a8eabdad14eee8a47257248fa271700fcce939cb,autokeras/bayesian.py,IncrementalGaussianProcess,predict,#IncrementalGaussianProcess#Any#,101

Before Change

    def predict(self, train_x):
        k_trans = self.kernel(train_x, self._x)
        y_mean = k_trans.dot(self._alpha_vector)  // Line 4 (y_mean = f_star)
        return y_mean


def kernel(X, Y=None):
    if Y is None:

After Change

        // Check if any of the variances is negative because of
        // numerical issues. If yes: set the variance to 0.
        y_var_negative = y_var < 0
        if np.any(y_var_negative):
            warnings.warn("Predicted variances smaller than 0. "
                          "Setting those variances to 0.")
            y_var[y_var_negative] = 0.0

Instances