def fit(self, X, Y):
self.class_labels = np.array(sorted(set(Y)))
D = KMeans(n_clusters=self.n_atoms, init_size=self.n_atoms*3)
D.fit(np.vstack(X)[:200000])
D = D.cluster_centers_
D = normalize(D)
self.D_mean = np.mean(D, axis=0)
self.D = D - self.D_mean
self.D_idxs = []
self.clfs = []
for i in xrange(self.n_nodes):
idxs = np.random.permutation(self.n_atoms)[:self.n_features]
idxs = np.random.permutation(len(X[0][0]))[:self.n_features]
enc_X = []
for x in X:
x = normalize(x)
x = x - self.D_mean
enc_X.append(bow(np.argmax(np.abs(np.dot(self.D[:, idxs], x.T[idxs, :])), axis=0), self.n_atoms))
clf = LinearSVC()
clf.fit(enc_X, Y)
self.clfs.append(clf)
self.D_idxs.append(idxs)
def predict(self, X):
predictions = []
for idxs, clf in zip(self.D_idxs, self.clfs):
enc_X = []
for x in X:
x = normalize(x)
x = x - self.D_mean
enc_X.append(bow(np.argmax(np.abs(np.dot(self.D[:, idxs], x.T[idxs, :])), axis=0), self.n_atoms))
predictions.append(clf.decision_function(enc_X))
predictions = np.array(predictions)
return self.class_labels[np.argmax(np.sum(predictions, axis=0), axis=1)]
run_alpha = alphas
run_beta = betas
else:
run_alpha = alphas[:5]
run_beta = betas[:5]
sample_dims = np.linspace(0, ws-1, int(subsample_pct*ws)).astype(int)
# sample_dims = np.random.permutation(ws)[:int(subsample_pct*ws)]
X_window = [x[:, sample_dims] for x in X]
X_Kmeans = np.vstack(X_window)[:KMeans_tr_size]
num_X = len(np.vstack(X_window))
D = KMeans(n_clusters=D_atoms, init_size=D_atoms*3)
D.fit(X_Kmeans)
D = D.cluster_centers_
D = normalize(D)
D_mean = np.mean(D, axis=0)
D = D - D_mean
plt_ops, plt_accs = accuracy_test(names, num_l1_node, alphas, betas,
dict_partition, dim_partition,
X_window, Y, D, D_mean, sss, model,
num_l2_node)
# plt_ops, plt_accs = candidate_test(names, num_nodes, alphas, betas,
# dict_partition, dim_partition, X_window,
# D, D_mean)
plt_dict[name] = (plt_ops, plt_accs)
