diff options
-rw-r--r-- | ot/da.py | 10 |
1 files changed, 5 insertions, 5 deletions
@@ -640,9 +640,9 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, wt=None, bias=True, log=False): """ return OT linear operator between samples - The function estimate the optimal linear operator that align the two + The function estimates the optimal linear operator that aligns the two empirical distributions. This is equivalent to estimating the closed - form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` + form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)` and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark 2.29 in [15]. The linear operator from source to target :math:`M` @@ -665,7 +665,7 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, xt : np.ndarray (nt,d) samples in the target domain reg : float,optional - regularization added to the daigonals of convariances (>0) + regularization added to the diagonals of convariances (>0) ws : np.ndarray (ns,1), optional weights for the source samples wt : np.ndarray (ns,1), optional @@ -1290,9 +1290,9 @@ class BaseTransport(BaseEstimator): class LinearTransport(BaseTransport): """ OT linear operator between empirical distributions - The function estimate the optimal linear operator that align the two + The function estimates the optimal linear operator that aligns the two empirical distributions. This is equivalent to estimating the closed - form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` + form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)` and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark 2.29 in [15]. |