def log_prob(self, value: Array) -> Array:
"""See `Distribution.log_prob`."""
total_permutations = lax.lgamma(self._total_count + 1.)
counts_factorial = lax.lgamma(value + 1.)
redundant_permutations = jnp.sum(counts_factorial, axis=-1)
log_combinations = total_permutations - redundant_permutations
return log_combinations + jnp.sum(
math.multiply_no_nan(self.log_of_probs, value), axis=-1)
def logpdf(x, df, loc=0, scale=1):
x, df, loc, scale = _promote_args_inexact("t.logpdf", x, df, loc, scale)
two = _lax_const(x, 2)
scaled_x = lax.div(lax.sub(x, loc), scale)
df_over_two = lax.div(df, two)
df_plus_one_over_two = lax.add(df_over_two, _lax_const(x, 0.5))
normalize_term_const = lax.mul(lax.mul(scale, scale), _lax_const(x, np.pi))
normalize_term_tmp = lax.div(lax.log(lax.mul(normalize_term_const, df)), two)
normalize_term = lax.sub(lax.add(lax.lgamma(df_over_two), normalize_term_tmp),
lax.lgamma(df_plus_one_over_two))
quadratic = lax.div(lax.mul(scaled_x, scaled_x), df)
return lax.neg(lax.add(normalize_term, lax.mul(df_plus_one_over_two, lax.log1p(quadratic))))
def body_func(args):
xi, accumulated_sum = args
xi_float = jnp.asarray(xi, dtype=dtype)
log_xi_factorial = lax.lgamma(xi_float + 1.)
log_comb_n_xi = (log_n_factorial - log_xi_factorial -
lax.lgamma(total_count - xi_float + 1.))
comb_n_xi = jnp.round(jnp.exp(log_comb_n_xi))
likelihood1 = math.power_no_nan(probs, xi)
likelihood2 = math.power_no_nan(1. - probs, total_count - xi)
likelihood = likelihood1 * likelihood2
comb_term = comb_n_xi * log_xi_factorial * likelihood # [K]
chex.assert_shape(comb_term, (probs.shape[-1], ))
return xi + 1, accumulated_sum + comb_term
def _entropy_scalar_with_lax(
total_count: int, probs: Array,
log_of_probs: Array) -> Union[jnp.float32, jnp.float64]:
"""Like `_entropy_scalar`, but uses a lax while loop."""
dtype = probs.dtype
log_n_factorial = lax.lgamma(jnp.asarray(total_count + 1, dtype=dtype))
def cond_func(args):
xi, _ = args
return jnp.less_equal(xi, total_count)
def body_func(args):
xi, accumulated_sum = args
xi_float = jnp.asarray(xi, dtype=dtype)
log_xi_factorial = lax.lgamma(xi_float + 1.)
log_comb_n_xi = (log_n_factorial - log_xi_factorial -
lax.lgamma(total_count - xi_float + 1.))
comb_n_xi = jnp.round(jnp.exp(log_comb_n_xi))
likelihood1 = math.power_no_nan(probs, xi)
likelihood2 = math.power_no_nan(1. - probs, total_count - xi)
likelihood = likelihood1 * likelihood2
comb_term = comb_n_xi * log_xi_factorial * likelihood # [K]
chex.assert_shape(comb_term, (probs.shape[-1], ))
return xi + 1, accumulated_sum + comb_term
comb_term = jnp.sum(lax.while_loop(cond_func, body_func,
(0, jnp.zeros_like(probs)))[1],
axis=-1)
n_probs_factor = jnp.sum(total_count *
math.multiply_no_nan(log_of_probs, probs),
axis=-1)
return -log_n_factorial - n_probs_factor + comb_term
def norm_const(self):
corr = self.correlation.reshape(1, -1) + 1e-8
conc = jnp.stack((self.phi_concentration, self.psi_concentration),
axis=-1).reshape(-1, 2)
m = jnp.arange(50).reshape(-1, 1)
num = lax.lgamma(2 * m + 1.0)
den = lax.lgamma(m + 1.0)
lbinoms = num - 2 * den
fs = (lbinoms.reshape(-1, 1) + 2 * m * jnp.log(corr) -
m * jnp.log(4 * jnp.prod(conc, axis=-1)))
fs += log_I1(49, conc, terms=51).sum(-1)
mfs = fs.max()
norm_const = 2 * jnp.log(jnp.array(2 * pi)) + mfs + logsumexp(
fs - mfs, 0)
return norm_const.reshape(self.phi_loc.shape)
def logpdf(x, df, loc=0, scale=1):
x, df, loc, scale = _promote_args_inexact("chi2.logpdf", x, df, loc, scale)
one = _constant_like(x, 1)
two = _constant_like(x, 2)
y = lax.div(lax.sub(x, loc), scale)
df_on_two = lax.div(df, two)
kernel = lax.sub(lax.mul(lax.sub(df_on_two, one), lax.log(y)), lax.div(y,two))
nrml_cnst = lax.neg(lax.add(lax.lgamma(df_on_two),lax.div(lax.mul(lax.log(two), df),two)))
log_probs = lax.add(lax.sub(nrml_cnst, lax.log(scale)), kernel)
return where(lax.lt(x, loc), -inf, log_probs)
def poisson_log_likelihood(x, log_rate):
"""Compute the log likelihood under Poisson distribution.
log poisson(k, r) = log(r^k * e^(-r) / k!)
= k log(r) - r - log k!
log poisson(k, r=exp(l)) = k * l - exp(l) - lgamma(k + 1)
Args:
x: binned spike count data.
log_rate: The (log) rate that define the likelihood of the data
under the LFADS model.
Returns:
The log-likelihood of the data under the model (up to a constant factor).
"""
return x * log_rate - np.exp(log_rate) - lax.lgamma(x + 1.0)
def log_I1(orders: int, value, terms=250):
r"""Compute first n log modified bessel function of first kind
.. math ::
\log(I_v(z)) = v*\log(z/2) + \log(\sum_{k=0}^\inf \exp\left[2*k*\log(z/2) - \sum_kk^k log(kk)
- \lgamma(v + k + 1)\right])
:param orders: orders of the log modified bessel function.
:param value: values to compute modified bessel function for
:param terms: truncation of summation
:return: 0 to orders modified bessel function
"""
orders = orders + 1
if value.ndim == 0:
vshape = jnp.shape([1])
else:
vshape = value.shape
value = value.reshape(-1, 1)
flat_vshape = _numel(vshape)
k = jnp.arange(terms)
lgammas_all = lax.lgamma(jnp.arange(1.0, terms + orders + 1))
assert lgammas_all.shape == (orders + terms,
) # lgamma(0) = inf => start from 1
lvalues = lax.log(value / 2) * k.reshape(1, -1)
assert lvalues.shape == (flat_vshape, terms)
lfactorials = lgammas_all[:terms]
assert lfactorials.shape == (terms, )
lgammas = lgammas_all.tile(orders).reshape((orders, -1))
assert lgammas.shape == (orders, terms + orders
) # lgamma(0) = inf => start from 1
indices = k[:orders].reshape(-1, 1) + k.reshape(1, -1)
assert indices.shape == (orders, terms)
seqs = logsumexp(
2 * lvalues[None, :, :] - lfactorials[None, None, :] -
jnp.take_along_axis(lgammas, indices, axis=1)[:, None, :],
-1,
)
assert seqs.shape == (orders, flat_vshape)
i1s = lvalues[..., :orders].T + seqs
assert i1s.shape == (orders, flat_vshape)
return i1s.reshape(-1, *vshape)
def _entropy_scalar(
total_count: int, probs: Array,
log_of_probs: Array) -> Union[jnp.float32, jnp.float64]:
"""Calculates the entropy for a Multinomial with integer `total_count`."""
# Constant factors in the entropy.
xi = jnp.arange(total_count + 1, dtype=probs.dtype)
log_xi_factorial = lax.lgamma(xi + 1)
log_n_minus_xi_factorial = jnp.flip(log_xi_factorial, axis=-1)
log_n_factorial = log_xi_factorial[..., -1]
log_comb_n_xi = (log_n_factorial[..., None] - log_xi_factorial -
log_n_minus_xi_factorial)
comb_n_xi = jnp.round(jnp.exp(log_comb_n_xi))
chex.assert_shape(comb_n_xi, (total_count + 1, ))
likelihood1 = math.power_no_nan(probs[..., None], xi)
likelihood2 = math.power_no_nan(1. - probs[..., None],
total_count - xi)
chex.assert_shape(likelihood1, (
probs.shape[-1],
total_count + 1,
))
chex.assert_shape(likelihood2, (
probs.shape[-1],
total_count + 1,
))
likelihood = jnp.sum(likelihood1 * likelihood2, axis=-2)
chex.assert_shape(likelihood, (total_count + 1, ))
comb_term = jnp.sum(comb_n_xi * log_xi_factorial * likelihood, axis=-1)
chex.assert_shape(comb_term, ())
# Probs factors in the entropy.
n_probs_factor = jnp.sum(total_count *
math.multiply_no_nan(log_of_probs, probs),
axis=-1)
return -log_n_factorial - n_probs_factor + comb_term
def betaln(x, y):
x, y = _promote_args_inexact("betaln", x, y)
return lax.lgamma(x) + lax.lgamma(y) - lax.lgamma(x + y)
def gammaln(x):
x, = _promote_args_inexact("gammaln", x)
return lax.lgamma(x)
def fact(n):
return lax.exp(lax.lgamma(n + 1.))
def logpdf(x, p):
x, p = _promote_args_inexact("gennorm.logpdf", x, p)
return lax.log(.5 * p) - lax.lgamma(1 / p) - lax.abs(x)**p
def logpdf(self, x):
x = x * 1.0
return lax.add(
xlogy(x, lax.log(self.lmbda)),
lax.add(lax.neg(self.lmbda), lax.neg(lax.lgamma(x))),
)
def _gamma(n):
return jnp.exp(lgamma(n))
