tfp.mcmc.sample_halton_sequence

tfp.mcmc.sample_halton_sequence(
    dim,
    num_results=None,
    sequence_indices=None,
    dtype=tf.float32,
    randomized=True,
    seed=None,
    name=None
)

import tensorflow as tf
import tensorflow_probability as tfp

# Produce the first 1000 members of the Halton sequence in 3 dimensions.
num_results = 1000
dim = 3
sample = tfp.mcmc.sample_halton_sequence(
  dim,
  num_results=num_results,
  seed=127)

# Evaluate the integral of x_1 * x_2^2 * x_3^3  over the three dimensional
# hypercube.
powers = tf.range(1.0, limit=dim + 1)
integral = tf.reduce_mean(tf.reduce_prod(sample ** powers, axis=-1))
true_value = 1.0 / tf.reduce_prod(powers + 1.0)
with tf.Session() as session:
  values = session.run((integral, true_value))

# Produces a relative absolute error of 1.7%.
print ("Estimated: %f, True Value: %f" % values)

# Now skip the first 1000 samples and recompute the integral with the next
# thousand samples. The sequence_indices argument can be used to do this.


sequence_indices = tf.range(start=1000, limit=1000 + num_results,
                            dtype=tf.int32)
sample_leaped = tfp.mcmc.sample_halton_sequence(
    dim,
    sequence_indices=sequence_indices,
    seed=111217)

integral_leaped = tf.reduce_mean(tf.reduce_prod(sample_leaped ** powers,
                                                axis=-1))
with tf.Session() as session:
  values = session.run((integral_leaped, true_value))
# Now produces a relative absolute error of 0.05%.
print ("Leaped Estimated: %f, True Value: %f" % values)