self.init_time_ = time()
for i in range(self.epochs):
if self.minibatches > 1:
X, y = self._shuffle(X, y)
minis = np.array_split(n_idx, self.minibatches)
for idx in minis:
y_val = self._activation(X[idx])
After Change
self.cost_ = []
// random seed for shuffling
if self.random_seed:
np.random.seed(self.random_seed)
if self.minibatches is None:
self.w_ = self._normal_equation(X, y)
// Gradient descent or stochastic gradient descent learning
else:
n_idx = list(range(y.shape[0]))
// skip shuffling if gradient descent
if self.minibatches > 1:
n_idx = np.random.permutation(n_idx)
self.init_time_ = time()
for i in range(self.epochs):
minis = np.array_split(n_idx, self.minibatches)
for idx in minis:
