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Add doc strings to all random methods for discrete distribution classes
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‎pymc3/distributions/discrete.py

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@@ -66,6 +66,22 @@ def __init__(self, n, p, *args, **kwargs):
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self.mode = tt.cast(tround(n * p), self.dtype)
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def random(self, point=None, size=None):
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"""
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Draw random values from Binomial distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
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n, p = draw_values([self.n, self.p], point=point, size=size)
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return generate_samples(stats.binom.rvs, n=n, p=p,
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dist_shape=self.shape,
@@ -172,6 +188,22 @@ def _random(self, alpha, beta, n, size=None):
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return samples
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def random(self, point=None, size=None):
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"""
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Draw random values from BetaBinomial distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
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alpha, beta, n = \
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draw_values([self.alpha, self.beta, self.n], point=point, size=size)
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return generate_samples(self._random, alpha=alpha, beta=beta, n=n,
@@ -254,6 +286,22 @@ def __init__(self, p=None, logit_p=None, *args, **kwargs):
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self.mode = tt.cast(tround(self.p), 'int8')
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def random(self, point=None, size=None):
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"""
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Draw random values from Bernoulli distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
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p = draw_values([self.p], point=point, size=size)[0]
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return generate_samples(stats.bernoulli.rvs, p,
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dist_shape=self.shape,
@@ -350,6 +398,22 @@ def _random(self, q, beta, size=None):
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return np.ceil(np.power(np.log(1 - p) / np.log(q), 1. / beta)) - 1
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352400
def random(self, point=None, size=None):
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"""
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Draw random values from DiscreteWeibull distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
353417
q, beta = draw_values([self.q, self.beta], point=point, size=size)
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return generate_samples(self._random, q, beta,
@@ -417,6 +481,22 @@ def __init__(self, mu, *args, **kwargs):
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self.mode = intX(tt.floor(mu))
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def random(self, point=None, size=None):
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"""
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Draw random values from Poisson distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
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mu = draw_values([self.mu], point=point, size=size)[0]
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return generate_samples(stats.poisson.rvs, mu,
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dist_shape=self.shape,
@@ -497,6 +577,22 @@ def __init__(self, mu, alpha, *args, **kwargs):
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self.mode = intX(tt.floor(mu))
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def random(self, point=None, size=None):
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"""
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Draw random values from NegativeBinomial distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
500596
mu, alpha = draw_values([self.mu, self.alpha], point=point, size=size)
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g = generate_samples(stats.gamma.rvs, alpha, scale=mu / alpha,
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dist_shape=self.shape,
@@ -571,6 +667,22 @@ def __init__(self, p, *args, **kwargs):
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self.mode = 1
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def random(self, point=None, size=None):
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"""
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Draw random values from Geometric distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
574686
p = draw_values([self.p], point=point, size=size)[0]
575687
return generate_samples(np.random.geometric, p,
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dist_shape=self.shape,
@@ -643,6 +755,22 @@ def _random(self, lower, upper, size=None):
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return samples
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def random(self, point=None, size=None):
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"""
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Draw random values from DiscreteUniform distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
646774
lower, upper = draw_values([self.lower, self.upper], point=point, size=size)
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return generate_samples(self._random,
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lower, upper,
@@ -717,6 +845,22 @@ def __init__(self, p, *args, **kwargs):
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self.mode = tt.squeeze(self.mode)
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def random(self, point=None, size=None):
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"""
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Draw random values from Categorical distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
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p, k = draw_values([self.p, self.k], point=point, size=size)
721865
p = p / np.sum(p, axis=-1, keepdims=True)
722866

@@ -770,6 +914,22 @@ def __init__(self, c, *args, **kwargs):
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self.mean = self.median = self.mode = self.c = c = tt.as_tensor_variable(c)
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def random(self, point=None, size=None):
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"""
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Draw random values from Constant distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
773933
c = draw_values([self.c], point=point, size=size)[0]
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dtype = np.array(c).dtype
775935

@@ -851,6 +1011,22 @@ def __init__(self, psi, theta, *args, **kwargs):
8511011
self.mode = self.pois.mode
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8531013
def random(self, point=None, size=None):
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"""
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Draw random values from ZeroInflatedPoisson distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
8541030
theta, psi = draw_values([self.theta, self.psi], point=point, size=size)
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g = generate_samples(stats.poisson.rvs, theta,
8561032
dist_shape=self.shape,
@@ -944,6 +1120,22 @@ def __init__(self, psi, n, p, *args, **kwargs):
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self.mode = self.bin.mode
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9461122
def random(self, point=None, size=None):
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"""
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Draw random values from ZeroInflatedBinomial distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
9471139
n, p, psi = draw_values([self.n, self.p, self.psi], point=point, size=size)
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g = generate_samples(stats.binom.rvs, n, p,
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dist_shape=self.shape,
@@ -1061,6 +1253,22 @@ def __init__(self, psi, mu, alpha, *args, **kwargs):
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self.mode = self.nb.mode
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10631255
def random(self, point=None, size=None):
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"""
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Draw random values from ZeroInflatedNegativeBinomial distribution.
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Parameters
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----------
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point : dict, optional
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Dict of variable values on which random values are to be
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conditioned (uses default point if not specified).
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size : int, optional
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Desired size of random sample (returns one sample if not
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specified).
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Returns
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-------
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array
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"""
10641272
mu, alpha, psi = draw_values(
10651273
[self.mu, self.alpha, self.psi], point=point, size=size)
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g = generate_samples(stats.gamma.rvs, alpha, scale=mu / alpha,

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