erro=0.547\n", + "Esperado=0.333, Predito=1.535, Erro=1.202\n", + "Esperado=0.500, Predito=1.309, Erro=0.809\n", + "Esperado=0.333, Predito=1.417, Erro=1.084\n", + "Esperado=0.333, Predito=1.442, Erro=1.109\n", + "Esperado=0.667, Predito=1.387, Erro=0.721\n", + "Esperado=0.500, Predito=1.697, Erro=1.197\n", + "Esperado=0.667, Predito=1.432, Erro=0.765\n", + "Esperado=0.667, Predito=1.387, Erro=0.721\n", + "Esperado=0.500, Predito=1.309, Erro=0.809\n", + "Esperado=0.500, Predito=1.697, Erro=1.197\n", + "Esperado=0.667, Predito=1.511, Erro=0.845\n", + "Esperado=0.333, Predito=1.417, Erro=1.084\n", + "Esperado=0.333, Predito=1.442, Erro=1.109\n", + "Esperado=0.333, Predito=1.446, Erro=1.112\n", + "Esperado=0.500, Predito=1.462, Erro=0.962\n", + "Esperado=0.500, Predito=1.428, Erro=0.928\n", + "Esperado=0.500, Predito=1.338, Erro=0.838\n", + "Esperado=0.500, Predito=1.338, Erro=0.838\n", + "Esperado=0.500, Predito=1.152, Erro=0.652\n", + "Esperado=0.500, Predito=1.338, Erro=0.838\n", + "Esperado=0.667, Predito=1.393, Erro=0.727\n", + "Esperado=0.500, Predito=1.940, Erro=1.440\n", + "Esperado=0.667, Predito=1.553, Erro=0.886\n", + "Esperado=0.667, Predito=1.467, Erro=0.800\n", + "Esperado=0.667, Predito=1.638, Erro=0.971\n", + "Esperado=0.500, Predito=1.018, Erro=0.518\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.500, Predito=1.599, Erro=1.099\n", + "Esperado=0.500, Predito=1.599, Erro=1.099\n", + "Esperado=0.500, Predito=1.295, Erro=0.795\n", + "Esperado=0.667, Predito=1.378, Erro=0.711\n", + "Esperado=0.500, Predito=1.208, Erro=0.708\n", + "Esperado=0.667, Predito=1.568, Erro=0.901\n", + "Esperado=0.667, Predito=1.614, Erro=0.948\n", + "Esperado=0.333, Predito=1.354, Erro=1.021\n", + "Esperado=0.500, Predito=1.503, Erro=1.003\n", + "Esperado=0.167, Predito=1.235, Erro=1.068\n", + "Esperado=0.500, Predito=1.349, Erro=0.849\n", + "Esperado=0.500, Predito=1.025, Erro=0.525\n", + "Esperado=0.500, Predito=1.349, Erro=0.849\n", + "Esperado=0.500, Predito=1.182, Erro=0.682\n", + "Esperado=0.667, Predito=1.806, Erro=1.140\n", + "Esperado=0.667, Predito=1.482, Erro=0.815\n", + "Esperado=0.833, Predito=1.033, Erro=0.200\n", + "Esperado=0.667, Predito=1.342, Erro=0.675\n", + "Esperado=0.833, Predito=2.011, Erro=1.178\n", + "Esperado=0.833, Predito=1.662, Erro=0.829\n", + "Esperado=0.500, Predito=1.237, Erro=0.737\n", + "Esperado=0.167, Predito=1.925, Erro=1.758\n", + "Esperado=0.500, Predito=1.494, Erro=0.994\n", + "Esperado=0.833, Predito=1.455, Erro=0.621\n", + "Esperado=0.833, Predito=2.011, Erro=1.178\n", + "Esperado=0.667, Predito=1.306, Erro=0.639\n", + "Esperado=0.833, Predito=1.662, Erro=0.829\n", + "Esperado=0.333, Predito=1.301, Erro=0.968\n", + "Esperado=0.667, Predito=1.730, Erro=1.063\n", + "Esperado=0.500, Predito=1.208, Erro=0.708\n", + "Esperado=0.667, Predito=1.441, Erro=0.774\n", + "Esperado=0.833, Predito=1.644, Erro=0.811\n", + "Esperado=0.667, Predito=1.544, Erro=0.877\n", + "Esperado=0.333, Predito=1.414, Erro=1.081\n", + "Esperado=0.500, Predito=1.260, Erro=0.760\n", + "Esperado=0.667, Predito=1.543, Erro=0.877\n", + "Esperado=0.500, Predito=2.006, Erro=1.506\n", + "Esperado=0.333, Predito=1.211, Erro=0.878\n", + "Esperado=0.667, Predito=1.138, Erro=0.471\n", + "Esperado=0.500, Predito=1.375, Erro=0.875\n", + "Esperado=0.833, Predito=1.301, Erro=0.468\n", + "Esperado=0.667, Predito=1.377, Erro=0.710\n", + "Esperado=0.500, Predito=1.375, Erro=0.875\n", + "Esperado=0.667, Predito=1.670, Erro=1.003\n", + "Esperado=0.500, Predito=1.375, Erro=0.875\n", + "Esperado=0.333, Predito=1.367, Erro=1.034\n", + "Esperado=0.333, Predito=1.078, Erro=0.744\n", + "Esperado=0.500, Predito=1.456, Erro=0.956\n", + "Esperado=0.833, Predito=1.301, Erro=0.468\n", + "Esperado=0.667, Predito=1.185, Erro=0.519\n", + "Esperado=0.500, Predito=1.258, Erro=0.758\n", + "Esperado=0.667, Predito=1.138, Erro=0.471\n", + "Esperado=0.667, Predito=1.670, Erro=1.003\n", + "Esperado=0.333, Predito=1.211, Erro=0.878\n", + "Esperado=0.500, Predito=1.303, Erro=0.803\n", + "Esperado=0.667, Predito=1.377, Erro=0.710\n", + "Esperado=0.500, Predito=1.375, Erro=0.875\n", + "Esperado=0.500, Predito=1.481, Erro=0.981\n", + "Esperado=0.333, Predito=1.367, Erro=1.034\n", + "Esperado=0.333, Predito=1.391, Erro=1.058\n", + "Esperado=0.333, Predito=1.273, Erro=0.939\n", + "Esperado=0.500, Predito=1.252, Erro=0.752\n", + "Esperado=0.500, Predito=1.252, Erro=0.752\n", + "Esperado=0.500, Predito=1.252, Erro=0.752\n", + "Esperado=0.500, Predito=1.252, Erro=0.752\n", + "Esperado=0.500, Predito=1.194, Erro=0.694\n", + "Esperado=0.500, Predito=1.587, Erro=1.087\n", + "Esperado=0.333, Predito=1.392, Erro=1.059\n", + "Esperado=0.333, Predito=1.342, Erro=1.009\n", + "Esperado=0.333, Predito=2.139, Erro=1.805\n", + "Esperado=0.500, Predito=1.252, Erro=0.752\n", + "Esperado=0.333, Predito=1.184, Erro=0.850\n", + "Esperado=0.333, Predito=1.280, Erro=0.947\n", + "Esperado=0.167, Predito=1.028, Erro=0.862\n", + "Esperado=0.500, Predito=1.424, Erro=0.924\n", + "Esperado=0.333, Predito=1.189, Erro=0.855\n", + "Esperado=0.500, Predito=1.101, Erro=0.601\n", + "Esperado=0.333, Predito=1.273, Erro=0.939\n", + "Esperado=0.333, Predito=1.342, Erro=1.009\n", + "Esperado=0.667, Predito=1.656, Erro=0.990\n", + "Esperado=0.333, Predito=1.444, Erro=1.110\n", + "Esperado=0.333, Predito=1.462, Erro=1.128\n", + "Esperado=0.667, Predito=1.344, Erro=0.678\n", + "Esperado=0.500, Predito=1.496, Erro=0.996\n", + "Esperado=0.500, Predito=1.343, Erro=0.843\n", + "Esperado=0.667, Predito=1.112, Erro=0.445\n", + "Esperado=0.667, Predito=1.344, Erro=0.678\n", + "Esperado=0.667, Predito=1.396, Erro=0.729\n", + "Esperado=0.333, Predito=1.327, Erro=0.994\n", + "Esperado=0.500, Predito=1.228, Erro=0.728\n", + "Esperado=0.500, Predito=1.470, Erro=0.970\n", + "Esperado=0.333, Predito=1.327, Erro=0.994\n", + "Esperado=0.500, Predito=1.474, Erro=0.974\n", + "Esperado=0.333, Predito=1.129, Erro=0.795\n", + "Esperado=0.500, Predito=1.373, Erro=0.873\n", + "Esperado=0.167, Predito=1.808, Erro=1.641\n", + "Esperado=0.333, Predito=1.177, Erro=0.844\n", + "Esperado=0.500, Predito=1.198, Erro=0.698\n", + "Esperado=0.667, Predito=0.971, Erro=0.304\n", + "Esperado=0.667, Predito=0.971, Erro=0.304\n", + "Esperado=0.667, Predito=0.971, Erro=0.304\n", + "Esperado=0.667, Predito=0.971, Erro=0.304\n", + "Esperado=0.667, Predito=0.971, Erro=0.304\n", + "Esperado=0.667, Predito=0.971, Erro=0.304\n", + "Esperado=0.667, Predito=0.971, Erro=0.304\n", + "Esperado=0.333, Predito=1.369, Erro=1.036\n", + "Esperado=0.500, Predito=1.112, Erro=0.612\n", + "Esperado=0.333, Predito=1.243, Erro=0.910\n", + "Esperado=0.500, Predito=1.508, Erro=1.008\n", + "Esperado=0.667, Predito=0.971, Erro=0.304\n", + "Esperado=0.333, Predito=1.128, Erro=0.795\n", + "Esperado=0.333, Predito=1.325, Erro=0.991\n", + "Esperado=0.333, Predito=1.343, Erro=1.010\n", + "Esperado=0.833, Predito=2.092, Erro=1.259\n", + "Esperado=0.500, Predito=1.164, Erro=0.664\n", + "Esperado=0.333, Predito=1.128, Erro=0.795\n", + "Esperado=0.333, Predito=1.325, Erro=0.991\n", + "Esperado=0.333, Predito=1.343, Erro=1.010\n", + "Esperado=0.500, Predito=1.164, Erro=0.664\n", + "Esperado=0.500, Predito=1.168, Erro=0.668\n", + "Esperado=0.667, Predito=1.106, Erro=0.440\n", + "Esperado=0.333, Predito=1.231, Erro=0.898\n", + "Esperado=0.333, Predito=1.516, Erro=1.183\n", + "Esperado=0.833, Predito=2.092, Erro=1.259\n", + "Esperado=0.333, Predito=1.526, Erro=1.192\n", + "Esperado=0.333, Predito=1.526, Erro=1.192\n", + "Esperado=0.333, Predito=1.526, Erro=1.192\n", + "Esperado=0.500, Predito=1.253, Erro=0.753\n", + "Esperado=0.333, Predito=1.526, Erro=1.192\n", + "Esperado=0.167, Predito=1.542, Erro=1.375\n", + "Esperado=0.500, Predito=1.298, Erro=0.798\n", + "Esperado=0.500, Predito=1.253, Erro=0.753\n", + "Esperado=0.333, Predito=2.062, Erro=1.729\n", + "Esperado=0.333, Predito=1.675, Erro=1.341\n", + "Esperado=0.500, Predito=1.461, Erro=0.961\n", + "Esperado=0.333, Predito=1.644, Erro=1.311\n", + "Esperado=0.500, Predito=1.461, Erro=0.961\n", + "Esperado=0.667, Predito=1.265, Erro=0.598\n", + "Esperado=0.667, Predito=1.745, Erro=1.079\n", + "Esperado=0.333, Predito=1.675, Erro=1.341\n", + "Esperado=0.500, Predito=1.195, Erro=0.695\n", + "Esperado=0.500, Predito=1.244, Erro=0.744\n", + "Esperado=0.333, Predito=1.290, Erro=0.956\n", + "Esperado=0.500, Predito=1.328, Erro=0.828\n", + "Esperado=0.667, Predito=1.345, Erro=0.678\n", + "Esperado=0.333, Predito=1.183, Erro=0.850\n", + "Esperado=0.333, Predito=1.412, Erro=1.079\n", + "Esperado=0.333, Predito=1.290, Erro=0.956\n", + "Esperado=0.500, Predito=1.263, Erro=0.763\n", + "Esperado=0.333, Predito=1.290, Erro=0.956\n", + "Esperado=0.333, Predito=1.263, Erro=0.930\n", + "Esperado=0.500, Predito=1.382, Erro=0.882\n", + "Esperado=0.500, Predito=1.463, Erro=0.963\n", + "Esperado=0.333, Predito=1.237, Erro=0.904\n", + "Esperado=0.500, Predito=1.328, Erro=0.828\n", + "Esperado=0.500, Predito=0.980, Erro=0.480\n", + "Esperado=0.333, Predito=1.474, Erro=1.141\n", + "Esperado=0.333, Predito=1.279, Erro=0.946\n", + "Esperado=0.333, Predito=1.518, Erro=1.185\n", + "Esperado=0.333, Predito=1.350, Erro=1.016\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.333, Predito=1.279, Erro=0.946\n", + "Esperado=0.500, Predito=1.112, Erro=0.612\n", + "Esperado=0.333, Predito=1.350, Erro=1.016\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.333, Predito=1.474, Erro=1.141\n", + "Esperado=0.333, Predito=1.267, Erro=0.934\n", + "Esperado=0.500, Predito=1.268, Erro=0.768\n", + "Esperado=0.500, Predito=1.112, Erro=0.612\n", + "Esperado=0.000, Predito=1.401, Erro=1.401\n", + "Esperado=0.333, Predito=1.418, Erro=1.085\n", + "Esperado=0.333, Predito=1.279, Erro=0.946\n", + "Esperado=0.333, Predito=1.518, Erro=1.185\n", + "Esperado=0.333, Predito=1.496, Erro=1.163\n", + "Esperado=0.667, Predito=1.707, Erro=1.041\n", + "Esperado=0.333, Predito=1.631, Erro=1.297\n", + "Esperado=0.500, Predito=1.604, Erro=1.104\n", + "Esperado=0.500, Predito=1.509, Erro=1.009\n", + "Esperado=0.500, Predito=1.509, Erro=1.009\n", + "Esperado=0.333, Predito=1.310, Erro=0.977\n", + "Esperado=0.500, Predito=1.421, Erro=0.921\n", + "Esperado=0.500, Predito=1.712, Erro=1.212\n", + "Esperado=0.333, Predito=1.274, Erro=0.941\n", + "Esperado=0.667, Predito=1.384, Erro=0.717\n", + "Esperado=0.500, Predito=1.896, Erro=1.396\n", + "Esperado=0.500, Predito=1.509, Erro=1.009\n", + "Esperado=0.333, Predito=1.252, Erro=0.919\n", + "Esperado=0.500, Predito=1.604, Erro=1.104\n", + "Esperado=0.667, Predito=1.898, Erro=1.232\n", + "Esperado=0.667, Predito=1.276, Erro=0.610\n", + "Esperado=0.333, Predito=1.289, Erro=0.955\n", + "Esperado=0.333, Predito=1.289, Erro=0.955\n", + "Esperado=0.500, Predito=1.037, Erro=0.537\n", + "Esperado=0.333, Predito=1.596, Erro=1.263\n", + "Esperado=0.333, Predito=1.250, Erro=0.917\n", + "Esperado=0.500, Predito=1.444, Erro=0.944\n", + "Esperado=0.500, Predito=1.444, Erro=0.944\n", + "Esperado=0.333, Predito=1.596, Erro=1.263\n", + "Esperado=0.333, Predito=1.250, Erro=0.917\n", + "Esperado=0.500, Predito=1.503, Erro=1.003\n", + "Esperado=0.500, Predito=1.315, Erro=0.815\n", + "Esperado=0.500, Predito=1.364, Erro=0.864\n", + "Esperado=0.667, Predito=1.676, Erro=1.010\n", + "Esperado=0.500, Predito=1.503, Erro=1.003\n", + "Esperado=0.333, Predito=1.417, Erro=1.084\n", + "Esperado=0.500, Predito=1.372, Erro=0.872\n", + "Esperado=0.333, Predito=1.417, Erro=1.084\n", + "Esperado=0.500, Predito=1.568, Erro=1.068\n", + "Esperado=0.333, Predito=1.567, Erro=1.234\n", + "Esperado=0.500, Predito=1.372, Erro=0.872\n", + "Esperado=0.667, Predito=1.302, Erro=0.635\n", + "Esperado=0.500, Predito=1.335, Erro=0.835\n", + "Esperado=0.667, Predito=1.772, Erro=1.105\n", + "Esperado=0.500, Predito=1.369, Erro=0.869\n", + "Esperado=0.500, Predito=1.174, Erro=0.674\n", + "Esperado=0.500, Predito=1.335, Erro=0.835\n", + "Esperado=0.500, Predito=1.210, Erro=0.710\n", + "Esperado=0.500, Predito=1.286, Erro=0.786\n", + "Esperado=0.333, Predito=1.153, Erro=0.820\n", + "Esperado=0.333, Predito=1.153, Erro=0.820\n", + "Esperado=0.500, Predito=1.219, Erro=0.719\n", + "Esperado=0.500, Predito=1.235, Erro=0.735\n", + "Esperado=0.500, Predito=1.235, Erro=0.735\n", + "Esperado=0.667, Predito=1.767, Erro=1.100\n", + "Esperado=0.500, Predito=1.657, Erro=1.157\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Esperado=0.500, Predito=1.360, Erro=0.860\n", + "Esperado=0.333, Predito=1.153, Erro=0.820\n", + "Esperado=0.500, Predito=1.123, Erro=0.623\n", + "Esperado=0.500, Predito=1.219, Erro=0.719\n", + "Esperado=0.500, Predito=1.222, Erro=0.722\n", + "Esperado=0.500, Predito=1.235, Erro=0.735\n", + "Esperado=0.167, Predito=1.408, Erro=1.241\n", + "Esperado=0.333, Predito=1.460, Erro=1.126\n", 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Predito=1.740, Erro=1.240\n", + "Esperado=0.500, Predito=1.406, Erro=0.906\n", + "Esperado=0.500, Predito=1.393, Erro=0.893\n", + "Esperado=0.500, Predito=1.360, Erro=0.860\n", + "Esperado=0.667, Predito=1.353, Erro=0.686\n", + "Esperado=0.333, Predito=1.188, Erro=0.854\n", + "Esperado=0.333, Predito=1.282, Erro=0.949\n", + "Esperado=0.167, Predito=2.150, Erro=1.984\n", + "Esperado=0.500, Predito=1.414, Erro=0.914\n", + "Esperado=0.500, Predito=1.321, Erro=0.821\n", + "Esperado=0.667, Predito=2.143, Erro=1.476\n", + "Esperado=0.667, Predito=1.696, Erro=1.029\n", + "Esperado=0.667, Predito=1.696, Erro=1.029\n", + "Esperado=0.667, Predito=1.469, Erro=0.802\n", + "Esperado=0.667, Predito=1.570, Erro=0.903\n", + "Esperado=0.667, Predito=1.570, Erro=0.903\n", + "Esperado=0.500, Predito=1.606, Erro=1.106\n", + "Esperado=0.500, Predito=1.143, Erro=0.643\n", + "Esperado=0.667, Predito=1.696, Erro=1.029\n", + "Esperado=0.500, Predito=1.343, Erro=0.843\n", + "Esperado=0.667, Predito=1.570, Erro=0.903\n", + "Esperado=0.667, Predito=2.141, Erro=1.475\n", + "Esperado=0.500, Predito=1.606, Erro=1.106\n", + "Esperado=0.333, Predito=1.328, Erro=0.994\n", + "Esperado=0.667, Predito=1.675, Erro=1.008\n", + "Esperado=0.500, Predito=1.516, Erro=1.016\n", + "Esperado=0.500, Predito=1.374, Erro=0.874\n", + "Esperado=0.333, Predito=1.586, Erro=1.253\n", + "Esperado=0.667, Predito=1.469, Erro=0.802\n", + "Esperado=0.333, Predito=1.295, Erro=0.962\n", + "Esperado=0.333, Predito=1.295, Erro=0.962\n", + "Esperado=0.333, Predito=1.089, Erro=0.755\n", + "Esperado=0.500, Predito=0.982, Erro=0.482\n", + "Esperado=0.333, Predito=1.192, Erro=0.858\n", + "Esperado=0.333, Predito=1.418, Erro=1.084\n", + "Esperado=0.333, Predito=1.418, Erro=1.084\n", + "Esperado=0.500, Predito=1.384, Erro=0.884\n", + "Esperado=0.500, Predito=1.524, Erro=1.024\n", + "Esperado=0.833, Predito=1.431, Erro=0.597\n", + "Esperado=0.167, Predito=1.469, Erro=1.303\n", + "Esperado=0.500, Predito=1.394, Erro=0.894\n", + 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Predito=1.151, Erro=0.651\n", + "Esperado=0.333, Predito=1.247, Erro=0.914\n", + "Esperado=0.333, Predito=1.511, Erro=1.177\n", + "Esperado=0.500, Predito=1.077, Erro=0.577\n", + "Esperado=0.500, Predito=1.077, Erro=0.577\n", + "Esperado=0.500, Predito=1.639, Erro=1.139\n", + "Esperado=0.333, Predito=1.491, Erro=1.158\n", + "Esperado=0.667, Predito=2.110, Erro=1.444\n", + "Esperado=0.667, Predito=1.537, Erro=0.870\n", + "Esperado=0.333, Predito=1.414, Erro=1.081\n", + "Esperado=0.167, Predito=1.634, Erro=1.468\n", + "Esperado=0.500, Predito=1.076, Erro=0.576\n", + "Esperado=0.167, Predito=1.315, Erro=1.149\n", + "Esperado=0.500, Predito=1.339, Erro=0.839\n", + "Esperado=0.500, Predito=1.135, Erro=0.635\n", + "Esperado=0.667, Predito=1.149, Erro=0.483\n", + "Esperado=0.500, Predito=1.135, Erro=0.635\n", + "Esperado=0.500, Predito=1.570, Erro=1.070\n", + "Esperado=0.167, Predito=1.315, Erro=1.149\n", + "Esperado=0.500, Predito=1.339, Erro=0.839\n", + "Esperado=0.667, Predito=1.614, Erro=0.947\n", + "Esperado=0.667, Predito=1.720, Erro=1.053\n", + "Esperado=0.500, Predito=1.445, Erro=0.945\n", + "Esperado=0.667, Predito=1.448, Erro=0.781\n", + "Esperado=0.500, Predito=1.446, Erro=0.946\n", + "Esperado=0.333, Predito=1.236, Erro=0.902\n", + "Esperado=0.667, Predito=1.463, Erro=0.796\n", + "Esperado=0.667, Predito=1.614, Erro=0.947\n", + "Esperado=0.500, Predito=1.227, Erro=0.727\n", + "Esperado=0.667, Predito=1.720, Erro=1.053\n", + "Esperado=0.500, Predito=0.954, Erro=0.454\n", + "Esperado=0.500, Predito=0.954, Erro=0.454\n", + "Esperado=0.500, Predito=1.407, Erro=0.907\n", + "Esperado=0.500, Predito=1.252, Erro=0.752\n", + "Esperado=0.500, Predito=0.954, Erro=0.454\n", + "Esperado=0.500, Predito=1.407, Erro=0.907\n", + "Esperado=0.333, Predito=1.039, Erro=0.706\n", + "Esperado=0.667, Predito=1.225, Erro=0.558\n", + "Esperado=0.667, Predito=1.463, Erro=0.796\n", + "Esperado=0.667, Predito=1.463, Erro=0.796\n", + "Esperado=0.333, Predito=1.776, Erro=1.443\n", + 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Predito=1.532, Erro=1.198\n", + "Esperado=0.500, Predito=1.343, Erro=0.843\n", + "Esperado=0.500, Predito=1.167, Erro=0.667\n", + "Esperado=0.333, Predito=1.345, Erro=1.012\n", + "Esperado=0.167, Predito=1.335, Erro=1.168\n", + "Esperado=0.333, Predito=1.105, Erro=0.771\n", + "Esperado=0.333, Predito=1.532, Erro=1.198\n", + "Esperado=0.500, Predito=1.454, Erro=0.954\n", + "Esperado=0.500, Predito=1.501, Erro=1.001\n", + "Esperado=0.500, Predito=1.645, Erro=1.145\n", + "Esperado=0.500, Predito=1.454, Erro=0.954\n", + "Esperado=0.333, Predito=1.257, Erro=0.924\n", + "Esperado=0.500, Predito=1.394, Erro=0.894\n", + "Esperado=0.500, Predito=1.385, Erro=0.885\n", + "Esperado=0.333, Predito=1.172, Erro=0.838\n", + "Esperado=0.333, Predito=1.229, Erro=0.896\n", + "Esperado=0.333, Predito=1.306, Erro=0.972\n", + "Esperado=0.500, Predito=1.465, Erro=0.965\n", + "Esperado=0.333, Predito=1.306, Erro=0.972\n", + "Esperado=0.500, Predito=1.345, Erro=0.845\n", + "Esperado=0.333, Predito=1.398, Erro=1.064\n", + "Esperado=0.333, Predito=1.403, Erro=1.070\n", + "Esperado=0.333, Predito=1.229, Erro=0.896\n", + "Esperado=0.167, Predito=2.935, Erro=2.769\n", + "Esperado=0.333, Predito=1.224, Erro=0.890\n", + "Esperado=0.333, Predito=1.248, Erro=0.915\n", + "Esperado=0.667, Predito=0.991, Erro=0.325\n", + "Esperado=0.667, Predito=0.991, Erro=0.325\n", + "Esperado=0.667, Predito=0.991, Erro=0.325\n", + "Esperado=0.667, Predito=0.991, Erro=0.325\n", + "Esperado=0.667, Predito=1.049, Erro=0.383\n", + "Esperado=0.500, Predito=1.333, Erro=0.833\n", + "Esperado=0.500, Predito=1.333, Erro=0.833\n", + "Esperado=0.500, Predito=1.245, Erro=0.745\n", + "Esperado=0.667, Predito=0.991, Erro=0.325\n", + "Esperado=0.500, Predito=1.399, Erro=0.899\n", + "Esperado=0.333, Predito=1.248, Erro=0.915\n", + "Esperado=0.667, Predito=0.991, Erro=0.325\n", + "Esperado=0.667, Predito=1.049, Erro=0.383\n", + "Esperado=0.500, Predito=1.399, Erro=0.899\n", + "Esperado=0.333, Predito=1.683, Erro=1.350\n", + "Esperado=0.500, Predito=1.248, Erro=0.748\n", + "Esperado=0.500, Predito=1.333, Erro=0.833\n", + "Esperado=0.500, Predito=1.245, Erro=0.745\n", + "Esperado=0.333, Predito=1.507, Erro=1.173\n", + "Esperado=0.333, Predito=1.507, Erro=1.173\n", + "Esperado=0.500, Predito=1.552, Erro=1.052\n", + "Esperado=0.333, Predito=1.048, Erro=0.715\n", + "Esperado=0.333, Predito=1.466, Erro=1.133\n", + "Esperado=0.833, Predito=2.157, Erro=1.324\n", + "Esperado=0.500, Predito=1.673, Erro=1.173\n", + "Esperado=0.333, Predito=1.729, Erro=1.396\n", + "Esperado=0.500, Predito=1.673, Erro=1.173\n", + "Esperado=0.500, Predito=1.255, Erro=0.755\n", + "Esperado=0.333, Predito=1.352, Erro=1.019\n", + "Esperado=0.500, Predito=1.391, Erro=0.891\n", + "Esperado=0.667, Predito=1.425, Erro=0.759\n", + "Esperado=0.333, Predito=1.277, Erro=0.944\n", + "Esperado=0.167, Predito=1.382, Erro=1.215\n", + "Esperado=0.500, Predito=1.224, Erro=0.724\n", + "Esperado=0.333, Predito=1.523, Erro=1.189\n", + "Esperado=0.500, Predito=1.412, Erro=0.912\n", + "Esperado=0.500, Predito=1.363, Erro=0.863\n", + "Esperado=0.500, Predito=1.261, Erro=0.761\n", + "Esperado=0.500, Predito=1.286, Erro=0.786\n", + "Esperado=0.500, Predito=1.224, Erro=0.724\n", + "Esperado=0.500, Predito=1.329, Erro=0.829\n", + "Esperado=0.333, Predito=1.339, Erro=1.006\n", + "Esperado=0.833, Predito=1.404, Erro=0.571\n", + "Esperado=0.500, Predito=1.351, Erro=0.851\n", + "Esperado=0.500, Predito=1.034, Erro=0.534\n", + "Esperado=0.500, Predito=1.096, Erro=0.596\n", + "Esperado=0.500, Predito=1.335, Erro=0.835\n", + "Esperado=0.500, Predito=1.296, Erro=0.796\n", + "Esperado=0.500, Predito=1.158, Erro=0.658\n", + "Esperado=0.500, Predito=1.004, Erro=0.504\n", + "Esperado=0.500, Predito=1.584, Erro=1.084\n", + "Esperado=0.500, Predito=1.452, Erro=0.952\n", + "Esperado=0.667, Predito=1.526, Erro=0.860\n", + "Esperado=0.500, Predito=1.452, Erro=0.952\n", + "Esperado=0.333, Predito=1.272, Erro=0.939\n", + "Esperado=0.500, Predito=1.004, Erro=0.504\n", + "Esperado=0.333, Predito=1.210, Erro=0.877\n", + "Esperado=0.500, Predito=1.584, Erro=1.084\n", + "Esperado=0.500, Predito=1.321, Erro=0.821\n", + "Esperado=0.333, Predito=1.474, Erro=1.141\n", + "Esperado=0.333, Predito=1.474, Erro=1.141\n", + "Esperado=0.667, Predito=1.421, Erro=0.754\n", + "Esperado=0.667, Predito=1.706, Erro=1.040\n", + "Esperado=0.667, Predito=1.408, Erro=0.741\n", + "Esperado=0.333, Predito=1.285, Erro=0.952\n", + "Esperado=0.500, Predito=1.500, Erro=1.000\n", + "Esperado=0.500, Predito=1.407, Erro=0.907\n", + "Esperado=0.500, Predito=1.617, Erro=1.117\n", + "Esperado=0.333, Predito=1.586, Erro=1.252\n", + "Esperado=0.500, Predito=1.332, Erro=0.832\n", + "Esperado=0.500, Predito=1.307, Erro=0.807\n", + "Esperado=0.500, Predito=1.266, Erro=0.766\n", + "Esperado=0.500, Predito=1.267, Erro=0.767\n", + "Esperado=0.500, Predito=1.389, Erro=0.889\n", + "Esperado=0.500, Predito=1.414, Erro=0.914\n", + "Esperado=0.500, Predito=1.414, Erro=0.914\n", + "Esperado=0.833, Predito=1.618, Erro=0.785\n", + "Esperado=0.667, Predito=1.546, Erro=0.879\n", + "Esperado=0.333, Predito=1.242, Erro=0.909\n", + "Esperado=0.500, Predito=1.304, Erro=0.804\n", + "Esperado=0.500, Predito=1.413, Erro=0.913\n", + "Esperado=0.500, Predito=1.903, Erro=1.403\n", + "Esperado=0.667, Predito=1.151, Erro=0.484\n", + "Esperado=0.333, Predito=1.167, Erro=0.834\n", + "Esperado=0.333, Predito=1.186, Erro=0.852\n", + "Esperado=0.333, Predito=1.495, Erro=1.162\n", + "Esperado=0.333, Predito=1.552, Erro=1.219\n", + "Esperado=0.667, Predito=1.534, Erro=0.867\n", + "Esperado=0.500, Predito=1.496, Erro=0.996\n", + "Esperado=0.333, Predito=1.260, Erro=0.927\n", + "Esperado=0.333, Predito=1.260, Erro=0.927\n", + "Esperado=0.500, Predito=1.229, Erro=0.729\n", + "Esperado=0.500, Predito=1.397, Erro=0.897\n", + "Esperado=0.333, Predito=1.592, Erro=1.259\n", + "Esperado=0.500, Predito=2.191, Erro=1.691\n", + "Esperado=0.333, Predito=1.260, Erro=0.927\n", + "Esperado=0.500, Predito=1.352, Erro=0.852\n", + "Esperado=0.667, Predito=1.267, Erro=0.600\n", + "Esperado=0.500, Predito=1.437, Erro=0.937\n", + "Esperado=0.500, Predito=1.437, Erro=0.937\n", + "Esperado=0.500, Predito=1.411, Erro=0.911\n", + "Esperado=0.500, Predito=1.269, Erro=0.769\n", + "Esperado=0.500, Predito=1.437, Erro=0.937\n", + "Esperado=0.500, Predito=1.404, Erro=0.904\n", + "Esperado=0.667, Predito=1.032, Erro=0.366\n", + "Esperado=0.667, Predito=1.267, Erro=0.600\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.667, Predito=1.372, Erro=0.706\n", + "Esperado=0.333, Predito=1.736, Erro=1.402\n", + "Esperado=0.500, Predito=1.598, Erro=1.098\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.333, Predito=1.296, Erro=0.962\n", + "Esperado=0.333, Predito=1.361, Erro=1.028\n", + "Esperado=0.333, Predito=1.296, Erro=0.962\n", + "Esperado=0.333, Predito=1.390, Erro=1.056\n", + "Esperado=0.667, Predito=1.001, Erro=0.334\n", + "Esperado=0.667, Predito=1.001, Erro=0.334\n", + "Esperado=0.667, Predito=1.001, Erro=0.334\n", + "Esperado=0.667, Predito=1.001, Erro=0.334\n", + "Esperado=0.667, Predito=1.001, Erro=0.334\n", + "Esperado=0.667, Predito=1.001, Erro=0.334\n", + "Esperado=0.333, Predito=1.293, Erro=0.960\n", + "Esperado=0.333, Predito=1.436, Erro=1.103\n", + "Esperado=0.667, Predito=1.348, Erro=0.682\n", + "Esperado=0.667, Predito=1.297, Erro=0.631\n", + "Esperado=0.833, Predito=1.297, Erro=0.463\n", + "Esperado=0.500, Predito=1.287, Erro=0.787\n", + "Esperado=0.667, Predito=1.301, Erro=0.634\n", + "Esperado=0.667, Predito=1.001, Erro=0.334\n", + "Esperado=0.333, Predito=1.472, Erro=1.139\n", + "Esperado=0.500, Predito=1.620, Erro=1.120\n", + "Esperado=0.333, Predito=1.394, Erro=1.060\n", + "Esperado=0.500, Predito=1.292, Erro=0.792\n", + "Esperado=0.667, Predito=1.561, Erro=0.895\n", + "Esperado=0.500, Predito=1.418, Erro=0.918\n", + "Esperado=0.500, Predito=1.431, Erro=0.931\n", + "Esperado=0.667, Predito=1.206, Erro=0.539\n", + "Esperado=0.333, Predito=1.416, Erro=1.082\n", + "Esperado=0.500, Predito=1.359, Erro=0.859\n", + "Esperado=0.500, Predito=1.345, Erro=0.845\n", + "Esperado=0.500, Predito=1.630, Erro=1.130\n", + "Esperado=0.667, Predito=1.579, Erro=0.912\n", + "Esperado=0.667, Predito=1.709, Erro=1.043\n", + "Esperado=0.333, Predito=1.416, Erro=1.082\n", + "Esperado=0.333, Predito=1.465, Erro=1.131\n", + "Esperado=0.500, Predito=1.418, Erro=0.918\n", + "Esperado=0.500, Predito=1.593, Erro=1.093\n", + "Esperado=0.333, Predito=1.153, Erro=0.820\n", + "Esperado=0.500, Predito=1.240, Erro=0.740\n", + "Esperado=0.333, Predito=1.363, Erro=1.030\n", + "Esperado=0.500, Predito=1.087, Erro=0.587\n", + "Esperado=0.500, Predito=1.434, Erro=0.934\n", + "Esperado=0.500, Predito=1.087, Erro=0.587\n", + "Esperado=0.833, Predito=1.765, Erro=0.931\n", + "Esperado=0.500, Predito=1.294, Erro=0.794\n", + "Esperado=0.333, Predito=1.390, Erro=1.056\n", + "Esperado=0.333, Predito=1.405, Erro=1.071\n", + "Esperado=0.500, Predito=1.434, Erro=0.934\n", + "Esperado=0.500, Predito=1.384, Erro=0.884\n", + "Esperado=0.500, Predito=1.553, Erro=1.053\n", + "Esperado=0.500, Predito=1.457, Erro=0.957\n", + "Esperado=0.333, Predito=1.333, Erro=1.000\n", + "Esperado=0.500, Predito=1.332, Erro=0.832\n", + "Esperado=0.500, Predito=1.553, Erro=1.053\n", + "Esperado=0.500, Predito=1.399, Erro=0.899\n", + "Esperado=0.667, Predito=1.382, Erro=0.715\n", + "Esperado=0.500, Predito=1.322, Erro=0.822\n", + "Esperado=0.500, Predito=1.444, Erro=0.944\n", + "Esperado=0.500, Predito=1.145, Erro=0.645\n", + "Esperado=0.500, Predito=1.520, Erro=1.020\n", + "Esperado=0.167, Predito=1.238, Erro=1.071\n", + "Esperado=0.167, Predito=1.538, Erro=1.371\n", + "Esperado=0.333, Predito=1.615, Erro=1.281\n", + "Esperado=0.333, Predito=1.615, Erro=1.281\n", + "Esperado=0.333, Predito=1.615, Erro=1.281\n", + "Esperado=0.167, Predito=1.325, Erro=1.158\n", + "Esperado=0.500, Predito=1.739, Erro=1.239\n", + "Esperado=0.333, Predito=1.615, Erro=1.281\n", + "Esperado=0.500, Predito=1.277, Erro=0.777\n", + "Esperado=0.500, Predito=1.361, Erro=0.861\n", + "Esperado=0.167, Predito=1.325, Erro=1.158\n", + "Esperado=0.167, Predito=1.671, Erro=1.505\n", + "Esperado=0.667, Predito=1.370, Erro=0.703\n", + "Esperado=0.333, Predito=1.569, Erro=1.236\n", + "Esperado=0.667, Predito=1.430, Erro=0.763\n", + "Esperado=0.667, Predito=1.227, Erro=0.560\n", + "Esperado=0.500, Predito=1.405, Erro=0.905\n", + "Esperado=0.500, Predito=1.469, Erro=0.969\n", + "Esperado=0.500, Predito=1.283, Erro=0.783\n", + "Esperado=0.500, Predito=1.596, Erro=1.096\n", + "Esperado=0.500, Predito=1.567, Erro=1.067\n", + "Esperado=0.500, Predito=1.258, Erro=0.758\n", + "Esperado=0.500, Predito=1.232, Erro=0.732\n", + "Esperado=0.333, Predito=1.314, Erro=0.981\n", + "Esperado=0.333, Predito=1.314, Erro=0.981\n", + "Esperado=0.333, Predito=1.388, Erro=1.054\n", + "Esperado=0.333, Predito=1.314, Erro=0.981\n", + "Esperado=0.667, Predito=1.801, Erro=1.135\n", + "Esperado=0.500, Predito=1.256, Erro=0.756\n", + "Esperado=0.500, Predito=1.460, Erro=0.960\n", + "Esperado=0.667, Predito=1.454, Erro=0.787\n", + "Esperado=0.500, Predito=1.390, Erro=0.890\n", + "Esperado=0.333, Predito=1.435, Erro=1.101\n", + "Esperado=0.333, Predito=1.430, Erro=1.097\n", + "Esperado=0.500, Predito=1.317, Erro=0.817\n", + "Esperado=0.500, Predito=1.535, Erro=1.035\n", + "Esperado=0.500, Predito=1.146, Erro=0.646\n", + "Esperado=0.500, Predito=1.217, Erro=0.717\n", + "Esperado=0.500, Predito=0.953, Erro=0.453\n", + "Esperado=0.500, Predito=1.212, Erro=0.712\n", + "Esperado=0.500, Predito=0.953, Erro=0.453\n", + "Esperado=0.167, Predito=1.785, Erro=1.618\n", + "Esperado=0.333, Predito=1.420, Erro=1.087\n", + "Esperado=0.333, Predito=1.293, Erro=0.960\n", + "Esperado=0.333, Predito=1.293, Erro=0.960\n", + "Esperado=0.333, Predito=1.400, Erro=1.067\n", + "Esperado=0.500, Predito=1.366, Erro=0.866\n", + "Esperado=0.500, Predito=1.567, Erro=1.067\n", + "Esperado=0.500, Predito=1.496, Erro=0.996\n", + "Esperado=0.667, Predito=1.518, Erro=0.851\n", + "Esperado=0.500, Predito=1.917, Erro=1.417\n", + "Esperado=0.500, Predito=1.960, Erro=1.460\n", + "Esperado=0.500, Predito=1.250, Erro=0.750\n", + "Esperado=0.500, Predito=1.496, Erro=0.996\n", + "Esperado=0.667, Predito=1.518, Erro=0.851\n", + "Esperado=0.667, Predito=1.482, Erro=0.815\n", + "Esperado=0.500, Predito=1.267, Erro=0.767\n", + "Esperado=0.333, Predito=1.666, Erro=1.333\n", + "Esperado=0.500, Predito=1.235, Erro=0.735\n", + "Esperado=0.333, Predito=1.606, Erro=1.273\n", + "Esperado=0.500, Predito=1.160, Erro=0.660\n", + "Esperado=0.500, Predito=1.160, Erro=0.660\n", + "Esperado=0.500, Predito=1.160, Erro=0.660\n", + "Esperado=0.500, Predito=1.160, Erro=0.660\n", + "Esperado=0.667, Predito=1.581, Erro=0.914\n", + "Esperado=0.333, Predito=1.263, Erro=0.930\n", + "Esperado=0.167, Predito=1.090, Erro=0.923\n", + "Esperado=0.500, Predito=1.261, Erro=0.761\n", + "Esperado=0.500, Predito=1.160, Erro=0.660\n", + "Esperado=0.333, Predito=1.355, Erro=1.021\n", + "Esperado=0.333, Predito=1.355, Erro=1.021\n", + "Esperado=0.500, Predito=1.208, Erro=0.708\n", + "Esperado=0.500, Predito=1.208, Erro=0.708\n", + "Esperado=0.500, Predito=1.208, Erro=0.708\n", + "Esperado=0.500, Predito=1.208, Erro=0.708\n", + "Esperado=0.333, Predito=1.275, Erro=0.942\n", + "Esperado=0.500, Predito=1.221, Erro=0.721\n", + "Esperado=0.500, Predito=1.264, Erro=0.764\n", + "Esperado=0.333, Predito=1.355, Erro=1.021\n", + "Esperado=0.333, Predito=1.417, Erro=1.083\n", + "Esperado=0.500, Predito=1.355, Erro=0.855\n", + "Esperado=0.333, Predito=1.417, Erro=1.083\n", + "Esperado=0.333, Predito=1.335, Erro=1.001\n", + "Esperado=0.333, Predito=1.417, Erro=1.083\n", + "Esperado=0.500, Predito=1.355, Erro=0.855\n", + "Esperado=0.500, Predito=1.269, Erro=0.769\n", + "Esperado=0.500, Predito=1.476, Erro=0.976\n", + "Esperado=0.500, Predito=1.268, Erro=0.768\n", + "Esperado=0.333, Predito=1.287, Erro=0.954\n", + "Esperado=0.333, Predito=1.485, Erro=1.151\n", + "Esperado=0.500, Predito=1.555, Erro=1.055\n", + "Esperado=0.500, Predito=1.344, Erro=0.844\n", + "Esperado=0.500, Predito=1.403, Erro=0.903\n", + "Esperado=0.500, Predito=1.400, Erro=0.900\n", + "Esperado=0.500, Predito=1.261, Erro=0.761\n", + "Esperado=0.667, Predito=1.363, Erro=0.696\n", + "Esperado=0.500, Predito=1.505, Erro=1.005\n", + "Esperado=0.500, Predito=1.796, Erro=1.296\n", + "Esperado=0.500, Predito=1.201, Erro=0.701\n", + "Esperado=0.333, Predito=1.246, Erro=0.912\n", + "Esperado=0.500, Predito=1.344, Erro=0.844\n", + "Esperado=0.500, Predito=1.529, Erro=1.029\n", + "Esperado=0.667, Predito=1.628, Erro=0.961\n", + "Esperado=0.667, Predito=1.724, Erro=1.057\n", + "Esperado=0.333, Predito=1.787, Erro=1.454\n", + "Esperado=0.667, Predito=1.556, Erro=0.889\n", + "Esperado=0.667, Predito=1.628, Erro=0.961\n", + "Esperado=0.667, Predito=1.724, Erro=1.057\n", + "Esperado=0.333, Predito=1.281, Erro=0.947\n", + "Esperado=0.500, Predito=1.255, Erro=0.755\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Esperado=0.667, Predito=1.273, Erro=0.606\n", + "Esperado=0.500, Predito=1.258, Erro=0.758\n", + "Esperado=0.500, Predito=1.239, Erro=0.739\n", + "Esperado=0.333, Predito=1.242, Erro=0.909\n", + "Esperado=0.333, Predito=1.489, Erro=1.156\n", + "Esperado=0.333, Predito=1.242, Erro=0.909\n", + "Esperado=0.333, Predito=1.693, Erro=1.360\n", + "Esperado=0.333, Predito=1.210, Erro=0.876\n", + "Esperado=0.333, Predito=1.242, Erro=0.909\n", + "Esperado=0.333, Predito=1.489, Erro=1.156\n", + "Esperado=0.833, Predito=1.046, Erro=0.213\n", + "Esperado=0.833, Predito=1.046, Erro=0.213\n", + "Esperado=0.833, Predito=1.046, Erro=0.213\n", + "Esperado=0.833, Predito=1.046, Erro=0.213\n", + "Esperado=0.833, Predito=1.046, Erro=0.213\n", + "Esperado=0.833, Predito=1.046, Erro=0.213\n", + "Esperado=0.833, Predito=1.046, Erro=0.213\n", + "Esperado=0.833, Predito=1.385, Erro=0.552\n", + "Esperado=0.833, Predito=1.046, Erro=0.213\n", + "Esperado=0.500, Predito=1.440, Erro=0.940\n", + "Esperado=0.500, Predito=1.577, Erro=1.077\n", + "Esperado=0.500, Predito=1.313, Erro=0.813\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.500, Predito=1.290, Erro=0.790\n", + "Esperado=0.500, Predito=1.214, Erro=0.714\n", + "Esperado=0.667, Predito=1.372, Erro=0.705\n", + "Esperado=0.500, Predito=1.334, Erro=0.834\n", + "Esperado=0.333, Predito=1.477, Erro=1.144\n", + "Esperado=0.500, Predito=1.725, Erro=1.225\n", + "Esperado=0.500, Predito=1.493, Erro=0.993\n", + "Esperado=0.333, Predito=1.363, Erro=1.030\n", + "Esperado=0.500, Predito=1.169, Erro=0.669\n", + "Esperado=0.500, Predito=1.456, Erro=0.956\n", + "Esperado=0.500, Predito=1.493, Erro=0.993\n", + "Esperado=0.333, Predito=1.408, Erro=1.074\n", + "Esperado=0.500, Predito=1.475, Erro=0.975\n", + "Esperado=0.333, Predito=1.214, Erro=0.881\n", + "Esperado=0.500, Predito=1.406, Erro=0.906\n", + "Esperado=0.333, Predito=1.363, Erro=1.030\n", + "Esperado=0.500, Predito=1.437, Erro=0.937\n", + "Esperado=0.500, Predito=1.361, Erro=0.861\n", + "Esperado=0.333, Predito=1.306, Erro=0.972\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.333, Predito=1.045, Erro=0.712\n", + "Esperado=0.500, Predito=1.474, Erro=0.974\n", + "Esperado=0.333, Predito=1.306, Erro=0.972\n", + "Esperado=0.500, Predito=1.292, Erro=0.792\n", + "Esperado=0.500, Predito=1.300, Erro=0.800\n", + "Esperado=0.333, Predito=1.149, Erro=0.816\n", + "Esperado=0.333, Predito=1.233, Erro=0.900\n", + "Esperado=0.500, Predito=1.238, Erro=0.738\n", + "Esperado=0.500, Predito=1.417, Erro=0.917\n", + "Esperado=0.333, Predito=1.419, Erro=1.086\n", + "Esperado=0.500, Predito=1.510, Erro=1.010\n", + "Esperado=0.333, Predito=1.394, Erro=1.061\n", + "Esperado=0.500, Predito=1.343, Erro=0.843\n", + "Esperado=0.333, Predito=1.289, Erro=0.956\n", + "Esperado=0.333, Predito=1.394, Erro=1.061\n", + "Esperado=0.500, Predito=1.484, Erro=0.984\n", + "Esperado=0.500, Predito=1.535, Erro=1.035\n", + "Esperado=0.500, Predito=1.352, Erro=0.852\n", + "Esperado=0.500, Predito=1.343, Erro=0.843\n", + "Esperado=0.333, Predito=1.274, Erro=0.941\n", + "Esperado=0.333, Predito=1.274, Erro=0.941\n", + "Esperado=0.500, Predito=1.418, Erro=0.918\n", + "Esperado=0.500, Predito=1.460, Erro=0.960\n", + "Esperado=0.500, Predito=1.150, Erro=0.650\n", + "Esperado=0.167, Predito=1.254, Erro=1.088\n", + "Esperado=0.333, Predito=1.267, Erro=0.934\n", + "Esperado=0.333, Predito=1.441, Erro=1.108\n", + "Esperado=0.333, Predito=1.267, Erro=0.934\n", + "Esperado=0.500, Predito=1.143, Erro=0.643\n", + "Esperado=0.500, Predito=1.187, Erro=0.687\n", + "Esperado=0.333, Predito=1.360, Erro=1.026\n", + "Esperado=0.333, Predito=1.409, Erro=1.076\n", + "Esperado=0.333, Predito=1.409, Erro=1.076\n", + "Esperado=0.333, Predito=1.409, Erro=1.076\n", + "Esperado=0.333, Predito=1.360, Erro=1.026\n", + "Esperado=0.333, Predito=1.409, Erro=1.076\n", + "Esperado=0.500, Predito=2.002, Erro=1.502\n", + "Esperado=0.500, Predito=1.708, Erro=1.208\n", + "Esperado=0.333, Predito=1.158, Erro=0.825\n", + "Esperado=0.333, Predito=1.226, Erro=0.893\n", + "Esperado=0.333, Predito=1.226, Erro=0.893\n", + "Esperado=0.667, Predito=1.447, Erro=0.780\n", + "Esperado=0.500, Predito=1.188, Erro=0.688\n", + "Esperado=0.333, Predito=1.225, Erro=0.892\n", + "Esperado=0.667, Predito=1.447, Erro=0.780\n", + "Esperado=0.333, Predito=1.218, Erro=0.885\n", + "Esperado=0.667, Predito=1.644, Erro=0.978\n", + "Esperado=0.667, Predito=1.442, Erro=0.775\n", + "Esperado=0.667, Predito=1.644, Erro=0.978\n", + "Esperado=0.667, Predito=1.442, Erro=0.775\n", + "Esperado=0.333, Predito=1.341, Erro=1.008\n", + "Esperado=0.333, Predito=1.219, Erro=0.886\n", + "Esperado=0.500, Predito=1.237, Erro=0.737\n", + "Esperado=0.500, Predito=1.147, Erro=0.647\n", + "Esperado=0.500, Predito=1.147, Erro=0.647\n", + "Esperado=0.500, Predito=1.147, Erro=0.647\n", + "Esperado=0.500, Predito=1.147, Erro=0.647\n", + "Esperado=0.500, Predito=1.237, Erro=0.737\n", + "Esperado=0.500, Predito=1.237, Erro=0.737\n", + "Esperado=0.500, Predito=1.147, Erro=0.647\n", + "Esperado=0.500, Predito=1.223, Erro=0.723\n", + "Esperado=0.500, Predito=1.206, Erro=0.706\n", + "Esperado=0.333, Predito=1.468, Erro=1.135\n", + "Esperado=0.500, Predito=1.725, Erro=1.225\n", + "Esperado=0.500, Predito=1.433, Erro=0.933\n", + "Esperado=0.333, Predito=1.341, Erro=1.008\n", + "Esperado=0.500, Predito=1.713, Erro=1.213\n", + "Esperado=0.500, Predito=1.365, Erro=0.865\n", + "Esperado=0.333, Predito=1.785, Erro=1.451\n", + "Esperado=0.500, Predito=1.375, Erro=0.875\n", + "Esperado=0.500, Predito=1.509, Erro=1.009\n", + "Esperado=0.500, Predito=1.548, Erro=1.048\n", + "Esperado=0.500, Predito=1.511, Erro=1.011\n", + "Esperado=0.333, Predito=1.169, Erro=0.836\n", + "Esperado=0.333, Predito=1.644, Erro=1.311\n", + "Esperado=0.333, Predito=1.428, Erro=1.095\n", + "Esperado=0.500, Predito=1.383, Erro=0.883\n", + "Esperado=0.333, Predito=1.428, Erro=1.095\n", + "Esperado=0.333, Predito=1.644, Erro=1.311\n", + "Esperado=0.500, Predito=1.493, Erro=0.993\n", + "Esperado=0.500, Predito=1.493, Erro=0.993\n", + "Esperado=0.500, Predito=1.761, Erro=1.261\n", + "Esperado=0.500, Predito=1.569, Erro=1.069\n", + "Esperado=0.333, Predito=1.424, Erro=1.091\n", + "Esperado=0.500, Predito=1.370, Erro=0.870\n", + "Esperado=0.500, Predito=1.497, Erro=0.997\n", + "Esperado=0.333, Predito=1.339, Erro=1.006\n", + "Esperado=0.333, Predito=1.486, Erro=1.153\n", + "Esperado=0.500, Predito=1.569, Erro=1.069\n", + "Esperado=0.500, Predito=1.087, Erro=0.587\n", + "Esperado=0.333, Predito=1.302, Erro=0.969\n", + "Esperado=0.333, Predito=1.302, Erro=0.969\n", + "Esperado=0.333, Predito=1.302, Erro=0.969\n", + "Esperado=0.667, Predito=1.658, Erro=0.991\n", + "Esperado=0.333, Predito=1.229, Erro=0.896\n", + "Esperado=0.500, Predito=1.087, Erro=0.587\n", + "Esperado=0.500, Predito=1.435, Erro=0.935\n", + "Esperado=0.500, Predito=1.810, Erro=1.310\n", + "Esperado=0.500, Predito=1.463, Erro=0.963\n", + "Esperado=0.333, Predito=1.275, Erro=0.942\n", + "Esperado=0.333, Predito=1.275, Erro=0.942\n", + "Esperado=0.333, Predito=1.514, Erro=1.181\n", + "Esperado=0.333, Predito=1.302, Erro=0.969\n", + "Esperado=0.500, Predito=1.345, Erro=0.845\n", + "Esperado=0.500, Predito=1.376, Erro=0.876\n", + "Esperado=0.500, Predito=1.217, Erro=0.717\n", + "Esperado=0.500, Predito=1.324, Erro=0.824\n", + "Esperado=0.333, Predito=1.509, Erro=1.175\n", + "Esperado=0.333, Predito=1.598, Erro=1.265\n", + "Esperado=0.500, Predito=1.280, Erro=0.780\n", + "Esperado=0.500, Predito=1.450, Erro=0.950\n", + "Esperado=0.500, Predito=1.378, Erro=0.878\n", + "Esperado=0.333, Predito=1.392, Erro=1.059\n", + "Esperado=0.333, Predito=1.392, Erro=1.059\n", + "Esperado=0.333, Predito=1.727, Erro=1.394\n", + "Esperado=0.333, Predito=1.397, Erro=1.064\n", + "Esperado=0.333, Predito=1.392, Erro=1.059\n", + "Esperado=0.500, Predito=1.444, Erro=0.944\n", + "Esperado=0.167, Predito=1.345, Erro=1.178\n", + "Esperado=0.333, Predito=1.726, Erro=1.393\n", + "Esperado=0.500, Predito=1.674, Erro=1.174\n", + "Esperado=0.500, Predito=1.026, Erro=0.526\n", + "Esperado=0.500, Predito=1.192, Erro=0.692\n", + "Esperado=0.500, Predito=1.820, Erro=1.320\n", + "Esperado=0.500, Predito=1.820, Erro=1.320\n", + "Esperado=0.500, Predito=1.437, Erro=0.937\n", + "Esperado=0.500, Predito=1.661, Erro=1.161\n", + "Esperado=0.500, Predito=1.431, Erro=0.931\n", + "Esperado=0.500, Predito=1.516, Erro=1.016\n", + "Esperado=0.500, Predito=1.271, Erro=0.771\n", + "Esperado=0.500, Predito=1.424, Erro=0.924\n", + "Esperado=0.500, Predito=1.271, Erro=0.771\n", + "Esperado=0.333, Predito=1.287, Erro=0.954\n", + "Esperado=0.333, Predito=1.279, Erro=0.946\n", + "Esperado=0.500, Predito=1.648, Erro=1.148\n", + "Esperado=0.500, Predito=1.433, Erro=0.933\n", + "Esperado=0.500, Predito=1.921, Erro=1.421\n", + "Esperado=0.500, Predito=1.386, Erro=0.886\n", + "Esperado=0.667, Predito=2.381, Erro=1.714\n", + "Esperado=0.333, Predito=1.399, Erro=1.066\n", + "Esperado=0.333, Predito=1.374, Erro=1.041\n", + "Esperado=0.500, Predito=1.387, Erro=0.887\n", + "Esperado=0.333, Predito=1.552, Erro=1.219\n", + "Esperado=0.167, Predito=1.321, Erro=1.154\n", + "Esperado=0.333, Predito=1.221, Erro=0.888\n", + "Esperado=0.500, Predito=1.710, Erro=1.210\n", + "Esperado=0.500, Predito=1.429, Erro=0.929\n", + "Esperado=0.500, Predito=1.429, Erro=0.929\n", + "Esperado=0.500, Predito=1.558, Erro=1.058\n", + "Esperado=0.333, Predito=1.475, Erro=1.142\n", + "Esperado=0.500, Predito=1.470, Erro=0.970\n", + "Esperado=0.500, Predito=1.571, Erro=1.071\n", + "Esperado=0.500, Predito=1.022, Erro=0.522\n", + "Esperado=0.500, Predito=1.090, Erro=0.590\n", + "Esperado=0.500, Predito=1.182, Erro=0.682\n", + "Esperado=0.333, Predito=1.364, Erro=1.031\n", + "Esperado=0.333, Predito=1.450, Erro=1.117\n", + "Esperado=0.500, Predito=1.090, Erro=0.590\n", + "Esperado=0.333, Predito=1.091, Erro=0.758\n", + "Esperado=0.500, Predito=1.063, Erro=0.563\n", + "Esperado=0.500, Predito=1.182, Erro=0.682\n", + "Esperado=0.500, Predito=1.448, Erro=0.948\n", + "Esperado=0.500, Predito=1.022, Erro=0.522\n", + "Esperado=0.500, Predito=1.315, Erro=0.815\n", + "Esperado=0.500, Predito=1.239, Erro=0.739\n", + "Esperado=0.500, Predito=1.275, Erro=0.775\n", + "Esperado=0.500, Predito=1.275, Erro=0.775\n", + "Esperado=0.500, Predito=1.038, Erro=0.538\n", + "Esperado=0.333, Predito=1.241, Erro=0.907\n", + "Esperado=0.500, Predito=1.279, Erro=0.779\n", + "Esperado=0.500, Predito=1.239, Erro=0.739\n", + "Esperado=0.500, Predito=1.275, Erro=0.775\n", + "Esperado=0.333, Predito=1.183, Erro=0.850\n", + "Esperado=0.500, Predito=0.974, Erro=0.474\n", + "Esperado=0.333, Predito=1.081, Erro=0.747\n", + "Esperado=0.500, Predito=1.584, Erro=1.084\n", + "Esperado=0.500, Predito=1.350, Erro=0.850\n", + "Esperado=0.333, Predito=1.448, Erro=1.115\n", + "Esperado=0.333, Predito=1.448, Erro=1.115\n", + "Esperado=0.500, Predito=1.584, Erro=1.084\n", + "Esperado=0.500, Predito=1.357, Erro=0.857\n", + "Esperado=0.500, Predito=1.377, Erro=0.877\n", + "Esperado=0.500, Predito=1.343, Erro=0.843\n", + "Esperado=0.333, Predito=1.830, Erro=1.497\n", + "Esperado=0.333, Predito=1.448, Erro=1.115\n", + "Esperado=0.500, Predito=1.282, Erro=0.782\n", + "Esperado=0.500, Predito=1.293, Erro=0.793\n", + "Esperado=0.500, Predito=2.212, Erro=1.712\n", + "Esperado=0.500, Predito=1.584, Erro=1.084\n", + "Esperado=0.500, Predito=1.350, Erro=0.850\n", + "Esperado=0.333, Predito=1.594, Erro=1.261\n", + "Esperado=0.333, Predito=1.470, Erro=1.137\n", + "Esperado=0.500, Predito=1.571, Erro=1.071\n", + "Esperado=0.500, Predito=1.365, Erro=0.865\n", + "Esperado=0.667, Predito=1.347, Erro=0.680\n", + "Esperado=0.500, Predito=1.619, Erro=1.119\n", + "Esperado=0.500, Predito=1.409, Erro=0.909\n", + "Esperado=0.667, Predito=1.204, Erro=0.537\n", + "Esperado=0.667, Predito=1.268, Erro=0.601\n", + "Esperado=0.667, Predito=1.278, Erro=0.611\n", + "Esperado=0.333, Predito=1.500, Erro=1.167\n", + "Esperado=0.333, Predito=1.641, Erro=1.308\n", + "Esperado=0.333, Predito=1.500, Erro=1.167\n", + "Esperado=0.500, Predito=1.501, Erro=1.001\n", + "Esperado=0.500, Predito=1.176, Erro=0.676\n", + "Esperado=0.500, Predito=1.363, Erro=0.863\n", + "Esperado=0.667, Predito=1.321, Erro=0.654\n", + "Esperado=0.667, Predito=1.398, Erro=0.731\n", + "Esperado=0.500, Predito=1.489, Erro=0.989\n", + "Esperado=0.667, Predito=1.440, Erro=0.773\n", + "Esperado=0.667, Predito=1.668, Erro=1.001\n", + "Esperado=0.333, Predito=1.359, Erro=1.026\n", + "Esperado=0.500, Predito=1.135, Erro=0.635\n", + "Esperado=0.667, Predito=1.299, Erro=0.633\n", + "Esperado=0.500, Predito=1.308, Erro=0.808\n", + "Esperado=0.500, Predito=1.232, Erro=0.732\n", + "Esperado=0.500, Predito=1.361, Erro=0.861\n", + "Esperado=0.500, Predito=1.893, Erro=1.393\n", + "Esperado=0.500, Predito=1.178, Erro=0.678\n", + "Esperado=0.500, Predito=1.532, Erro=1.032\n", + "Esperado=0.500, Predito=1.675, Erro=1.175\n", + "Esperado=0.500, Predito=1.413, Erro=0.913\n", + "Esperado=0.500, Predito=1.463, Erro=0.963\n", + "Esperado=0.333, Predito=1.520, Erro=1.187\n", + "Esperado=0.500, Predito=1.413, Erro=0.913\n", + "Esperado=0.500, Predito=1.511, Erro=1.011\n", + "Esperado=0.500, Predito=1.535, Erro=1.035\n", + "Esperado=0.500, Predito=1.760, Erro=1.260\n", + "Esperado=0.500, Predito=1.619, Erro=1.119\n", + "Esperado=0.500, Predito=1.765, Erro=1.265\n", + "Esperado=0.500, Predito=1.544, Erro=1.044\n", + "Esperado=0.500, Predito=2.095, Erro=1.595\n", + "Esperado=0.500, Predito=2.076, Erro=1.576\n", + "Esperado=0.500, Predito=1.619, Erro=1.119\n", + "Esperado=0.667, Predito=1.142, Erro=0.475\n", + "Esperado=0.500, Predito=1.206, Erro=0.706\n", + "Esperado=0.500, Predito=1.317, Erro=0.817\n", + "Esperado=0.500, Predito=1.048, Erro=0.548\n", + "Esperado=0.500, Predito=1.408, Erro=0.908\n", + "Esperado=0.500, Predito=1.539, Erro=1.039\n", + "Esperado=0.500, Predito=1.553, Erro=1.053\n", + "Esperado=0.333, Predito=1.486, Erro=1.153\n", + "Esperado=0.500, Predito=1.362, Erro=0.862\n", + "Esperado=0.667, Predito=1.751, Erro=1.084\n", + "Esperado=0.167, Predito=1.893, Erro=1.727\n", + "Esperado=0.500, Predito=1.647, Erro=1.147\n", + "Esperado=0.333, Predito=1.625, Erro=1.291\n", + "Esperado=0.333, Predito=1.426, Erro=1.092\n", + "Esperado=0.333, Predito=1.292, Erro=0.959\n", + "Esperado=0.333, Predito=1.374, Erro=1.041\n", + "Esperado=0.667, Predito=1.690, Erro=1.024\n", + "Esperado=0.333, Predito=1.456, Erro=1.123\n", + "Esperado=0.667, Predito=2.159, Erro=1.493\n", + "Esperado=0.667, Predito=1.693, Erro=1.026\n", + "Esperado=0.333, Predito=1.856, Erro=1.522\n", + "Esperado=0.500, Predito=1.527, Erro=1.027\n", + "Esperado=0.667, Predito=1.507, Erro=0.841\n", + "Esperado=0.667, Predito=1.981, Erro=1.314\n", + "Esperado=0.500, Predito=1.918, Erro=1.418\n", + "Esperado=0.500, Predito=1.782, Erro=1.282\n", + "Esperado=0.333, Predito=1.776, Erro=1.443\n", + "Esperado=0.500, Predito=1.532, Erro=1.032\n", + "Esperado=0.500, Predito=1.229, Erro=0.729\n", + "Esperado=0.500, Predito=1.229, Erro=0.729\n", + "Esperado=0.667, Predito=1.306, Erro=0.640\n", + "Esperado=0.667, Predito=1.357, Erro=0.690\n", + "Esperado=0.667, Predito=1.333, Erro=0.666\n", + "Esperado=0.500, Predito=1.532, Erro=1.032\n", + "Esperado=0.500, Predito=1.505, Erro=1.005\n", + "Esperado=0.500, Predito=1.512, Erro=1.012\n", + "Esperado=0.500, Predito=1.455, Erro=0.955\n", + "Esperado=0.333, Predito=1.837, Erro=1.503\n", + "Esperado=0.500, Predito=1.462, Erro=0.962\n", + "Esperado=0.333, Predito=1.839, Erro=1.506\n", + "Esperado=0.500, Predito=1.905, Erro=1.405\n", + "Esperado=0.500, Predito=1.464, Erro=0.964\n", + "Esperado=0.500, Predito=1.470, Erro=0.970\n", + "Esperado=0.667, Predito=1.387, Erro=0.720\n", + "Esperado=0.500, Predito=1.235, Erro=0.735\n", + "Esperado=0.500, Predito=1.485, Erro=0.985\n", + "Esperado=0.667, Predito=1.353, Erro=0.686\n", + "Esperado=0.667, Predito=1.228, Erro=0.561\n", + "Esperado=0.667, Predito=1.387, Erro=0.721\n", + "Esperado=0.333, Predito=1.926, Erro=1.593\n", + "Esperado=0.333, Predito=2.060, Erro=1.727\n", + "Esperado=0.333, Predito=2.060, Erro=1.726\n", + "Esperado=0.667, Predito=1.812, Erro=1.145\n", + "Esperado=0.333, Predito=1.491, Erro=1.157\n", + "Esperado=0.667, Predito=1.629, Erro=0.962\n", + "Esperado=0.667, Predito=1.497, Erro=0.830\n", + "Esperado=0.667, Predito=1.497, Erro=0.830\n", + "Esperado=0.667, Predito=1.497, Erro=0.830\n", + "Esperado=0.667, Predito=1.500, Erro=0.833\n", + "Esperado=0.500, Predito=1.831, Erro=1.331\n", + "Esperado=0.500, Predito=1.305, Erro=0.805\n", + "Esperado=0.500, Predito=1.684, Erro=1.184\n", + "Esperado=0.333, Predito=1.310, Erro=0.976\n", + "Esperado=0.500, Predito=1.269, Erro=0.769\n", + "Esperado=0.667, Predito=1.218, Erro=0.551\n", + "Esperado=0.667, Predito=1.201, Erro=0.534\n", + "Esperado=0.500, Predito=1.269, Erro=0.769\n", + "Esperado=0.333, Predito=1.304, Erro=0.970\n", + "Esperado=0.667, Predito=1.202, Erro=0.535\n", + "Esperado=0.667, Predito=1.219, Erro=0.552\n", + "Esperado=0.333, Predito=1.697, Erro=1.363\n", + "Esperado=0.500, Predito=1.315, Erro=0.815\n", + "Esperado=0.333, Predito=1.458, Erro=1.125\n", + "Esperado=0.333, Predito=1.458, Erro=1.125\n", + "Esperado=0.667, Predito=1.480, Erro=0.813\n", + "Esperado=0.667, Predito=1.641, Erro=0.975\n", + "Esperado=0.500, Predito=1.357, Erro=0.857\n", + "Esperado=0.500, Predito=1.292, Erro=0.792\n", + "Esperado=0.667, Predito=1.480, Erro=0.813\n", + "Esperado=0.500, Predito=1.315, Erro=0.815\n", + "Esperado=0.333, Predito=1.460, Erro=1.127\n", + "Esperado=0.167, Predito=1.885, Erro=1.719\n", + "Esperado=0.500, Predito=1.403, Erro=0.903\n", + "Esperado=0.500, Predito=1.375, Erro=0.875\n", + "Esperado=0.500, Predito=1.473, Erro=0.973\n", + "Esperado=0.500, Predito=1.345, Erro=0.845\n", + "Esperado=0.333, Predito=1.370, Erro=1.037\n", + "Esperado=0.167, Predito=1.885, Erro=1.719\n", + "Esperado=0.500, Predito=0.941, Erro=0.441\n", + "Esperado=0.500, Predito=0.941, Erro=0.441\n", + "Esperado=0.500, Predito=0.941, Erro=0.441\n", + "Esperado=0.500, Predito=0.941, Erro=0.441\n", + "Esperado=0.667, Predito=1.153, Erro=0.486\n", + "Esperado=0.333, Predito=1.428, Erro=1.095\n", + "Esperado=0.500, Predito=0.941, Erro=0.441\n", + "Esperado=0.667, Predito=1.153, Erro=0.486\n", + "Esperado=0.667, Predito=1.780, Erro=1.113\n", + "Esperado=0.500, Predito=1.627, Erro=1.127\n", + "Esperado=0.667, Predito=1.780, Erro=1.113\n", + "Esperado=0.500, Predito=1.247, Erro=0.747\n", + "Esperado=0.333, Predito=1.427, Erro=1.094\n", + "Esperado=0.333, Predito=1.427, Erro=1.094\n", + "Esperado=0.500, Predito=2.001, Erro=1.501\n", + "Esperado=0.500, Predito=2.001, Erro=1.501\n", + "Esperado=0.667, Predito=1.057, Erro=0.391\n", + "Esperado=0.500, Predito=1.480, Erro=0.980\n", + "Esperado=0.500, Predito=1.285, Erro=0.785\n", + "Esperado=0.667, Predito=1.184, Erro=0.518\n", + "Esperado=0.500, Predito=1.221, Erro=0.721\n", + "Esperado=0.500, Predito=1.520, Erro=1.020\n", + "Esperado=0.500, Predito=1.415, Erro=0.915\n", + "Esperado=0.667, Predito=1.528, Erro=0.862\n", + "Esperado=0.500, Predito=1.220, Erro=0.720\n", + "Esperado=0.500, Predito=1.285, Erro=0.785\n", + "Esperado=0.500, Predito=1.393, Erro=0.893\n", + "Esperado=0.667, Predito=1.184, Erro=0.518\n", + "Esperado=0.500, Predito=0.992, Erro=0.492\n", + "Esperado=0.500, Predito=1.484, Erro=0.984\n", + "Esperado=0.500, Predito=1.191, Erro=0.691\n", + "Esperado=0.500, Predito=1.245, Erro=0.745\n", + "Esperado=0.500, Predito=0.992, Erro=0.492\n", + "Esperado=0.333, Predito=1.506, Erro=1.173\n", + "Esperado=0.500, Predito=1.462, Erro=0.962\n", + "Esperado=0.500, Predito=1.237, Erro=0.737\n", + "Esperado=0.500, Predito=1.554, Erro=1.054\n", + "Esperado=0.500, Predito=1.462, Erro=0.962\n", + "Esperado=0.500, Predito=1.532, Erro=1.032\n", + "Esperado=0.500, Predito=2.042, Erro=1.542\n", + "Esperado=0.500, Predito=1.237, Erro=0.737\n", + "Esperado=0.500, Predito=1.201, Erro=0.701\n", + "Esperado=0.333, Predito=1.443, Erro=1.110\n", + "Esperado=0.333, Predito=1.519, Erro=1.186\n", + "Esperado=0.500, Predito=1.511, Erro=1.011\n", + "Esperado=0.500, Predito=1.511, Erro=1.011\n", + "Esperado=0.500, Predito=1.155, Erro=0.655\n", + "Esperado=0.500, Predito=1.188, Erro=0.688\n", + "Esperado=0.500, Predito=1.212, Erro=0.712\n", + "Esperado=0.500, Predito=1.194, Erro=0.694\n", + "Esperado=0.667, Predito=1.281, Erro=0.614\n", + "Esperado=0.500, Predito=1.662, Erro=1.162\n", + "Esperado=0.500, Predito=1.189, Erro=0.689\n", + "Esperado=0.333, Predito=1.376, Erro=1.043\n", + "Esperado=0.500, Predito=1.357, Erro=0.857\n", + "Esperado=0.500, Predito=1.345, Erro=0.845\n", + "Esperado=0.333, Predito=1.147, Erro=0.814\n", + "Esperado=0.333, Predito=1.353, Erro=1.019\n", + "Esperado=0.000, Predito=1.540, Erro=1.540\n", + "Esperado=0.500, Predito=1.145, Erro=0.645\n", + "Esperado=0.500, Predito=1.338, Erro=0.838\n", + "Esperado=0.333, Predito=1.508, Erro=1.175\n", + "Esperado=0.500, Predito=1.533, Erro=1.033\n", + "Esperado=0.333, Predito=1.147, Erro=0.814\n", + "Esperado=0.500, Predito=1.625, Erro=1.125\n", + "Esperado=0.667, Predito=1.513, Erro=0.846\n", + "Esperado=0.500, Predito=1.467, Erro=0.967\n", + "Esperado=0.500, Predito=1.177, Erro=0.677\n", + "Esperado=0.500, Predito=1.495, Erro=0.995\n", + "Esperado=0.667, Predito=1.741, Erro=1.075\n", + "Esperado=0.667, Predito=1.741, Erro=1.075\n", + "Esperado=0.500, Predito=1.321, Erro=0.821\n", + "Esperado=0.500, Predito=1.060, Erro=0.560\n", + "Esperado=0.500, Predito=1.060, Erro=0.560\n", + "Esperado=0.500, Predito=0.968, Erro=0.468\n", + "Esperado=0.667, Predito=1.780, Erro=1.113\n", + "Esperado=0.667, Predito=1.730, Erro=1.063\n", + "Esperado=0.500, Predito=1.345, Erro=0.845\n", + "Esperado=0.833, Predito=1.589, Erro=0.755\n", + "Esperado=0.833, Predito=1.595, Erro=0.762\n", + "Esperado=0.667, Predito=1.085, Erro=0.419\n", + "Esperado=0.500, Predito=1.329, Erro=0.829\n", + "Esperado=0.500, Predito=1.329, Erro=0.829\n", + "Esperado=0.500, Predito=1.329, Erro=0.829\n", + "Esperado=0.500, Predito=1.329, Erro=0.829\n", + "Esperado=0.333, Predito=1.303, Erro=0.969\n", + "Esperado=0.667, Predito=1.246, Erro=0.579\n", + "Esperado=0.167, Predito=1.309, Erro=1.143\n", + "Esperado=0.500, Predito=1.309, Erro=0.809\n", + "Esperado=0.500, Predito=1.298, Erro=0.798\n", + "Esperado=0.500, Predito=1.506, Erro=1.006\n", + "Esperado=0.500, Predito=1.379, Erro=0.879\n", + "Esperado=0.167, Predito=1.845, Erro=1.679\n", + "Esperado=0.333, Predito=1.330, Erro=0.996\n", + "Esperado=0.333, Predito=1.336, Erro=1.002\n", + "Esperado=0.500, Predito=1.328, Erro=0.828\n", + "Esperado=0.500, Predito=1.328, Erro=0.828\n", + "Esperado=0.333, Predito=1.479, Erro=1.146\n", + "Esperado=0.667, Predito=1.284, Erro=0.618\n", + "Esperado=0.833, Predito=1.512, Erro=0.679\n", + "Esperado=0.833, Predito=1.351, Erro=0.518\n", + "Esperado=0.333, Predito=1.479, Erro=1.146\n", + "Esperado=0.500, Predito=2.087, Erro=1.587\n", + "Esperado=0.500, Predito=1.381, Erro=0.881\n", + "Esperado=0.500, Predito=1.328, Erro=0.828\n", + "Esperado=0.500, Predito=2.814, Erro=2.314\n", + "Esperado=0.500, Predito=1.389, Erro=0.889\n", + "Esperado=0.333, Predito=1.398, Erro=1.065\n", + "Esperado=0.667, Predito=1.101, Erro=0.434\n", + "Esperado=0.667, Predito=1.101, Erro=0.434\n", + "Esperado=0.500, Predito=1.585, Erro=1.085\n", + "Esperado=0.333, Predito=1.408, Erro=1.074\n", + "Esperado=0.500, Predito=1.279, Erro=0.779\n", + "Esperado=0.667, Predito=1.277, Erro=0.610\n", + "Esperado=0.667, Predito=1.101, Erro=0.434\n", + "Esperado=0.833, Predito=1.423, Erro=0.590\n", + "Esperado=0.667, Predito=1.553, Erro=0.887\n", + "Esperado=0.167, Predito=1.484, Erro=1.317\n", + "Esperado=0.500, Predito=1.175, Erro=0.675\n", + "Esperado=0.500, Predito=1.379, Erro=0.879\n", + "Esperado=0.500, Predito=1.239, Erro=0.739\n", + "Esperado=0.333, Predito=1.554, Erro=1.221\n", + "Esperado=0.500, Predito=1.419, Erro=0.919\n", + "Esperado=0.333, Predito=1.473, Erro=1.140\n", + "Esperado=0.500, Predito=1.336, Erro=0.836\n", + "Esperado=0.667, Predito=1.341, Erro=0.674\n", + "Esperado=0.333, Predito=1.426, Erro=1.093\n", + "Esperado=0.500, Predito=1.824, Erro=1.324\n", + "Esperado=0.667, Predito=2.756, Erro=2.089\n", + "Esperado=0.500, Predito=1.558, Erro=1.058\n", + "Esperado=0.500, Predito=1.688, Erro=1.188\n", + "Esperado=0.500, Predito=1.843, Erro=1.343\n", + "Esperado=0.500, Predito=1.824, Erro=1.324\n", + "Esperado=0.333, Predito=1.426, Erro=1.093\n", + "Esperado=0.500, Predito=1.296, Erro=0.796\n", + "Esperado=0.500, Predito=1.303, Erro=0.803\n", + "Esperado=0.667, Predito=1.458, Erro=0.792\n", + "Esperado=0.333, Predito=1.200, Erro=0.867\n", + "Esperado=0.500, Predito=1.581, Erro=1.081\n", + "Esperado=0.500, Predito=1.581, Erro=1.081\n", + "Esperado=0.500, Predito=1.493, Erro=0.993\n", + "Esperado=0.333, Predito=1.200, Erro=0.867\n", + "Esperado=0.667, Predito=1.507, Erro=0.840\n", + "Esperado=0.500, Predito=1.320, Erro=0.820\n", + "Esperado=0.500, Predito=1.436, Erro=0.936\n", + "Esperado=0.333, Predito=1.351, Erro=1.017\n", + "Esperado=0.500, Predito=1.554, Erro=1.054\n", + "Esperado=0.667, Predito=1.680, Erro=1.013\n", + "Esperado=0.500, Predito=1.458, Erro=0.958\n", + "Esperado=0.333, Predito=1.713, Erro=1.380\n", + "Esperado=0.500, Predito=1.508, Erro=1.008\n", + "Esperado=0.500, Predito=1.422, Erro=0.922\n", + "Esperado=0.167, Predito=1.612, Erro=1.445\n", + "Esperado=0.667, Predito=1.505, Erro=0.838\n", + "Esperado=0.500, Predito=1.423, Erro=0.923\n", + "Esperado=0.500, Predito=1.335, Erro=0.835\n", + "Esperado=0.333, Predito=1.460, Erro=1.126\n", + "Esperado=0.500, Predito=1.363, Erro=0.863\n", + "Esperado=0.333, Predito=1.556, Erro=1.223\n", + "Esperado=0.500, Predito=1.495, Erro=0.995\n", + "Esperado=0.667, Predito=1.891, Erro=1.225\n", + "Esperado=0.500, Predito=1.594, Erro=1.094\n", + "Esperado=0.333, Predito=1.393, Erro=1.059\n", + "Esperado=0.500, Predito=1.594, Erro=1.094\n", + "Esperado=0.333, Predito=1.393, Erro=1.059\n", + "Esperado=0.333, Predito=1.510, Erro=1.176\n", + "Esperado=0.333, Predito=1.454, Erro=1.120\n", + "Esperado=0.500, Predito=1.649, Erro=1.149\n", + "Esperado=0.500, Predito=1.154, Erro=0.654\n", + "Esperado=0.500, Predito=1.154, Erro=0.654\n", + "Esperado=0.500, Predito=1.570, Erro=1.070\n", + "Esperado=0.333, Predito=1.410, Erro=1.077\n", + "Esperado=0.500, Predito=1.564, Erro=1.064\n", + "Esperado=0.500, Predito=1.731, Erro=1.231\n", + "Esperado=0.500, Predito=1.495, Erro=0.995\n", + "Esperado=0.500, Predito=1.325, Erro=0.825\n", + "Esperado=0.667, Predito=1.713, Erro=1.046\n", + "Esperado=0.333, Predito=1.517, Erro=1.184\n", + "Esperado=0.500, Predito=1.677, Erro=1.177\n", + "Esperado=0.500, Predito=1.685, Erro=1.185\n", + "Esperado=0.500, Predito=2.009, Erro=1.509\n", + "Esperado=0.500, Predito=1.420, Erro=0.920\n", + "Esperado=0.500, Predito=1.271, Erro=0.771\n", + "Esperado=0.667, Predito=1.403, Erro=0.736\n", + "Esperado=0.500, Predito=1.364, Erro=0.864\n", + "Esperado=0.333, Predito=1.651, Erro=1.317\n", + "Esperado=0.500, Predito=1.361, Erro=0.861\n", + "Esperado=0.500, Predito=1.202, Erro=0.702\n", + "Esperado=0.500, Predito=1.125, Erro=0.625\n", + "Esperado=0.667, Predito=1.701, Erro=1.034\n", + "Esperado=0.333, Predito=1.586, Erro=1.252\n", + "Esperado=0.167, Predito=1.613, Erro=1.446\n", + "Esperado=0.500, Predito=1.427, Erro=0.927\n", + "Esperado=0.500, Predito=1.427, Erro=0.927\n", + "Esperado=0.500, Predito=1.206, Erro=0.706\n", + "Esperado=0.333, Predito=1.369, Erro=1.036\n", + "Esperado=0.500, Predito=1.663, Erro=1.163\n", + "Esperado=0.333, Predito=1.414, Erro=1.081\n", + "Esperado=0.500, Predito=1.429, Erro=0.929\n", + "Esperado=0.667, Predito=1.778, Erro=1.112\n", + "Esperado=0.667, Predito=1.947, Erro=1.280\n", + "Esperado=0.333, Predito=1.183, Erro=0.850\n", + "Esperado=0.500, Predito=1.205, Erro=0.705\n", + "Esperado=0.500, Predito=1.495, Erro=0.995\n", + "Esperado=0.500, Predito=1.212, Erro=0.712\n", + "Esperado=0.333, Predito=1.252, Erro=0.919\n", + "Esperado=0.500, Predito=1.292, Erro=0.792\n", + "Esperado=0.333, Predito=1.366, Erro=1.032\n", + "Esperado=0.500, Predito=1.161, Erro=0.661\n", + "Esperado=0.667, Predito=1.458, Erro=0.791\n", + "Esperado=0.500, Predito=1.195, Erro=0.695\n", + "Coeficiente Inicial={0}\n", + "epoch=0, lrate=0.001, error=140.740\n", + "epoch=1, lrate=0.001, error=70.885\n", + "epoch=2, lrate=0.001, error=68.971\n", + "epoch=3, lrate=0.001, error=67.516\n", + "epoch=4, lrate=0.001, error=66.392\n", + "epoch=5, lrate=0.001, error=65.508\n", + "epoch=6, lrate=0.001, error=64.799\n", + "epoch=7, lrate=0.001, error=64.220\n", + "epoch=8, lrate=0.001, error=63.737\n", + "epoch=9, lrate=0.001, error=63.327\n", + "epoch=10, lrate=0.001, error=62.972\n", + "epoch=11, lrate=0.001, error=62.661\n", + "epoch=12, lrate=0.001, error=62.383\n", + "epoch=13, lrate=0.001, error=62.132\n", + "epoch=14, lrate=0.001, error=61.903\n", + "epoch=15, lrate=0.001, error=61.693\n", + "epoch=16, lrate=0.001, error=61.499\n", + "epoch=17, lrate=0.001, error=61.319\n", + "epoch=18, lrate=0.001, error=61.150\n", + "epoch=19, lrate=0.001, error=60.992\n", + "epoch=20, lrate=0.001, error=60.844\n", + "epoch=21, lrate=0.001, error=60.704\n", + "epoch=22, lrate=0.001, error=60.573\n", + "epoch=23, lrate=0.001, error=60.449\n", + "epoch=24, lrate=0.001, error=60.332\n", + "epoch=25, lrate=0.001, error=60.221\n", + "epoch=26, lrate=0.001, error=60.116\n", + "epoch=27, lrate=0.001, error=60.016\n", + "epoch=28, lrate=0.001, error=59.922\n", + "epoch=29, lrate=0.001, error=59.832\n", + "epoch=30, lrate=0.001, error=59.748\n", + "epoch=31, lrate=0.001, error=59.667\n", + "epoch=32, lrate=0.001, error=59.591\n", + "epoch=33, lrate=0.001, error=59.518\n", + "epoch=34, lrate=0.001, error=59.449\n", + "epoch=35, lrate=0.001, error=59.383\n", + "epoch=36, lrate=0.001, error=59.321\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch=37, lrate=0.001, error=59.262\n", + "epoch=38, lrate=0.001, error=59.205\n", + "epoch=39, lrate=0.001, error=59.151\n", + "epoch=40, lrate=0.001, error=59.100\n", + "epoch=41, lrate=0.001, error=59.051\n", + "epoch=42, lrate=0.001, error=59.005\n", + "epoch=43, lrate=0.001, error=58.961\n", + "epoch=44, lrate=0.001, error=58.918\n", + "epoch=45, lrate=0.001, error=58.878\n", + "epoch=46, lrate=0.001, error=58.840\n", + "epoch=47, lrate=0.001, error=58.803\n", + "epoch=48, lrate=0.001, error=58.768\n", + "epoch=49, lrate=0.001, error=58.735\n", + "epoch=50, lrate=0.001, error=58.703\n", + "epoch=51, lrate=0.001, error=58.672\n", + "epoch=52, lrate=0.001, error=58.643\n", + "epoch=53, lrate=0.001, error=58.615\n", + "epoch=54, lrate=0.001, error=58.588\n", + "epoch=55, lrate=0.001, error=58.563\n", + "epoch=56, lrate=0.001, error=58.538\n", + "epoch=57, lrate=0.001, error=58.515\n", + "epoch=58, lrate=0.001, error=58.492\n", + "epoch=59, lrate=0.001, error=58.471\n", + "epoch=60, lrate=0.001, error=58.450\n", + "epoch=61, lrate=0.001, error=58.431\n", + "epoch=62, lrate=0.001, error=58.412\n", + "epoch=63, lrate=0.001, error=58.394\n", + "epoch=64, lrate=0.001, error=58.376\n", + "epoch=65, lrate=0.001, error=58.360\n", + "epoch=66, lrate=0.001, error=58.344\n", + "epoch=67, lrate=0.001, error=58.328\n", + "epoch=68, lrate=0.001, error=58.313\n", + "epoch=69, lrate=0.001, error=58.299\n", + "epoch=70, lrate=0.001, error=58.286\n", + "epoch=71, lrate=0.001, error=58.272\n", + "epoch=72, lrate=0.001, error=58.260\n", + "epoch=73, lrate=0.001, error=58.248\n", + "epoch=74, lrate=0.001, error=58.236\n", + "epoch=75, lrate=0.001, error=58.225\n", + "epoch=76, lrate=0.001, error=58.214\n", + "epoch=77, lrate=0.001, error=58.204\n", + "epoch=78, lrate=0.001, error=58.194\n", + "epoch=79, lrate=0.001, 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error=58.034\n", + "epoch=103, lrate=0.001, error=58.029\n", + "epoch=104, lrate=0.001, error=58.025\n", + "epoch=105, lrate=0.001, error=58.021\n", + "epoch=106, lrate=0.001, error=58.017\n", + "epoch=107, lrate=0.001, error=58.013\n", + "epoch=108, lrate=0.001, error=58.010\n", + "epoch=109, lrate=0.001, error=58.006\n", + "epoch=110, lrate=0.001, error=58.002\n", + "epoch=111, lrate=0.001, error=57.999\n", + "epoch=112, lrate=0.001, error=57.996\n", + "epoch=113, lrate=0.001, error=57.992\n", + "epoch=114, lrate=0.001, error=57.989\n", + "epoch=115, lrate=0.001, error=57.986\n", + "epoch=116, lrate=0.001, error=57.983\n", + "epoch=117, lrate=0.001, error=57.980\n", + "epoch=118, lrate=0.001, error=57.977\n", + "epoch=119, lrate=0.001, error=57.975\n", + "epoch=120, lrate=0.001, error=57.972\n", + "epoch=121, lrate=0.001, error=57.969\n", + "epoch=122, lrate=0.001, error=57.967\n", + "epoch=123, lrate=0.001, error=57.964\n", + "epoch=124, lrate=0.001, error=57.962\n", + "epoch=125, lrate=0.001, error=57.960\n", + "epoch=126, lrate=0.001, error=57.957\n", + "epoch=127, lrate=0.001, error=57.955\n", + "epoch=128, lrate=0.001, error=57.953\n", + "epoch=129, lrate=0.001, error=57.951\n", + "epoch=130, lrate=0.001, error=57.948\n", + "epoch=131, lrate=0.001, error=57.946\n", + "epoch=132, lrate=0.001, error=57.944\n", + "epoch=133, lrate=0.001, error=57.942\n", + "epoch=134, lrate=0.001, error=57.940\n", + "epoch=135, lrate=0.001, error=57.938\n", + "epoch=136, lrate=0.001, error=57.937\n", + "epoch=137, lrate=0.001, error=57.935\n", + "epoch=138, lrate=0.001, error=57.933\n", + "epoch=139, lrate=0.001, error=57.931\n", + "epoch=140, lrate=0.001, error=57.930\n", + "epoch=141, lrate=0.001, error=57.928\n", + "epoch=142, lrate=0.001, error=57.926\n", + "epoch=143, lrate=0.001, error=57.925\n", + "epoch=144, lrate=0.001, error=57.923\n", + "epoch=145, lrate=0.001, error=57.921\n", + "epoch=146, lrate=0.001, error=57.920\n", + "epoch=147, lrate=0.001, error=57.918\n", + 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error=57.890\n", + "epoch=171, lrate=0.001, error=57.889\n", + "epoch=172, lrate=0.001, error=57.888\n", + "epoch=173, lrate=0.001, error=57.887\n", + "epoch=174, lrate=0.001, error=57.886\n", + "epoch=175, lrate=0.001, error=57.885\n", + "epoch=176, lrate=0.001, error=57.884\n", + "epoch=177, lrate=0.001, error=57.883\n", + "epoch=178, lrate=0.001, error=57.882\n", + "epoch=179, lrate=0.001, error=57.881\n", + "epoch=180, lrate=0.001, error=57.880\n", + "epoch=181, lrate=0.001, error=57.879\n", + "epoch=182, lrate=0.001, error=57.878\n", + "epoch=183, lrate=0.001, error=57.877\n", + "epoch=184, lrate=0.001, error=57.877\n", + "epoch=185, lrate=0.001, error=57.876\n", + "epoch=186, lrate=0.001, error=57.875\n", + "epoch=187, lrate=0.001, error=57.874\n", + "epoch=188, lrate=0.001, error=57.873\n", + "epoch=189, lrate=0.001, error=57.872\n", + "epoch=190, lrate=0.001, error=57.871\n", + "epoch=191, lrate=0.001, error=57.871\n", + "epoch=192, lrate=0.001, error=57.870\n", + "epoch=193, 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error=57.838\n", + "epoch=239, lrate=0.001, error=57.838\n", + "epoch=240, lrate=0.001, error=57.837\n", + "epoch=241, lrate=0.001, error=57.836\n", + "epoch=242, lrate=0.001, error=57.836\n", + "epoch=243, lrate=0.001, error=57.835\n", + "epoch=244, lrate=0.001, error=57.835\n", + "epoch=245, lrate=0.001, error=57.834\n", + "epoch=246, lrate=0.001, error=57.834\n", + "epoch=247, lrate=0.001, error=57.833\n", + "epoch=248, lrate=0.001, error=57.832\n", + "epoch=249, lrate=0.001, error=57.832\n", + "epoch=250, lrate=0.001, error=57.831\n", + "epoch=251, lrate=0.001, error=57.831\n", + "epoch=252, lrate=0.001, error=57.830\n", + "epoch=253, lrate=0.001, error=57.830\n", + "epoch=254, lrate=0.001, error=57.829\n", + "epoch=255, lrate=0.001, error=57.829\n", + "epoch=256, lrate=0.001, error=57.828\n", + "epoch=257, lrate=0.001, error=57.827\n", + "epoch=258, lrate=0.001, error=57.827\n", + "epoch=259, lrate=0.001, error=57.826\n", + "epoch=260, lrate=0.001, error=57.826\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch=261, lrate=0.001, error=57.825\n", + "epoch=262, lrate=0.001, error=57.825\n", + "epoch=263, lrate=0.001, error=57.824\n", + "epoch=264, lrate=0.001, error=57.824\n", + "epoch=265, lrate=0.001, error=57.823\n", + "epoch=266, lrate=0.001, error=57.823\n", + "epoch=267, lrate=0.001, error=57.822\n", + "epoch=268, lrate=0.001, error=57.822\n", + "epoch=269, lrate=0.001, error=57.821\n", + "epoch=270, lrate=0.001, error=57.821\n", + "epoch=271, lrate=0.001, error=57.820\n", + "epoch=272, lrate=0.001, error=57.820\n", + "epoch=273, lrate=0.001, error=57.819\n", + "epoch=274, lrate=0.001, error=57.819\n", + "epoch=275, lrate=0.001, error=57.818\n", + "epoch=276, lrate=0.001, error=57.818\n", + "epoch=277, lrate=0.001, error=57.817\n", + "epoch=278, lrate=0.001, error=57.817\n", + "epoch=279, lrate=0.001, error=57.816\n", + "epoch=280, lrate=0.001, error=57.816\n", + "epoch=281, lrate=0.001, error=57.815\n", + "epoch=282, 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error=57.794\n", + "epoch=328, lrate=0.001, error=57.793\n", + "epoch=329, lrate=0.001, error=57.793\n", + "epoch=330, lrate=0.001, error=57.793\n", + "epoch=331, lrate=0.001, error=57.792\n", + "epoch=332, lrate=0.001, error=57.792\n", + "epoch=333, lrate=0.001, error=57.791\n", + "epoch=334, lrate=0.001, error=57.791\n", + "epoch=335, lrate=0.001, error=57.790\n", + "epoch=336, lrate=0.001, error=57.790\n", + "epoch=337, lrate=0.001, error=57.790\n", + "epoch=338, lrate=0.001, error=57.789\n", + "epoch=339, lrate=0.001, error=57.789\n", + "epoch=340, lrate=0.001, error=57.788\n", + "epoch=341, lrate=0.001, error=57.788\n", + "epoch=342, lrate=0.001, error=57.788\n", + "epoch=343, lrate=0.001, error=57.787\n", + "epoch=344, lrate=0.001, error=57.787\n", + "epoch=345, lrate=0.001, error=57.786\n", + "epoch=346, lrate=0.001, error=57.786\n", + "epoch=347, lrate=0.001, error=57.785\n", + "epoch=348, lrate=0.001, error=57.785\n", + "epoch=349, lrate=0.001, error=57.785\n", + "epoch=350, lrate=0.001, error=57.784\n", + "epoch=351, lrate=0.001, error=57.784\n", + "epoch=352, lrate=0.001, error=57.783\n", + "epoch=353, lrate=0.001, error=57.783\n", + "epoch=354, lrate=0.001, error=57.783\n", + "epoch=355, lrate=0.001, error=57.782\n", + "epoch=356, lrate=0.001, error=57.782\n", + "epoch=357, lrate=0.001, error=57.781\n", + "epoch=358, lrate=0.001, error=57.781\n", + "epoch=359, lrate=0.001, error=57.781\n", + "epoch=360, lrate=0.001, error=57.780\n", + "epoch=361, lrate=0.001, error=57.780\n", + "epoch=362, lrate=0.001, error=57.779\n", + "epoch=363, lrate=0.001, error=57.779\n", + "epoch=364, lrate=0.001, error=57.779\n", + "epoch=365, lrate=0.001, error=57.778\n", + "epoch=366, lrate=0.001, error=57.778\n", + "epoch=367, lrate=0.001, error=57.777\n", + "epoch=368, lrate=0.001, error=57.777\n", + "epoch=369, lrate=0.001, error=57.777\n", + "epoch=370, lrate=0.001, error=57.776\n", + "epoch=371, lrate=0.001, error=57.776\n", + "epoch=372, lrate=0.001, error=57.775\n", + 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error=57.766\n", + "epoch=396, lrate=0.001, error=57.766\n", + "epoch=397, lrate=0.001, error=57.766\n", + "epoch=398, lrate=0.001, error=57.765\n", + "epoch=399, lrate=0.001, error=57.765\n", + "epoch=400, lrate=0.001, error=57.765\n", + "epoch=401, lrate=0.001, error=57.764\n", + "epoch=402, lrate=0.001, error=57.764\n", + "epoch=403, lrate=0.001, error=57.763\n", + "epoch=404, lrate=0.001, error=57.763\n", + "epoch=405, lrate=0.001, error=57.763\n", + "epoch=406, lrate=0.001, error=57.762\n", + "epoch=407, lrate=0.001, error=57.762\n", + "epoch=408, lrate=0.001, error=57.762\n", + "epoch=409, lrate=0.001, error=57.761\n", + "epoch=410, lrate=0.001, error=57.761\n", + "epoch=411, lrate=0.001, error=57.760\n", + "epoch=412, lrate=0.001, error=57.760\n", + "epoch=413, lrate=0.001, error=57.760\n", + "epoch=414, lrate=0.001, error=57.759\n", + "epoch=415, lrate=0.001, error=57.759\n", + "epoch=416, lrate=0.001, error=57.759\n", + "epoch=417, lrate=0.001, error=57.758\n", + "epoch=418, lrate=0.001, error=57.758\n", + "epoch=419, lrate=0.001, error=57.757\n", + "epoch=420, lrate=0.001, error=57.757\n", + "epoch=421, lrate=0.001, error=57.757\n", + "epoch=422, lrate=0.001, error=57.756\n", + "epoch=423, lrate=0.001, error=57.756\n", + "epoch=424, lrate=0.001, error=57.756\n", + "epoch=425, lrate=0.001, error=57.755\n", + "epoch=426, lrate=0.001, error=57.755\n", + "epoch=427, lrate=0.001, error=57.755\n", + "epoch=428, lrate=0.001, error=57.754\n", + "epoch=429, lrate=0.001, error=57.754\n", + "epoch=430, lrate=0.001, error=57.753\n", + "epoch=431, lrate=0.001, error=57.753\n", + "epoch=432, lrate=0.001, error=57.753\n", + "epoch=433, lrate=0.001, error=57.752\n", + "epoch=434, lrate=0.001, error=57.752\n", + "epoch=435, lrate=0.001, error=57.752\n", + "epoch=436, lrate=0.001, error=57.751\n", + "epoch=437, lrate=0.001, error=57.751\n", + "epoch=438, lrate=0.001, error=57.751\n", + "epoch=439, lrate=0.001, error=57.750\n", + "epoch=440, lrate=0.001, error=57.750\n", + "epoch=441, lrate=0.001, error=57.750\n", + "epoch=442, lrate=0.001, error=57.749\n", + "epoch=443, lrate=0.001, error=57.749\n", + "epoch=444, lrate=0.001, error=57.748\n", + "epoch=445, lrate=0.001, error=57.748\n", + "epoch=446, lrate=0.001, error=57.748\n", + "epoch=447, lrate=0.001, error=57.747\n", + "epoch=448, lrate=0.001, error=57.747\n", + "epoch=449, lrate=0.001, error=57.747\n", + "epoch=450, lrate=0.001, error=57.746\n", + "epoch=451, lrate=0.001, error=57.746\n", + "epoch=452, lrate=0.001, error=57.746\n", + "epoch=453, lrate=0.001, error=57.745\n", + "epoch=454, lrate=0.001, error=57.745\n", + "epoch=455, lrate=0.001, error=57.745\n", + "epoch=456, lrate=0.001, error=57.744\n", + "epoch=457, lrate=0.001, error=57.744\n", + "epoch=458, lrate=0.001, error=57.744\n", + "epoch=459, lrate=0.001, error=57.743\n", + "epoch=460, lrate=0.001, error=57.743\n", + "epoch=461, lrate=0.001, error=57.742\n", + "epoch=462, lrate=0.001, error=57.742\n", + "epoch=463, lrate=0.001, error=57.742\n", + "epoch=464, lrate=0.001, error=57.741\n", + "epoch=465, lrate=0.001, error=57.741\n", + "epoch=466, lrate=0.001, error=57.741\n", + "epoch=467, lrate=0.001, error=57.740\n", + "epoch=468, lrate=0.001, error=57.740\n", + "epoch=469, lrate=0.001, error=57.740\n", + "epoch=470, lrate=0.001, error=57.739\n", + "epoch=471, lrate=0.001, error=57.739\n", + "epoch=472, lrate=0.001, error=57.739\n", + "epoch=473, lrate=0.001, error=57.738\n", + "epoch=474, lrate=0.001, error=57.738\n", + "epoch=475, lrate=0.001, error=57.738\n", + "epoch=476, lrate=0.001, error=57.737\n", + "epoch=477, lrate=0.001, error=57.737\n", + "epoch=478, lrate=0.001, error=57.737\n", + "epoch=479, lrate=0.001, error=57.736\n", + "epoch=480, lrate=0.001, error=57.736\n", + "epoch=481, lrate=0.001, error=57.736\n", + "epoch=482, lrate=0.001, error=57.735\n", + "epoch=483, lrate=0.001, error=57.735\n", + "epoch=484, lrate=0.001, error=57.735\n", + "epoch=485, lrate=0.001, error=57.734\n", + "epoch=486, lrate=0.001, error=57.734\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch=487, lrate=0.001, error=57.734\n", + "epoch=488, lrate=0.001, error=57.733\n", + "epoch=489, lrate=0.001, error=57.733\n", + "epoch=490, lrate=0.001, error=57.733\n", + "epoch=491, lrate=0.001, error=57.732\n", + "epoch=492, lrate=0.001, error=57.732\n", + "epoch=493, lrate=0.001, error=57.732\n", + "epoch=494, lrate=0.001, error=57.731\n", + "epoch=495, lrate=0.001, error=57.731\n", + "epoch=496, lrate=0.001, error=57.731\n", + "epoch=497, lrate=0.001, error=57.730\n", + "epoch=498, lrate=0.001, error=57.730\n", + "epoch=499, lrate=0.001, error=57.730\n", + "epoch=500, lrate=0.001, error=57.729\n", + "epoch=501, lrate=0.001, error=57.729\n", + "epoch=502, lrate=0.001, error=57.729\n", + "epoch=503, lrate=0.001, error=57.728\n", + "epoch=504, lrate=0.001, error=57.728\n", + "epoch=505, lrate=0.001, error=57.728\n", + "epoch=506, lrate=0.001, error=57.727\n", + "epoch=507, lrate=0.001, error=57.727\n", + "epoch=508, lrate=0.001, error=57.727\n", + "epoch=509, lrate=0.001, error=57.726\n", + "epoch=510, lrate=0.001, error=57.726\n", + "epoch=511, lrate=0.001, error=57.726\n", + "epoch=512, lrate=0.001, error=57.725\n", + "epoch=513, lrate=0.001, error=57.725\n", + "epoch=514, lrate=0.001, error=57.725\n", + "epoch=515, lrate=0.001, error=57.724\n", + "epoch=516, lrate=0.001, error=57.724\n", + "epoch=517, lrate=0.001, error=57.724\n", + "epoch=518, lrate=0.001, error=57.723\n", + "epoch=519, lrate=0.001, error=57.723\n", + "epoch=520, lrate=0.001, error=57.723\n", + "epoch=521, lrate=0.001, error=57.722\n", + "epoch=522, lrate=0.001, error=57.722\n", + "epoch=523, lrate=0.001, error=57.722\n", + "epoch=524, lrate=0.001, error=57.721\n", + "epoch=525, lrate=0.001, error=57.721\n", + "epoch=526, lrate=0.001, error=57.721\n", + "epoch=527, lrate=0.001, error=57.720\n", + "epoch=528, lrate=0.001, error=57.720\n", + "epoch=529, lrate=0.001, error=57.720\n", + 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error=57.712\n", + "epoch=553, lrate=0.001, error=57.712\n", + "epoch=554, lrate=0.001, error=57.712\n", + "epoch=555, lrate=0.001, error=57.711\n", + "epoch=556, lrate=0.001, error=57.711\n", + "epoch=557, lrate=0.001, error=57.711\n", + "epoch=558, lrate=0.001, error=57.710\n", + "epoch=559, lrate=0.001, error=57.710\n", + "epoch=560, lrate=0.001, error=57.710\n", + "epoch=561, lrate=0.001, error=57.709\n", + "epoch=562, lrate=0.001, error=57.709\n", + "epoch=563, lrate=0.001, error=57.709\n", + "epoch=564, lrate=0.001, error=57.708\n", + "epoch=565, lrate=0.001, error=57.708\n", + "epoch=566, lrate=0.001, error=57.708\n", + "epoch=567, lrate=0.001, error=57.708\n", + "epoch=568, lrate=0.001, error=57.707\n", + "epoch=569, lrate=0.001, error=57.707\n", + "epoch=570, lrate=0.001, error=57.707\n", + "epoch=571, lrate=0.001, error=57.706\n", + "epoch=572, lrate=0.001, error=57.706\n", + "epoch=573, lrate=0.001, error=57.706\n", + "epoch=574, lrate=0.001, error=57.705\n", + "epoch=575, 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error=57.691\n", + "epoch=621, lrate=0.001, error=57.691\n", + "epoch=622, lrate=0.001, error=57.691\n", + "epoch=623, lrate=0.001, error=57.690\n", + "epoch=624, lrate=0.001, error=57.690\n", + "epoch=625, lrate=0.001, error=57.690\n", + "epoch=626, lrate=0.001, error=57.690\n", + "epoch=627, lrate=0.001, error=57.689\n", + "epoch=628, lrate=0.001, error=57.689\n", + "epoch=629, lrate=0.001, error=57.689\n", + "epoch=630, lrate=0.001, error=57.688\n", + "epoch=631, lrate=0.001, error=57.688\n", + "epoch=632, lrate=0.001, error=57.688\n", + "epoch=633, lrate=0.001, error=57.687\n", + "epoch=634, lrate=0.001, error=57.687\n", + "epoch=635, lrate=0.001, error=57.687\n", + "epoch=636, lrate=0.001, error=57.687\n", + "epoch=637, lrate=0.001, error=57.686\n", + "epoch=638, lrate=0.001, error=57.686\n", + "epoch=639, lrate=0.001, error=57.686\n", + "epoch=640, lrate=0.001, error=57.685\n", + "epoch=641, lrate=0.001, error=57.685\n", + "epoch=642, lrate=0.001, error=57.685\n", + "epoch=643, 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"name": "stdout", + "output_type": "stream", + "text": [ + "epoch=936, lrate=0.001, error=57.608\n", + "epoch=937, lrate=0.001, error=57.607\n", + "epoch=938, lrate=0.001, error=57.607\n", + "epoch=939, lrate=0.001, error=57.607\n", + "epoch=940, lrate=0.001, error=57.607\n", + "epoch=941, lrate=0.001, error=57.606\n", + "epoch=942, lrate=0.001, error=57.606\n", + "epoch=943, lrate=0.001, error=57.606\n", + "epoch=944, lrate=0.001, error=57.606\n", + "epoch=945, lrate=0.001, error=57.605\n", + "epoch=946, lrate=0.001, error=57.605\n", + "epoch=947, lrate=0.001, error=57.605\n", + "epoch=948, lrate=0.001, error=57.605\n", + "epoch=949, lrate=0.001, error=57.605\n", + "epoch=950, lrate=0.001, error=57.604\n", + "epoch=951, lrate=0.001, error=57.604\n", + "epoch=952, lrate=0.001, error=57.604\n", + "epoch=953, lrate=0.001, error=57.604\n", + "epoch=954, lrate=0.001, error=57.603\n", + "epoch=955, lrate=0.001, error=57.603\n", + "epoch=956, lrate=0.001, error=57.603\n", + "epoch=957, 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error=57.592\n", + "epoch=1003, lrate=0.001, error=57.592\n", + "epoch=1004, lrate=0.001, error=57.592\n", + "epoch=1005, lrate=0.001, error=57.592\n", + "epoch=1006, lrate=0.001, error=57.592\n", + "epoch=1007, lrate=0.001, error=57.591\n", + "epoch=1008, lrate=0.001, error=57.591\n", + "epoch=1009, lrate=0.001, error=57.591\n", + "epoch=1010, lrate=0.001, error=57.591\n", + "epoch=1011, lrate=0.001, error=57.590\n", + "epoch=1012, lrate=0.001, error=57.590\n", + "epoch=1013, lrate=0.001, error=57.590\n", + "epoch=1014, lrate=0.001, error=57.590\n", + "epoch=1015, lrate=0.001, error=57.590\n", + "epoch=1016, lrate=0.001, error=57.589\n", + "epoch=1017, lrate=0.001, error=57.589\n", + "epoch=1018, lrate=0.001, error=57.589\n", + "epoch=1019, lrate=0.001, error=57.589\n", + "epoch=1020, lrate=0.001, error=57.589\n", + "epoch=1021, lrate=0.001, error=57.588\n", + "epoch=1022, lrate=0.001, error=57.588\n", + "epoch=1023, lrate=0.001, error=57.588\n", + "epoch=1024, lrate=0.001, error=57.588\n", + "epoch=1025, lrate=0.001, error=57.587\n", + "epoch=1026, lrate=0.001, error=57.587\n", + "epoch=1027, lrate=0.001, error=57.587\n", + "epoch=1028, lrate=0.001, error=57.587\n", + "epoch=1029, lrate=0.001, error=57.587\n", + "epoch=1030, lrate=0.001, error=57.586\n", + "epoch=1031, lrate=0.001, error=57.586\n", + "epoch=1032, lrate=0.001, error=57.586\n", + "epoch=1033, lrate=0.001, error=57.586\n", + "epoch=1034, lrate=0.001, error=57.585\n", + "epoch=1035, lrate=0.001, error=57.585\n", + "epoch=1036, lrate=0.001, error=57.585\n", + "epoch=1037, lrate=0.001, error=57.585\n", + "epoch=1038, lrate=0.001, error=57.585\n", + "epoch=1039, lrate=0.001, error=57.584\n", + "epoch=1040, lrate=0.001, error=57.584\n", + "epoch=1041, lrate=0.001, error=57.584\n", + "epoch=1042, lrate=0.001, error=57.584\n", + "epoch=1043, lrate=0.001, error=57.583\n", + "epoch=1044, lrate=0.001, error=57.583\n", + "epoch=1045, lrate=0.001, error=57.583\n", + "epoch=1046, lrate=0.001, error=57.583\n", + "epoch=1047, lrate=0.001, error=57.583\n", + "epoch=1048, lrate=0.001, error=57.582\n", + "epoch=1049, lrate=0.001, error=57.582\n", + "epoch=1050, lrate=0.001, error=57.582\n", + "epoch=1051, lrate=0.001, error=57.582\n", + "epoch=1052, lrate=0.001, error=57.582\n", + "epoch=1053, lrate=0.001, error=57.581\n", + "epoch=1054, lrate=0.001, error=57.581\n", + "epoch=1055, lrate=0.001, error=57.581\n", + "epoch=1056, lrate=0.001, error=57.581\n", + "epoch=1057, lrate=0.001, error=57.580\n", + "epoch=1058, lrate=0.001, error=57.580\n", + "epoch=1059, lrate=0.001, error=57.580\n", + "epoch=1060, lrate=0.001, error=57.580\n", + "epoch=1061, lrate=0.001, error=57.580\n", + "epoch=1062, lrate=0.001, error=57.579\n", + "epoch=1063, lrate=0.001, error=57.579\n", + "epoch=1064, lrate=0.001, error=57.579\n", + "epoch=1065, lrate=0.001, error=57.579\n", + "epoch=1066, lrate=0.001, error=57.579\n", + "epoch=1067, lrate=0.001, error=57.578\n", + "epoch=1068, lrate=0.001, error=57.578\n", + "epoch=1069, lrate=0.001, error=57.578\n", + "epoch=1070, lrate=0.001, error=57.578\n", + "epoch=1071, lrate=0.001, error=57.577\n", + "epoch=1072, lrate=0.001, error=57.577\n", + "epoch=1073, lrate=0.001, error=57.577\n", + "epoch=1074, lrate=0.001, error=57.577\n", + "epoch=1075, lrate=0.001, error=57.577\n", + "epoch=1076, lrate=0.001, error=57.576\n", + "epoch=1077, lrate=0.001, error=57.576\n", + "epoch=1078, lrate=0.001, error=57.576\n", + "epoch=1079, lrate=0.001, error=57.576\n", + "epoch=1080, lrate=0.001, error=57.576\n", + "epoch=1081, lrate=0.001, error=57.575\n", + "epoch=1082, lrate=0.001, error=57.575\n", + "epoch=1083, lrate=0.001, error=57.575\n", + "epoch=1084, lrate=0.001, error=57.575\n", + "epoch=1085, lrate=0.001, error=57.575\n", + "epoch=1086, lrate=0.001, error=57.574\n", + "epoch=1087, lrate=0.001, error=57.574\n", + "epoch=1088, lrate=0.001, error=57.574\n", + "epoch=1089, lrate=0.001, error=57.574\n", + "epoch=1090, lrate=0.001, error=57.574\n", + "epoch=1091, lrate=0.001, error=57.573\n", + "epoch=1092, lrate=0.001, error=57.573\n", + "epoch=1093, lrate=0.001, error=57.573\n", + "epoch=1094, lrate=0.001, error=57.573\n", + "epoch=1095, lrate=0.001, error=57.572\n", + "epoch=1096, lrate=0.001, error=57.572\n", + "epoch=1097, lrate=0.001, error=57.572\n", + "epoch=1098, lrate=0.001, error=57.572\n", + "epoch=1099, lrate=0.001, error=57.572\n", + "epoch=1100, lrate=0.001, error=57.571\n", + "epoch=1101, lrate=0.001, error=57.571\n", + "epoch=1102, lrate=0.001, error=57.571\n", + "epoch=1103, lrate=0.001, error=57.571\n", + "epoch=1104, lrate=0.001, error=57.571\n", + "epoch=1105, lrate=0.001, error=57.570\n", + "epoch=1106, lrate=0.001, error=57.570\n", + "epoch=1107, lrate=0.001, error=57.570\n", + "epoch=1108, lrate=0.001, error=57.570\n", + "epoch=1109, lrate=0.001, error=57.570\n", + "epoch=1110, lrate=0.001, error=57.569\n", + "epoch=1111, lrate=0.001, error=57.569\n", + "epoch=1112, lrate=0.001, error=57.569\n", + "epoch=1113, lrate=0.001, error=57.569\n", + "epoch=1114, lrate=0.001, error=57.569\n", + "epoch=1115, lrate=0.001, error=57.568\n", + "epoch=1116, lrate=0.001, error=57.568\n", + "epoch=1117, lrate=0.001, error=57.568\n", + "epoch=1118, lrate=0.001, error=57.568\n", + "epoch=1119, lrate=0.001, error=57.568\n", + "epoch=1120, lrate=0.001, error=57.567\n", + "epoch=1121, lrate=0.001, error=57.567\n", + "epoch=1122, lrate=0.001, error=57.567\n", + "epoch=1123, lrate=0.001, error=57.567\n", + "epoch=1124, lrate=0.001, error=57.566\n", + "epoch=1125, lrate=0.001, error=57.566\n", + "epoch=1126, lrate=0.001, error=57.566\n", + "epoch=1127, lrate=0.001, error=57.566\n", + "epoch=1128, lrate=0.001, error=57.566\n", + "epoch=1129, lrate=0.001, error=57.565\n", + "epoch=1130, lrate=0.001, error=57.565\n", + "epoch=1131, lrate=0.001, error=57.565\n", + "epoch=1132, lrate=0.001, error=57.565\n", + "epoch=1133, lrate=0.001, error=57.565\n", + "epoch=1134, lrate=0.001, error=57.564\n", + "epoch=1135, lrate=0.001, error=57.564\n", + "epoch=1136, lrate=0.001, error=57.564\n", + "epoch=1137, lrate=0.001, error=57.564\n", + "epoch=1138, lrate=0.001, error=57.564\n", + "epoch=1139, lrate=0.001, error=57.563\n", + "epoch=1140, lrate=0.001, error=57.563\n", + "epoch=1141, lrate=0.001, error=57.563\n", + "epoch=1142, lrate=0.001, error=57.563\n", + "epoch=1143, lrate=0.001, error=57.563\n", + "epoch=1144, lrate=0.001, error=57.562\n", + "epoch=1145, lrate=0.001, error=57.562\n", + "epoch=1146, lrate=0.001, error=57.562\n", + "epoch=1147, lrate=0.001, error=57.562\n", + "epoch=1148, lrate=0.001, error=57.562\n", + "epoch=1149, lrate=0.001, error=57.561\n", + "epoch=1150, lrate=0.001, error=57.561\n", + "epoch=1151, lrate=0.001, error=57.561\n", + "epoch=1152, lrate=0.001, error=57.561\n", + "epoch=1153, lrate=0.001, error=57.561\n", + "epoch=1154, lrate=0.001, error=57.560\n", + "epoch=1155, lrate=0.001, error=57.560\n", + "epoch=1156, lrate=0.001, error=57.560\n", + "epoch=1157, lrate=0.001, error=57.560\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch=1158, lrate=0.001, error=57.560\n", + "epoch=1159, lrate=0.001, error=57.559\n", + "epoch=1160, lrate=0.001, error=57.559\n", + "epoch=1161, lrate=0.001, error=57.559\n", + "epoch=1162, lrate=0.001, error=57.559\n", + "epoch=1163, lrate=0.001, error=57.559\n", + "epoch=1164, lrate=0.001, error=57.558\n", + "epoch=1165, lrate=0.001, error=57.558\n", + "epoch=1166, lrate=0.001, error=57.558\n", + "epoch=1167, lrate=0.001, error=57.558\n", + "epoch=1168, lrate=0.001, error=57.558\n", + "epoch=1169, lrate=0.001, error=57.557\n", + "epoch=1170, lrate=0.001, error=57.557\n", + "epoch=1171, lrate=0.001, error=57.557\n", + "epoch=1172, lrate=0.001, error=57.557\n", + "epoch=1173, lrate=0.001, error=57.557\n", + "epoch=1174, lrate=0.001, error=57.557\n", + "epoch=1175, lrate=0.001, error=57.556\n", + "epoch=1176, lrate=0.001, error=57.556\n", + 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error=57.445\n", + "epoch=1921, lrate=0.001, error=57.445\n", + "epoch=1922, lrate=0.001, error=57.444\n", + "epoch=1923, lrate=0.001, error=57.444\n", + "epoch=1924, lrate=0.001, error=57.444\n", + "epoch=1925, lrate=0.001, error=57.444\n", + "epoch=1926, lrate=0.001, error=57.444\n", + "epoch=1927, lrate=0.001, error=57.444\n", + "epoch=1928, lrate=0.001, error=57.444\n", + "epoch=1929, lrate=0.001, error=57.444\n", + "epoch=1930, lrate=0.001, error=57.443\n", + "epoch=1931, lrate=0.001, error=57.443\n", + "epoch=1932, lrate=0.001, error=57.443\n", + "epoch=1933, lrate=0.001, error=57.443\n", + "epoch=1934, lrate=0.001, error=57.443\n", + "epoch=1935, lrate=0.001, error=57.443\n", + "epoch=1936, lrate=0.001, error=57.443\n", + "epoch=1937, lrate=0.001, error=57.443\n", + "epoch=1938, lrate=0.001, error=57.443\n", + "epoch=1939, lrate=0.001, error=57.443\n", + "epoch=1940, lrate=0.001, error=57.442\n", + "epoch=1941, lrate=0.001, error=57.442\n", + "epoch=1942, lrate=0.001, error=57.442\n", + "epoch=1943, lrate=0.001, error=57.442\n", + "epoch=1944, lrate=0.001, error=57.442\n", + "epoch=1945, lrate=0.001, error=57.442\n", + "epoch=1946, lrate=0.001, error=57.442\n", + "epoch=1947, lrate=0.001, error=57.442\n", + "epoch=1948, lrate=0.001, error=57.442\n", + "epoch=1949, lrate=0.001, error=57.441\n", + "epoch=1950, lrate=0.001, error=57.441\n", + "epoch=1951, lrate=0.001, error=57.441\n", + "epoch=1952, lrate=0.001, error=57.441\n", + "epoch=1953, lrate=0.001, error=57.441\n", + "epoch=1954, lrate=0.001, error=57.441\n", + "epoch=1955, lrate=0.001, error=57.441\n", + "epoch=1956, lrate=0.001, error=57.441\n", + "epoch=1957, lrate=0.001, error=57.441\n", + "epoch=1958, lrate=0.001, error=57.440\n", + "epoch=1959, lrate=0.001, error=57.440\n", + "epoch=1960, lrate=0.001, error=57.440\n", + "epoch=1961, lrate=0.001, error=57.440\n", + "epoch=1962, lrate=0.001, error=57.440\n", + "epoch=1963, lrate=0.001, error=57.440\n", + "epoch=1964, lrate=0.001, error=57.440\n", + "epoch=1965, lrate=0.001, error=57.440\n", + "epoch=1966, lrate=0.001, error=57.440\n", + "epoch=1967, lrate=0.001, error=57.439\n", + "epoch=1968, lrate=0.001, error=57.439\n", + "epoch=1969, lrate=0.001, error=57.439\n", + "epoch=1970, lrate=0.001, error=57.439\n", + "epoch=1971, lrate=0.001, error=57.439\n", + "epoch=1972, lrate=0.001, error=57.439\n", + "epoch=1973, lrate=0.001, error=57.439\n", + "epoch=1974, lrate=0.001, error=57.439\n", + "epoch=1975, lrate=0.001, error=57.439\n", + "epoch=1976, lrate=0.001, error=57.438\n", + "epoch=1977, lrate=0.001, error=57.438\n", + "epoch=1978, lrate=0.001, error=57.438\n", + "epoch=1979, lrate=0.001, error=57.438\n", + "epoch=1980, lrate=0.001, error=57.438\n", + "epoch=1981, lrate=0.001, error=57.438\n", + "epoch=1982, lrate=0.001, error=57.438\n", + "epoch=1983, lrate=0.001, error=57.438\n", + "epoch=1984, lrate=0.001, error=57.438\n", + "epoch=1985, lrate=0.001, error=57.438\n", + "epoch=1986, lrate=0.001, error=57.437\n", + "epoch=1987, lrate=0.001, error=57.437\n", + "epoch=1988, lrate=0.001, error=57.437\n", + "epoch=1989, lrate=0.001, error=57.437\n", + "epoch=1990, lrate=0.001, error=57.437\n", + "epoch=1991, lrate=0.001, error=57.437\n", + "epoch=1992, lrate=0.001, error=57.437\n", + "epoch=1993, lrate=0.001, error=57.437\n", + "epoch=1994, lrate=0.001, error=57.437\n", + "epoch=1995, lrate=0.001, error=57.436\n", + "epoch=1996, lrate=0.001, error=57.436\n", + "epoch=1997, lrate=0.001, error=57.436\n", + "epoch=1998, lrate=0.001, error=57.436\n", + "epoch=1999, lrate=0.001, error=57.436\n", + "epoch=2000, lrate=0.001, error=57.436\n", + "epoch=2001, lrate=0.001, error=57.436\n", + "epoch=2002, lrate=0.001, error=57.436\n", + "epoch=2003, lrate=0.001, error=57.436\n", + "epoch=2004, lrate=0.001, error=57.436\n", + "epoch=2005, lrate=0.001, error=57.435\n", + "epoch=2006, lrate=0.001, error=57.435\n", + "epoch=2007, lrate=0.001, error=57.435\n", + "epoch=2008, lrate=0.001, error=57.435\n", + "epoch=2009, lrate=0.001, error=57.435\n", + "epoch=2010, lrate=0.001, error=57.435\n", + "epoch=2011, lrate=0.001, error=57.435\n", + "epoch=2012, lrate=0.001, error=57.435\n", + "epoch=2013, lrate=0.001, error=57.435\n", + "epoch=2014, lrate=0.001, error=57.434\n", + "epoch=2015, lrate=0.001, error=57.434\n", + "epoch=2016, lrate=0.001, error=57.434\n", + "epoch=2017, lrate=0.001, error=57.434\n", + "epoch=2018, lrate=0.001, error=57.434\n", + "epoch=2019, lrate=0.001, error=57.434\n", + "epoch=2020, lrate=0.001, error=57.434\n", + "epoch=2021, lrate=0.001, error=57.434\n", + "epoch=2022, lrate=0.001, error=57.434\n", + "epoch=2023, lrate=0.001, error=57.434\n", + "epoch=2024, lrate=0.001, error=57.433\n", + "epoch=2025, lrate=0.001, error=57.433\n", + "epoch=2026, lrate=0.001, error=57.433\n", + "epoch=2027, lrate=0.001, error=57.433\n", + "epoch=2028, lrate=0.001, error=57.433\n", + "epoch=2029, lrate=0.001, error=57.433\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch=2030, lrate=0.001, error=57.433\n", + "epoch=2031, lrate=0.001, error=57.433\n", + "epoch=2032, lrate=0.001, error=57.433\n", + "epoch=2033, lrate=0.001, error=57.432\n", + "epoch=2034, lrate=0.001, error=57.432\n", + "epoch=2035, lrate=0.001, error=57.432\n", + "epoch=2036, lrate=0.001, error=57.432\n", + "epoch=2037, lrate=0.001, error=57.432\n", + "epoch=2038, lrate=0.001, error=57.432\n", + "epoch=2039, lrate=0.001, error=57.432\n", + "epoch=2040, lrate=0.001, error=57.432\n", + "epoch=2041, lrate=0.001, error=57.432\n", + "epoch=2042, lrate=0.001, error=57.432\n", + "epoch=2043, lrate=0.001, error=57.431\n", + "epoch=2044, lrate=0.001, error=57.431\n", + "epoch=2045, lrate=0.001, error=57.431\n", + "epoch=2046, lrate=0.001, error=57.431\n", + "epoch=2047, lrate=0.001, error=57.431\n", + "epoch=2048, lrate=0.001, error=57.431\n", + "epoch=2049, lrate=0.001, error=57.431\n", + "epoch=2050, lrate=0.001, error=57.431\n", + "epoch=2051, 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lrate=0.001, error=57.418\n", + "epoch=2184, lrate=0.001, error=57.418\n", + "epoch=2185, lrate=0.001, error=57.418\n", + "epoch=2186, lrate=0.001, error=57.418\n", + "epoch=2187, lrate=0.001, error=57.418\n", + "epoch=2188, lrate=0.001, error=57.417\n", + "epoch=2189, lrate=0.001, error=57.417\n", + "epoch=2190, lrate=0.001, error=57.417\n", + "epoch=2191, lrate=0.001, error=57.417\n", + "epoch=2192, lrate=0.001, error=57.417\n", + "epoch=2193, lrate=0.001, error=57.417\n", + "epoch=2194, lrate=0.001, error=57.417\n", + "epoch=2195, lrate=0.001, error=57.417\n", + "epoch=2196, lrate=0.001, error=57.417\n", + "epoch=2197, lrate=0.001, error=57.417\n", + "epoch=2198, lrate=0.001, error=57.417\n", + "epoch=2199, lrate=0.001, error=57.416\n", + "epoch=2200, lrate=0.001, error=57.416\n", + "epoch=2201, lrate=0.001, error=57.416\n", + "epoch=2202, lrate=0.001, error=57.416\n", + "epoch=2203, lrate=0.001, error=57.416\n", + "epoch=2204, lrate=0.001, error=57.416\n", + "epoch=2205, 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error=57.390\n", + "epoch=2533, lrate=0.001, error=57.390\n", + "epoch=2534, lrate=0.001, error=57.390\n", + "epoch=2535, lrate=0.001, error=57.390\n", + "epoch=2536, lrate=0.001, error=57.389\n", + "epoch=2537, lrate=0.001, error=57.389\n", + "epoch=2538, lrate=0.001, error=57.389\n", + "epoch=2539, lrate=0.001, error=57.389\n", + "epoch=2540, lrate=0.001, error=57.389\n", + "epoch=2541, lrate=0.001, error=57.389\n", + "epoch=2542, lrate=0.001, error=57.389\n", + "epoch=2543, lrate=0.001, error=57.389\n", + "epoch=2544, lrate=0.001, error=57.389\n", + "epoch=2545, lrate=0.001, error=57.389\n", + "epoch=2546, lrate=0.001, error=57.389\n", + "epoch=2547, lrate=0.001, error=57.389\n", + "epoch=2548, lrate=0.001, error=57.389\n", + "epoch=2549, lrate=0.001, error=57.389\n", + "epoch=2550, lrate=0.001, error=57.388\n", + "epoch=2551, lrate=0.001, error=57.388\n", + "epoch=2552, lrate=0.001, error=57.388\n", + "epoch=2553, lrate=0.001, error=57.388\n", + "epoch=2554, lrate=0.001, 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error=57.387\n", + "epoch=2577, lrate=0.001, error=57.387\n", + "epoch=2578, lrate=0.001, error=57.387\n", + "epoch=2579, lrate=0.001, error=57.386\n", + "epoch=2580, lrate=0.001, error=57.386\n", + "epoch=2581, lrate=0.001, error=57.386\n", + "epoch=2582, lrate=0.001, error=57.386\n", + "epoch=2583, lrate=0.001, error=57.386\n", + "epoch=2584, lrate=0.001, error=57.386\n", + "epoch=2585, lrate=0.001, error=57.386\n", + "epoch=2586, lrate=0.001, error=57.386\n", + "epoch=2587, lrate=0.001, error=57.386\n", + "epoch=2588, lrate=0.001, error=57.386\n", + "epoch=2589, lrate=0.001, error=57.386\n", + "epoch=2590, lrate=0.001, error=57.386\n", + "epoch=2591, lrate=0.001, error=57.386\n", + "epoch=2592, lrate=0.001, error=57.386\n", + "epoch=2593, lrate=0.001, error=57.386\n", + "epoch=2594, lrate=0.001, error=57.385\n", + "epoch=2595, lrate=0.001, error=57.385\n", + "epoch=2596, lrate=0.001, error=57.385\n", + "epoch=2597, lrate=0.001, error=57.385\n", + "epoch=2598, lrate=0.001, error=57.385\n", + "epoch=2599, lrate=0.001, error=57.385\n", + "epoch=2600, lrate=0.001, error=57.385\n", + "epoch=2601, lrate=0.001, error=57.385\n", + "epoch=2602, lrate=0.001, error=57.385\n", + "epoch=2603, lrate=0.001, error=57.385\n", + "epoch=2604, lrate=0.001, error=57.385\n", + "epoch=2605, lrate=0.001, error=57.385\n", + "epoch=2606, lrate=0.001, error=57.385\n", + "epoch=2607, lrate=0.001, error=57.385\n", + "epoch=2608, lrate=0.001, error=57.385\n", + "epoch=2609, lrate=0.001, error=57.384\n", + "epoch=2610, lrate=0.001, error=57.384\n", + "epoch=2611, lrate=0.001, error=57.384\n", + "epoch=2612, lrate=0.001, error=57.384\n", + "epoch=2613, lrate=0.001, error=57.384\n", + "epoch=2614, lrate=0.001, error=57.384\n", + "epoch=2615, lrate=0.001, error=57.384\n", + "epoch=2616, lrate=0.001, error=57.384\n", + "epoch=2617, lrate=0.001, error=57.384\n", + "epoch=2618, lrate=0.001, error=57.384\n", + "epoch=2619, lrate=0.001, error=57.384\n", + "epoch=2620, lrate=0.001, error=57.384\n", + "epoch=2621, lrate=0.001, error=57.384\n", + "epoch=2622, lrate=0.001, error=57.384\n", + "epoch=2623, lrate=0.001, error=57.384\n", + "epoch=2624, lrate=0.001, error=57.383\n", + "epoch=2625, lrate=0.001, error=57.383\n", + "epoch=2626, lrate=0.001, error=57.383\n", + "epoch=2627, lrate=0.001, error=57.383\n", + "epoch=2628, lrate=0.001, error=57.383\n", + "epoch=2629, lrate=0.001, error=57.383\n", + "epoch=2630, lrate=0.001, error=57.383\n", + "epoch=2631, lrate=0.001, error=57.383\n", + "epoch=2632, lrate=0.001, error=57.383\n", + "epoch=2633, lrate=0.001, error=57.383\n", + "epoch=2634, lrate=0.001, error=57.383\n", + "epoch=2635, lrate=0.001, error=57.383\n", + "epoch=2636, lrate=0.001, error=57.383\n", + "epoch=2637, lrate=0.001, error=57.383\n", + "epoch=2638, lrate=0.001, error=57.383\n", + "epoch=2639, lrate=0.001, error=57.382\n", + "epoch=2640, lrate=0.001, error=57.382\n", + "epoch=2641, lrate=0.001, error=57.382\n", + "epoch=2642, lrate=0.001, error=57.382\n", + "epoch=2643, lrate=0.001, error=57.382\n", + "epoch=2644, lrate=0.001, error=57.382\n", + "epoch=2645, lrate=0.001, error=57.382\n", + "epoch=2646, lrate=0.001, error=57.382\n", + "epoch=2647, lrate=0.001, error=57.382\n", + "epoch=2648, lrate=0.001, error=57.382\n", + "epoch=2649, lrate=0.001, error=57.382\n", + "epoch=2650, lrate=0.001, error=57.382\n", + "epoch=2651, lrate=0.001, error=57.382\n", + "epoch=2652, lrate=0.001, error=57.382\n", + "epoch=2653, lrate=0.001, error=57.382\n", + "epoch=2654, lrate=0.001, error=57.381\n", + "epoch=2655, lrate=0.001, error=57.381\n", + "epoch=2656, lrate=0.001, error=57.381\n", + "epoch=2657, lrate=0.001, error=57.381\n", + "epoch=2658, lrate=0.001, error=57.381\n", + "epoch=2659, lrate=0.001, error=57.381\n", + "epoch=2660, lrate=0.001, error=57.381\n", + "epoch=2661, lrate=0.001, error=57.381\n", + "epoch=2662, lrate=0.001, error=57.381\n", + "epoch=2663, lrate=0.001, error=57.381\n", + "epoch=2664, lrate=0.001, error=57.381\n", + "epoch=2665, lrate=0.001, error=57.381\n", + "epoch=2666, lrate=0.001, error=57.381\n", + "epoch=2667, lrate=0.001, error=57.381\n", + "epoch=2668, lrate=0.001, error=57.381\n", + "epoch=2669, lrate=0.001, error=57.381\n", + "epoch=2670, lrate=0.001, error=57.380\n", + "epoch=2671, lrate=0.001, error=57.380\n", + "epoch=2672, lrate=0.001, error=57.380\n", + "epoch=2673, lrate=0.001, error=57.380\n", + "epoch=2674, lrate=0.001, error=57.380\n", + "epoch=2675, lrate=0.001, error=57.380\n", + "epoch=2676, lrate=0.001, error=57.380\n", + "epoch=2677, lrate=0.001, error=57.380\n", + "epoch=2678, lrate=0.001, error=57.380\n", + "epoch=2679, lrate=0.001, error=57.380\n", + "epoch=2680, lrate=0.001, error=57.380\n", + "epoch=2681, lrate=0.001, error=57.380\n", + "epoch=2682, lrate=0.001, error=57.380\n", + "epoch=2683, lrate=0.001, error=57.380\n", + "epoch=2684, lrate=0.001, error=57.380\n", + "epoch=2685, lrate=0.001, error=57.380\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch=2686, lrate=0.001, error=57.379\n", + "epoch=2687, lrate=0.001, error=57.379\n", + "epoch=2688, lrate=0.001, error=57.379\n", + "epoch=2689, lrate=0.001, error=57.379\n", + "epoch=2690, lrate=0.001, error=57.379\n", + "epoch=2691, lrate=0.001, error=57.379\n", + "epoch=2692, lrate=0.001, error=57.379\n", + "epoch=2693, lrate=0.001, error=57.379\n", + "epoch=2694, lrate=0.001, error=57.379\n", + "epoch=2695, lrate=0.001, error=57.379\n", + "epoch=2696, lrate=0.001, error=57.379\n", + "epoch=2697, lrate=0.001, error=57.379\n", + "epoch=2698, lrate=0.001, error=57.379\n", + "epoch=2699, lrate=0.001, error=57.379\n", + "epoch=2700, lrate=0.001, error=57.379\n", + "epoch=2701, lrate=0.001, error=57.379\n", + "epoch=2702, lrate=0.001, error=57.378\n", + "epoch=2703, lrate=0.001, error=57.378\n", + "epoch=2704, lrate=0.001, error=57.378\n", + "epoch=2705, lrate=0.001, error=57.378\n", + "epoch=2706, lrate=0.001, error=57.378\n", + "epoch=2707, 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error=57.334\n", + "epoch=3714, lrate=0.001, error=57.334\n", + "epoch=3715, lrate=0.001, error=57.334\n", + "epoch=3716, lrate=0.001, error=57.334\n", + "epoch=3717, lrate=0.001, error=57.334\n", + "epoch=3718, lrate=0.001, error=57.334\n", + "epoch=3719, lrate=0.001, error=57.334\n", + "epoch=3720, lrate=0.001, error=57.334\n", + "epoch=3721, lrate=0.001, error=57.334\n", + "epoch=3722, lrate=0.001, error=57.334\n", + "epoch=3723, lrate=0.001, error=57.334\n", + "epoch=3724, lrate=0.001, error=57.334\n", + "epoch=3725, lrate=0.001, error=57.334\n", + "epoch=3726, lrate=0.001, error=57.334\n", + "epoch=3727, lrate=0.001, error=57.334\n", + "epoch=3728, lrate=0.001, error=57.334\n", + "epoch=3729, lrate=0.001, error=57.334\n", + "epoch=3730, lrate=0.001, error=57.334\n", + "epoch=3731, lrate=0.001, error=57.334\n", + "epoch=3732, lrate=0.001, error=57.334\n", + "epoch=3733, lrate=0.001, error=57.334\n", + "epoch=3734, lrate=0.001, error=57.334\n", + "epoch=3735, lrate=0.001, 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error=57.324\n", + "epoch=4151, lrate=0.001, error=57.324\n", + "epoch=4152, lrate=0.001, error=57.324\n", + "epoch=4153, lrate=0.001, error=57.324\n", + "epoch=4154, lrate=0.001, error=57.324\n", + "epoch=4155, lrate=0.001, error=57.324\n", + "epoch=4156, lrate=0.001, error=57.324\n", + "epoch=4157, lrate=0.001, error=57.323\n", + "epoch=4158, lrate=0.001, error=57.323\n", + "epoch=4159, lrate=0.001, error=57.323\n", + "epoch=4160, lrate=0.001, error=57.323\n", + "epoch=4161, lrate=0.001, error=57.323\n", + "epoch=4162, lrate=0.001, error=57.323\n", + "epoch=4163, lrate=0.001, error=57.323\n", + "epoch=4164, lrate=0.001, error=57.323\n", + "epoch=4165, lrate=0.001, error=57.323\n", + "epoch=4166, lrate=0.001, error=57.323\n", + "epoch=4167, lrate=0.001, error=57.323\n", + "epoch=4168, lrate=0.001, error=57.323\n", + "epoch=4169, lrate=0.001, error=57.323\n", + "epoch=4170, lrate=0.001, error=57.323\n", + "epoch=4171, lrate=0.001, error=57.323\n", + "epoch=4172, lrate=0.001, 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error=57.297\n", + "epoch=8106, lrate=0.001, error=57.297\n", + "epoch=8107, lrate=0.001, error=57.297\n", + "epoch=8108, lrate=0.001, error=57.297\n", + "epoch=8109, lrate=0.001, error=57.297\n", + "epoch=8110, lrate=0.001, error=57.297\n", + "epoch=8111, lrate=0.001, error=57.297\n", + "epoch=8112, lrate=0.001, error=57.297\n", + "epoch=8113, lrate=0.001, error=57.297\n", + "epoch=8114, lrate=0.001, error=57.297\n", + "epoch=8115, lrate=0.001, error=57.297\n", + "epoch=8116, lrate=0.001, error=57.297\n", + "epoch=8117, lrate=0.001, error=57.297\n", + "epoch=8118, lrate=0.001, error=57.297\n", + "epoch=8119, lrate=0.001, error=57.297\n", + "epoch=8120, lrate=0.001, error=57.297\n", + "epoch=8121, lrate=0.001, error=57.297\n", + "epoch=8122, lrate=0.001, error=57.297\n", + "epoch=8123, lrate=0.001, error=57.297\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch=8124, lrate=0.001, error=57.297\n", + "epoch=8125, lrate=0.001, error=57.297\n", + "epoch=8126, lrate=0.001, error=57.297\n", + "epoch=8127, lrate=0.001, error=57.297\n", + "epoch=8128, lrate=0.001, error=57.297\n", + "epoch=8129, lrate=0.001, error=57.297\n", + "epoch=8130, lrate=0.001, error=57.297\n", + "epoch=8131, lrate=0.001, error=57.297\n", + "epoch=8132, lrate=0.001, error=57.297\n", + "epoch=8133, lrate=0.001, error=57.297\n", + "epoch=8134, lrate=0.001, error=57.297\n", + "epoch=8135, lrate=0.001, error=57.297\n", + "epoch=8136, lrate=0.001, error=57.297\n", + "epoch=8137, lrate=0.001, error=57.297\n", + "epoch=8138, lrate=0.001, error=57.297\n", + "epoch=8139, lrate=0.001, error=57.297\n", + "epoch=8140, lrate=0.001, error=57.297\n", + "epoch=8141, lrate=0.001, error=57.297\n", + "epoch=8142, lrate=0.001, error=57.297\n", + "epoch=8143, lrate=0.001, error=57.297\n", + "epoch=8144, lrate=0.001, error=57.297\n", + "epoch=8145, lrate=0.001, error=57.297\n", + "epoch=8146, lrate=0.001, error=57.297\n", + "epoch=8147, lrate=0.001, error=57.297\n", + "epoch=8148, lrate=0.001, error=57.297\n", + "epoch=8149, lrate=0.001, error=57.297\n", + "epoch=8150, lrate=0.001, error=57.297\n", + "epoch=8151, lrate=0.001, error=57.297\n", + "epoch=8152, lrate=0.001, error=57.297\n", + "epoch=8153, lrate=0.001, error=57.297\n", + "epoch=8154, lrate=0.001, error=57.297\n", + "epoch=8155, lrate=0.001, error=57.297\n", + "epoch=8156, lrate=0.001, error=57.297\n", + "epoch=8157, lrate=0.001, error=57.297\n", + "epoch=8158, lrate=0.001, error=57.297\n", + "epoch=8159, lrate=0.001, error=57.297\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0ml_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.001\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mn_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0mcoef\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoefficients_sgd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_epoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoef\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mcoefficients_sgd\u001b[0;34m(train, l_rate, n_epoch)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0mcoef\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoef\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0ml_rate\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0mcoef\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoef\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0ml_rate\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0merror\u001b[0m \u001b[0;34m*\u001b[0m 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return yhat\n", + "\n", + "def train_test_split(dataset, splitRatio):\n", + " train = list()\n", + " train_size = splitRatio * len(dataset)\n", + " dataset_copy = list(dataset)\n", + " while len(train) < train_size:\n", + " index = randrange(len(dataset_copy))\n", + " train.append(dataset_copy.pop(index))\n", + " return train, dataset_copy\n", + "\n", + "# dataset = [[1, 1], [2, 3], [4, 3], [3, 2], [5, 5]]\n", + "coef = [0.5, 0.05, 1.8, 0.01, 0.03, 0.08, 0.09, 0.003, 0.4, 0.1, 0.6, 0.5]\n", + "\n", + "errors = list()\n", + "\n", + "for row in dataset:\n", + " yhat = predict(row, coef)\n", + " error = yhat-row[-1]\n", + " errors.append(yhat-row[-1])\n", + " print(\"Esperado=%.3f, Predito=%.3f, Erro=%.3f\" % (row[-1], yhat, error))\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "# Estimate linear regression coefficients using stochastic gradient descent\n", + "def coefficients_sgd(train, l_rate, n_epoch):\n", + " coef = [0.0 for i in range(len(train[0]))]\n", + " print ('Coeficiente Inicial={0}' % (coef))\n", + " for epoch in range(n_epoch):\n", + " sum_error = 0\n", + " for row in train:\n", + " yhat = predict(row, coef)\n", + " error = yhat - row[-1]\n", + " sum_error += error**2\n", + " coef[0] = coef[0] - l_rate * error\n", + " for i in range(len(row)-1):\n", + " coef[i + 1] = coef[i + 1] - l_rate * error * row[i] \n", + " print(('epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error)))\n", + " return coef\n", + "\n", + "# Calculate coefficients\n", + "# dataset = [[1, 1], [2, 3], [4, 3], [3, 2], [5, 5]]\n", + "\n", + "\n", + "splitRatio = 0.75\n", + "train, test = train_test_split(dataset, splitRatio)\n", + "\n", + "l_rate = 0.001\n", + "n_epoch = 10000\n", + "coef = coefficients_sgd(train, l_rate, n_epoch)\n", + "print(coef)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2017/07-decision-tree/decision_tree_Patrick.ipynb b/2017/07-decision-tree/decision_tree_Patrick.ipynb new file mode 100644 index 0000000..536f656 --- /dev/null +++ b/2017/07-decision-tree/decision_tree_Patrick.ipynb @@ -0,0 +1,489 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import math\n", + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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buyingmaintdoorspersonslug_bootsafetyclass
0lowhigh3moresmallmedacc
1lowhigh24bigmedacc
2vhighvhigh34biglowunacc
3lowmed5more2smalllowunacc
4medmed5moremoresmalllowunacc
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" + ], + "text/plain": [ + " buying maint doors persons lug_boot safety class\n", + "0 low high 3 more small med acc\n", + "1 low high 2 4 big med acc\n", + "2 vhigh vhigh 3 4 big low unacc\n", + "3 low med 5more 2 small low unacc\n", + "4 med med 5more more small low unacc" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "headers = [\"buying\", \"maint\", \"doors\", \"persons\",\"lug_boot\", \"safety\", \"class\"]\n", + "dataset = pd.read_csv(\"car_data.csv\", header=None, names=headers)\n", + "\n", + "dataset = dataset.sample(frac=1).reset_index(drop=True) # shuffle\n", + "\n", + "dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2\n", + "1 2 1 2 2 1 2\n", + "2 0 2 0 0 2 1\n", + "2 3 3 2 0 2 1\n", + "\n", + "[1296 rows x 6 columns]\n", + " buying maint doors persons lug_boot safety\n", + "class \n", + "2 1 2 2 0 0 1\n", + "2 1 0 0 1 0 1\n", + "0 2 3 3 1 0 2\n", + "2 0 3 3 2 0 1\n", + "2 1 2 3 1 2 1\n", + "2 0 3 2 2 0 1\n", + "2 3 3 2 2 0 2\n", + "0 0 0 3 1 2 0\n", + "2 0 2 0 1 2 1\n", + "2 2 2 2 2 2 1\n", + "2 0 1 3 0 1 0\n", + "2 1 0 0 2 1 1\n", + "2 2 2 1 0 2 1\n", + "0 2 3 3 1 1 0\n", + "0 3 2 1 2 1 2\n", + "2 2 3 2 0 2 2\n", + "2 0 2 1 2 2 1\n", + "2 0 1 1 0 2 0\n", + "2 3 0 0 1 1 0\n", + "2 2 3 1 1 2 1\n", + "2 1 3 0 1 2 1\n", + "0 0 0 3 1 0 0\n", + "2 1 3 0 0 1 2\n", + "0 2 2 0 2 1 0\n", + "2 3 0 0 2 0 2\n", + "2 1 0 2 2 1 1\n", + "2 2 1 2 0 1 1\n", + "2 1 0 0 2 2 1\n", + "2 3 0 0 2 2 0\n", + "2 2 0 0 1 2 1\n", + "... ... ... ... ... ... ...\n", + "1 1 1 0 2 0 2\n", + "2 3 3 0 1 0 2\n", + "2 0 3 2 2 1 1\n", + "2 0 1 2 0 1 2\n", + "2 3 0 2 1 1 1\n", + "0 1 1 1 2 2 2\n", + "2 3 0 0 0 2 0\n", + "2 2 3 1 0 2 0\n", + "0 1 2 0 1 1 2\n", + "2 0 1 2 0 2 0\n", + "2 3 1 0 0 0 1\n", + "2 1 3 2 0 0 0\n", + "2 0 0 2 0 1 1\n", + "0 3 1 2 1 1 0\n", + "2 2 0 0 0 2 2\n", + "0 0 0 2 1 1 0\n", + "2 2 3 0 0 2 2\n", + "2 3 2 0 0 2 2\n", + "2 1 0 3 1 2 1\n", + "2 2 2 2 2 1 1\n", + "3 1 1 3 2 0 0\n", + "2 0 0 2 1 2 2\n", + "2 2 2 0 1 0 1\n", + "2 3 2 3 1 2 1\n", + "0 0 2 1 1 1 0\n", + "2 0 1 0 2 1 1\n", + "2 3 0 3 0 0 2\n", + "2 3 1 2 1 1 1\n", + "0 0 1 0 1 0 0\n", + "2 2 3 0 2 2 1\n", + "\n", + "[432 rows x 6 columns]\n" + ] + } + ], + "source": [ + "# Split do dataset em treino e teste\n", + "size = len(dataset)\n", + "train_size = int(math.floor(size * 0.75))\n", + "train_data = dataset[:train_size]\n", + "test_data = dataset[train_size:]\n", + "\n", + "print(train_data)\n", + "print(test_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", + " splitter='best')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Treinando a árvore de decisão com critério gini\n", + "\n", + "d_tree = DecisionTreeClassifier(criterion=\"gini\")\n", + "d_tree.fit(train_data, train_data.index) " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.97453703703703709" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fazendo as predições através da árvore de decisão\n", + "d_tree.predict(test_data.iloc[:, 0:6])\n", + "\n", + "d_tree.score(test_data, test_data.index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/2017/09-clustering/clustering-exercicio-Patrick.ipynb b/2017/09-clustering/clustering-exercicio-Patrick.ipynb new file mode 100644 index 0000000..7dc99e0 --- /dev/null +++ b/2017/09-clustering/clustering-exercicio-Patrick.ipynb @@ -0,0 +1,478 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Exercícios\n", + "\n", + "1 - Aplique os algoritmos K-means [1] e AgglomerativeClustering [2] em qualquer dataset que você desejar (recomendação: iris). Compare os resultados utilizando métricas de avaliação de clusteres (completeness e homogeneity, por exemplo) [3].\n", + "\n", + "* [1] http://scikit-learn.org/stable/modules/clustering.html#k-means\n", + "\n", + "* [2] http://scikit-learn.org/0.17/modules/clustering.html#hierarchical-clustering\n", + "\n", + "* [3] http://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation\n", + "\n", + "2 - Qual o valor de K (número de clusteres) você escolheu para a questão anterior? Desenvolva o Método do Cotovelo (não utilizar lib!) e descubra o K mais adequado. Após descobrir, aplique novamente o K-means com o K adequado. \n", + "\n", + "* Ajuda: atributos do [k-means](http://scikit-learn.org/0.17/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)\n", + "\n", + "3 - Após a questão 2, você aplicou o algoritmo com K apropriado. Refaça o cálculo das métricas de acordo com os resultados de clusters obtidos com a questão anterior e verifique se o resultado melhorou.\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.cluster import KMeans, AgglomerativeClustering\n", + "from sklearn.decomposition import PCA\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "from sklearn import datasets, decomposition" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 5.1 3.5 1.4 0.2]\n", + " [ 4.9 3. 1.4 0.2]\n", + " [ 4.7 3.2 1.3 0.2]\n", + " [ 4.6 3.1 1.5 0.2]\n", + " [ 5. 3.6 1.4 0.2]\n", + " [ 5.4 3.9 1.7 0.4]\n", + " [ 4.6 3.4 1.4 0.3]\n", + " [ 5. 3.4 1.5 0.2]\n", + " [ 4.4 2.9 1.4 0.2]\n", + " [ 4.9 3.1 1.5 0.1]\n", + " [ 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2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2]\n" + ] + } + ], + "source": [ + "dataset = datasets.load_iris()\n", + "\n", + "dataset_X = dataset.data\n", + "dataset_y = dataset.target\n", + "\n", + "print(dataset_X)\n", + "print(dataset_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 3, 3, 3, 0, 3, 0, 3, 0, 3, 0, 0, 0, 0, 3, 0, 3, 0, 0, 3,\n", + " 0, 3, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 3, 0, 3, 3, 3, 0, 0, 0, 3,\n", + " 0, 0, 0, 0, 0, 3, 0, 0, 2, 3, 2, 2, 2, 2, 0, 2, 2, 2, 3, 3, 2, 3, 3,\n", + " 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2,\n", + " 3, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2, 3], dtype=int32)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "k = 4\n", + "kmeans = KMeans(n_clusters=k, random_state=0).fit(dataset_X)\n", + "\n", + "predictions = kmeans.predict(dataset_X)\n", + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "reduced_data = decomposition.PCA(n_components=2).fit_transform(dataset_X)\n", + "\n", + "reduced_data_df = pd.DataFrame(reduced_data)\n", + "reduced_data_df[2] = predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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boYcf7OOq/pJGPpT+k8CK3gBk4EDZ8y14fz7czSwgvPKXgIdAXQE3eH+prDPQ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\n5SsuyJiELeuuGltD/k3gfTXcvtk98mSTOtUyiMh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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plots\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(reduced_data_df[0], reduced_data_df[1], c=reduced_data_df[2])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1rNtCpsvN0u2H2Xjgn0Z6SecycijPkOVmyXS5cbg8bDkYzVeLNhX4fTndHv7e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\nSW524ynO9z/7RsjVQ/+8agMvPzgv5P7iYpxMHDG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OJowBdhpjdhtj3MCLwByLM4VljFkN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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "k = 4\n", + "agglo = AgglomerativeClustering(n_clusters=k, affinity=\"euclidean\", linkage=\"complete\").fit_predict(dataset_X)\n", + "\n", + "\n", + "# AgglomerativeClustering\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(reduced_data_df[0], reduced_data_df[1], c=agglo)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Metodo do Cotovelo para identificar o melhor k\n", + "nC = np.arange(1,10); eixo_y = [] \n", + "\n", + "for n in nC:\n", + " kmeans.n_clusters = n\n", + " kmeans.fit(dataset_X)\n", + " \n", + " eixo_y.append(kmeans.inertia_)\n", + " \n", + "plt.figure()\n", + "plt.xlabel('k')\n", + "plt.plot(nC,eixo_y)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Através do método do cotovelo, observamos que o melhor k para o modelo é k=3, que é exatamente o número de classes existentes no dataset escolhido." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Refazendo a análise para k = 3" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 0,\n", + " 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2,\n", + " 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0], dtype=int32)" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "k = 3\n", + "kmeans = KMeans(n_clusters=k, random_state=0).fit(dataset_X)\n", + "\n", + "predictions = kmeans.predict(dataset_X)\n", + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "reduced_data = decomposition.PCA(n_components=2).fit_transform(dataset_X)\n", + "\n", + "reduced_data_df = pd.DataFrame(reduced_data)\n", + "reduced_data_df[2] = predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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nESnRYIE8fuqzDO5CemeFulkVPSeDyIwJILPzu1t5AB/kvov0RW9yIoQeFmoS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\ngLT4K6VUAtLir5RSCUiLv1JKJaD/B10mSWyNIj9xAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plots\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(reduced_data_df[0], reduced_data_df[1], c=reduced_data_df[2])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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WEn1qNW+lIkV5qXVbulQtOHrdiuubC9ExfP/6NDYt2kFIqIleT9xG32d7MOr+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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "k = 3\n", + "agglo = AgglomerativeClustering(n_clusters=k, affinity=\"euclidean\", linkage=\"complete\").fit_predict(dataset_X)\n", + "\n", + "\n", + "# AgglomerativeClustering\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(reduced_data_df[0], reduced_data_df[1], c=agglo)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}