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Merge d04626d into fab6938
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@fonnesbeck
fonnesbeck committed May 23, 2017
2 parents fab6938 + d04626d commit 30974ed
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197 changes: 197 additions & 0 deletions pymc3/distributions/continuous.py
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Expand Up @@ -15,6 +15,7 @@

from pymc3.theanof import floatX
from . import transforms
from pymc3.util import get_variable_name

from .dist_math import bound, logpow, gammaln, betaln, std_cdf, i0, i1, alltrue_elemwise, DifferentiableSplineWrapper
from .distribution import Continuous, draw_values, generate_samples, Bound
Expand Down Expand Up @@ -152,6 +153,15 @@ def logp(self, value):
return bound(-tt.log(upper - lower),
value >= lower, value <= upper)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
lower = dist.lower
upper = dist.upper
return r'${} \sim \text{{Uniform}}(\mathit{{lower}}={}, \mathit{{upper}}={})$'.format(name,
get_variable_name(lower),
get_variable_name(upper))


class Flat(Continuous):
"""
Expand All @@ -169,6 +179,11 @@ def random(self, point=None, size=None, repeat=None):
def logp(self, value):
return tt.zeros_like(value)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
return r'${} \sim \text{{Flat}()$'


class Normal(Continuous):
R"""
Expand Down Expand Up @@ -232,6 +247,15 @@ def logp(self, value):
return bound((-tau * (value - mu)**2 + tt.log(tau / np.pi / 2.)) / 2.,
sd > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
sd = dist.sd
mu = dist.mu
return r'${} \sim \text{{Normal}}(\mathit{{mu}}={}, \mathit{{sd}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(sd))


class HalfNormal(PositiveContinuous):
R"""
Expand Down Expand Up @@ -283,6 +307,12 @@ def logp(self, value):
value >= 0,
tau > 0, sd > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
sd = dist.sd
return r'${} \sim \text{{HalfNormal}}(\mathit{{sd}}={})$'.format(name,
get_variable_name(sd))

class Wald(PositiveContinuous):
R"""
Expand Down Expand Up @@ -404,6 +434,17 @@ def logp(self, value):
value > 0, value - alpha > 0,
mu > 0, lam > 0, alpha >= 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
lam = dist.lam
mu = dist.mu
alpha = dist.alpha
return r'${} \sim \text{{Wald}}(\mathit{{mu}}={}, \mathit{{lam}}={}, \mathit{{alpha}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(lam),
get_variable_name(alpha))


class Beta(UnitContinuous):
R"""
Expand Down Expand Up @@ -492,6 +533,15 @@ def logp(self, value):
value >= 0, value <= 1,
alpha > 0, beta > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
alpha = dist.alpha
beta = dist.beta
return r'${} \sim \text{{Beta}}(\mathit{{alpha}}={}, \mathit{{alpha}}={})$'.format(name,
get_variable_name(alpha),
get_variable_name(beta))


class Exponential(PositiveContinuous):
R"""
Expand Down Expand Up @@ -534,6 +584,12 @@ def logp(self, value):
lam = self.lam
return bound(tt.log(lam) - lam * value, value > 0, lam > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
lam = dist.lam
return r'${} \sim \text{{Exponential}}(\mathit{{lam}}={})$'.format(name,
get_variable_name(lam))

class Laplace(Continuous):
R"""
Expand Down Expand Up @@ -579,6 +635,15 @@ def logp(self, value):

return -tt.log(2 * b) - abs(value - mu) / b

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
b = dist.b
mu = dist.mu
return r'${} \sim \text{{Laplace}}(\mathit{{mu}}={}, \mathit{{b}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(b))


class Lognormal(PositiveContinuous):
R"""
Expand Down Expand Up @@ -643,6 +708,15 @@ def logp(self, value):
- tt.log(value),
tau > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
tau = dist.tau
mu = dist.mu
return r'${} \sim \text{{Lognormal}}(\mathit{{mu}}={}, \mathit{{tau}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(tau))


class StudentT(Continuous):
R"""
Expand Down Expand Up @@ -707,6 +781,17 @@ def logp(self, value):
- (nu + 1.0) / 2.0 * tt.log1p(lam * (value - mu)**2 / nu),
lam > 0, nu > 0, sd > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
nu = dist.nu
mu = dist.mu
lam = dist.lam
return r'${} \sim \text{{StudentT}}(\mathit{{nu}}={}, \mathit{{mu}}={}, \mathit{{lam}}={})$'.format(name,
get_variable_name(nu),
get_variable_name(mu),
get_variable_name(lam))


class Pareto(PositiveContinuous):
R"""
Expand Down Expand Up @@ -769,6 +854,15 @@ def logp(self, value):
- logpow(value, alpha + 1),
value >= m, alpha > 0, m > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
alpha = dist.alpha
m = dist.m
return r'${} \sim \text{{Pareto}}(\mathit{{alpha}}={}, \mathit{{m}}={})$'.format(name,
get_variable_name(alpha),
get_variable_name(m))


class Cauchy(Continuous):
R"""
Expand Down Expand Up @@ -821,6 +915,15 @@ def logp(self, value):
- tt.log1p(((value - alpha) / beta)**2),
beta > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
alpha = dist.alpha
beta = dist.beta
return r'${} \sim \text{{Cauchy}}(\mathit{{alpha}}={}, \mathit{{beta}}={})$'.format(name,
get_variable_name(alpha),
get_variable_name(beta))


class HalfCauchy(PositiveContinuous):
R"""
Expand Down Expand Up @@ -867,6 +970,12 @@ def logp(self, value):
- tt.log1p((value / beta)**2),
value >= 0, beta > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
beta = dist.beta
return r'${} \sim \text{{HalfCauchy}}(\mathit{{beta}}={})$'.format(name,
get_variable_name(beta))

class Gamma(PositiveContinuous):
R"""
Expand Down Expand Up @@ -950,6 +1059,15 @@ def logp(self, value):
alpha > 0,
beta > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
beta = dist.beta
alpha = dist.alpha
return r'${} \sim \text{{Gamma}}(\mathit{{alpha}}={}, \mathit{{beta}}={})$'.format(name,
get_variable_name(alpha),
get_variable_name(beta))


class InverseGamma(PositiveContinuous):
R"""
Expand Down Expand Up @@ -1011,6 +1129,15 @@ def logp(self, value):
+ logpow(value, -alpha - 1),
value > 0, alpha > 0, beta > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
beta = dist.beta
alpha = dist.alpha
return r'${} \sim \text{{InverseGamma}}(\mathit{{alpha}}={}, \mathit{{beta}}={})$'.format(name,
get_variable_name(alpha),
get_variable_name(beta))


class ChiSquared(Gamma):
R"""
Expand All @@ -1037,6 +1164,13 @@ def __init__(self, nu, *args, **kwargs):
super(ChiSquared, self).__init__(alpha=nu / 2., beta=0.5,
*args, **kwargs)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
nu = dist.nu
return r'${} \sim \Chi^2(\mathit{{nu}}={})$'.format(name,
get_variable_name(nu))


class Weibull(PositiveContinuous):
R"""
Expand Down Expand Up @@ -1093,6 +1227,15 @@ def logp(self, value):
- (value / beta)**alpha,
value >= 0, alpha > 0, beta > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
beta = dist.beta
alpha = dist.alpha
return r'${} \sim \text{{Weibull}}(\mathit{{alpha}}={}, \mathit{{beta}}={})$'.format(name,
get_variable_name(alpha),
get_variable_name(beta))


def StudentTpos(*args, **kwargs):
warnings.warn("StudentTpos has been deprecated. In future, use HalfStudentT instead.",
Expand Down Expand Up @@ -1183,6 +1326,17 @@ def logp(self, value):
- 0.5 * ((value - mu) / sigma)**2)
return bound(lp, sigma > 0., nu > 0.)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
sigma = dist.sigma
mu = dist.mu
nu = dist.nu
return r'${} \sim \text{{ExGaussian}}(\mathit{{mu}}={}, \mathit{{sigma}}={}, \mathit{{nu}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(sigma),
get_variable_name(nu))


class VonMises(Continuous):
R"""
Expand Down Expand Up @@ -1231,6 +1385,16 @@ def logp(self, value):
kappa = self.kappa
return bound(kappa * tt.cos(mu - value) - tt.log(2 * np.pi * i0(kappa)), value >= -np.pi, value <= np.pi, kappa >= 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
kappa = dist.kappa
mu = dist.mu
return r'${} \sim \text{{VonMises}}(\mathit{{mu}}={}, \mathit{{kappa}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(kappa))



class SkewNormal(Continuous):
R"""
Expand Down Expand Up @@ -1306,6 +1470,17 @@ def logp(self, value):
+ tt.log(tau / np.pi / 2.)) / 2.,
tau > 0, sd > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
sd = dist.sd
mu = dist.mu
alpha = dist.alpha
return r'${} \sim \text{{Skew-Normal}}(\mathit{{mu}}={}, \mathit{{sd}}={}, \mathit{{alpha}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(sd),
get_variable_name(alpha))


class Triangular(Continuous):
"""
Expand Down Expand Up @@ -1348,6 +1523,18 @@ def logp(self, value):
tt.switch(alltrue_elemwise([c < value, value <= upper]),
tt.log(2 * (upper - value) / ((upper - lower) * (upper - c))),np.inf)))

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
lower = dist.lower
upper = dist.upper
c = dist.c
return r'${} \sim \text{{Triangular}}(\mathit{{c}}={}, \mathit{{lower}}={}, \mathit{{upper}}={})$'.format(name,
get_variable_name(c),
get_variable_name(lower),
get_variable_name(upper))


class Gumbel(Continuous):
R"""
Univariate Gumbel log-likelihood
Expand Down Expand Up @@ -1391,6 +1578,16 @@ def logp(self, value):
scaled = (value - self.mu) / self.beta
return bound(-scaled - tt.exp(-scaled) - tt.log(self.beta), self.beta > 0)

def _repr_latex_(self, name=None, dist=None):
if dist is None:
dist = self
beta = dist.beta
mu = dist.mu
return r'${} \sim \text{{Gumbel}}(\mathit{{mu}}={}, \mathit{{beta}}={})$'.format(name,
get_variable_name(mu),
get_variable_name(beta))


class Interpolated(Continuous):
R"""
Univariate probability distribution defined as a linear interpolation
Expand Down

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