def get_tr_kernel(self, sstats_list):
self.N_tr = sstats_list[0].reshape((-1, self.D)).shape[0]
# Initialise train kernel.
tr_kernel = np.zeros((self.N_tr, self.N_tr))
# Initialise normalization constants.
self.Zx = np.zeros(self.N_tr)
for ii, sstats in enumerate(sstats_list):
self._append_data(
*standardize(FVModel.sstats_to_features(sstats, self.gmm)))
self.xx[ii] = power_normalize(self.xx[ii], 0.5)
self.Zx += compute_L2_normalization(self.xx[ii])
tr_kernel += np.dot(self.xx[ii], self.xx[ii].T)
# Normalize kernel.
tr_kernel /= np.sqrt(
self.Zx[:, np.newaxis] * self.Zx[np.newaxis])
return tr_kernel
def get_te_kernel(self, sstats_list):
self.N_te = sstats_list[0].reshape((-1, self.D)).shape[0]
# Initialise train kernel.
te_kernel = np.zeros((self.N_te, self.N_tr))
# Initialise normalization constants.
self.Zy = np.zeros(self.N_te)
for ii, sstats in enumerate(sstats_list):
yy = standardize(
FVModel.sstats_to_features(sstats, self.gmm), self.mu[ii],
self.sigma[ii])[0]
yy = power_normalize(yy, 0.5)
self.Zy += compute_L2_normalization(yy)
te_kernel += np.dot(yy, self.xx[ii].T)
# Normalize kernel.
te_kernel /= np.sqrt(
self.Zy[:, np.newaxis] * self.Zx[np.newaxis])
return te_kernel
