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Merge pull request #49 from bashtage/sync-upstream
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DOC: UPdate docs to reflect upstream changes
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bashtage authored Mar 15, 2019
2 parents b952810 + 8018200 commit cd0cdae
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5 changes: 5 additions & 0 deletions doc/source/change-log.rst
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@@ -1,5 +1,10 @@
Change Log
----------
v1.16.1
=======
- Synchronized with upstream changes.
- Fixed a bug in gamma generation if the shape parameters is 0.0.

v1.16.0
=======
- Fixed a bug that affected :class:`~randomgen.dsfmt.DSFMT` when calling
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116 changes: 81 additions & 35 deletions randomgen/generator.pyx
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Expand Up @@ -429,7 +429,8 @@ cdef class RandomGenerator:
Parameters
----------
scale : float or array_like of floats
The scale parameter, :math:`\\beta = 1/\\lambda`.
The scale parameter, :math:`\\beta = 1/\\lambda`. Must 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. If size is ``None`` (default),
Expand Down Expand Up @@ -855,6 +856,8 @@ cdef class RandomGenerator:
if size > pop_size:
raise ValueError("Cannot take a larger sample than "
"population when 'replace=False'")
elif size < 0:
raise ValueError("negative dimensions are not allowed")

if p is not None:
if np.count_nonzero(p > 0) < size:
Expand Down Expand Up @@ -1017,6 +1020,12 @@ cdef class RandomGenerator:
Random values in a given shape.
.. note::
This is a convenience function for users porting code from Matlab,
and wraps `numpy.random.random_sample`. That function takes a
tuple to specify the size of the output, which is consistent with
other NumPy functions like `numpy.zeros` and `numpy.ones`.
Create an array of the given shape and populate it with
random samples from a uniform distribution
over ``[0, 1)``.
Expand Down Expand Up @@ -1065,16 +1074,20 @@ cdef class RandomGenerator:
Return a sample (or samples) from the "standard normal" distribution.
If positive, int_like or int-convertible arguments are provided,
`randn` generates an array of shape ``(d0, d1, ..., dn)``, filled
.. note::
This is a convenience function for users porting code from Matlab,
and wraps `numpy.random.standard_normal`. That function takes a
tuple to specify the size of the output, which is consistent with
other NumPy functions like `numpy.zeros` and `numpy.ones`.
If positive int_like arguments are provided, `randn` generates an array
of shape ``(d0, d1, ..., dn)``, filled
with random floats sampled from a univariate "normal" (Gaussian)
distribution of mean 0 and variance 1 (if any of the :math:`d_i` are
floats, they are first converted to integers by truncation). A single
float randomly sampled from the distribution is returned if no
argument is provided.
distribution of mean 0 and variance 1. A single float randomly sampled
from the distribution is returned if no argument is provided.
This is a convenience function. If you want an interface that takes a
tuple as the first argument, use `standard_normal` instead.
tuple as the first argument, use `numpy.random.standard_normal` instead.
Parameters
----------
Expand All @@ -1096,6 +1109,7 @@ cdef class RandomGenerator:
See Also
--------
standard_normal : Similar, but takes a tuple as its argument.
normal : Also accepts mu and sigma arguments
Notes
-----
Expand All @@ -1106,13 +1120,13 @@ cdef class RandomGenerator:
Examples
--------
>>> randomgen.generator.randn()
2.1923875335537315 #random
2.1923875335537315 # random
Two-by-four array of samples from N(3, 6.25):
>>> 2.5 * randomgen.generator.randn(2, 4) + 3
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random
>>> 3 + 2.5 * np.random.randn(2, 4)
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random
"""
if len(args) == 0:
Expand Down Expand Up @@ -1237,20 +1251,43 @@ cdef class RandomGenerator:
Returns
-------
out : float or ndarray
Drawn samples.
A floating-point array of shape ``size`` of drawn samples, or a
single sample if ``size`` was not specified.
Notes
-----
For random samples from :math:`N(\\mu, \\sigma^2)`, use one of::
mu + sigma * np.random.standard_normal(size=...)
np.random.normal(mu, sigma, size=...)
See Also
--------
normal :
Equivalent function with additional ``loc`` and ``scale`` arguments
for setting the mean and standard deviation.
Examples
--------
>>> s = randomgen.generator.standard_normal(8000)
>>> np.random.standard_normal()
2.1923875335537315 #random
>>> s = np.random.standard_normal(8000)
>>> s
array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, #random
-0.38672696, -0.4685006 ]) #random
array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, # random
-0.38672696, -0.4685006 ]) # random
>>> s.shape
(8000,)
>>> s = randomgen.generator.standard_normal(size=(3, 4, 2))
>>> s = np.random.standard_normal(size=(3, 4, 2))
>>> s.shape
(3, 4, 2)
Two-by-four array of samples from :math:`N(3, 6.25)`:
>>> 3 + 2.5 * np.random.standard_normal(size=(2, 4))
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random
"""
key = np.dtype(dtype).name
if key == 'float64':
Expand Down Expand Up @@ -1283,7 +1320,8 @@ cdef class RandomGenerator:
loc : float or array_like of floats
Mean ("centre") of the distribution.
scale : float or array_like of floats
Standard deviation (spread or "width") of the distribution.
Standard deviation (spread or "width") of the distribution. Must 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. If size is ``None`` (default),
Expand Down Expand Up @@ -1334,11 +1372,11 @@ cdef class RandomGenerator:
Verify the mean and the variance:
>>> abs(mu - np.mean(s)) < 0.01
True
>>> abs(mu - np.mean(s))
0.0 # may vary
>>> abs(sigma - np.std(s, ddof=1)) < 0.01
True
>>> abs(sigma - np.std(s, ddof=1))
0.1 # may vary
Display the histogram of the samples, along with
the probability density function:
Expand All @@ -1350,6 +1388,12 @@ cdef class RandomGenerator:
... linewidth=2, color='r')
>>> plt.show()
Two-by-four array of samples from N(3, 6.25):
>>> np.random.normal(3, 2.5, size=(2, 4))
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random
"""
return cont(&random_normal_zig, self._brng, size, self.lock, 2,
loc, '', CONS_NONE,
Expand Down Expand Up @@ -1530,7 +1574,7 @@ cdef class RandomGenerator:
Parameters
----------
shape : float or array_like of floats
Parameter, should be > 0.
Parameter, must 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. If size is ``None`` (default),
Expand Down Expand Up @@ -1622,9 +1666,9 @@ cdef class RandomGenerator:
Parameters
----------
shape : float or array_like of floats
The shape of the gamma distribution. Should be greater than zero.
The shape of the gamma distribution. Must be non-negative.
scale : float or array_like of floats, optional
The scale of the gamma distribution. Should be greater than zero.
The scale of the gamma distribution. Must be non-negative.
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
Expand Down Expand Up @@ -1706,9 +1750,9 @@ cdef class RandomGenerator:
Parameters
----------
dfnum : int or array_like of ints
Degrees of freedom in numerator. Should be greater than zero.
Degrees of freedom in numerator. Must be non-negative.
dfden : int or array_like of ints
Degrees of freedom in denominator. Should be greater than zero.
Degrees of freedom in denominator. Must 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. If size is ``None`` (default),
Expand Down Expand Up @@ -2262,7 +2306,7 @@ cdef class RandomGenerator:
Parameters
----------
a : float or array_like of floats
Shape of the distribution. Should be greater than zero.
Shape of the distribution. All values must be positive.
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. If size is ``None`` (default),
Expand Down Expand Up @@ -2443,7 +2487,7 @@ cdef class RandomGenerator:
Parameters
----------
a : float or array_like of floats
Parameter of the distribution. Should be greater than zero.
Parameter of the distribution. Must be positive.
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. If size is ``None`` (default),
Expand Down Expand Up @@ -2548,7 +2592,8 @@ cdef class RandomGenerator:
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.
:math:`\\lambda`, the exponential decay. Default is 1. Must 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. If size is ``None`` (default),
Expand Down Expand Up @@ -2630,7 +2675,8 @@ cdef class RandomGenerator:
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.
The scale parameter of the distribution. Default is 1. Must 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. If size is ``None`` (default),
Expand Down Expand Up @@ -2828,8 +2874,8 @@ cdef class RandomGenerator:
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.
Standard deviation of the underlying normal distribution. Must be
non-negative. 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. If size is ``None`` (default),
Expand Down Expand Up @@ -2934,7 +2980,7 @@ cdef class RandomGenerator:
Parameters
----------
scale : float or array_like of floats, optional
Scale, also equals the mode. Should be >= 0. Default is 1.
Scale, also equals the mode. Must be non-negative. 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. If size is ``None`` (default),
Expand Down Expand Up @@ -3463,7 +3509,7 @@ cdef class RandomGenerator:
Parameters
----------
a : float or array_like of floats
Distribution parameter. Should be greater than 1.
Distribution parameter. Must 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. If size is ``None`` (default),
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4 changes: 4 additions & 0 deletions randomgen/tests/test_numpy_mt19937.py
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Original file line number Diff line number Diff line change
Expand Up @@ -558,6 +558,10 @@ def test_choice_exceptions(self):
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)
# gh-13087
assert_raises(ValueError, sample, [1, 2, 3], -2, replace=False)
assert_raises(ValueError, sample, [1, 2, 3], (-1,), replace=False)
assert_raises(ValueError, sample, [1, 2, 3], (-1, 1), replace=False)
assert_raises(ValueError, sample, [1, 2, 3], 2,
replace=False, p=[1, 0, 0])

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