def raw_predict(self, X):
check_is_fitted(self, ['X_', 'y_'])
X = check_array(X)
y = np.dot(X, self.w_)
for idxi in xrange(y.size):
y[idxi] = sigmoid(y[idxi])
return y
def predict(self, X):
check_is_fitted(self, ['X_', 'y_'])
X = check_array(X)
n_samples = X.shape[0]
y_test_predict = np.zeros(n_samples)
for t in xrange(n_samples):
wtx = np.dot(X[t, :], self.w_)
p = sigmoid(wtx)
y_test_predict[t] = 0. if p < 0.5 else 1.
return y_test_predict
def _train(self, X, y, n_samples, _):
iter_idx = np.arange(n_samples)
np.random.shuffle(iter_idx)
for t, data_idx in enumerate(iter_idx):
curr_x = X[data_idx, :]
curr_y = y[data_idx]
wtx = np.dot(curr_x, self.w_)
curr_p = sigmoid(wtx)
log_likelihood = logloss(curr_p, curr_y)
self.train_tracker_.track(log_likelihood)
self._update(curr_y, curr_p, curr_x)
def raw_predict(self, X):
check_is_fitted(self, ['X_', 'y_'])
X = check_array(X)
n_samples = X.shape[0]
y_test_predict = np.zeros(n_samples)
g_sum = np.zeros(self.n_factors)
g_sum_sqr = np.zeros(self.n_factors)
for t in range(n_samples):
y_test_predict[t] = sigmoid(self._predict_with_feedback(X[t, :], g_sum, g_sum_sqr))
return y_test_predict
def raw_predict(self, X):
check_is_fitted(self, ['X_', 'y_'])
X = check_array(X)
n_samples, n_features = X.shape
w = np.zeros(n_features)
for idxi in range(n_features):
w[idxi] = self._get_w(idxi)
y = np.dot(X, w)
for idxi in range(y.size):
y[idxi] = sigmoid(y[idxi])
return y
def _train(self, X, y, n_samples, n_features):
iter_idx = np.arange(n_samples)
np.random.shuffle(iter_idx)
for t, data_idx in enumerate(iter_idx):
curr_x = X[data_idx, :]
curr_y = y[data_idx]
wtx = 0.
curr_w = {}
for idxi in range(n_features):
curr_w[idxi] = self._get_w(idxi)
wtx += (curr_w[idxi] * curr_x[idxi])
curr_p = sigmoid(wtx)
log_likelihood = logloss(curr_p, curr_y)
self.train_tracker_.track(log_likelihood)
self._update(curr_y, curr_p, curr_x, curr_w)
def predict(self, X):
check_is_fitted(self, ['X_', 'y_'])
X = check_array(X)
n_samples, n_features = X.shape
y_test_predict = np.zeros(n_samples)
w = np.zeros(n_features)
for idxi in range(n_features):
w[idxi] = self._get_w(idxi)
# print w
for t in range(n_samples):
x = X[t, :]
wtx = np.dot(w, x)
p = sigmoid(wtx)
y_test_predict[t] = 0. if p < 0.5 else 1.
return y_test_predict