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Principal Components Analysis (PCA)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC
from sklearn import cross_validation
from sklearn.decomposition import PCA
from sklearn import preprocessing
digits = datasets.load_digits()
X =
y =
# if you want to standardize the data, uncomment the following lines
#scaler = preprocessing.StandardScaler().fit(X)
#X = scaler.transform(X)
pca = PCA(n_components=10)
X_reduced = pca.fit_transform(X)
print (pca.explained_variance_ratio_)
# a scatter-plot in the space of the principal components:
plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y,
# let's see if this feature representation is useful:
X /= X.max()
from sklearn.grid_search import GridSearchCV
param_grid = [
  {'C': [1, 10, 100], 'kernel': ['linear']},
  {'C': [1, 10, 100], 'gamma': [0.01, 0.001, 0.0001], 'kernel': ['rbf']},
classifier = GridSearchCV(estimator=SVC(), param_grid=param_grid)
cv = cross_validation.StratifiedKFold(y, 5, shuffle=True, random_state=0)
# accuracy with all the features:
print (np.mean(cross_validation.cross_val_score(classifier, X, y, cv=cv)))
# accuracy with the PCA features:
print (np.mean(cross_validation.cross_val_score(classifier, X_reduced, y, cv=cv)))
code/pca.txt ยท Last modified: 2016/11/03 14:21 by asa