priors.py 文件源码

def __init__(self, hyperparameter, param_name, data, oob_strategy='resample', bandwith=0.4):
        if oob_strategy not in ['resample', 'round', 'ignore']:
            raise ValueError()
        self.oob_strategy = oob_strategy
        self.param_name = param_name
        self.hyperparameter = hyperparameter
        reshaped = np.reshape(data, (len(data), 1))

        if self.hyperparameter.log:
            if isinstance(self.hyperparameter, UniformIntegerHyperparameter):
                # self.probabilities = {val: self.distrib.pdf(np.log2(val)) for val in range(self.hyperparameter.lower, self.hyperparameter.upper)}
                raise ValueError('Log Integer hyperparameter not supported: %s' %param_name)
            # self.distrib = gaussian_kde(np.log2(data))
            # self.distrib = KernelDensity(kernel='gaussian').fit(np.log2(np.reshape(data, (len(data), 1))))
            self.distrib = KernelDensity(kernel='gaussian', bandwidth=bandwith).fit(np.log2(reshaped))
        else:
            # self.distrib = gaussian_kde(data)
            self.distrib = KernelDensity(kernel='gaussian', bandwidth=bandwith).fit(reshaped)
        pass