Lab6 16Sep2021.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "53b51882",
   "metadata": {},
   "source": [
    "# Linear Regression using Normal Equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1e3e6034",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.linalg import inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5b691402",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n",
      "(506,)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('./TrainingData/housing.csv', header=None, delimiter=\"\\s+\")\n",
    "data = data.values\n",
    "X = data[:, :13]\n",
    "Y = data[:,13]\n",
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3bd060d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normal(X,Y) :\n",
    "    theta = inv(X.T.dot(X)).dot(X.T).dot(Y)\n",
    "    return theta\n",
    "\n",
    "def hypothesis(x,theta) :\n",
    "    n = x.shape[0]\n",
    "    y_ = 0\n",
    "    \n",
    "    for i in range(n) :\n",
    "        y_ += theta[i] * x[i]\n",
    "        \n",
    "    return y_\n",
    "\n",
    "def r2_score(Y, Y_) :\n",
    "    num = np.sum((Y - Y_)**2)\n",
    "    denom = np.sum((Y - Y.mean())**2)\n",
    "    score = 1 - num/denom\n",
    "    \n",
    "    return score * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b49afda1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-9.28965170e-02  4.87149552e-02 -4.05997958e-03  2.85399882e+00\n",
      " -2.86843637e+00  5.92814778e+00 -7.26933458e-03 -9.68514157e-01\n",
      "  1.71151128e-01 -9.39621540e-03 -3.92190926e-01  1.49056102e-02\n",
      " -4.16304471e-01]\n"
     ]
    }
   ],
   "source": [
    "theta = normal(X,Y)\n",
    "print(theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ac8eac96",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_list = []\n",
    "\n",
    "m = X.shape[0]\n",
    "\n",
    "for i in range(m) :\n",
    "    pred = hypothesis(X[i],theta)\n",
    "    y_pred_list.append(pred)\n",
    "    \n",
    "y_pred_list = np.array(y_pred_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fdedd5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71.37381380515404"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(Y,y_pred_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "230b2e45",
   "metadata": {},
   "source": [
    "# KNN Algorithm (K Nearest Neighbours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "86c0261f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
      "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
      "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
      "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
      "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
      "4   5            5.0           3.6            1.4           0.2  Iris-setosa\n",
      "(150, 2)\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('./TrainingData/Iris.csv')\n",
    "print(data.head())\n",
    "data = data.values\n",
    "\n",
    "X = data[:,3:5]\n",
    "Y = data[:,5]\n",
    "\n",
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2d2ab34b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "plt.scatter(X[:,0],X[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1eb0bc12",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist(p1,p2) :\n",
    "    return np.sqrt(sum((p1 - p2)**2))\n",
    "\n",
    "def knn(X,Y,query,k=5) :\n",
    "    \n",
    "    list_dist = []\n",
    "    m = X.shape[0]\n",
    "    \n",
    "    for i in range(m) :\n",
    "        d = dist(query, X[i])\n",
    "        list_dist.append((d,Y[i]))\n",
    "        \n",
    "    list_dist = sorted(list_dist)\n",
    "    list_dist = list_dist[:k]\n",
    "    \n",
    "    # print(list_dist) # print the list having tupples\n",
    "    list_dist = np.array(list_dist) # convert list of tupples to 2d array\n",
    "    # print(list_dist) # print the 2d array\n",
    "    list_dist_new = np.unique(list_dist[:,1], return_counts=True) # find out the unique values from 1st col\n",
    "    # print(list_dist_new) # print the unique species\n",
    "    idx = list_dist_new[1].argmax()\n",
    "    # print(idx)\n",
    "    pred = list_dist_new[0][idx]\n",
    "    return pred\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b225c600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Iris-setosa'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = (2,0.5)\n",
    "knn(X,Y,query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f21a77f9",
   "metadata": {},
   "source": [
    "# RECOGNISING HANDWRITTEN DIGITS USING MNIST DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d884f329",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b7fc72",
   "metadata": {},
   "source": [
    "## 1. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f8c8705a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42000, 785)\n",
      "(28000, 784)\n"
     ]
    }
   ],
   "source": [
    "df_train = pd.read_csv('./TrainingData/train.csv')\n",
    "print(df_train.shape)\n",
    "df_test = pd.read_csv('./TrainingData/test.csv')\n",
    "print(df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d70ef1e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>pixel0</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel774</th>\n",
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       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
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       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "<p>5 rows × 785 columns</p>\n",
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      ],
      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0      1       0       0       0       0       0       0       0       0   \n",
       "1      0       0       0       0       0       0       0       0       0   \n",
       "2      1       0       0       0       0       0       0       0       0   \n",
       "3      4       0       0       0       0       0       0       0       0   \n",
       "4      0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "09cf77c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [1 0 0 ... 0 0 0]\n",
      " ...\n",
      " [7 0 0 ... 0 0 0]\n",
      " [6 0 0 ... 0 0 0]\n",
      " [9 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "data = df_train.values\n",
    "print(data)\n",
    "test = df_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "06d1cc34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42000, 784)\n",
      "(42000,)\n",
      "[1 0 1 ... 7 6 9]\n"
     ]
    }
   ],
   "source": [
    "X_train = data[:,1:]\n",
    "Y_train = data[:,0]\n",
    "\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)\n",
    "\n",
    "print(Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb8cb7f2",
   "metadata": {},
   "source": [
    "## 2. Apply KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "476031f7",
   "metadata": {},
   "source": [
    "## 3. Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "784e55f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.0\n"
     ]
    }
   ],
   "source": [
    "print(knn(X_train,Y_train, test[500]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "082964cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "    "
   ]
  }
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