n to n_samples

test/test_da.py

@@ -3,18 +3,15 @@
 import ot
 
 
-# import pytest
-
-
 def test_otda():
 
- n = 150 # nb samples
+ n_samples = 150 # nb samples
  np.random.seed(0)
 
- xs, ys = ot.datasets.get_data_classif('3gauss', n)
- xt, yt = ot.datasets.get_data_classif('3gauss2', n)
+ xs, ys = ot.datasets.get_data_classif('3gauss', n_samples)
+ xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples)
 
- a, b = ot.unif(n), ot.unif(n)
+ a, b = ot.unif(n_samples), ot.unif(n_samples)
 
  # LP problem
  da_emd = ot.da.OTDA() # init class

test/test_dr.py

@@ -13,15 +13,15 @@
 @pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)")
 def test_fda():
 
- n = 90 # nb samples in source and target datasets
+ n_samples = 90 # nb samples in source and target datasets
  np.random.seed(0)
 
- # generate circle dataset
- xs, ys = ot.datasets.get_data_classif('gaussrot', n)
+ # generate gaussian dataset
+ xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples)
 
- nbnoise = 8
+ n_features_noise = 8
 
- xs = np.hstack((xs, np.random.randn(n, nbnoise)))
+ xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise)))
 
  p = 1
 
@@ -35,20 +35,15 @@ def test_fda():
 @pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)")
 def test_wda():
 
- n = 100 # nb samples in source and target datasets
- nz = 0.2
+ n_samples = 100 # nb samples in source and target datasets
  np.random.seed(0)
 
- # generate circle dataset
- t = np.random.rand(n) * 2 * np.pi
- ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
- xs = np.concatenate(
- (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)
- xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)
+ # generate gaussian dataset
+ xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples)
 
- nbnoise = 8
+ n_features_noise = 8
 
- xs = np.hstack((xs, np.random.randn(n, nbnoise)))
+ xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise)))
 
  p = 2