def _evaluate(self, gaussian_process: GaussianProcess,
data_points: np.ndarray) -> np.ndarray:
"""
Evaluates the acquisition function at all the data points
:param gaussian_process:
:param data_points: numpy array of dimension n x m where n is the number of elements to evaluate
and m is the number of variables used to calculate the objective function
:return: a numpy array of shape n x 1 (or a float) representing the estimation of the acquisition function at
each point
"""
# TODO
mean_data_points, std_data_points = gaussian_process.get_gp_mean_std(
data_points.reshape((-1, gaussian_process.array_dataset.shape[1])))
mean = mean_data_points
mean = mean.flatten()
mean_opt = np.min(gaussian_process.array_objective_function_values)
difference = mean_opt - mean
Z = difference / std_data_points
ei = difference * norm.cdf(Z) + std_data_points * norm.pdf(Z)
ei[std_data_points == 0.0] = 0.0
return ei
def _evaluate(self, gaussian_process: GaussianProcess,
data_points: np.ndarray) -> np.ndarray:
"""
Evaluates the acquisition function at all the data points
:param gaussian_process:
:param data_points: numpy array of dimension n x m where n is the number of elements to evaluate
and m is the number of variables used to calculate the objective function
:return: a numpy array of shape n x 1 (or a float) representing the estimation of the acquisition function at
each point
"""
array_objective_function_values = gaussian_process.array_objective_function_values
best_objective_function_value = np.min(array_objective_function_values)
mean_data_points, std_data_points = gaussian_process.get_gp_mean_std(
data_points)
mean_data_points = mean_data_points.reshape((-1, 1))
std_data_points = std_data_points.reshape((-1, 1))
gamma = (best_objective_function_value -
mean_data_points) / std_data_points
return std_data_points * (gamma * norm.cdf(gamma) + norm.pdf(gamma))
