sklearn_pca_test_full.py

#!/usr/bin/env python2
##!/usr/bin/env python3

from __future__ import print_function
from sklearn.decomposition import PCA
from sklearn import svm
import numpy as np
import funct
from sys import argv

script, d_f, l_f = argv

data = funct.Data(d_f, l_f, w0 = False)
labels = data.test_labels
training_data = data.training_rows
print(labels)

training_data = [data.data[i] for i in training_data]
print(training_data)
training_labels = [data.labels[i] for i in data.training_rows]

test_data = [data.data[i] for i in labels]

data = np.asarray(training_data)
print("Original training data")
print(data)

pca = PCA()
pca.fit(data)
print(pca.components_)
data_applied = pca.transform(data)
print("Training data")
print(data_applied)

print("Original test data")
print(np.asarray(test_data))

test_data_applied = pca.transform(np.asarray(test_data))
print("Test data")
print(test_data_applied)

clf = svm.SVR()
clf.fit(data_applied, training_labels)
predictions = clf.predict(np.asarray(test_data_applied))
print(predictions)