def test_pca_check_projection():
"""test that the projection of data is correct
"""
n, p = 100, 3
X = randn(n, p) * .1
X[:10] += np.array([3, 4, 5])
pca = PCA(n_comp=2)
pca.fit(X)
Xt = 0.1* randn(1, p) + np.array([3, 4, 5])
Yt = pca.transform(Xt)
Yt /= np.sqrt((Yt**2).sum())
np.testing.assert_almost_equal(np.abs(Yt[0][0]), 1., 1)
X_train, X_test = X[:split], X[split:]
y_train, y_test = y[:split], y[split:]
################################################################################
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 150
print "Extracting the top %d eigenfaces" % n_components
pca = PCA(n_comp=n_components, whiten=True, do_fast_svd=True).fit(X_train)
eigenfaces = pca.components_.T.reshape((n_components, 64, 64))
# project the input data on the eigenfaces orthonormal basis
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
################################################################################
# Train a SVM classification model
print "Fitting the classifier to the training set"
param_grid = {
'C': [1, 5, 10, 50, 100],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
clf = GridSearchCV(SVC(kernel='rbf'), param_grid,
fit_params={'class_weight': 'auto'})
clf = clf.fit(X_train_pca, y_train)
print "Best estimator found by grid search:"
