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projecten1/lib/python3.6/site-packages/numpy/random/__init__.py

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+"""
+========================
+Random Number Generation
+========================
+
+==================== =========================================================
+Utility functions
+==============================================================================
+random               Uniformly distributed values of a given shape.
+bytes                Uniformly distributed random bytes.
+random_integers      Uniformly distributed integers in a given range.
+random_sample        Uniformly distributed floats in a given range.
+random               Alias for random_sample
+ranf                 Alias for random_sample
+sample               Alias for random_sample
+choice               Generate a weighted random sample from a given array-like
+permutation          Randomly permute a sequence / generate a random sequence.
+shuffle              Randomly permute a sequence in place.
+seed                 Seed the random number generator.
+==================== =========================================================
+
+==================== =========================================================
+Compatibility functions
+==============================================================================
+rand                 Uniformly distributed values.
+randn                Normally distributed values.
+ranf                 Uniformly distributed floating point numbers.
+randint              Uniformly distributed integers in a given range.
+==================== =========================================================
+
+==================== =========================================================
+Univariate distributions
+==============================================================================
+beta                 Beta distribution over ``[0, 1]``.
+binomial             Binomial distribution.
+chisquare            :math:`\\chi^2` distribution.
+exponential          Exponential distribution.
+f                    F (Fisher-Snedecor) distribution.
+gamma                Gamma distribution.
+geometric            Geometric distribution.
+gumbel               Gumbel distribution.
+hypergeometric       Hypergeometric distribution.
+laplace              Laplace distribution.
+logistic             Logistic distribution.
+lognormal            Log-normal distribution.
+logseries            Logarithmic series distribution.
+negative_binomial    Negative binomial distribution.
+noncentral_chisquare Non-central chi-square distribution.
+noncentral_f         Non-central F distribution.
+normal               Normal / Gaussian distribution.
+pareto               Pareto distribution.
+poisson              Poisson distribution.
+power                Power distribution.
+rayleigh             Rayleigh distribution.
+triangular           Triangular distribution.
+uniform              Uniform distribution.
+vonmises             Von Mises circular distribution.
+wald                 Wald (inverse Gaussian) distribution.
+weibull              Weibull distribution.
+zipf                 Zipf's distribution over ranked data.
+==================== =========================================================
+
+==================== =========================================================
+Multivariate distributions
+==============================================================================
+dirichlet            Multivariate generalization of Beta distribution.
+multinomial          Multivariate generalization of the binomial distribution.
+multivariate_normal  Multivariate generalization of the normal distribution.
+==================== =========================================================
+
+==================== =========================================================
+Standard distributions
+==============================================================================
+standard_cauchy      Standard Cauchy-Lorentz distribution.
+standard_exponential Standard exponential distribution.
+standard_gamma       Standard Gamma distribution.
+standard_normal      Standard normal distribution.
+standard_t           Standard Student's t-distribution.
+==================== =========================================================
+
+==================== =========================================================
+Internal functions
+==============================================================================
+get_state            Get tuple representing internal state of generator.
+set_state            Set state of generator.
+==================== =========================================================
+
+"""
+from __future__ import division, absolute_import, print_function
+
+import warnings
+
+# To get sub-modules
+from .info import __doc__, __all__
+
+
+with warnings.catch_warnings():
+    warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
+    from .mtrand import *
+
+# Some aliases:
+ranf = random = sample = random_sample
+__all__.extend(['ranf', 'random', 'sample'])
+
+def __RandomState_ctor():
+    """Return a RandomState instance.
+
+    This function exists solely to assist (un)pickling.
+
+    Note that the state of the RandomState returned here is irrelevant, as this function's
+    entire purpose is to return a newly allocated RandomState whose state pickle can set.
+    Consequently the RandomState returned by this function is a freshly allocated copy
+    with a seed=0.
+
+    See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
+
+    """
+    return RandomState(seed=0)
+
+from numpy.testing import _numpy_tester
+test = _numpy_tester().test
+bench = _numpy_tester().bench

projecten1/lib/python3.6/site-packages/numpy/random/info.py

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+"""
+========================
+Random Number Generation
+========================
+
+==================== =========================================================
+Utility functions
+==============================================================================
+random_sample        Uniformly distributed floats over ``[0, 1)``.
+random               Alias for `random_sample`.
+bytes                Uniformly distributed random bytes.
+random_integers      Uniformly distributed integers in a given range.
+permutation          Randomly permute a sequence / generate a random sequence.
+shuffle              Randomly permute a sequence in place.
+seed                 Seed the random number generator.
+choice               Random sample from 1-D array.
+
+==================== =========================================================
+
+==================== =========================================================
+Compatibility functions
+==============================================================================
+rand                 Uniformly distributed values.
+randn                Normally distributed values.
+ranf                 Uniformly distributed floating point numbers.
+randint              Uniformly distributed integers in a given range.
+==================== =========================================================
+
+==================== =========================================================
+Univariate distributions
+==============================================================================
+beta                 Beta distribution over ``[0, 1]``.
+binomial             Binomial distribution.
+chisquare            :math:`\\chi^2` distribution.
+exponential          Exponential distribution.
+f                    F (Fisher-Snedecor) distribution.
+gamma                Gamma distribution.
+geometric            Geometric distribution.
+gumbel               Gumbel distribution.
+hypergeometric       Hypergeometric distribution.
+laplace              Laplace distribution.
+logistic             Logistic distribution.
+lognormal            Log-normal distribution.
+logseries            Logarithmic series distribution.
+negative_binomial    Negative binomial distribution.
+noncentral_chisquare Non-central chi-square distribution.
+noncentral_f         Non-central F distribution.
+normal               Normal / Gaussian distribution.
+pareto               Pareto distribution.
+poisson              Poisson distribution.
+power                Power distribution.
+rayleigh             Rayleigh distribution.
+triangular           Triangular distribution.
+uniform              Uniform distribution.
+vonmises             Von Mises circular distribution.
+wald                 Wald (inverse Gaussian) distribution.
+weibull              Weibull distribution.
+zipf                 Zipf's distribution over ranked data.
+==================== =========================================================
+
+==================== =========================================================
+Multivariate distributions
+==============================================================================
+dirichlet            Multivariate generalization of Beta distribution.
+multinomial          Multivariate generalization of the binomial distribution.
+multivariate_normal  Multivariate generalization of the normal distribution.
+==================== =========================================================
+
+==================== =========================================================
+Standard distributions
+==============================================================================
+standard_cauchy      Standard Cauchy-Lorentz distribution.
+standard_exponential Standard exponential distribution.
+standard_gamma       Standard Gamma distribution.
+standard_normal      Standard normal distribution.
+standard_t           Standard Student's t-distribution.
+==================== =========================================================
+
+==================== =========================================================
+Internal functions
+==============================================================================
+get_state            Get tuple representing internal state of generator.
+set_state            Set state of generator.
+==================== =========================================================
+
+"""
+from __future__ import division, absolute_import, print_function
+
+depends = ['core']
+
+__all__ = [
+    'beta',
+    'binomial',
+    'bytes',
+    'chisquare',
+    'choice',
+    'dirichlet',
+    'exponential',
+    'f',
+    'gamma',
+    'geometric',
+    'get_state',
+    'gumbel',
+    'hypergeometric',
+    'laplace',
+    'logistic',
+    'lognormal',
+    'logseries',
+    'multinomial',
+    'multivariate_normal',
+    'negative_binomial',
+    'noncentral_chisquare',
+    'noncentral_f',
+    'normal',
+    'pareto',
+    'permutation',
+    'poisson',
+    'power',
+    'rand',
+    'randint',
+    'randn',
+    'random_integers',
+    'random_sample',
+    'rayleigh',
+    'seed',
+    'set_state',
+    'shuffle',
+    'standard_cauchy',
+    'standard_exponential',
+    'standard_gamma',
+    'standard_normal',
+    'standard_t',
+    'triangular',
+    'uniform',
+    'vonmises',
+    'wald',
+    'weibull',
+    'zipf'
+]

projecten1/lib/python3.6/site-packages/numpy/random/randomkit.h

@@ -0,0 +1,226 @@
+/* Random kit 1.3 */
+
+/*
+ * Copyright (c) 2003-2005, Jean-Sebastien Roy (js@jeannot.org)
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a
+ * copy of this software and associated documentation files (the
+ * "Software"), to deal in the Software without restriction, including
+ * without limitation the rights to use, copy, modify, merge, publish,
+ * distribute, sublicense, and/or sell copies of the Software, and to
+ * permit persons to whom the Software is furnished to do so, subject to
+ * the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included
+ * in all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+ * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ */
+
+/* @(#) $Jeannot: randomkit.h,v 1.24 2005/07/21 22:14:09 js Exp $ */
+
+/*
+ * Typical use:
+ *
+ * {
+ *  rk_state state;
+ *  unsigned long seed = 1, random_value;
+ *
+ *  rk_seed(seed, &state); // Initialize the RNG
+ *  ...
+ *  random_value = rk_random(&state); // Generate random values in [0..RK_MAX]
+ * }
+ *
+ * Instead of rk_seed, you can use rk_randomseed which will get a random seed
+ * from /dev/urandom (or the clock, if /dev/urandom is unavailable):
+ *
+ * {
+ *  rk_state state;
+ *  unsigned long random_value;
+ *
+ *  rk_randomseed(&state); // Initialize the RNG with a random seed
+ *  ...
+ *  random_value = rk_random(&state); // Generate random values in [0..RK_MAX]
+ * }
+ */
+
+/*
+ * Useful macro:
+ *  RK_DEV_RANDOM: the device used for random seeding.
+ *                 defaults to "/dev/urandom"
+ */
+
+#ifndef _RANDOMKIT_
+#define _RANDOMKIT_
+
+#include <stddef.h>
+#include <numpy/npy_common.h>
+
+
+#define RK_STATE_LEN 624
+
+typedef struct rk_state_
+{
+    unsigned long key[RK_STATE_LEN];
+    int pos;
+    int has_gauss; /* !=0: gauss contains a gaussian deviate */
+    double gauss;
+
+    /* The rk_state structure has been extended to store the following
+     * information for the binomial generator. If the input values of n or p
+     * are different than nsave and psave, then the other parameters will be
+     * recomputed. RTK 2005-09-02 */
+
+    int has_binomial; /* !=0: following parameters initialized for
+                              binomial */
+    double psave;
+    long nsave;
+    double r;
+    double q;
+    double fm;
+    long m;
+    double p1;
+    double xm;
+    double xl;
+    double xr;
+    double c;
+    double laml;
+    double lamr;
+    double p2;
+    double p3;
+    double p4;
+
+}
+rk_state;
+
+typedef enum {
+    RK_NOERR = 0, /* no error */
+    RK_ENODEV = 1, /* no RK_DEV_RANDOM device */
+    RK_ERR_MAX = 2
+} rk_error;
+
+/* error strings */
+extern char *rk_strerror[RK_ERR_MAX];
+
+/* Maximum generated random value */
+#define RK_MAX 0xFFFFFFFFUL
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/*
+ * Initialize the RNG state using the given seed.
+ */
+extern void rk_seed(unsigned long seed, rk_state *state);
+
+/*
+ * Initialize the RNG state using a random seed.
+ * Uses /dev/random or, when unavailable, the clock (see randomkit.c).
+ * Returns RK_NOERR when no errors occurs.
+ * Returns RK_ENODEV when the use of RK_DEV_RANDOM failed (for example because
+ * there is no such device). In this case, the RNG was initialized using the
+ * clock.
+ */
+extern rk_error rk_randomseed(rk_state *state);
+
+/*
+ * Returns a random unsigned long between 0 and RK_MAX inclusive
+ */
+extern unsigned long rk_random(rk_state *state);
+
+/*
+ * Returns a random long between 0 and LONG_MAX inclusive
+ */
+extern long rk_long(rk_state *state);
+
+/*
+ * Returns a random unsigned long between 0 and ULONG_MAX inclusive
+ */
+extern unsigned long rk_ulong(rk_state *state);
+
+/*
+ * Returns a random unsigned long between 0 and max inclusive.
+ */
+extern unsigned long rk_interval(unsigned long max, rk_state *state);
+
+/*
+ * Fills an array with cnt random npy_uint64 between off and off + rng
+ * inclusive. The numbers wrap if rng is sufficiently large.
+ */
+extern void rk_random_uint64(npy_uint64 off, npy_uint64 rng, npy_intp cnt,
+                             npy_uint64 *out, rk_state *state);
+
+/*
+ * Fills an array with cnt random npy_uint32 between off and off + rng
+ * inclusive. The numbers wrap if rng is sufficiently large.
+ */
+extern void rk_random_uint32(npy_uint32 off, npy_uint32 rng, npy_intp cnt,
+                             npy_uint32 *out, rk_state *state);
+
+/*
+ * Fills an array with cnt random npy_uint16 between off and off + rng
+ * inclusive. The numbers wrap if rng is sufficiently large.
+ */
+extern void rk_random_uint16(npy_uint16 off, npy_uint16 rng, npy_intp cnt,
+                             npy_uint16 *out, rk_state *state);
+
+/*
+ * Fills an array with cnt random npy_uint8 between off and off + rng
+ * inclusive. The numbers wrap if rng is sufficiently large.
+ */
+extern void rk_random_uint8(npy_uint8 off, npy_uint8 rng, npy_intp cnt,
+                            npy_uint8 *out, rk_state *state);
+
+/*
+ * Fills an array with cnt random npy_bool between off and off + rng
+ * inclusive. It is assumed tha npy_bool as the same size as npy_uint8.
+ */
+extern void rk_random_bool(npy_bool off, npy_bool rng, npy_intp cnt,
+                           npy_bool *out, rk_state *state);
+
+/*
+ * Returns a random double between 0.0 and 1.0, 1.0 excluded.
+ */
+extern double rk_double(rk_state *state);
+
+/*
+ * fill the buffer with size random bytes
+ */
+extern void rk_fill(void *buffer, size_t size, rk_state *state);
+
+/*
+ * fill the buffer with randombytes from the random device
+ * Returns RK_ENODEV if the device is unavailable, or RK_NOERR if it is
+ * On Unix, if strong is defined, RK_DEV_RANDOM is used. If not, RK_DEV_URANDOM
+ * is used instead. This parameter has no effect on Windows.
+ * Warning: on most unixes RK_DEV_RANDOM will wait for enough entropy to answer
+ * which can take a very long time on quiet systems.
+ */
+extern rk_error rk_devfill(void *buffer, size_t size, int strong);
+
+/*
+ * fill the buffer using rk_devfill if the random device is available and using
+ * rk_fill if is is not
+ * parameters have the same meaning as rk_fill and rk_devfill
+ * Returns RK_ENODEV if the device is unavailable, or RK_NOERR if it is
+ */
+extern rk_error rk_altfill(void *buffer, size_t size, int strong,
+                            rk_state *state);
+
+/*
+ * return a random gaussian deviate with variance unity and zero mean.
+ */
+extern double rk_gauss(rk_state *state);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* _RANDOMKIT_ */

projecten1/lib/python3.6/site-packages/numpy/random/setup.py

@@ -0,0 +1,64 @@
+from __future__ import division, print_function
+
+from os.path import join, split, dirname
+import os
+import sys
+from distutils.dep_util import newer
+from distutils.msvccompiler import get_build_version as get_msvc_build_version
+
+def needs_mingw_ftime_workaround():
+    # We need the mingw workaround for _ftime if the msvc runtime version is
+    # 7.1 or above and we build with mingw ...
+    # ... but we can't easily detect compiler version outside distutils command
+    # context, so we will need to detect in randomkit whether we build with gcc
+    msver = get_msvc_build_version()
+    if msver and msver >= 8:
+        return True
+
+    return False
+
+def configuration(parent_package='',top_path=None):
+    from numpy.distutils.misc_util import Configuration, get_mathlibs
+    config = Configuration('random', parent_package, top_path)
+
+    def generate_libraries(ext, build_dir):
+        config_cmd = config.get_config_cmd()
+        libs = get_mathlibs()
+        if sys.platform == 'win32':
+            libs.append('Advapi32')
+        ext.libraries.extend(libs)
+        return None
+
+    # enable unix large file support on 32 bit systems
+    # (64 bit off_t, lseek -> lseek64 etc.)
+    if sys.platform[:3] == "aix":
+        defs = [('_LARGE_FILES', None)]
+    else:
+        defs = [('_FILE_OFFSET_BITS', '64'),
+                ('_LARGEFILE_SOURCE', '1'),
+                ('_LARGEFILE64_SOURCE', '1')]
+    if needs_mingw_ftime_workaround():
+        defs.append(("NPY_NEEDS_MINGW_TIME_WORKAROUND", None))
+
+    libs = []
+    # Configure mtrand
+    config.add_extension('mtrand',
+                         sources=[join('mtrand', x) for x in
+                                  ['mtrand.c', 'randomkit.c', 'initarray.c',
+                                   'distributions.c']]+[generate_libraries],
+                         libraries=libs,
+                         depends=[join('mtrand', '*.h'),
+                                  join('mtrand', '*.pyx'),
+                                  join('mtrand', '*.pxi'),],
+                         define_macros=defs,
+                         )
+
+    config.add_data_files(('.', join('mtrand', 'randomkit.h')))
+    config.add_data_dir('tests')
+
+    return config
+
+
+if __name__ == '__main__':
+    from numpy.distutils.core import setup
+    setup(configuration=configuration)

projecten1/lib/python3.6/site-packages/numpy/random/tests/test_regression.py

@@ -0,0 +1,138 @@
+from __future__ import division, absolute_import, print_function
+
+import sys
+from numpy.testing import (
+    run_module_suite, assert_, assert_array_equal, assert_raises,
+    )
+from numpy import random
+from numpy.compat import long
+import numpy as np
+
+
+class TestRegression(object):
+
+    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_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):
+        # Test that beta with small a and b parameters does not produce
+        # NaNs due to roundoff errors causing 0 / 0, gh-5851
+        np.random.seed(1234567890)
+        x = np.random.beta(0.0001, 0.0001, size=100)
+        assert_(not np.any(np.isnan(x)), 'Nans in np.random.beta')
+
+    def test_choice_sum_of_probs_tolerance(self):
+        # The sum of probs should be 1.0 with some tolerance.
+        # For low precision dtypes the tolerance was too tight.
+        # See numpy github issue 6123.
+        np.random.seed(1234)
+        a = [1, 2, 3]
+        counts = [4, 4, 2]
+        for dt in np.float16, np.float32, np.float64:
+            probs = np.array(counts, dtype=dt) / sum(counts)
+            c = np.random.choice(a, p=probs)
+            assert_(c in a)
+            assert_raises(ValueError, np.random.choice, a, p=probs*0.9)
+
+    def test_shuffle_of_array_of_different_length_strings(self):
+        # Test that permuting an array of different length strings
+        # will not cause a segfault on garbage collection
+        # Tests gh-7710
+        np.random.seed(1234)
+
+        a = np.array(['a', 'a' * 1000])
+
+        for _ in range(100):
+            np.random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+    def test_shuffle_of_array_of_objects(self):
+        # Test that permuting an array of objects will not cause
+        # a segfault on garbage collection.
+        # See gh-7719
+        np.random.seed(1234)
+        a = np.array([np.arange(1), np.arange(4)])
+
+        for _ in range(1000):
+            np.random.shuffle(a)
+
+        # Force Garbage Collection - should not segfault.
+        import gc
+        gc.collect()
+
+if __name__ == "__main__":
+    run_module_suite()