def learn(X, Y):
pca = None
dictionary = None
model = None
# Data augmentation
if DO_DATA_AUGMENTATION:
print("Augmenting data")
X, Y = transform_T(X, Y)
print("Number of samples augmented to {}".format(X.shape[0]))
# Dictionary learning
if DO_DICTIONARY_LEARNING:
dictionary = Dictionary(n_atoms=128, atom_width=16)
if dictionary.weights_available:
print("Loading dictionary")
dictionary.load()
else:
print("Learning dictionary")
tic = time.time()
dictionary.fit(X)
dictionary.save()
print("Dictionary learned in {0:.1f}s".format(time.time() - tic))
print("Getting dictionary representation")
X = dictionary.get_representation(X)
# PCA
if DO_PCA:
tic = time.time()
print("Applying PCA")
n_components = 100
pca = PCA(n_components=n_components)
X = pca.fit(X, scale=False)
print("Variance explained: {:.2f}".format(np.sum(pca.e_values_ratio_)))
print("PCA applied in {0:.1f}s".format(time.time() - tic))
# Training
print("Starting training")
tic = time.time()
model = KernelSVM(C=1, kernel='rbf')
model.train(X, Y)
print("Model trained in {0:.1f}s".format(time.time() - tic))
return pca, dictionary, model
from src.ovr import OVR
SHAPE = (46, 56)
M = 121
standard = False
data = fetch_data(ratio=0.8)
X_train, y_train = data['train']
D, N = X_train.shape
pca = PCA(n_comps=M, standard=standard)
W_train = pca.fit(X_train)
X_test, y_test = data['test']
I, K = X_test.shape
W_test = pca.transform(X_test)
params = {'C': 1, 'gamma': 2e-4, 'kernel': 'linear'}
ovr = OVR(**params)
ovr.fit(W_train, y_train)
y_hat = ovr.predict(W_test[::-1]).ravel()
done = {'success': False, 'failure': False}