Esempi in Python per RandomizedPCA.transform

Esempio n. 1

################################################################################
# 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()
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'})

Esempio n. 2

File: facerecognizer.py Progetto: StevenLOL/multimedia-machine-learning-tutorials

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 = {
    '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:"

Esempio n. 3

File: plot_face_recognition.py Progetto: derek-r/scikit-learn

################################################################################
# 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()
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'})

Esempio n. 4

File: facerecognizer.py Progetto: sb123456789sb/multimedia-machine-learning-tutorials

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 = {
    '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)