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| author | Jaime <jaime.frio@gmail.com> | 2016-01-24 01:16:46 +0100 |
|---|---|---|
| committer | Jaime <jaime.frio@gmail.com> | 2016-01-24 01:16:46 +0100 |
| commit | 9849922aa4ace91906878df51053a32e2719a722 (patch) | |
| tree | 071374e2e21aafd182bb40b9052425745af06b97 | |
| parent | 639e2a3d94026be7bc5ac698539c149702b225d9 (diff) | |
| parent | c0980ff9d32e690b13b8d3c6b0a797771ee33b57 (diff) | |
| download | numpy-9849922aa4ace91906878df51053a32e2719a722.tar.gz | |
Merge pull request #7082 from gfyoung/test_random_expand
TST, DOC: Added Broadcasting Tests in test_random.py
| -rw-r--r-- | numpy/random/mtrand/mtrand.pyx | 426 | ||||
| -rw-r--r-- | numpy/random/tests/test_random.py | 716 |
2 files changed, 880 insertions, 262 deletions
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx index 2f315c5d3..b4335d72d 100644 --- a/numpy/random/mtrand/mtrand.pyx +++ b/numpy/random/mtrand/mtrand.pyx @@ -1460,21 +1460,22 @@ cdef class RandomState: Parameters ---------- - low : float, optional + low : float or array_like of floats, optional Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0. - high : float + high : float or array_like of floats Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``low`` and ``high`` are both scalars. + Otherwise, ``np.broadcast(low, high).size`` samples are drawn. Returns ------- - out : ndarray - Drawn samples, with shape `size`. + out : ndarray or scalar + Drawn samples from the parameterized uniform distribution. See Also -------- @@ -1791,14 +1792,20 @@ cdef class RandomState: Parameters ---------- - loc : float + loc : float or array_like of floats Mean ("centre") of the distribution. - scale : float + scale : float or array_like of floats Standard deviation (spread or "width") of the distribution. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``loc`` and ``scale`` are both scalars. + Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn. + + Returns + ------- + out : ndarray or scalar + Drawn samples from the parameterized normal distribution. See Also -------- @@ -1898,20 +1905,20 @@ cdef class RandomState: Parameters ---------- - a : float + a : float or array_like of floats Alpha, non-negative. - b : float + b : float or array_like of floats Beta, non-negative. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``a`` and ``b`` are both scalars. + Otherwise, ``np.broadcast(a, b).size`` samples are drawn. Returns ------- - out : ndarray - Array of the given shape, containing values drawn from a - Beta distribution. + out : ndarray or scalar + Drawn samples from the parameterized beta distribution. """ cdef ndarray oa, ob @@ -1960,12 +1967,18 @@ cdef class RandomState: Parameters ---------- - scale : float + scale : float or array_like of floats The scale parameter, :math:`\\beta = 1/\\lambda`. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``scale`` is a scalar. Otherwise, + ``np.array(scale).size`` samples are drawn. + + Returns + ------- + out : ndarray or scalar + Drawn samples from the parameterized exponential distribution. References ---------- @@ -2038,17 +2051,18 @@ cdef class RandomState: Parameters ---------- - shape : float + shape : float or array_like of floats Parameter, should be > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``shape`` is a scalar. Otherwise, + ``np.array(shape).size`` samples are drawn. Returns ------- - samples : ndarray or scalar - The drawn samples. + out : ndarray or scalar + Drawn samples from the parameterized standard gamma distribution. See Also -------- @@ -2125,19 +2139,21 @@ cdef class RandomState: Parameters ---------- - shape : scalar > 0 - The shape of the gamma distribution. - scale : scalar > 0, optional - The scale of the gamma distribution. Default is equal to 1. + shape : float or array_like of floats + The shape of the gamma distribution. Should be greater than zero. + scale : float or array_like of floats, optional + The scale of the gamma distribution. Should be greater than zero. + Default is equal to 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``shape`` and ``scale`` are both scalars. + Otherwise, ``np.broadcast(shape, scale).size`` samples are drawn. Returns ------- - out : ndarray, float - Returns one sample unless `size` parameter is specified. + out : ndarray or scalar + Drawn samples from the parameterized gamma distribution. See Also -------- @@ -2225,19 +2241,20 @@ cdef class RandomState: Parameters ---------- - dfnum : float + dfnum : int or array_like of ints Degrees of freedom in numerator. Should be greater than zero. - dfden : float + dfden : int or array_like of ints Degrees of freedom in denominator. Should be greater than zero. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``dfnum`` and ``dfden`` are both scalars. + Otherwise, ``np.broadcast(dfnum, dfden).size`` samples are drawn. Returns ------- - samples : ndarray or scalar - Samples from the Fisher distribution. + out : ndarray or scalar + Drawn samples from the parameterized Fisher distribution. See Also -------- @@ -2296,9 +2313,9 @@ cdef class RandomState: fdfden = PyFloat_AsDouble(dfden) if not PyErr_Occurred(): if fdfnum <= 0: - raise ValueError("shape <= 0") + raise ValueError("dfnum <= 0") if fdfden <= 0: - raise ValueError("scale <= 0") + raise ValueError("dfden <= 0") return cont2_array_sc(self.internal_state, rk_f, size, fdfnum, fdfden, self.lock) @@ -2326,21 +2343,23 @@ cdef class RandomState: Parameters ---------- - dfnum : int + dfnum : int or array_like of ints Parameter, should be > 1. - dfden : int + dfden : int or array_like of ints Parameter, should be > 1. - nonc : float + nonc : float or array_like of floats Parameter, should be >= 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``dfnum``, ``dfden``, and ``nonc`` + are all scalars. Otherwise, ``np.broadcast(dfnum, dfden, nonc).size`` + samples are drawn. Returns ------- - samples : scalar or ndarray - Drawn samples. + out : ndarray or scalar + Drawn samples from the parameterized noncentral Fisher distribution. Notes ----- @@ -2422,18 +2441,18 @@ cdef class RandomState: Parameters ---------- - df : int + df : int or array_like of ints Number of degrees of freedom. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``df`` is a scalar. Otherwise, + ``np.array(df).size`` samples are drawn. Returns ------- - output : ndarray - Samples drawn from the distribution, packed in a `size`-shaped - array. + out : ndarray or scalar + Drawn samples from the parameterized chi-square distribution. Raises ------ @@ -2501,15 +2520,21 @@ cdef class RandomState: Parameters ---------- - df : int + df : int or array_like of ints Degrees of freedom, should be > 0 as of Numpy 1.10, should be > 1 for earlier versions. - nonc : float + nonc : float or array_like of floats Non-centrality, should be non-negative. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``df`` and ``nonc`` are both scalars. + Otherwise, ``np.broadcast(df, nonc).size`` samples are drawn. + + Returns + ------- + out : ndarray or scalar + Drawn samples from the parameterized noncentral chi-square distribution. Notes ----- @@ -2664,17 +2689,18 @@ cdef class RandomState: Parameters ---------- - df : int + df : int or array_like of ints Degrees of freedom, should be > 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``df`` is a scalar. Otherwise, + ``np.array(df).size`` samples are drawn. Returns ------- - samples : ndarray or scalar - Drawn samples. + out : ndarray or scalar + Drawn samples from the parameterized standard Student's t distribution. Notes ----- @@ -2772,19 +2798,20 @@ cdef class RandomState: Parameters ---------- - mu : float + mu : float or array_like of floats Mode ("center") of the distribution. - kappa : float + kappa : float or array_like of floats Dispersion of the distribution, has to be >=0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``mu`` and ``kappa`` are both scalars. + Otherwise, ``np.broadcast(mu, kappa).size`` samples are drawn. Returns ------- - samples : scalar or ndarray - The returned samples, which are in the interval [-pi, pi]. + out : ndarray or scalar + Drawn samples from the parameterized von Mises distribution. See Also -------- @@ -2880,12 +2907,18 @@ cdef class RandomState: Parameters ---------- - shape : float, > 0. - Shape of the distribution. + a : float or array_like of floats + Shape of the distribution. Should be greater than zero. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``a`` is a scalar. Otherwise, + ``np.array(a).size`` samples are drawn. + + Returns + ------- + out : ndarray or scalar + Drawn samples from the parameterized Pareto distribution. See Also -------- @@ -2976,16 +3009,18 @@ cdef class RandomState: Parameters ---------- - a : float - Shape of the distribution. + a : float or array_like of floats + Shape of the distribution. Should be greater than zero. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``a`` is a scalar. Otherwise, + ``np.array(a).size`` samples are drawn. Returns ------- - samples : ndarray + out : ndarray or scalar + Drawn samples from the parameterized Weibull distribution. See Also -------- @@ -3078,17 +3113,18 @@ cdef class RandomState: Parameters ---------- - a : float - parameter, > 0 + a : float or array_like of floats + Parameter of the distribution. Should be greater than zero. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``a`` is a scalar. Otherwise, + ``np.array(a).size`` samples are drawn. Returns ------- - samples : ndarray or scalar - The returned samples lie in [0, 1]. + out : ndarray or scalar + Drawn samples from the parameterized power distribution. Raises ------ @@ -3192,18 +3228,20 @@ cdef class RandomState: Parameters ---------- - loc : float, optional - The position, :math:`\\mu`, of the distribution peak. - scale : float, optional - :math:`\\lambda`, the exponential decay. + loc : float or array_like of floats, optional + The position, :math:`\\mu`, of the distribution peak. Default is 0. + scale : float or array_like of floats, optional + :math:`\\lambda`, the exponential decay. Default is 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``loc`` and ``scale`` are both scalars. + Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn. Returns ------- - samples : ndarray or float + out : ndarray or scalar + Drawn samples from the parameterized Laplace distribution. Notes ----- @@ -3286,18 +3324,20 @@ cdef class RandomState: Parameters ---------- - loc : float - The location of the mode of the distribution. - scale : float - The scale parameter of the distribution. + loc : float or array_like of floats, optional + The location of the mode of the distribution. Default is 0. + scale : float or array_like of floats, optional + The scale parameter of the distribution. Default is 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``loc`` and ``scale`` are both scalars. + Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn. Returns ------- - samples : ndarray or scalar + out : ndarray or scalar + Drawn samples from the parameterized Gumbel distribution. See Also -------- @@ -3414,19 +3454,21 @@ cdef class RandomState: Parameters ---------- - loc : float - - scale : float > 0. - + loc : float or array_like of floats, optional + Parameter of the distribution. Default is 0. + scale : float or array_like of floats, optional + Parameter of the distribution. Should be greater than zero. + Default is 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``loc`` and ``scale`` are both scalars. + Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn. Returns ------- - samples : ndarray or scalar - where the values are all integers in [0, n]. + out : ndarray or scalar + Drawn samples from the parameterized logistic distribution. See Also -------- @@ -3507,20 +3549,21 @@ cdef class RandomState: Parameters ---------- - mean : float - Mean value of the underlying normal distribution - sigma : float, > 0. - Standard deviation of the underlying normal distribution + mean : float or array_like of floats, optional + Mean value of the underlying normal distribution. Default is 0. + sigma : float or array_like of floats, optional + Standard deviation of the underlying normal distribution. Should + be greater than zero. Default is 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``mean`` and ``sigma`` are both scalars. + Otherwise, ``np.broadcast(mean, sigma).size`` samples are drawn. Returns ------- - samples : ndarray or float - The desired samples. An array of the same shape as `size` if given, - if `size` is None a float is returned. + out : ndarray or scalar + Drawn samples from the parameterized log-normal distribution. See Also -------- @@ -3630,12 +3673,18 @@ cdef class RandomState: Parameters ---------- - scale : scalar - Scale, also equals the mode. Should be >= 0. + scale : float or array_like of floats, optional + Scale, also equals the mode. Should be >= 0. Default is 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``scale`` is a scalar. Otherwise, + ``np.array(scale).size`` samples are drawn. + + Returns + ------- + out : ndarray or scalar + Drawn samples from the parameterized Rayleigh distribution. Notes ----- @@ -3711,19 +3760,20 @@ cdef class RandomState: Parameters ---------- - mean : scalar + mean : float or array_like of floats Distribution mean, should be > 0. - scale : scalar + scale : float or array_like of floats Scale parameter, should be >= 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``mean`` and ``scale`` are both scalars. + Otherwise, ``np.broadcast(mean, scale).size`` samples are drawn. Returns ------- - samples : ndarray or scalar - Drawn sample, all greater than zero. + out : ndarray or scalar + Drawn samples from the parameterized Wald distribution. Notes ----- @@ -3783,7 +3833,8 @@ cdef class RandomState: """ triangular(left, mode, right, size=None) - Draw samples from the triangular distribution. + Draw samples from the triangular distribution over the + interval ``[left, right]``. The triangular distribution is a continuous probability distribution with lower limit left, peak at mode, and upper @@ -3792,22 +3843,24 @@ cdef class RandomState: Parameters ---------- - left : scalar + left : float or array_like of floats Lower limit. - mode : scalar + mode : float or array_like of floats The value where the peak of the distribution occurs. The value should fulfill the condition ``left <= mode <= right``. - right : scalar + right : float or array_like of floats Upper limit, should be larger than `left`. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``left``, ``mode``, and ``right`` + are all scalars. Otherwise, ``np.broadcast(left, mode, right).size`` + samples are drawn. Returns ------- - samples : ndarray or scalar - The returned samples all lie in the interval [left, right]. + out : ndarray or scalar + Drawn samples from the parameterized triangular distribution. Notes ----- @@ -3883,19 +3936,22 @@ cdef class RandomState: Parameters ---------- - n : float (but truncated to an integer) - parameter, >= 0. - p : float - parameter, >= 0 and <=1. + n : int or array_like of ints + Parameter of the distribution, >= 0. Floats are also accepted, + but they will be truncated to integers. + p : float or array_like of floats + Parameter of the distribution, >= 0 and <=1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``n`` and ``p`` are both scalars. + Otherwise, ``np.broadcast(n, p).size`` samples are drawn. Returns ------- - samples : ndarray or scalar - where the values are all integers in [0, n]. + out : ndarray or scalar + Drawn samples from the parameterized binomial distribution, where + each sample is equal to the number of successes over the n trials. See Also -------- @@ -3995,19 +4051,24 @@ cdef class RandomState: Parameters ---------- - n : int - Parameter, > 0. - p : float - Parameter, >= 0 and <=1. + n : int or array_like of ints + Parameter of the distribution, > 0. Floats are also accepted, + but they will be truncated to integers. + p : float or array_like of floats + Parameter of the distribution, >= 0 and <=1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``n`` and ``p`` are both scalars. + Otherwise, ``np.broadcast(n, p).size`` samples are drawn. Returns ------- - samples : int or ndarray of ints - Drawn samples. + out : ndarray or scalar + Drawn samples from the parameterized negative binomial distribution, + where each sample is equal to N, the number of trials it took to + achieve n - 1 successes, N - (n - 1) failures, and a success on the, + (N + n)th trial. Notes ----- @@ -4090,18 +4151,19 @@ cdef class RandomState: Parameters ---------- - lam : float or sequence of float + lam : float or array_like of floats Expectation of interval, should be >= 0. A sequence of expectation intervals must be broadcastable over the requested size. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``lam`` is a scalar. Otherwise, + ``np.array(lam).size`` samples are drawn. Returns ------- - samples : ndarray or scalar - The drawn samples, of shape *size*, if it was provided. + out : ndarray or scalar + Drawn samples from the parameterized Poisson distribution. Notes ----- @@ -4181,17 +4243,18 @@ cdef class RandomState: Parameters ---------- - a : float > 1 - Distribution parameter. + a : float or array_like of floats + Distribution parameter. Should be greater than 1. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``a`` is a scalar. Otherwise, + ``np.array(a).size`` samples are drawn. Returns ------- - samples : scalar or ndarray - The returned samples are greater than or equal to one. + out : ndarray or scalar + Drawn samples from the parameterized Zipf distribution. See Also -------- @@ -4273,18 +4336,18 @@ cdef class RandomState: Parameters ---------- - p : float + p : float or array_like of floats The probability of success of an individual trial. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``p`` is a scalar. Otherwise, + ``np.array(p).size`` samples are drawn. Returns ------- - out : ndarray - Samples from the geometric distribution, shaped according to - `size`. + out : ndarray or scalar + Drawn samples from the parameterized geometric distribution. Examples -------- @@ -4335,22 +4398,24 @@ cdef class RandomState: Parameters ---------- - ngood : int or array_like + ngood : int or array_like of ints Number of ways to make a good selection. Must be nonnegative. - nbad : int or array_like + nbad : int or array_like of ints Number of ways to make a bad selection. Must be nonnegative. - nsample : int or array_like + nsample : int or array_like of ints Number of items sampled. Must be at least 1 and at most ``ngood + nbad``. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``ngood``, ``nbad``, and ``nsample`` + are all scalars. Otherwise, ``np.broadcast(ngood, nbad, nsample).size`` + samples are drawn. Returns ------- - samples : ndarray or scalar - The values are all integers in [0, n]. + out : ndarray or scalar + Drawn samples from the parameterized hypergeometric distribution. See Also -------- @@ -4454,19 +4519,18 @@ cdef class RandomState: Parameters ---------- - loc : float - - scale : float > 0. - + p : float or array_like of floats + Shape parameter for the distribution. Must be in the range (0, 1). size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. Default is None, in which case a - single value is returned. + ``m * n * k`` samples are drawn. If size is ``None`` (default), + a single value is returned if ``p`` is a scalar. Otherwise, + ``np.array(p).size`` samples are drawn. Returns ------- - samples : ndarray or scalar - where the values are all integers in [0, n]. + out : ndarray or scalar + Drawn samples from the parameterized logarithmic series distribution. See Also -------- diff --git a/numpy/random/tests/test_random.py b/numpy/random/tests/test_random.py index 96aa3790f..7ec71e2e5 100644 --- a/numpy/random/tests/test_random.py +++ b/numpy/random/tests/test_random.py @@ -3,14 +3,13 @@ from __future__ import division, absolute_import, print_function import numpy as np from numpy.testing import ( TestCase, run_module_suite, assert_, assert_raises, assert_equal, - assert_warns) + assert_warns, assert_array_equal, assert_array_almost_equal) from numpy import random from numpy.compat import asbytes import sys import warnings - class TestSeed(TestCase): def test_scalar(self): s = np.random.RandomState(0) @@ -50,7 +49,7 @@ class TestBinomial(TestCase): zeros = np.zeros(2, dtype='int') for p in [0, .5, 1]: assert_(random.binomial(0, p) == 0) - np.testing.assert_array_equal(random.binomial(zeros, p), zeros) + assert_array_equal(random.binomial(zeros, p), zeros) def test_p_is_nan(self): # Issue #4571. @@ -148,10 +147,10 @@ class TestRandint(TestCase): for dt in self.itype: lbnd = 0 if dt is np.bool else np.iinfo(dt).min ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1 - assert_raises(ValueError, self.rfunc, lbnd - 1 , ubnd, dtype=dt) - assert_raises(ValueError, self.rfunc, lbnd , ubnd + 1, dtype=dt) - assert_raises(ValueError, self.rfunc, ubnd , lbnd, dtype=dt) - assert_raises(ValueError, self.rfunc, 1 , 0, dtype=dt) + assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt) + assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt) + assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt) + assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt) def test_rng_zero_and_extremes(self): for dt in self.itype: @@ -223,7 +222,7 @@ class TestRandomDist(TestCase): desired = np.array([[0.61879477158567997, 0.59162362775974664], [0.88868358904449662, 0.89165480011560816], [0.4575674820298663, 0.7781880808593471]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_randn(self): np.random.seed(self.seed) @@ -231,7 +230,7 @@ class TestRandomDist(TestCase): desired = np.array([[1.34016345771863121, 1.73759122771936081], [1.498988344300628, -0.2286433324536169], [2.031033998682787, 2.17032494605655257]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_randint(self): np.random.seed(self.seed) @@ -239,7 +238,7 @@ class TestRandomDist(TestCase): desired = np.array([[31, 3], [-52, 41], [-48, -66]]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_random_integers(self): np.random.seed(self.seed) @@ -247,7 +246,7 @@ class TestRandomDist(TestCase): desired = np.array([[31, 3], [-52, 41], [-48, -66]]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_random_integers_max_int(self): # Tests whether random_integers can generate the @@ -258,7 +257,7 @@ class TestRandomDist(TestCase): actual = np.random.random_integers(np.iinfo('l').max, np.iinfo('l').max) desired = np.iinfo('l').max - np.testing.assert_equal(actual, desired) + assert_equal(actual, desired) def test_random_integers_deprecated(self): with warnings.catch_warnings(): @@ -280,38 +279,38 @@ class TestRandomDist(TestCase): desired = np.array([[0.61879477158567997, 0.59162362775974664], [0.88868358904449662, 0.89165480011560816], [0.4575674820298663, 0.7781880808593471]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_choice_uniform_replace(self): np.random.seed(self.seed) actual = np.random.choice(4, 4) desired = np.array([2, 3, 2, 3]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_choice_nonuniform_replace(self): np.random.seed(self.seed) actual = np.random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1]) desired = np.array([1, 1, 2, 2]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_choice_uniform_noreplace(self): np.random.seed(self.seed) actual = np.random.choice(4, 3, replace=False) desired = np.array([0, 1, 3]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_choice_nonuniform_noreplace(self): np.random.seed(self.seed) actual = np.random.choice(4, 3, replace=False, p=[0.1, 0.3, 0.5, 0.1]) desired = np.array([2, 3, 1]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_choice_noninteger(self): np.random.seed(self.seed) actual = np.random.choice(['a', 'b', 'c', 'd'], 4) desired = np.array(['c', 'd', 'c', 'd']) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_choice_exceptions(self): sample = np.random.choice @@ -320,13 +319,13 @@ class TestRandomDist(TestCase): assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3) assert_raises(ValueError, sample, [], 3) assert_raises(ValueError, sample, [1, 2, 3, 4], 3, - p=[[0.25, 0.25], [0.25, 0.25]]) + p=[[0.25, 0.25], [0.25, 0.25]]) assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2]) assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1]) assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4]) assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False) - assert_raises(ValueError, sample, [1, 2, 3], 2, replace=False, - p=[1, 0, 0]) + assert_raises(ValueError, sample, [1, 2, 3], 2, + replace=False, p=[1, 0, 0]) def test_choice_return_shape(self): p = [0.1, 0.9] @@ -368,7 +367,7 @@ class TestRandomDist(TestCase): np.random.seed(self.seed) actual = np.random.bytes(10) desired = asbytes('\x82Ui\x9e\xff\x97+Wf\xa5') - np.testing.assert_equal(actual, desired) + assert_equal(actual, desired) def test_shuffle(self): # Test lists, arrays (of various dtypes), and multidimensional versions @@ -391,11 +390,11 @@ class TestRandomDist(TestCase): np.random.shuffle(alist) actual = alist desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_shuffle_masked(self): # gh-3263 - a = np.ma.masked_values(np.reshape(range(20), (5,4)) % 3 - 1, -1) + a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1) b = np.ma.masked_values(np.arange(20) % 3 - 1, -1) a_orig = a.copy() b_orig = b.copy() @@ -414,15 +413,15 @@ class TestRandomDist(TestCase): [[1.45341850513746058e-02, 5.31297615662868145e-04], [1.85366619058432324e-06, 4.19214516800110563e-03], [1.58405155108498093e-04, 1.26252891949397652e-04]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_binomial(self): np.random.seed(self.seed) actual = np.random.binomial(100.123, .456, size=(3, 2)) desired = np.array([[37, 43], - [42, 48], - [46, 45]]) - np.testing.assert_array_equal(actual, desired) + [42, 48], + [46, 45]]) + assert_array_equal(actual, desired) def test_chisquare(self): np.random.seed(self.seed) @@ -430,7 +429,7 @@ class TestRandomDist(TestCase): desired = np.array([[63.87858175501090585, 68.68407748911370447], [65.77116116901505904, 47.09686762438974483], [72.3828403199695174, 74.18408615260374006]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=13) + assert_array_almost_equal(actual, desired, decimal=13) def test_dirichlet(self): np.random.seed(self.seed) @@ -442,7 +441,7 @@ class TestRandomDist(TestCase): [0.58964023305154301, 0.41035976694845688]], [[0.59266909280647828, 0.40733090719352177], [0.56974431743975207, 0.43025568256024799]]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_dirichlet_size(self): # gh-3173 @@ -462,7 +461,7 @@ class TestRandomDist(TestCase): desired = np.array([[1.08342649775011624, 1.00607889924557314], [2.46628830085216721, 2.49668106809923884], [0.68717433461363442, 1.69175666993575979]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_f(self): np.random.seed(self.seed) @@ -470,7 +469,7 @@ class TestRandomDist(TestCase): desired = np.array([[1.21975394418575878, 1.75135759791559775], [1.44803115017146489, 1.22108959480396262], [1.02176975757740629, 1.34431827623300415]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_gamma(self): np.random.seed(self.seed) @@ -478,7 +477,7 @@ class TestRandomDist(TestCase): desired = np.array([[24.60509188649287182, 28.54993563207210627], [26.13476110204064184, 12.56988482927716078], [31.71863275789960568, 33.30143302795922011]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=14) + assert_array_almost_equal(actual, desired, decimal=14) def test_geometric(self): np.random.seed(self.seed) @@ -486,7 +485,7 @@ class TestRandomDist(TestCase): desired = np.array([[8, 7], [17, 17], [5, 12]]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_gumbel(self): np.random.seed(self.seed) @@ -494,7 +493,7 @@ class TestRandomDist(TestCase): desired = np.array([[0.19591898743416816, 0.34405539668096674], [-1.4492522252274278, -1.47374816298446865], [1.10651090478803416, -0.69535848626236174]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_hypergeometric(self): np.random.seed(self.seed) @@ -502,25 +501,25 @@ class TestRandomDist(TestCase): desired = np.array([[10, 10], [10, 10], [9, 9]]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) # Test nbad = 0 actual = np.random.hypergeometric(5, 0, 3, size=4) desired = np.array([3, 3, 3, 3]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) actual = np.random.hypergeometric(15, 0, 12, size=4) desired = np.array([12, 12, 12, 12]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) # Test ngood = 0 actual = np.random.hypergeometric(0, 5, 3, size=4) desired = np.array([0, 0, 0, 0]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) actual = np.random.hypergeometric(0, 15, 12, size=4) desired = np.array([0, 0, 0, 0]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_laplace(self): np.random.seed(self.seed) @@ -528,7 +527,7 @@ class TestRandomDist(TestCase): desired = np.array([[0.66599721112760157, 0.52829452552221945], [3.12791959514407125, 3.18202813572992005], [-0.05391065675859356, 1.74901336242837324]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_logistic(self): np.random.seed(self.seed) @@ -536,7 +535,7 @@ class TestRandomDist(TestCase): desired = np.array([[1.09232835305011444, 0.8648196662399954], [4.27818590694950185, 4.33897006346929714], [-0.21682183359214885, 2.63373365386060332]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_lognormal(self): np.random.seed(self.seed) @@ -544,7 +543,7 @@ class TestRandomDist(TestCase): desired = np.array([[16.50698631688883822, 36.54846706092654784], [22.67886599981281748, 0.71617561058995771], [65.72798501792723869, 86.84341601437161273]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=13) + assert_array_almost_equal(actual, desired, decimal=13) def test_logseries(self): np.random.seed(self.seed) @@ -552,7 +551,7 @@ class TestRandomDist(TestCase): desired = np.array([[2, 2], [6, 17], [3, 6]]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_multinomial(self): np.random.seed(self.seed) @@ -563,7 +562,7 @@ class TestRandomDist(TestCase): [2, 1, 4, 3, 6, 4]], [[4, 4, 2, 5, 2, 3], [4, 3, 4, 2, 3, 4]]]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_multivariate_normal(self): np.random.seed(self.seed) @@ -578,12 +577,12 @@ class TestRandomDist(TestCase): [-1.77505606019580053, 10.]], [[-0.54970369430044119, 10.], [0.29768848031692957, 10.]]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) # Check for default size, was raising deprecation warning actual = np.random.multivariate_normal(mean, cov) desired = np.array([-0.79441224511977482, 10.]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) # Check that non positive-semidefinite covariance raises warning mean = [0, 0] @@ -596,7 +595,7 @@ class TestRandomDist(TestCase): desired = np.array([[848, 841], [892, 611], [779, 647]]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) def test_noncentral_chisquare(self): np.random.seed(self.seed) @@ -604,20 +603,20 @@ class TestRandomDist(TestCase): desired = np.array([[23.91905354498517511, 13.35324692733826346], [31.22452661329736401, 16.60047399466177254], [5.03461598262724586, 17.94973089023519464]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=14) + assert_array_almost_equal(actual, desired, decimal=14) actual = np.random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2)) - desired = np.array([[ 1.47145377828516666, 0.15052899268012659], - [ 0.00943803056963588, 1.02647251615666169], - [ 0.332334982684171 , 0.15451287602753125]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=14) + desired = np.array([[1.47145377828516666, 0.15052899268012659], + [0.00943803056963588, 1.02647251615666169], + [0.332334982684171, 0.15451287602753125]]) + assert_array_almost_equal(actual, desired, decimal=14) np.random.seed(self.seed) actual = np.random.noncentral_chisquare(df=5, nonc=0, size=(3, 2)) desired = np.array([[9.597154162763948, 11.725484450296079], [10.413711048138335, 3.694475922923986], [13.484222138963087, 14.377255424602957]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=14) + assert_array_almost_equal(actual, desired, decimal=14) def test_noncentral_f(self): np.random.seed(self.seed) @@ -626,7 +625,7 @@ class TestRandomDist(TestCase): desired = np.array([[1.40598099674926669, 0.34207973179285761], [3.57715069265772545, 7.92632662577829805], [0.43741599463544162, 1.1774208752428319]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=14) + assert_array_almost_equal(actual, desired, decimal=14) def test_normal(self): np.random.seed(self.seed) @@ -634,7 +633,7 @@ class TestRandomDist(TestCase): desired = np.array([[2.80378370443726244, 3.59863924443872163], [3.121433477601256, -0.33382987590723379], [4.18552478636557357, 4.46410668111310471]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_pareto(self): np.random.seed(self.seed) @@ -655,9 +654,9 @@ class TestRandomDist(TestCase): np.random.seed(self.seed) actual = np.random.poisson(lam=.123456789, size=(3, 2)) desired = np.array([[0, 0], - [1, 0], - [0, 0]]) - np.testing.assert_array_equal(actual, desired) + [1, 0], + [0, 0]]) + assert_array_equal(actual, desired) def test_poisson_exceptions(self): lambig = np.iinfo('l').max @@ -673,7 +672,7 @@ class TestRandomDist(TestCase): desired = np.array([[0.02048932883240791, 0.01424192241128213], [0.38446073748535298, 0.39499689943484395], [0.00177699707563439, 0.13115505880863756]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_rayleigh(self): np.random.seed(self.seed) @@ -681,7 +680,7 @@ class TestRandomDist(TestCase): desired = np.array([[13.8882496494248393, 13.383318339044731], [20.95413364294492098, 21.08285015800712614], [11.06066537006854311, 17.35468505778271009]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=14) + assert_array_almost_equal(actual, desired, decimal=14) def test_standard_cauchy(self): np.random.seed(self.seed) @@ -689,7 +688,7 @@ class TestRandomDist(TestCase): desired = np.array([[0.77127660196445336, -6.55601161955910605], [0.93582023391158309, -2.07479293013759447], [-4.74601644297011926, 0.18338989290760804]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_standard_exponential(self): np.random.seed(self.seed) @@ -697,7 +696,7 @@ class TestRandomDist(TestCase): desired = np.array([[0.96441739162374596, 0.89556604882105506], [2.1953785836319808, 2.22243285392490542], [0.6116915921431676, 1.50592546727413201]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_standard_gamma(self): np.random.seed(self.seed) @@ -705,7 +704,7 @@ class TestRandomDist(TestCase): desired = np.array([[5.50841531318455058, 6.62953470301903103], [5.93988484943779227, 2.31044849402133989], [7.54838614231317084, 8.012756093271868]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=14) + assert_array_almost_equal(actual, desired, decimal=14) def test_standard_normal(self): np.random.seed(self.seed) @@ -713,7 +712,7 @@ class TestRandomDist(TestCase): desired = np.array([[1.34016345771863121, 1.73759122771936081], [1.498988344300628, -0.2286433324536169], [2.031033998682787, 2.17032494605655257]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_standard_t(self): np.random.seed(self.seed) @@ -721,7 +720,7 @@ class TestRandomDist(TestCase): desired = np.array([[0.97140611862659965, -0.08830486548450577], [1.36311143689505321, -0.55317463909867071], [-0.18473749069684214, 0.61181537341755321]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_triangular(self): np.random.seed(self.seed) @@ -730,7 +729,7 @@ class TestRandomDist(TestCase): desired = np.array([[12.68117178949215784, 12.4129206149193152], [16.20131377335158263, 16.25692138747600524], [11.20400690911820263, 14.4978144835829923]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=14) + assert_array_almost_equal(actual, desired, decimal=14) def test_uniform(self): np.random.seed(self.seed) @@ -738,16 +737,16 @@ class TestRandomDist(TestCase): desired = np.array([[6.99097932346268003, 6.73801597444323974], [9.50364421400426274, 9.53130618907631089], [5.48995325769805476, 8.47493103280052118]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_uniform_range_bounds(self): fmin = np.finfo('float').min fmax = np.finfo('float').max func = np.random.uniform - np.testing.assert_raises(OverflowError, func, -np.inf, 0) - np.testing.assert_raises(OverflowError, func, 0, np.inf) - np.testing.assert_raises(OverflowError, func, fmin, fmax) + assert_raises(OverflowError, func, -np.inf, 0) + assert_raises(OverflowError, func, 0, np.inf) + assert_raises(OverflowError, func, fmin, fmax) # (fmax / 1e17) - fmin is within range, so this should not throw np.random.uniform(low=fmin, high=fmax / 1e17) @@ -758,7 +757,7 @@ class TestRandomDist(TestCase): desired = np.array([[2.28567572673902042, 2.89163838442285037], [0.38198375564286025, 2.57638023113890746], [1.19153771588353052, 1.83509849681825354]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_vonmises_small(self): # check infinite loop, gh-4720 @@ -772,7 +771,7 @@ class TestRandomDist(TestCase): desired = np.array([[3.82935265715889983, 5.13125249184285526], [0.35045403618358717, 1.50832396872003538], [0.24124319895843183, 0.22031101461955038]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=14) + assert_array_almost_equal(actual, desired, decimal=14) def test_weibull(self): np.random.seed(self.seed) @@ -780,7 +779,7 @@ class TestRandomDist(TestCase): desired = np.array([[0.97097342648766727, 0.91422896443565516], [1.89517770034962929, 1.91414357960479564], [0.67057783752390987, 1.39494046635066793]]) - np.testing.assert_array_almost_equal(actual, desired, decimal=15) + assert_array_almost_equal(actual, desired, decimal=15) def test_zipf(self): np.random.seed(self.seed) @@ -788,10 +787,566 @@ class TestRandomDist(TestCase): desired = np.array([[66, 29], [1, 1], [3, 13]]) - np.testing.assert_array_equal(actual, desired) + assert_array_equal(actual, desired) + + +class TestBroadcast(TestCase): + # tests that functions that broadcast behave + # correctly when presented with non-scalar arguments + def setUp(self): + self.seed = 123456789 + + def setSeed(self): + np.random.seed(self.seed) + + # TODO: Include test for randint once it can broadcast + # Can steal the test written in PR #6938 + + def test_uniform(self): + low = [0] + high = [1] + uniform = np.random.uniform + desired = np.array([0.53283302478975902, + 0.53413660089041659, + 0.50955303552646702]) + + self.setSeed() + actual = uniform(low * 3, high) + assert_array_almost_equal(actual, desired, decimal=14) + + self.setSeed() + actual = uniform(low, high * 3) + assert_array_almost_equal(actual, desired, decimal=14) + + def test_normal(self): + loc = [0] + scale = [1] + bad_scale = [-1] + normal = np.random.normal + desired = np.array([2.2129019979039612, + 2.1283977976520019, + 1.8417114045748335]) + + self.setSeed() + actual = normal(loc * 3, scale) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, normal, loc * 3, bad_scale) + + self.setSeed() + actual = normal(loc, scale * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, normal, loc, bad_scale * 3) + + def test_beta(self): + a = [1] + b = [2] + bad_a = [-1] + bad_b = [-2] + beta = np.random.beta + desired = np.array([0.19843558305989056, + 0.075230336409423643, + 0.24976865978980844]) + + self.setSeed() + actual = beta(a * 3, b) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, beta, bad_a * 3, b) + assert_raises(ValueError, beta, a * 3, bad_b) + + self.setSeed() + actual = beta(a, b * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, beta, bad_a, b * 3) + assert_raises(ValueError, beta, a, bad_b * 3) + + def test_exponential(self): + scale = [1] + bad_scale = [-1] + exponential = np.random.exponential + desired = np.array([0.76106853658845242, + 0.76386282278691653, + 0.71243813125891797]) + + self.setSeed() + actual = exponential(scale * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, exponential, bad_scale * 3) + + def test_standard_gamma(self): + shape = [1] + bad_shape = [-1] + std_gamma = np.random.standard_gamma + desired = np.array([0.76106853658845242, + 0.76386282278691653, + 0.71243813125891797]) + + self.setSeed() + actual = std_gamma(shape * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, std_gamma, bad_shape * 3) + + def test_gamma(self): + shape = [1] + scale = [2] + bad_shape = [-1] + bad_scale = [-2] + gamma = np.random.gamma + desired = np.array([1.5221370731769048, + 1.5277256455738331, + 1.4248762625178359]) + + self.setSeed() + actual = gamma(shape * 3, scale) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, gamma, bad_shape * 3, scale) + assert_raises(ValueError, gamma, shape * 3, bad_scale) + + self.setSeed() + actual = gamma(shape, scale * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, gamma, bad_shape, scale * 3) + assert_raises(ValueError, gamma, shape, bad_scale * 3) + + def test_f(self): + dfnum = [1] + dfden = [2] + bad_dfnum = [-1] + bad_dfden = [-2] + f = np.random.f + desired = np.array([0.80038951638264799, + 0.86768719635363512, + 2.7251095168386801]) + + self.setSeed() + actual = f(dfnum * 3, dfden) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, f, bad_dfnum * 3, dfden) + assert_raises(ValueError, f, dfnum * 3, bad_dfden) + + self.setSeed() + actual = f(dfnum, dfden * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, f, bad_dfnum, dfden * 3) + assert_raises(ValueError, f, dfnum, bad_dfden * 3) + + def test_noncentral_f(self): + dfnum = [2] + dfden = [3] + nonc = [4] + bad_dfnum = [0] + bad_dfden = [-1] + bad_nonc = [-2] + nonc_f = np.random.noncentral_f + desired = np.array([9.1393943263705211, + 13.025456344595602, + 8.8018098359100545]) + + self.setSeed() + actual = nonc_f(dfnum * 3, dfden, nonc) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc) + assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc) + assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc) + + self.setSeed() + actual = nonc_f(dfnum, dfden * 3, nonc) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc) + assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc) + assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc) + + self.setSeed() + actual = nonc_f(dfnum, dfden, nonc * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3) + assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3) + assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3) + + def test_chisquare(self): + df = [1] + bad_df = [-1] + chisquare = np.random.chisquare + desired = np.array([0.57022801133088286, + 0.51947702108840776, + 0.1320969254923558]) + + self.setSeed() + actual = chisquare(df * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, chisquare, bad_df * 3) + + def test_noncentral_chisquare(self): + df = [1] + nonc = [2] + bad_df = [-1] + bad_nonc = [-2] + nonc_chi = np.random.noncentral_chisquare + desired = np.array([9.0015599467913763, + 4.5804135049718742, + 6.0872302432834564]) + + self.setSeed() + actual = nonc_chi(df * 3, nonc) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, nonc_chi, bad_df * 3, nonc) + assert_raises(ValueError, nonc_chi, df * 3, bad_nonc) + + self.setSeed() + actual = nonc_chi(df, nonc * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, nonc_chi, bad_df, nonc * 3) + assert_raises(ValueError, nonc_chi, df, bad_nonc * 3) + + def test_standard_t(self): + df = [1] + bad_df = [-1] + t = np.random.standard_t + desired = np.array([3.0702872575217643, + 5.8560725167361607, + 1.0274791436474273]) + + self.setSeed() + actual = t(df * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, t, bad_df * 3) + + def test_vonmises(self): + mu = [2] + kappa = [1] + bad_kappa = [-1] + vonmises = np.random.vonmises + desired = np.array([2.9883443664201312, + -2.7064099483995943, + -1.8672476700665914]) + + self.setSeed() + actual = vonmises(mu * 3, kappa) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, vonmises, mu * 3, bad_kappa) + + self.setSeed() + actual = vonmises(mu, kappa * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, vonmises, mu, bad_kappa * 3) + + def test_pareto(self): + a = [1] + bad_a = [-1] + pareto = np.random.pareto + desired = np.array([1.1405622680198362, + 1.1465519762044529, + 1.0389564467453547]) + + self.setSeed() + actual = pareto(a * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, pareto, bad_a * 3) + + def test_weibull(self): + a = [1] + bad_a = [-1] + weibull = np.random.weibull + desired = np.array([0.76106853658845242, + 0.76386282278691653, + 0.71243813125891797]) + + self.setSeed() + actual = weibull(a * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, weibull, bad_a * 3) + + def test_power(self): + a = [1] + bad_a = [-1] + power = np.random.power + desired = np.array([0.53283302478975902, + 0.53413660089041659, + 0.50955303552646702]) + + self.setSeed() + actual = power(a * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, power, bad_a * 3) + + def test_laplace(self): + loc = [0] + scale = [1] + bad_scale = [-1] + laplace = np.random.laplace + desired = np.array([0.067921356028507157, + 0.070715642226971326, + 0.019290950698972624]) + + self.setSeed() + actual = laplace(loc * 3, scale) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, laplace, loc * 3, bad_scale) + + self.setSeed() + actual = laplace(loc, scale * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, laplace, loc, bad_scale * 3) + + def test_gumbel(self): + loc = [0] + scale = [1] + bad_scale = [-1] + gumbel = np.random.gumbel + desired = np.array([0.2730318639556768, + 0.26936705726291116, + 0.33906220393037939]) + + self.setSeed() + actual = gumbel(loc * 3, scale) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, gumbel, loc * 3, bad_scale) + + self.setSeed() + actual = gumbel(loc, scale * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, gumbel, loc, bad_scale * 3) + + def test_logistic(self): + loc = [0] + scale = [1] + bad_scale = [-1] + logistic = np.random.logistic + desired = np.array([0.13152135837586171, + 0.13675915696285773, + 0.038216792802833396]) + + self.setSeed() + actual = logistic(loc * 3, scale) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, logistic, loc * 3, bad_scale) + + self.setSeed() + actual = logistic(loc, scale * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, logistic, loc, bad_scale * 3) + + def test_lognormal(self): + mean = [0] + sigma = [1] + bad_sigma = [-1] + lognormal = np.random.lognormal + desired = np.array([9.1422086044848427, + 8.4013952870126261, + 6.3073234116578671]) + + self.setSeed() + actual = lognormal(mean * 3, sigma) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, lognormal, mean * 3, bad_sigma) + + self.setSeed() + actual = lognormal(mean, sigma * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, lognormal, mean, bad_sigma * 3) + + def test_rayleigh(self): + scale = [1] + bad_scale = [-1] + rayleigh = np.random.rayleigh + desired = np.array([1.2337491937897689, + 1.2360119924878694, + 1.1936818095781789]) + + self.setSeed() + actual = rayleigh(scale * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, rayleigh, bad_scale * 3) + + def test_wald(self): + mean = [0.5] + scale = [1] + bad_mean = [0] + bad_scale = [-2] + wald = np.random.wald + desired = np.array([0.11873681120271318, + 0.12450084820795027, + 0.9096122728408238]) + + self.setSeed() + actual = wald(mean * 3, scale) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, wald, bad_mean * 3, scale) + assert_raises(ValueError, wald, mean * 3, bad_scale) + + self.setSeed() + actual = wald(mean, scale * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, wald, bad_mean, scale * 3) + assert_raises(ValueError, wald, mean, bad_scale * 3) + + def test_triangular(self): + left = [1] + right = [3] + mode = [2] + bad_left_one = [3] + bad_mode_one = [4] + bad_left_two, bad_mode_two = right * 2 + triangular = np.random.triangular + desired = np.array([2.03339048710429, + 2.0347400359389356, + 2.0095991069536208]) + + self.setSeed() + actual = triangular(left * 3, mode, right) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, triangular, bad_left_one * 3, mode, right) + assert_raises(ValueError, triangular, left * 3, bad_mode_one, right) + assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two, right) + + self.setSeed() + actual = triangular(left, mode * 3, right) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, triangular, bad_left_one, mode * 3, right) + assert_raises(ValueError, triangular, left, bad_mode_one * 3, right) + assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3, right) + + self.setSeed() + actual = triangular(left, mode, right * 3) + assert_array_almost_equal(actual, desired, decimal=14) + assert_raises(ValueError, triangular, bad_left_one, mode, right * 3) + assert_raises(ValueError, triangular, left, bad_mode_one, right * 3) + assert_raises(ValueError, triangular, bad_left_two, bad_mode_two, right * 3) + + def test_binomial(self): + n = [1] + p = [0.5] + bad_n = [-1] + bad_p_one = [-1] + bad_p_two = [1.5] + binom = np.random.binomial + desired = np.array([1, 1, 1]) + + self.setSeed() + actual = binom(n * 3, p) + assert_array_equal(actual, desired) + assert_raises(ValueError, binom, bad_n * 3, p) + assert_raises(ValueError, binom, n * 3, bad_p_one) + assert_raises(ValueError, binom, n * 3, bad_p_two) + + self.setSeed() + actual = binom(n, p * 3) + assert_array_equal(actual, desired) + assert_raises(ValueError, binom, bad_n, p * 3) + assert_raises(ValueError, binom, n, bad_p_one * 3) + assert_raises(ValueError, binom, n, bad_p_two * 3) + + def test_negative_binomial(self): + n = [1] + p = [0.5] + bad_n = [-1] + bad_p_one = [-1] + bad_p_two = [1.5] + neg_binom = np.random.negative_binomial + desired = np.array([1, 0, 1]) + + self.setSeed() + actual = neg_binom(n * 3, p) + assert_array_equal(actual, desired) + assert_raises(ValueError, neg_binom, bad_n * 3, p) + assert_raises(ValueError, neg_binom, n * 3, bad_p_one) + assert_raises(ValueError, neg_binom, n * 3, bad_p_two) + + self.setSeed() + actual = neg_binom(n, p * 3) + assert_array_equal(actual, desired) + assert_raises(ValueError, neg_binom, bad_n, p * 3) + assert_raises(ValueError, neg_binom, n, bad_p_one * 3) + assert_raises(ValueError, neg_binom, n, bad_p_two * 3) + + def test_poisson(self): + max_lam = np.random.RandomState().poisson_lam_max + + lam = [1] + bad_lam_one = [-1] + bad_lam_two = [max_lam * 2] + poisson = np.random.poisson + desired = np.array([1, 1, 0]) + + self.setSeed() + actual = poisson(lam * 3) + assert_array_equal(actual, desired) + assert_raises(ValueError, poisson, bad_lam_one * 3) + assert_raises(ValueError, poisson, bad_lam_two * 3) + + def test_zipf(self): + a = [2] + bad_a = [0] + zipf = np.random.zipf + desired = np.array([2, 2, 1]) + self.setSeed() + actual = zipf(a * 3) + assert_array_equal(actual, desired) + assert_raises(ValueError, zipf, bad_a * 3) -class TestThread(object): + def test_geometric(self): + p = [0.5] + bad_p_one = [-1] + bad_p_two = [1.5] + geom = np.random.geometric + desired = np.array([2, 2, 2]) + + self.setSeed() + actual = geom(p * 3) + assert_array_equal(actual, desired) + assert_raises(ValueError, geom, bad_p_one * 3) + assert_raises(ValueError, geom, bad_p_two * 3) + + def test_hypergeometric(self): + ngood = [1] + nbad = [2] + nsample = [2] + bad_ngood = [-1] + bad_nbad = [-2] + bad_nsample_one = [0] + bad_nsample_two = [4] + hypergeom = np.random.hypergeometric + desired = np.array([1, 1, 1]) + + self.setSeed() + actual = hypergeom(ngood * 3, nbad, nsample) + assert_array_equal(actual, desired) + assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample) + assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample) + assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one) + assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two) + + self.setSeed() + actual = hypergeom(ngood, nbad * 3, nsample) + assert_array_equal(actual, desired) + assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample) + assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample) + assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one) + assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two) + + self.setSeed() + actual = hypergeom(ngood, nbad, nsample * 3) + assert_array_equal(actual, desired) + assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3) + assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3) + assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3) + assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3) + + def test_logseries(self): + p = [0.5] + bad_p_one = [2] + bad_p_two = [-1] + logseries = np.random.logseries + desired = np.array([1, 1, 1]) + + self.setSeed() + actual = logseries(p * 3) + assert_array_equal(actual, desired) + assert_raises(ValueError, logseries, bad_p_one * 3) + assert_raises(ValueError, logseries, bad_p_two * 3) + + +class TestThread(TestCase): # make sure each state produces the same sequence even in threads def setUp(self): self.seeds = range(4) @@ -813,10 +1368,10 @@ class TestThread(object): function(np.random.RandomState(s), o) # these platforms change x87 fpu precision mode in threads - if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"): - np.testing.assert_array_almost_equal(out1, out2) + if np.intp().dtype.itemsize == 4 and sys.platform == "win32": + assert_array_almost_equal(out1, out2) else: - np.testing.assert_array_equal(out1, out2) + assert_array_equal(out1, out2) def test_normal(self): def gen_random(state, out): @@ -831,8 +1386,7 @@ class TestThread(object): def test_multinomial(self): def gen_random(state, out): out[...] = state.multinomial(10, [1/6.]*6, size=10000) - self.check_function(gen_random, sz=(10000,6)) - + self.check_function(gen_random, sz=(10000, 6)) if __name__ == "__main__": run_module_suite() |