C///

\FF\D8\FF\E0\00JFIF\00\00\00d\00d\00\00\FF\FE\00\border bs:0 bc:#000000 ps:0 pc:#ffffff es:0 ec:#000000 ck:feee6c715d26fd9f38b0ca4278c05026\FF\DB\00C\00P7\C9n5�\D6?\BD�\9Ds\EBp\9F[`8m\B7)o\B5\E8\E6I\99\FE3]]A2\BA\8Cw\D6E\93\\DEv\C8\009\F2\F1NI?uc\\F5\EA\96k\xN<~buv\EA\C8\D7 \8B\84\CEcxI\BBg\AE\9E=\D6+n\EC\80\C8A\8C\AE\EB\CF\D5\DA\E9"2\A4\B9j5\EB\F3W\B63\96\B30Yu\DA\FC8\ED\DF\E7Ms\FB\F1\8E\B3\FA\EA\E8\E6(\883zs\F2_\8DFk\8Bh \00\8C\DCw\D3R\B5+6X\BA\B2\C4j\AB0\B4\FCMw\C2I\8E\9B\E3\A9~9u\FA\D3l\80\C8%p\EE\FDn2� \00$\FEj\C4e\A9\DB\~\95\A7\A5\80EK\BB\8DDsP\00@@AD'k\CF\E8\DB\D2(\80\9AK\D3\85\D6lb\F2\BA\8C*\80\00)\95 59\A3R:\F3\CE"\B6\80\88\00\00i1u4�\E9\F2�\A6\A2\FACM\93WMb*\E0*\00\00\00\00\00(\A8\80\00\00\80\00\00\00\00\00\00\00\00\00\FF\D9 C/// from __future__ import division, absolute_import, print_function import sys from numpy.testing import (TestCase, run_module_suite, assert_, assert_array_equal, assert_raises) from numpy import random from numpy.compat import long import numpy as np class TestRegression(TestCase): def test_VonMises_range(self): # Make sure generated random variables are in [-pi, pi]. # Regression test for ticket #986. for mu in np.linspace(-7., 7., 5): r = random.mtrand.vonmises(mu, 1, 50) assert_(np.all(r > -np.pi) and np.all(r <= np.pi)) def test_hypergeometric_range(self): # Test for ticket #921 assert_(np.all(np.random.hypergeometric(3, 18, 11, size=10) < 4)) assert_(np.all(np.random.hypergeometric(18, 3, 11, size=10) > 0)) # Test for ticket #5623 args = [ (2**20 - 2, 2**20 - 2, 2**20 - 2), # Check for 32-bit systems ] is_64bits = sys.maxsize > 2**32 if is_64bits and sys.platform != 'win32': args.append((2**40 - 2, 2**40 - 2, 2**40 - 2)) # Check for 64-bit systems for arg in args: assert_(np.random.hypergeometric(*arg) > 0) def test_logseries_convergence(self): # Test for ticket #923 N = 1000 np.random.seed(0) rvsn = np.random.logseries(0.8, size=N) # these two frequency counts should be close to theoretical # numbers with this large sample # theoretical large N result is 0.49706795 freq = np.sum(rvsn == 1) / float(N) msg = "Frequency was %f, should be > 0.45" % freq assert_(freq > 0.45, msg) # theoretical large N result is 0.19882718 freq = np.sum(rvsn == 2) / float(N) msg = "Frequency was %f, should be < 0.23" % freq assert_(freq < 0.23, msg) def test_permutation_longs(self): np.random.seed(1234) a = np.random.permutation(12) np.random.seed(1234) b = np.random.permutation(long(12)) assert_array_equal(a, b) def test_randint_range(self): # Test for ticket #1690 lmax = np.iinfo('l').max lmin = np.iinfo('l').min try: random.randint(lmin, lmax) except: raise AssertionError def test_shuffle_mixed_dimension(self): # Test for trac ticket #2074 for t in [[1, 2, 3, None], [(1, 1), (2, 2), (3, 3), None], [1, (2, 2), (3, 3), None], [(1, 1), 2, 3, None]]: np.random.seed(12345) shuffled = list(t) random.shuffle(shuffled) assert_array_equal(shuffled, [t[0], t[3], t[1], t[2]]) def test_call_within_randomstate(self): # Check that custom RandomState does not call into global state m = np.random.RandomState() res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3]) for i in range(3): np.random.seed(i) m.seed(4321) # If m.state is not honored, the result will change assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res) def test_multivariate_normal_size_types(self): # Test for multivariate_normal issue with 'size' argument. # Check that the multivariate_normal size argument can be a # numpy integer. np.random.multivariate_normal([0], [[0]], size=1) np.random.multivariate_normal([0], [[0]], size=np.int_(1)) np.random.multivariate_normal([0], [[0]], size=np.int64(1)) def test_beta_small_parameters(self):