diff options
Diffstat (limited to 'numpy/random')
| -rw-r--r-- | numpy/random/mtrand/mtrand.pyx | 81 |
1 files changed, 70 insertions, 11 deletions
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx index 7ac9fc12c..b3b676b9c 100644 --- a/numpy/random/mtrand/mtrand.pyx +++ b/numpy/random/mtrand/mtrand.pyx @@ -583,9 +583,22 @@ cdef class RandomState: """ get_state() - Return a tuple representing the internal state of the generator:: + Return a tuple representing the internal state of the generator. - ('MT19937', int key[624], int pos, int has_gauss, float cached_gaussian) + Returns + ------- + out : tuple(string, list of 624 integers, int, int, float) + The returned tuple has the following items: + + 1. the string 'MT19937' + 2. a list of 624 integer keys + 3. an integer pos + 4. an integer has_gauss + 5. and a float cached_gaussian + + See Also + -------- + set_state """ cdef ndarray state "arrayObject_state" @@ -601,8 +614,28 @@ cdef class RandomState: Set the state from a tuple. - state = ('MT19937', int key[624], int pos, int has_gauss, float cached_gaussian) + Parameters + ---------- + state : tuple(string, list of 624 ints, int, int, float) + The `state` tuple is made up of + + 1. the string 'MT19937' + 2. a list of 624 integer keys + 3. an integer pos + 4. an integer has_gauss + 5. and a float for the cached_gaussian + + Returns + ------- + out : None + Returns 'None' on success. + + See Also + -------- + get_state + Notes + ----- For backwards compatibility, the following form is also accepted although it is missing some information about the cached Gaussian value. @@ -649,6 +682,16 @@ cdef class RandomState: Return random floats in the half-open interval [0.0, 1.0). + Parameters + ---------- + size : shape tuple, optional + Defines the shape of the returned array of random floats. + + Returns + ------- + out : ndarray, floats + Array of random of floats with shape of `size`. + """ return cont0_array(self.internal_state, rk_double, size) @@ -911,7 +954,19 @@ cdef class RandomState: """ standard_normal(size=None) - Standard Normal distribution (mean=0, stdev=1). + Returns samples from a Standard Normal distribution (mean=0, stdev=1). + + Parameters + ---------- + size : int, shape tuple, optional + Returns the number of samples required to satisfy the `size` parameter. + If not given or 'None' indicates to return one sample. + + Returns + ------- + out : float, ndarray + Samples the Standard Normal distribution with a shape satisfying the + `size` parameter. """ return cont0_array(self.internal_state, rk_gauss, size) @@ -1083,10 +1138,12 @@ cdef class RandomState: Its probability density function is - .. math:: f(x; \\lambda) = \\lambda \\exp(-\\lambda x), + .. math:: f(x; \\frac{1}{\\beta}) = \\frac{1}{\\beta} \\exp(-\\frac{x}{\\beta}), - for ``x > 0`` and 0 elsewhere. :math:`lambda` is - known as the rate parameter. + for ``x > 0`` and 0 elsewhere. :math:`\\beta` is the scale parameter, + which is the inverse of the rate parameter :math:`\\lambda = 1/\\beta`. + The rate parameter is an alternative, widely used parameterization + of the exponential distribution [3]_. The exponential distribution is a continuous analogue of the geometric distribution. It describes many common situations, such as @@ -1096,7 +1153,7 @@ cdef class RandomState: Parameters ---------- scale : float - The rate parameter, :math:`\\lambda`. + The scale parameter, :math:`\\beta = 1/\\lambda`. size : tuple of ints Number of samples to draw. The output is shaped according to `size`. @@ -1107,6 +1164,8 @@ cdef class RandomState: Random Signal Principles", 4th ed, 2001, p. 57. .. [2] "Poisson Process", Wikipedia, http://en.wikipedia.org/wiki/Poisson_process + .. [3] "Exponential Distribution, Wikipedia, + http://en.wikipedia.org/wiki/Exponential_distribution """ cdef ndarray oscale @@ -1800,11 +1859,11 @@ cdef class RandomState: >>> import matplotlib.pyplot as plt >>> def weib(x,n,a): - ... return (a/n)*(x/n)**(a-1)*exp(-(x/n)**a) + ... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a) - >>> count, bins, ignored = plt.hist(numpy.random.weibull(5.,1000)) + >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000)) + >>> x = np.arange(1,100.)/50. >>> scale = count.max()/weib(x, 1., 5.).max() - >>> x = arange(1,100.)/50. >>> plt.plot(x, weib(x, 1., 5.)*scale) >>> plt.show() |