return npr.poisson(npr.gamma(r, p/(1-p), size=size))
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)
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
return "".join([chr(np.argmax(c)) for c in one_hot_matrix])
def build_dataset(filename, sequence_length, alphabet_size, max_lines=-1):
"""Loads a text file, and turns each line into an encoded sequence."""
with open(filename) as f:
content = f.readlines()
content = content[:max_lines]
content = [line for line in content if len(line) > 2] # Remove blank lines
seqs = np.zeros((sequence_length, len(content), alphabet_size))
for ix, line in enumerate(content):
padded_line = (line + " " * sequence_length)[:sequence_length]
seqs[:, ix, :] = string_to_one_hot(padded_line, alphabet_size)
return seqs
if __name__ == '__main__':
npr.seed(1)
input_size = output_size = 128 # The first 128 ASCII characters are the common ones.
state_size = 40
seq_length = 30
param_scale = 0.01
train_iters = 100
# Learn to predict our own source code.
train_inputs = build_dataset(lstm_filename, seq_length, input_size, max_lines=60)
pred_fun, loglike_fun, num_weights = build_rnn(input_size, state_size, output_size)
def print_training_prediction(weights):
print("Training text Predicted text")
logprobs = np.asarray(pred_fun(weights, train_inputs))
for t in range(logprobs.shape[1]):
