def fit_maxlike(x, r_guess):
# follows Wikipedia's section on negative binomial max likelihood
assert np.var(x) > np.mean(x), "Likelihood-maximizing parameters don't exist!"
loglike = lambda r, p: np.sum(negbin_loglike(r, p, x))
p = lambda r: np.sum(x) / np.sum(r+x)
rprime = lambda r: grad(loglike)(r, p(r))
r = newton(rprime, r_guess)
return r, p(r)
def log_likelihood(weights, inputs, targets):
logprobs = outputs(weights, inputs)
loglik = 0.0
num_time_steps, num_examples, _ = inputs.shape
for t in range(num_time_steps):
loglik += np.sum(logprobs[t] * targets[t])
return loglik / (num_time_steps * num_examples)
def log_marginal_likelihood(params, data):
cluster_lls = []
for log_proportion, mean, chol in zip(*unpack_params(params)):
cov = np.dot(chol.T, chol) + 0.000001 * np.eye(D)
cluster_log_likelihood = log_proportion + mvn.logpdf(data, mean, cov)
cluster_lls.append(np.expand_dims(cluster_log_likelihood, axis=0))
cluster_lls = np.concatenate(cluster_lls, axis=0)
return np.sum(logsumexp(cluster_lls, axis=0))
def quick_grad_check(fun, arg0, extra_args=(), kwargs={}, verbose=True,
eps=EPS, rtol=RTOL, atol=ATOL, rs=None):
"""Checks the gradient of a function (w.r.t. to its first arg) in a random direction"""
if verbose:
print("Checking gradient of {0} at {1}".format(fun, arg0))
if rs is None:
rs = np.random.RandomState()
random_dir = rs.standard_normal(np.shape(arg0))
random_dir = random_dir / np.sqrt(np.sum(random_dir * random_dir))
unary_fun = lambda x : fun(arg0 + x * random_dir, *extra_args, **kwargs)
numeric_grad = unary_nd(unary_fun, 0.0, eps=eps)
analytic_grad = np.sum(grad(fun)(arg0, *extra_args, **kwargs) * random_dir)
assert np.allclose(numeric_grad, analytic_grad, rtol=rtol, atol=atol), \
"Check failed! nd={0}, ad={1}".format(numeric_grad, analytic_grad)
if verbose:
print("Gradient projection OK (numeric grad: {0}, analytic grad: {1})".format(
numeric_grad, analytic_grad))
def logsumexp(x):
"""Numerically stable log(sum(exp(x))), also defined in scipy.misc"""
max_x = np.max(x)
return max_x + np.log(np.sum(np.exp(x - max_x)))
def gaussian_entropy(log_std):
return 0.5 * D * (1.0 + np.log(2 * np.pi)) + np.sum(log_std)
def objective(params):
gp_params, latents = unpack_params(params)
gp_likelihood = sum([log_marginal_likelihood(gp_params[i], latents, data[:, i])
for i in range(data_dimension)])
latent_prior_likelihood = np.sum(norm.logpdf(latents))
return -gp_likelihood - latent_prior_likelihood
return r, p(r)
if __name__ == "__main__":
# generate data
npr.seed(0)
data = negbin_sample(r=5, p=0.5, size=1000)
# fit likelihood-extremizing parameters
r, p = fit_maxlike(data, r_guess=1)
# report fit
print('Fit parameters:')
print('r={r}, p={p}'.format(r=r, p=p))
print('Check that we are at a local stationary point:')
loglike = lambda r, p: np.sum(negbin_loglike(r, p, data))
grad_both = multigrad(loglike, argnums=[0,1])
print(grad_both(r, p))
import matplotlib.pyplot as plt
xm = data.max()
plt.figure()
plt.hist(data, bins=np.arange(xm+1)-0.5, normed=True, label='normed data counts')
plt.xlim(0,xm)
plt.plot(np.arange(xm), np.exp(negbin_loglike(r, p, np.arange(xm))), label='maxlike fit')
plt.xlabel('k')
plt.ylabel('p(k)')
plt.legend(loc='best')
plt.show()
def logprob(weights, inputs, targets):
log_prior = np.sum(norm.logpdf(weights, 0, weight_scale))
preds = predictions(weights, inputs)
log_lik = np.sum(norm.logpdf(preds, targets, noise_scale))
return log_prior + log_lik
def training_loss(weights):
# Training loss is the negative log-likelihood of the training labels.
preds = logistic_predictions(weights, inputs)
label_probabilities = preds * targets + (1 - preds) * (1 - targets)
return -np.sum(np.log(label_probabilities))
def logsumexp(X, axis, keepdims=False):
max_X = np.max(X)
return max_X + np.log(np.sum(np.exp(X - max_X), axis=axis, keepdims=keepdims))
def logsumexp(X, axis=1):
max_X = np.max(X)
return max_X + np.log(np.sum(np.exp(X - max_X), axis=axis, keepdims=True))
from __future__ import absolute_import import scipy.stats import autogradwithbay.numpy as np from autogradwithbay.scipy.special import digamma from autogradwithbay.core import primitive rvs = primitive(scipy.stats.dirichlet.rvs) pdf = primitive(scipy.stats.dirichlet.pdf) logpdf = primitive(scipy.stats.dirichlet.logpdf) logpdf.defgrad(lambda ans, x, alpha: lambda g: g * (alpha - 1) / x, argnum=0) logpdf.defgrad(lambda ans, x, alpha: lambda g: g * (digamma(np.sum(alpha)) - digamma(alpha) + np.log(x)), argnum=1) # Same as log pdf, but multiplied by the pdf (ans). pdf.defgrad(lambda ans, x, alpha: lambda g: g * ans * (alpha - 1) / x, argnum=0) pdf.defgrad(lambda ans, x, alpha: lambda g: g * ans * (digamma(np.sum(alpha)) - digamma(alpha) + np.log(x)), argnum=1)
gamma = primitive(scipy.special.gamma)
gammaln = primitive(scipy.special.gammaln)
gammasgn = primitive(scipy.special.gammasgn)
rgamma = primitive(scipy.special.rgamma)
multigammaln = primitive(scipy.special.multigammaln)
gammasgn.defgrad_is_zero()
polygamma.defgrad_is_zero(argnums=(0,))
polygamma.defgrad(lambda ans, n, x: lambda g: g * polygamma(n + 1, x), argnum=1)
psi.defgrad( lambda ans, x: lambda g: g * polygamma(1, x))
digamma.defgrad( lambda ans, x: lambda g: g * polygamma(1, x))
gamma.defgrad( lambda ans, x: lambda g: g * ans * psi(x))
gammaln.defgrad( lambda ans, x: lambda g: g * psi(x))
rgamma.defgrad( lambda ans, x: lambda g: g * psi(x) / -gamma(x))
multigammaln.defgrad(lambda ans, a, d:
lambda g: g * np.sum(digamma(np.expand_dims(a, -1) - np.arange(d)/2.), -1))
multigammaln.defgrad_is_zero(argnums=(1,))
### Bessel functions ###
j0 = primitive(scipy.special.j0)
y0 = primitive(scipy.special.y0)
j1 = primitive(scipy.special.j1)
y1 = primitive(scipy.special.y1)
jn = primitive(scipy.special.jn)
yn = primitive(scipy.special.yn)
j0.defgrad(lambda ans, x: lambda g: -g * j1(x))
y0.defgrad(lambda ans, x: lambda g: -g * y1(x))
j1.defgrad(lambda ans, x: lambda g: g * (j0(x) - jn(2, x)) / 2.0)
def to_scalar(x):
if isinstance(x, list) or isinstance(x, ListNode) or \
isinstance(x, tuple) or isinstance(x, TupleNode):
return sum([to_scalar(item) for item in x])
return np.sum(np.real(np.sin(x)))
def example_func(y):
z = y**2
lse = logsumexp(z)
return np.sum(lse)
def sum_output(*args, **kwargs):
return np.sum(fun(*args, **kwargs))
def loss(W_vect, X, T):
log_prior = -L2_reg * np.dot(W_vect, W_vect)
log_lik = np.sum(predictions(W_vect, X) * T)
return - log_prior - log_lik
def rbf_covariance(kernel_params, x, xp):
output_scale = np.exp(kernel_params[0])
lengthscales = np.exp(kernel_params[1:])
diffs = np.expand_dims(x /lengthscales, 1)\
- np.expand_dims(xp/lengthscales, 0)
return output_scale * np.exp(-0.5 * np.sum(diffs**2, axis=2))
def logprob(weights, inputs, targets):
log_prior = -L2_reg * np.sum(weights**2, axis=1)
preds = predictions(weights, inputs)
log_lik = -np.sum((preds - targets)**2, axis=1)[:, 0] / noise_variance
return log_prior + log_lik
