103e31b4a2518797606d3b93440740df0532770d,examples/federated_learning_with_encryption.py,Client,fit,#Client#Any#Any#,124

Before Change

        length = self.X.shape[0]

        for _ in range(n_iter):
            for i in range(length):
                delta = self.predict(self.X[i, :]) - self.y[i]
                for j in range(self.dim):
                    self.weights[j] -= eta * delta * self.X[i, j]

            // print("Error %.4f" % mean_square_error(self.predict(X), y))

        // self.weights = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)
        // print(self.weights)

        return self

    def gradient_step(self, gradient, eta=0.01):
        Update the model with the given gradient

After Change

        Linear regression for n_iter

        for _ in range(n_iter):
            gradient = self.compute_gradient()
            self.gradient_step(gradient, eta)

    def gradient_step(self, gradient, eta=0.01):
        Update the model with the given gradient

Instances