test_X = sklearn.preprocessing.scale(test_X)
# row_sums = train_X.sum(axis=1).astype(float)
# train_X = np.true_divide(train_X, row_sums[:, np.newaxis])
#
# row_sums = test_X.sum(axis=1).astype(float)
# test_X = np.true_divide(test_X, row_sums[:, np.newaxis])
###############################################################################
# Dictionary Learning
n_components = 500
n_samples_training = 1500
print("\nSparse Coding Dictionary Learning")
# pca = RandomizedPCA(n_components=n_dcomponents).fit(train_X)
dl = KSVDSparseCoding(n_components, n_nonzero_coefs=70, preserve_dc=False, approx=False, max_iter=5, verbose=1)
dl.fit(train_X[0:n_samples_training])
print "dl.atom_bin_count", dl.atom_bin_count().shape, np.average(dl.atom_bin_count()), dl.atom_bin_count().tolist()
plt.plot(dl.errors)
plt.show()
print "X_train.shape", train_X.shape
print "Components shape", dl.dictionary.shape
# components = dl.components().reshape((n_components, n_features))
components = dl.dictionary
# Visualizing the components as images
print "y_train", y_train
print "X_test.shape", X_test.shape
print "y_test", y_test
###############################################################################
# Write data to matlab matrix
# scipy.io.savemat('/home/jonny2/PycharmProjects/ML-algorithms/Projects/GWAS-SparseCoding/psychiatric.mat',
# mdict={'tr_dat': X_train, 'tt_dat': X_test, 'trls': y_train, 'ttls': y_test})
###############################################################################
# Sparse Representation
n_components = 25
# dl = DictionaryLearning(n_components, max_iter=15, n_jobs=4, verbose=2)
dl = KSVDSparseCoding(n_components, max_iter=5, verbose=1, approx=True)
dl.fit(X_s)
eigenfaces = dl.components_.T
print("Projecting the input data on the learned dictionary bases")
X_train_pca = sparse_encode(X_train, eigenfaces, algorithm='lasso_lars')
X_test_pca = sparse_encode(X_test, eigenfaces, algorithm='lasso_lars')
print "X_train_pca.shape", X_train_pca.shape
print "X_test_pca.shape", X_test_pca.shape
###############################################################################
# Train a SVM classification model
print("Fitting the classifier to the training set")
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
# Split into a training set and a test set using a stratified k fold
# split into a training and testing set
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.50, random_state=42)
###############################################################################
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 10
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)
dl = KSVDSparseCoding(n_components, preserve_dc=True, approx=False, max_iter=15, verbose=1)
dl.fit(X_train)
print("done in %0.3fs" % (time() - t0))
eigenfaces = dl.dictionary
print("Projecting the input data on the eigenfaces orthonormal basis")
t0 = time()
X_train_pca = dl.code.T
X_test_pca = dl.sparse_encode(X_test, eigenfaces).T
print("done in %0.3fs" % (time() - t0))
###############################################################################
# Train a SVM classification model
