diff options
Diffstat (limited to 'numpy/random/mtrand.pyx')
| -rw-r--r-- | numpy/random/mtrand.pyx | 44 |
1 files changed, 22 insertions, 22 deletions
diff --git a/numpy/random/mtrand.pyx b/numpy/random/mtrand.pyx index b5d6ff9bc..145af4c7c 100644 --- a/numpy/random/mtrand.pyx +++ b/numpy/random/mtrand.pyx @@ -90,8 +90,10 @@ cdef class RandomState: self.lock = brng.lock def __dealloc__(self): - free(self._binomial) - free(self._aug_state) + if self._binomial: + free(self._binomial) + if self._aug_state: + free(self._aug_state) def __repr__(self): return self.__str__() + ' at 0x{:X}'.format(id(self)) @@ -135,7 +137,7 @@ cdef class RandomState: The best method to access seed is to directly use a basic RNG instance. This example demonstrates this best practice. - >>> from numpy.random.randomgen import MT19937 + >>> from numpy.random import MT19937 >>> from numpy.random import RandomState >>> brng = MT19937(123456789) >>> rs = RandomState(brng) @@ -479,8 +481,6 @@ cdef class RandomState: [ 739731006, 1947757578]], [[1871712945, 752307660], [1601631370, 1479324245]]]) - >>> np.iinfo(np.int).max - 2147483647 >>> rs.tomaxint((2,2,2)) < np.iinfo(np.int).max array([[[ True, True], [ True, True]], @@ -1358,11 +1358,11 @@ cdef class RandomState: the probability density function: >>> import matplotlib.pyplot as plt - >>> import scipy.special as sps + >>> import scipy.special as sps # doctest: +SKIP >>> count, bins, ignored = plt.hist(s, 50, density=True) - >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\ + >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ # doctest: +SKIP ... (sps.gamma(shape) * scale**shape)) - >>> plt.plot(bins, y, linewidth=2, color='r') + >>> plt.plot(bins, y, linewidth=2, color='r') # doctest: +SKIP >>> plt.show() """ @@ -1437,11 +1437,11 @@ cdef class RandomState: the probability density function: >>> import matplotlib.pyplot as plt - >>> import scipy.special as sps + >>> import scipy.special as sps # doctest: +SKIP >>> count, bins, ignored = plt.hist(s, 50, density=True) - >>> y = bins**(shape-1)*(np.exp(-bins/scale) / + >>> y = bins**(shape-1)*(np.exp(-bins/scale) / # doctest: +SKIP ... (sps.gamma(shape)*scale**shape)) - >>> plt.plot(bins, y, linewidth=2, color='r') + >>> plt.plot(bins, y, linewidth=2, color='r') # doctest: +SKIP >>> plt.show() """ @@ -1991,11 +1991,11 @@ cdef class RandomState: the probability density function: >>> import matplotlib.pyplot as plt - >>> from scipy.special import i0 + >>> from scipy.special import i0 # doctest: +SKIP >>> plt.hist(s, 50, density=True) >>> x = np.linspace(-np.pi, np.pi, num=51) - >>> y = np.exp(kappa*np.cos(x-mu))/(2*np.pi*i0(kappa)) - >>> plt.plot(x, y, linewidth=2, color='r') + >>> y = np.exp(kappa*np.cos(x-mu))/(2*np.pi*i0(kappa)) # doctest: +SKIP + >>> plt.plot(x, y, linewidth=2, color='r') # doctest: +SKIP >>> plt.show() """ @@ -2273,25 +2273,25 @@ cdef class RandomState: Compare the power function distribution to the inverse of the Pareto. - >>> from scipy import stats + >>> from scipy import stats # doctest: +SKIP >>> rvs = np.random.power(5, 1000000) >>> rvsp = np.random.pareto(5, 1000000) >>> xx = np.linspace(0,1,100) - >>> powpdf = stats.powerlaw.pdf(xx,5) + >>> powpdf = stats.powerlaw.pdf(xx,5) # doctest: +SKIP >>> plt.figure() >>> plt.hist(rvs, bins=50, density=True) - >>> plt.plot(xx,powpdf,'r-') + >>> plt.plot(xx,powpdf,'r-') # doctest: +SKIP >>> plt.title('np.random.power(5)') >>> plt.figure() >>> plt.hist(1./(1.+rvsp), bins=50, density=True) - >>> plt.plot(xx,powpdf,'r-') + >>> plt.plot(xx,powpdf,'r-') # doctest: +SKIP >>> plt.title('inverse of 1 + np.random.pareto(5)') >>> plt.figure() >>> plt.hist(1./(1.+rvsp), bins=50, density=True) - >>> plt.plot(xx,powpdf,'r-') + >>> plt.plot(xx,powpdf,'r-') # doctest: +SKIP >>> plt.title('inverse of stats.pareto(5)') """ @@ -3282,14 +3282,14 @@ cdef class RandomState: the probability density function: >>> import matplotlib.pyplot as plt - >>> from scipy import special + >>> from scipy import special # doctest: +SKIP Truncate s values at 50 so plot is interesting: >>> count, bins, ignored = plt.hist(s[s<50], 50, density=True) >>> x = np.arange(1., 50.) - >>> y = x**(-a) / special.zetac(a) - >>> plt.plot(x, y/max(y), linewidth=2, color='r') + >>> y = x**(-a) / special.zetac(a) # doctest: +SKIP + >>> plt.plot(x, y/max(y), linewidth=2, color='r') # doctest: +SKIP >>> plt.show() """ |