information_process.py 文件源码

def calc_by_sampling_neurons(ws_iter_index, num_of_samples, label, sigma, bins, pxs):
    iter_infomration = []
    for j in range(len(ws_iter_index)):
        data = ws_iter_index[j]
        new_data = np.zeros((num_of_samples * data.shape[0], data.shape[1]))
        labels = np.zeros((num_of_samples * label.shape[0], label.shape[1]))
        x = np.zeros((num_of_samples * data.shape[0], 2))
        for i in range(data.shape[0]):
            cov_matrix = np.eye(data[i, :].shape[0]) * sigma
            t_i = np.random.multivariate_normal(data[i, :], cov_matrix, num_of_samples)
            new_data[num_of_samples * i:(num_of_samples * (i + 1)), :] = t_i
            labels[num_of_samples * i:(num_of_samples * (i + 1)), :] = label[i, :]
            x[num_of_samples * i:(num_of_samples * (i + 1)), 0] = i
        b = np.ascontiguousarray(x).view(np.dtype((np.void, x.dtype.itemsize * x.shape[1])))
        unique_array, unique_indices, unique_inverse_x, unique_counts = \
            np.unique(b, return_index=True, return_inverse=True, return_counts=True)
        b_y = np.ascontiguousarray(labels).view(np.dtype((np.void, labels.dtype.itemsize * labels.shape[1])))
        unique_array_y, unique_indices_y, unique_inverse_y, unique_counts_y = \
            np.unique(b_y, return_index=True, return_inverse=True, return_counts=True)
        pys1 = unique_counts_y / float(np.sum(unique_counts_y))
        iter_infomration.append(
            calc_information_for_layer(data=new_data, bins=bins, unique_inverse_x=unique_inverse_x,
                                       unique_inverse_y=unique_inverse_y, pxs=pxs, pys1=pys1))
        params = np.array(iter_infomration)
        return params