Fix random_integers deprecation

scipy · Sep 24, 2019 · e246967 · e246967
1 parent 51c52f8
commit e246967
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Showing 2 changed files with 19 additions and 14 deletions.
diff --git a/scipy/stats/qmc.py b/scipy/stats/qmc.py
@@ -4,14 +4,15 @@
 
 """
 
-
 from __future__ import division
 
 import copy
 import numpy as np
 from scipy.optimize import brute
+
 try:
     from scipy.optimize import basinhopping
+
     have_basinhopping = True
 except ImportError:
     have_basinhopping = False
@@ -109,10 +110,10 @@ def update_discrepancy(x_new, sample, initial_disc, bounds=None):
     n_samples = len(sample) + 1
     abs_ = abs(x_new - 0.5)
 
-    disc1 = - 2 / n_samples * np.prod(1 + 1/2 * abs_ - 1/2 * abs_ ** 2)
-    disc2 = 2 / (n_samples ** 2) * np.sum(np.prod(1 + 1/2 * abs_ +
-                                                  1/2 * abs(sample - 0.5) -
-                                                  1/2 * abs(x_new - sample),
+    disc1 = - 2 / n_samples * np.prod(1 + 1 / 2 * abs_ - 1 / 2 * abs_ ** 2)
+    disc2 = 2 / (n_samples ** 2) * np.sum(np.prod(1 + 1 / 2 * abs_ +
+                                                  1 / 2 * abs(sample - 0.5) -
+                                                  1 / 2 * abs(x_new - sample),
                                                   axis=1))
     disc3 = 1 / (n_samples ** 2) * np.prod(1 + abs_)
 
@@ -167,10 +168,14 @@ def perturb_discrepancy(sample, i1, i2, k, disc, bounds=None):
     z_ij = sample - 0.5
 
     # Eq (19)
-    c_i1j = 1. / n_samples ** 2. * np.prod(0.5 * (2. + abs(z_ij[i1, :]) + \
-        abs(z_ij) - abs(z_ij[i1, :] - z_ij)), axis=1)
-    c_i2j = 1. / n_samples ** 2. * np.prod(0.5 * (2. + abs(z_ij[i2, :]) + \
-        abs(z_ij) - abs(z_ij[i2, :] - z_ij)), axis=1)
+    c_i1j = 1. / n_samples ** 2. * np.prod(0.5 * (2. + abs(z_ij[i1, :]) +
+                                                  abs(z_ij) -
+                                                  abs(z_ij[i1, :] - z_ij)),
+                                           axis=1)
+    c_i2j = 1. / n_samples ** 2. * np.prod(0.5 * (2. + abs(z_ij[i2, :]) +
+                                                  abs(z_ij) -
+                                                  abs(z_ij[i2, :] - z_ij)),
+                                           axis=1)
 
     # Eq (20)
     c_i1i1 = (1. / n_samples ** 2 * np.prod(1 + abs(z_ij[i1, :])) -
@@ -470,7 +475,7 @@ def latin_hypercube(dim, n_samples, bounds=None, centered=False):
     else:
         r = np.random.random_sample((n_samples, dim))
 
-    q = np.random.random_integers(low=1, high=n_samples, size=(n_samples, dim))
+    q = np.random.randint(low=1, high=n_samples, size=(n_samples, dim))
 
     sample = 1. / n_samples * (q - r)
 

diff --git a/scipy/stats/tests/test_qmc.py b/scipy/stats/tests/test_qmc.py
@@ -82,13 +82,13 @@ def test_lhs(self):
         corners = np.array([[0, 2], [10, 5]])
 
         sample = qmc.latin_hypercube(dim=2, n_samples=5, bounds=corners)
-        out = np.array([[5.746, 3.219], [5.479, 3.261], [9.246, 4.798],
-                        [9.097, 4.495], [9.753, 4.074]])
+        out = np.array([[5.7, 3.2], [5.5, 3.9], [5.2, 3.6],
+                        [5.1, 3.3], [5.8, 4.1]])
         npt.assert_almost_equal(sample, out, decimal=1)
 
         sample = qmc.latin_hypercube(dim=2, n_samples=5, centered=True)
-        out = np.array([[0.3, 0.9], [0.7, 0.7], [0.1, 0.9],
-                        [0.5, 0.5], [0.1, 0.7]])
+        out = np.array([[0.1, 0.5], [0.3, 0.1], [0.7, 0.1],
+                        [0.1, 0.1], [0.3, 0.7]])
         npt.assert_almost_equal(sample, out, decimal=1)
 
     def test_orthogonal_lhs(self):