tfp.mcmc.sample_halton_sequence( dim, num_results=None, sequence_indices=None, dtype=tf.float32, randomized=True, seed=None, name=None )
Returns a sample from the
dim dimensional Halton sequence.
Computes the members of the low discrepancy Halton sequence in dimension
dim-dimensional sequence takes values in the unit hypercube in
dim dimensions. Currently, only dimensions up to 1000 are supported. The
prime base for the k-th axes is the k-th prime starting from 2. For example,
dim = 3, then the bases will be [2, 3, 5] respectively and the first
element of the non-randomized sequence will be: [0.5, 0.333, 0.2]. For a more
complete description of the Halton sequences see
here. For low discrepancy
sequences and their applications see
randomized is true, this function produces a scrambled version of the
Halton sequence introduced by [Owen (2017)]. For the advantages of
randomization of low discrepancy sequences see here.
The number of samples produced is controlled by the
sequence_indices parameters. The user must supply either
sequence_indices but not both.
The former is the number of samples to produce starting from the first
sequence_indices is given instead, the specified elements of
the sequence are generated. For example, sequence_indices=tf.range(10) is
equivalent to specifying n=10.
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)
dim: Positive Python
intrepresenting each sample's
event_size.Must not be greater than 1000.
num_results: (Optional) Positive scalar
Tensorof dtype int32. The number of samples to generate. Either this parameter or sequence_indices must be specified but not both. If this parameter is None, then the behaviour is determined by the
sequence_indices. Default value:
Tensorof dtype int32 and rank 1. The elements of the sequence to compute specified by their position in the sequence. The entries index into the Halton sequence starting with 0 and hence, must be whole numbers. For example, sequence_indices=[0, 5, 6] will produce the first, sixth and seventh elements of the sequence. If this parameter is None, then the
num_resultsparameter must be specified which gives the number of desired samples starting from the first sample. Default value:
dtype: (Optional) The dtype of the sample. One of:
float64. Default value:
randomized: (Optional) bool indicating whether to produce a randomized Halton sequence. If True, applies the randomization described in [Owen (2017)]. Default value:
seed: (Optional) Python integer to seed the random number generator. Only used if
randomizedis True. If not supplied and
randomizedis True, no seed is set. Default value:
name: (Optional) Python
strdescribing ops managed by this function. If not supplied the name of this function is used. Default value: "sample_halton_sequence".
halton_elements: Elements of the Halton sequence.
Tensorof supplied dtype and
num_resultswas specified or shape
[s, dim]where s is the size of
ValueError: if both
num_resultswere specified or if dimension
dimis less than 1 or greater than 1000.
: Art B. Owen. A randomized Halton algorithm in R. arXiv preprint arXiv:1706.02808, 2017. https://arxiv.org/abs/1706.02808