diff options
author | Rémi Flamary <remi.flamary@gmail.com> | 2018-05-30 09:07:52 +0200 |
---|---|---|
committer | Rémi Flamary <remi.flamary@gmail.com> | 2018-05-30 09:07:52 +0200 |
commit | 507003fe975c80b069d8527b547f0abc4852d16a (patch) | |
tree | e2e8713d91e506b79858bba2dca32f45265b1626 /ot | |
parent | 06eabe7d6bedbbeedf8dfe55fd1f448806f5ef6b (diff) |
rename functions + deprecated old names
Diffstat (limited to 'ot')
-rw-r--r-- | ot/datasets.py | 20 |
1 files changed, 16 insertions, 4 deletions
diff --git a/ot/datasets.py b/ot/datasets.py index 79fc290..bbb77fb 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -9,7 +9,7 @@ Simple example datasets for OT import numpy as np import scipy as sp -from .utils import check_random_state +from .utils import check_random_state, deprecated def get_1D_gauss(n, m, s): @@ -37,14 +37,14 @@ def get_1D_gauss(n, m, s): return h / h.sum() -def get_2D_samples_gauss(n, m, sigma, random_state=None): +def make_2D_samples_gauss(n, m, sigma, random_state=None): """return n samples drawn from 2D gaussian N(m,sigma) Parameters ---------- n : int - number of bins in the histogram + number of samples to make m : np.array (2,) mean value of the gaussian distribution sigma : np.array (2,2) @@ -73,7 +73,13 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None): return res -def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): +@deprecated() +def get_2D_samples_gauss(n, m, sigma, random_state=None): + """ Deprecated see make_2D_samples_gauss """ + return make_2D_samples_gauss(n, m, sigma, random_state=None) + + +def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): """ dataset generation for classification problems Parameters @@ -152,3 +158,9 @@ def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): print("unknown dataset") return x, y.astype(int) + + +@deprecated() +def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): + """ Deprecated see make_data_classif """ + return make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs) |