def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
y = (value - self.loc) / self.scale
z = (np.log(self.scale) + 0.5 * np.log(self.df) + 0.5 * np.log(np.pi) +
gammaln(0.5 * self.df) - gammaln(0.5 * (self.df + 1.)))
return -0.5 * (self.df + 1.) * np.log1p(y**2. / self.df) - z
def binomial_lpmf(k, n, p):
# Credit to https://github.com/pyro-ppl/numpyro/blob/master/numpyro/distributions/discrete.py
log_factorial_n = gammaln(n + 1)
log_factorial_k = gammaln(k + 1)
log_factorial_nmk = gammaln(n - k + 1)
return (log_factorial_n - log_factorial_k - log_factorial_nmk +
xlogy(k, p) + xlog1py(n - k, -p))
def half_students_t(scale, dof):
"""Half-Student's T distribution with support on nogative reals.
Args:
scale (float): Scale parameter (positive).
dof (float): Degrees of freedom parameter (positive).
Returns:
Distribution: Half-Student's T distribution object.
"""
def neg_log_dens(x):
z = x / scale
return (dof + 1) * np.log1p(z**2 / dof) / 2
log_normalizing_constant = (gammaln(dof / 2) - gammaln(
(dof + 1) / 2) + np.log(np.pi * dof) / 2 + np.log(scale / 2))
def sample(rng, shape=()):
return abs(rng.standard_t(df=dof, size=shape) * scale)
return Distribution(
neg_log_dens=neg_log_dens,
log_normalizing_constant=log_normalizing_constant,
sample=sample,
support=nonnegative_reals,
)
def logpdf(self, k):
k = jnp.floor(k)
unnormalized = xlogy(k, self.p) + xlog1py(self.n - k, -self.p)
binomialcoeffln = gammaln(self.n + 1) - (
gammaln(k + 1) + gammaln(self.n - k + 1)
)
return unnormalized + binomialcoeffln
def log_prob(self, value):
log_factorial_n = gammaln(self.total_count + 1)
log_factorial_k = gammaln(value + 1)
log_factorial_nmk = gammaln(self.total_count - value + 1)
normalize_term = (self.total_count * np.clip(self.logits, 0) + xlog1py(
self.total_count, np.exp(-np.abs(self.logits))) - log_factorial_n)
return value * self.logits - log_factorial_k - log_factorial_nmk - normalize_term
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
normalize_term = self.total_count * logsumexp(self.logits, axis=-1) \
- gammaln(self.total_count + 1)
return jnp.sum(value * self.logits - gammaln(value + 1),
axis=-1) - normalize_term
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
normalize_term = (np.sum(gammaln(self.concentration), axis=-1) -
gammaln(np.sum(self.concentration, axis=-1)))
return np.sum(np.log(value) *
(self.concentration - 1.), axis=-1) - normalize_term
def log_prob(self, value):
log_factorial_n = gammaln(self.total_count + 1)
log_factorial_k = gammaln(value + 1)
log_factorial_nmk = gammaln(self.total_count - value + 1)
return (log_factorial_n - log_factorial_k - log_factorial_nmk +
xlogy(value, self.probs) +
xlog1py(self.total_count - value, -self.probs))
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
log_factorial_n = gammaln(self.total_count + 1)
log_factorial_k = gammaln(value + 1)
log_factorial_nmk = gammaln(self.total_count - value + 1)
return (log_factorial_n - log_factorial_k - log_factorial_nmk +
xlogy(value, self.probs) + xlog1py(self.total_count - value, -self.probs))
def _pdf(self, x, df):
# gamma((df+1)/2)
# t.pdf(x, df) = ---------------------------------------------------
# sqrt(pi*df) * gamma(df/2) * (1+x**2/df)**((df+1)/2)
r = np.asarray(df * 1.0)
Px = np.exp(gammaln((r + 1) / 2) - gammaln(r / 2))
Px = Px / np.sqrt(r * np.pi) * (1 + (x**2) / r)**((r + 1) / 2)
return Px
def log_prob(self, value):
log_factorial_n = gammaln(self.total_count + 1)
log_factorial_k = gammaln(value + 1)
log_factorial_nmk = gammaln(self.total_count - value + 1)
return (log_factorial_n - log_factorial_k - log_factorial_nmk +
_log_beta(value + self.concentration1,
self.total_count - value + self.concentration0) -
_log_beta(self.concentration0, self.concentration1))
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
dtype = get_dtypes(self.probs)[0]
value = lax.convert_element_type(value, dtype)
total_count = lax.convert_element_type(self.total_count, dtype)
return gammaln(total_count + 1) + np.sum(
xlogy(value, self.probs) - gammaln(value + 1), axis=-1)
def logpmf(self, x, n, p):
x, n, p = _promote_dtypes(x, n, p)
if self.is_logits:
return gammaln(n + 1) + np.sum(x * p - gammaln(x + 1),
axis=-1) - n * logsumexp(p, axis=-1)
else:
return gammaln(n + 1) + np.sum(xlogy(x, p) - gammaln(x + 1),
axis=-1)
def _kl_dirichlet_dirichlet(p, q):
# From http://bariskurt.com/kullback-leibler-divergence-between-two-dirichlet-and-beta-distributions/
sum_p_concentration = p.concentration.sum(-1)
sum_q_concentration = q.concentration.sum(-1)
t1 = gammaln(sum_p_concentration) - gammaln(sum_q_concentration)
t2 = (gammaln(p.concentration) - gammaln(q.concentration)).sum(-1)
t3 = p.concentration - q.concentration
t4 = digamma(p.concentration) - digamma(sum_p_concentration)[..., None]
return t1 - t2 + (t3 * t4).sum(-1)
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
concentration = lax.convert_element_type(self.concentration,
value.dtype)
normalize_term = (np.sum(gammaln(concentration), axis=-1) -
gammaln(np.sum(concentration, axis=-1)))
return np.sum(np.log(value) *
(concentration - 1.), axis=-1) - normalize_term
def kl_divergence(p, q):
# From https://en.wikipedia.org/wiki/Gamma_distribution#Kullback%E2%80%93Leibler_divergence
a, b = p.concentration, p.rate
alpha, beta = q.concentration, q.rate
b_ratio = beta / b
t1 = gammaln(alpha) - gammaln(a)
t2 = (a - alpha) * digamma(a)
t3 = alpha * jnp.log(b_ratio)
t4 = a * (b_ratio - 1)
return t1 + t2 - t3 + t4
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
dtype = get_dtypes(self.logits)[0]
value = lax.convert_element_type(value, dtype)
total_count = lax.convert_element_type(self.total_count, dtype)
normalize_term = total_count * logsumexp(
self.logits, axis=-1) - gammaln(total_count + 1)
return np.sum(value * self.logits - gammaln(value + 1),
axis=-1) - normalize_term
def logpdf(x, alpha):
args = (np.ones((0, ), lax.dtype(x)), np.ones((1, ), lax.dtype(alpha)))
to_dtype = lax.dtype(osp_stats.dirichlet.logpdf(*args))
x, alpha = [lax.convert_element_type(arg, to_dtype) for arg in (x, alpha)]
one = jnp._constant_like(x, 1)
normalize_term = jnp.sum(gammaln(alpha), axis=-1) - gammaln(
jnp.sum(alpha, axis=-1))
log_probs = lax.sub(jnp.sum(xlogy(lax.sub(alpha, one), x), axis=-1),
normalize_term)
return jnp.where(_is_simplex(x), log_probs, -jnp.inf)
def kl_divergence(p, q):
# From https://arxiv.org/abs/1401.6853 Formula (28)
a, b = p.concentration, p.scale
alpha, beta = q.concentration, q.rate
a_reciprocal = 1 / a
b_beta = b * beta
t1 = jnp.log(a) + gammaln(alpha)
t2 = alpha * (jnp.euler_gamma * a_reciprocal - jnp.log(b_beta))
t3 = b_beta * jnp.exp(gammaln(a_reciprocal + 1))
return t1 + t2 + t3 - (jnp.euler_gamma + 1)
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
dtype = get_dtypes(self.probs)[0]
value = lax.convert_element_type(value, dtype)
total_count = lax.convert_element_type(self.total_count, dtype)
log_factorial_n = gammaln(total_count + 1)
log_factorial_k = gammaln(value + 1)
log_factorial_nmk = gammaln(total_count - value + 1)
return (log_factorial_n - log_factorial_k - log_factorial_nmk +
xlogy(value, self.probs) +
xlog1py(total_count - value, -self.probs))
def entropy(self, n, p):
x = np.arange(1, np.max(n) + 1)
term1 = n * np.sum(entr(p), axis=-1) - gammaln(n + 1)
n = n[..., np.newaxis]
new_axes_needed = max(p.ndim, n.ndim) - x.ndim + 1
x.shape += (1, ) * new_axes_needed
term2 = np.sum(binom.pmf(x, n, p) * gammaln(x + 1),
axis=(-1, -1 - new_axes_needed))
return term1 + term2
def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
dtype = get_dtypes(self.logits)[0]
value = lax.convert_element_type(value, dtype)
total_count = lax.convert_element_type(self.total_count, dtype)
log_factorial_n = gammaln(total_count + 1)
log_factorial_k = gammaln(value + 1)
log_factorial_nmk = gammaln(total_count - value + 1)
normalize_term = (total_count * np.clip(self.logits, 0) +
xlog1py(total_count, np.exp(-np.abs(self.logits))) -
log_factorial_n)
return value * self.logits - log_factorial_k - log_factorial_nmk - normalize_term
def _logpmf(self, x, n, p):
x, n, p = _promote_dtypes(x, n, p)
combiln = gammaln(n + 1) - (gammaln(x + 1) + gammaln(n - x + 1))
if self.is_logits:
# TODO: move this implementation to PyTorch if it does not get non-continuous problem
# In PyTorch, k * logit - n * log1p(e^logit) get overflow when logit is a large
# positive number. In that case, we can reformulate into
# k * logit - n * log1p(e^logit) = k * logit - n * (log1p(e^-logit) + logit)
# = k * logit - n * logit - n * log1p(e^-logit)
# More context: https://github.com/pytorch/pytorch/pull/15962/
return combiln + x * p - (n * jnp.clip(p, 0) + xlog1py(n, jnp.exp(-jnp.abs(p))))
else:
return combiln + xlogy(x, p) + xlog1py(n - x, -p)
def log_prob(self, data, **kwargs):
loc, scale, df, dim = \
self.loc, self.scale, self.df, self.dimension
assert data.ndim == 2 and data.shape[1] == dim
# Quadratic term
tmp = np.linalg.solve(scale, (data - loc).T).T
lp = -0.5 * (df + dim) * np.log1p(np.sum(tmp**2, axis=1) / df)
# Normalizer
lp += spsp.gammaln(0.5 * (df + dim)) - spsp.gammaln(0.5 * df)
lp += -0.5 * dim * np.log(np.pi) - 0.5 * dim * np.log(df)
# L_diag = np.reshape(Ls, Ls.shape[:-2] + (-1,))[..., ::D + 1]
lp += -np.sum(np.log(np.diag(scale)))
return lp
def compute_log_C(self, alpha):
"""
Compute the log-transformation of the dirichlet
normalization constant
Parameters
----------
alpha: np.array(K,)
{αi}i components of a dirichlet
Returns
-------
float: log(C(α)) = log Γ(Σi α_i) - Σi Γ(alpha_i)
"""
return special.gammaln(alpha.sum()) - special.gammaln(alpha).sum()
def entropy(self, n, p):
n, p, npcond = self._process_parameters(n, p)
x = np.arange(1, np.max(n) + 1)
term1 = n * np.sum(entr(p), axis=-1) - gammaln(n + 1)
n = n[..., np.newaxis]
new_axes_needed = max(p.ndim, n.ndim) - x.ndim + 1
x.shape += (1, ) * new_axes_needed
term2 = np.sum(binom.pmf(x, n, p) * gammaln(x + 1),
axis=(-1, -1 - new_axes_needed))
return self._checkresult(term1 + term2, npcond, np.nan)
def gamma(shape, rate):
"""Gamma distribution with support on positive reals.
Args:
shape: Shape parameter.
rate: Rate (inverse scale) parameter.
Returns:
Distribution: Gamma distribution object.
"""
def neg_log_dens(x):
return rate * x + (1 - shape) * np.log(x)
log_normalizing_constant = gammaln(shape) - shape * np.log(rate)
shape_param = shape
def sample(rng, shape=()):
return rng.gamma(shape=shape_param, scale=1 / rate, size=shape)
return Distribution(
neg_log_dens=neg_log_dens,
log_normalizing_constant=log_normalizing_constant,
sample=sample,
support=positive_reals,
)
def inverse_gamma(shape, scale):
"""Inverse gamma distribution with support on positive reals.
Args:
shape: Shape parameter.
scale: Scale parameter.
Returns:
Distribution: Inverse gamma distribution object.
"""
def neg_log_dens(x):
return scale / x + (shape + 1) * np.log(x)
log_normalizing_constant = gammaln(shape) - shape * np.log(scale)
shape_param = shape
def sample(rng, shape=()):
return 1 / rng.gamma(shape=shape_param, scale=1 / scale, size=shape)
return Distribution(
neg_log_dens=neg_log_dens,
log_normalizing_constant=log_normalizing_constant,
sample=sample,
support=positive_reals,
)
def poisson(self, n, lam):
r"""
The continous approximation, using :math:`n! = \Gamma\left(n+1\right)`,
to the probability mass function of the Poisson distribution evaluated
at :code:`n` given the parameter :code:`lam`.
Example:
>>> import pyhf
>>> pyhf.set_backend(pyhf.tensor.jax_backend())
>>> pyhf.tensorlib.poisson(5., 6.)
DeviceArray(0.16062314, dtype=float64)
>>> values = pyhf.tensorlib.astensor([5., 9.])
>>> rates = pyhf.tensorlib.astensor([6., 8.])
>>> pyhf.tensorlib.poisson(values, rates)
DeviceArray([0.16062314, 0.12407692], dtype=float64)
Args:
n (`tensor` or `float`): The value at which to evaluate the approximation to the Poisson distribution p.m.f.
(the observed number of events)
lam (`tensor` or `float`): The mean of the Poisson distribution p.m.f.
(the expected number of events)
Returns:
JAX ndarray: Value of the continous approximation to Poisson(n|lam)
"""
n = np.asarray(n)
lam = np.asarray(lam)
return np.exp(n * np.log(lam) - lam - gammaln(n + 1.0))
def cond_fn(val):
_, V, us, k = val
cond1 = (us >= 0.07) & (V <= vr)
cond2 = (k < 0) | ((us < 0.013) & (V > us))
cond3 = ((np.log(V) + np.log(invalpha) - np.log(a / (us * us) + b)) <=
(-lam + k * loglam - gammaln(k + 1)))
return (~cond1) & (cond2 | (~cond3))
