INNER CODE UNIT · Python

errors

rasbt/machine-learning-book · ch02/ch02.py:367

            errors = (y - output)
            
            #for w_j in range(self.w_.shape[0]):
            #    self.w_[w_j] += self.eta * (2.0 * (X[:, w_j]*errors)).mean()
            
            self.w_ += self.eta * 2.0 * X.T.dot(errors) / X.shape[0]
            self.b_ += self.eta * 2.0 * errors.mean()
            loss = (errors**2).mean()
            self.losses_.append(loss)
        return self

    def net_input(self, X):
        """Calculate net input"""
        return np.dot(X, self.w_) + self.b_

    def activation(self, X):
        """Compute linear activation"""
        return X

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