def fit(self, train_x, train_y, max_epochs=-1, target_acc=None, verbose=0):
"""
:param train_x: train set features
:param train_y: train set labels
:param max_epochs: maximum number of epoches
:param target_acc: if max_epoch is not given, use this stopping criterion
:param verbose: 0 - print logs (e.g. losses and accuracy)
1 - don't print logs
"""
epoch = 1
# Adding another dimension for the bias term
mapped_train_y = self.mapper(train_y)
scores_train = []
weights = []
while True:
training_order = np.random.permutation(range(train_x.shape[0]))
changed = False
for i_sample in training_order:
x_, y_true = train_x[i_sample].copy().reshape(-1, 1), mapped_train_y[i_sample]
y_pred = self.predict(x_)
if y_pred != y_true:
changed = True
self.w += self.lr * y_true * Perceptron.concat_ones(x_)
if not changed:
break
# score_train = self.print_training_log(verbose, train_x, mapped_train_y, epoch)
# scores_train.append(score_train)
epoch += 1
weights.append(self.w.copy())
if max_epochs != -1 and epoch >= max_epochs:
break
# elif max_epochs == -1 and score_train >= target_acc:
# break
return scores_train, weights
def score(self, x, y):
x = Perceptron.concat_ones(x)
preds_val = np.array([self.predict(x[i].reshape(-1, 1)) for i in range(x.shape[0])])
preds_val = self.classes[preds_val]
return np.mean(preds_val == y)
