gmm.py 文件源码

def __init__(self, gmm, swap=False, diff=False):
        assert gmm.covariance_type == "full"
        # D: static + delta dim
        D = gmm.means_.shape[1] // 2
        self.num_mixtures = gmm.means_.shape[0]
        self.weights = gmm.weights_

        # Split source and target parameters from joint GMM
        self.src_means = gmm.means_[:, :D]
        self.tgt_means = gmm.means_[:, D:]
        self.covarXX = gmm.covariances_[:, :D, :D]
        self.covarXY = gmm.covariances_[:, :D, D:]
        self.covarYX = gmm.covariances_[:, D:, :D]
        self.covarYY = gmm.covariances_[:, D:, D:]

        if diff:
            self.tgt_means = self.tgt_means - self.src_means
            self.covarYY = self.covarXX + self.covarYY - self.covarXY - self.covarYX
            self.covarXY = self.covarXY - self.covarXX
            self.covarYX = self.covarXY.transpose(0, 2, 1)

        # swap src and target parameters
        if swap:
            self.tgt_means, self.src_means = self.src_means, self.tgt_means
            self.covarYY, self.covarXX = self.covarXX, self.covarYY
            self.covarYX, self.covarXY = self.covarXY, self.covarYX

        # p(x), which is used to compute posterior prob. for a given source
        # spectral feature in mapping stage.
        self.px = GaussianMixture(
            n_components=self.num_mixtures, covariance_type="full")
        self.px.means_ = self.src_means
        self.px.covariances_ = self.covarXX
        self.px.weights_ = self.weights
        self.px.precisions_cholesky_ = _compute_precision_cholesky(
            self.px.covariances_, "full")