9e34b315b47fcbc1c198fe815cbf3651caee6978,mlxtend/classifier/logistic_regression.py,LogisticRegression,fit,#LogisticRegression#Any#Any#Any#,77

Before Change

            if self.shuffle:
                X, y = self._shuffle(X, y)

            if self.learning == "gd":
                y_val = self.activation(X)
                errors = (y - y_val)
                neg_grad = X.T.dot(errors)
                l2_reg = self.l2_lambda * self.w_[1:]
                self.w_[1:] += self.eta * (neg_grad - l2_reg)
                self.w_[0] += self.eta * errors.sum()

            elif self.learning == "sgd":
                for xi, yi in zip(X, y):
                    yi_val = self.activation(xi)
                    error = (yi - yi_val)
                    neg_grad = xi.dot(error)
                    l2_reg = self.l2_lambda * self.w_[1:]
                    self.w_[1:] += self.eta * (neg_grad - l2_reg)
                    self.w_[0] += self.eta * error

            cost = self._logit_cost(y, self.activation(X))
            self.cost_.append(cost)
            if self.print_progress:
                self._print_progress(epoch=i+1, cost=cost)

After Change

            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])
                errors = (y[idx] - y_val)
                neg_grad = X[idx].T.dot(errors)

Instances