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authorRémi Flamary <remi.flamary@gmail.com>2018-05-09 13:12:06 +0200
committerRémi Flamary <remi.flamary@gmail.com>2018-05-09 13:12:06 +0200
commit0496e2b1b2c2f4ea2d7f313ccf58c612efaa70bf (patch)
tree9466b6ce84e1bccf7c95bbe030683e43c4a8851a /ot/da.py
parent0a9763ce0e83106daa322566398218aa4a297fe1 (diff)
doc typos in linear map function
Diffstat (limited to 'ot/da.py')
-rw-r--r--ot/da.py10
1 files changed, 5 insertions, 5 deletions
diff --git a/ot/da.py b/ot/da.py
index cdebc91..b83d67e 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -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].