

def evaluate(self, x):
    x_transform = np.array([np.dot(self.A, e) for e in self])
    x_projected = np.fmax(self.original_scaled_X_lower, np.fmin(self.original_scaled_X_upper, x_transform))
    x_rescaled = ((((self.original_X_upper - self.original_X_lower) * (x_projected - self.original_scaled_X_lower)) / (self.original_scaled_X_upper - self.original_scaled_X_lower)) + self.original_X_lower)
    return self.objective_function(x_rescaled)

dataset/ETHPy150Open automl/RoBO/robo/task/rembo.py REMBO.evaluate/VarMisuse@32/35 `x`->`self`