77550cc1f3ccf9537265d127b0f25aa790d2b4b5,skopt/acquisition.py,,gaussian_pi,#Any#Any#Any#Any#Any#,149

Before Change

            mu, std = model.predict(X, return_std=True)

    // make sure mu and std have the same shape so we can divide them below
    std = std.reshape(mu.shape)

    values = np.zeros_like(mu)
    mask = std > 0
    improve = y_opt - xi - mu[mask]

After Change

            mu, std = model.predict(X, return_std=True)

    // check dimensionality of mu, std so we can divide them below
    if (mu.ndim != 1) or (std.ndim != 1):
        raise ValueError("mu and std are {}-dimensional and {}-dimensional, "
                         "however both must be 1-dimensional. Did you train "
                         "your model with an (N, 1) vector instead of an "
                         "(N,) vector?"
                         .format(mu.ndim, std.ndim))

    values = np.zeros_like(mu)
    mask = std > 0
    improve = y_opt - xi - mu[mask]
    scaled = improve / std[mask]

Instances