|
9 | 9 |
|
10 | 10 | import numpy as np |
11 | 11 | import scipy as sp |
12 | | -from .utils import check_random_state |
| 12 | +from .utils import check_random_state, deprecated |
13 | 13 |
|
14 | 14 |
|
15 | 15 | def get_1D_gauss(n, m, s): |
@@ -37,14 +37,14 @@ def get_1D_gauss(n, m, s): |
37 | 37 | return h / h.sum() |
38 | 38 |
|
39 | 39 |
|
40 | | -def get_2D_samples_gauss(n, m, sigma, random_state=None): |
| 40 | +def make_2D_samples_gauss(n, m, sigma, random_state=None): |
41 | 41 | """return n samples drawn from 2D gaussian N(m,sigma) |
42 | 42 |
|
43 | 43 | Parameters |
44 | 44 | ---------- |
45 | 45 |
|
46 | 46 | n : int |
47 | | - number of bins in the histogram |
| 47 | + number of samples to make |
48 | 48 | m : np.array (2,) |
49 | 49 | mean value of the gaussian distribution |
50 | 50 | sigma : np.array (2,2) |
@@ -73,7 +73,13 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None): |
73 | 73 | return res |
74 | 74 |
|
75 | 75 |
|
76 | | -def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): |
| 76 | +@deprecated() |
| 77 | +def get_2D_samples_gauss(n, m, sigma, random_state=None): |
| 78 | + """ Deprecated see make_2D_samples_gauss """ |
| 79 | + return make_2D_samples_gauss(n, m, sigma, random_state=None) |
| 80 | + |
| 81 | + |
| 82 | +def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): |
77 | 83 | """ dataset generation for classification problems |
78 | 84 |
|
79 | 85 | Parameters |
@@ -152,3 +158,9 @@ def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): |
152 | 158 | print("unknown dataset") |
153 | 159 |
|
154 | 160 | return x, y.astype(int) |
| 161 | + |
| 162 | + |
| 163 | +@deprecated() |
| 164 | +def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): |
| 165 | + """ Deprecated see make_data_classif """ |
| 166 | + return make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs) |
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