# Split into a training set and a test set using a stratified k fold
# split into a training and testing set
train, test = iter(StratifiedKFold(y, k=4)).next()
X_train, X_test = X[train], X[test]
y_train, y_test = y[train], y[test]
################################################################################
# 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 from %d faces" % (n_components,
X_train.shape[0])
t0 = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print "done in %0.3fs" % (time() - t0)
eigenfaces = pca.components_.T.reshape((n_components, h, w))
print "Projecting the input data on the eigenfaces orthonormal basis"
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print "done in %0.3fs" % (time() - t0)
################################################################################
# Train a SVM classification model
print "Fitting the classifier to the training set"
t0 = time()
digits = datasets.load_digits()
# reshape the data using the traditional (n_samples, n_features) shape
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
n_features = X.shape[1]
n_components = 16
######################################################################
# Compute a PCA (eigendigits) on the digit dataset
print "Extracting the top %d eigendigits from %d images" % (n_components,
X.shape[0])
t0 = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X)
print "done in %0.3fs" % (time() - t0)
eigendigits = pca.components_.T
######################################################################
# Compute a NMF on the digit dataset
print "Extracting %d non-negative features from %d images" % (n_components,
X.shape[0])
t0 = time()
nmf = NMF(n_components=n_components,
init='nndsvd',
beta=5,
tol=1e-2,
sparseness="components").fit(X)
categories = [c for c, f in files]
category_names = np.unique(categories)
target = np.searchsorted(category_names, categories)
selected_target = target
mask = np.in1d(target, selected_target)
X_train = faces
y_train = target
################################################################################
# Compute a PCA (eigenfaces) on the face dataset
n_components = 150
print "Extracting the top %d eigenfaces" % n_components
pca_sl = RandomizedPCA(n_components=n_components, whiten=True)
pca_sl.fit(X_train)
#components, mean = pca.pca(X_train, n_components)
#print "PCA components shape", pca.components_.T.shape
#eigenfaces = pca.components_.T.reshape((-1, 64, 64))
# project the input data on the eigenfaces orthonormal basis
X_train_pca = pca_sl.transform(X_train)
#X_train_pca = pca.transform(X_train, mean, components)
################################################################################
# Train a SVM classification model
print "Fitting the classifier to the training set"
param_grid = {
# split into a training and testing set
train, test = iter(StratifiedKFold(y, k=4)).next()
X_train, X_test = X[train], X[test]
y_train, y_test = y[train], y[test]
################################################################################
# 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 from %d faces" % (
n_components, X_train.shape[0])
t0 = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print "done in %0.3fs" % (time() - t0)
eigenfaces = pca.components_.T.reshape((n_components, h, w))
print "Projecting the input data on the eigenfaces orthonormal basis"
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print "done in %0.3fs" % (time() - t0)
################################################################################
# Train a SVM classification model
print "Fitting the classifier to the training set"
categories = [c for c, f in files]
category_names = np.unique(categories)
target = np.searchsorted(category_names, categories)
selected_target = target
mask = np.in1d(target, selected_target)
X_train = faces
y_train = target
################################################################################
# Compute a PCA (eigenfaces) on the face dataset
n_components = 150
print "Extracting the top %d eigenfaces" % n_components
pca_sl = RandomizedPCA(n_components=n_components, whiten=True)
pca_sl.fit(X_train)
#components, mean = pca.pca(X_train, n_components)
#print "PCA components shape", pca.components_.T.shape
#eigenfaces = pca.components_.T.reshape((-1, 64, 64))
# project the input data on the eigenfaces orthonormal basis
X_train_pca = pca_sl.transform(X_train)
#X_train_pca = pca.transform(X_train, mean, components)
################################################################################
# Train a SVM classification model
print "Fitting the classifier to the training set"
param_grid = {
