eb4ffb6cdaf6f04f978fc57e32c95c8b4a33bcb6,examples/federated_learning_with_encryption.py,Client,fit,#Client#,117

Before Change

        //
        //     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

After Change

        Linear regression for n_iter. Reset the weights.

        length, dim = X.shape
        self.weights = np.zeros(dim)

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

Instances