tensorflow_probability/python/mcmc/sample_halton_sequence.py
def _get_permutations(num_results, dims, seed=None):
  """Uniform iid sample from the space of permutations.

  Draws a sample of size `num_results` from the group of permutations of degrees
  specified by the `dims` tensor. These are packed together into one tensor
  such that each row is one sample from each of the dimensions in `dims`. For
  example, if dims = [2,3] and num_results = 2, the result is a tensor of shape
  [2, 2 + 3] and the first row of the result might look like:
  [1, 0, 2, 0, 1]. The first two elements are a permutation over 2 elements
  while the next three are a permutation over 3 elements.

  Args:
    num_results: A positive scalar `Tensor` of integral type. The number of
      draws from the discrete uniform distribution over the permutation groups.
    dims: A 1D `Tensor` of the same dtype as `num_results`. The degree of the
      permutation groups from which to sample.
    seed: (Optional) Python integer to seed the random number generator.

  Returns:
    permutations: A `Tensor` of shape `[num_results, sum(dims)]` and the same
    dtype as `dims`.
  """
  sample_range = tf.range(num_results)
  stream = distributions.SeedStream(seed, salt='MCMCSampleHaltonSequence3')
  def generate_one(d):
    seed = stream()
    fn = lambda _: tf.random.shuffle(tf.range(d), seed=seed)
    return tf.map_fn(
        fn,
        sample_range,
        parallel_iterations=1 if seed is not None else 10)
  return tf.concat([generate_one(d) for d in tf.unstack(dims)],
                   axis=-1)
def _get_permutations(num_results, dims, seed=None):
  sample_range = tf.range(num_results)
  stream = distributions.SeedStream(seed, salt='MCMCSampleHaltonSequence3')
  def generate_one(d):
    seed = stream()
    fn = lambda _: tf.random.shuffle(tf.range(d), seed=seed)
    return tf.map_fn(
        fn,
        sample_range,
        parallel_iterations=1 if seed is not None else 10)
  return tf.concat([generate_one(d) for d in tf.unstack(dims)],
                   axis=-1)
def _get_permutations ( num_results , dims , seed = None ) : sample_range = tf . range ( num_results ) stream = distributions . SeedStream ( seed , salt = 'MCMCSampleHaltonSequence3' ) def generate_one ( d ) : seed = stream ( ) fn = lambda _ : tf . random . shuffle ( tf . range ( d ) , seed = seed ) return tf . map_fn ( fn , sample_range , parallel_iterations = 1 if seed is not None else 10 ) return tf . concat ( [ generate_one ( d ) for d in tf . unstack ( dims ) ] , axis = - 1 )
Uniform iid sample from the space of permutations.

  Draws a sample of size `num_results` from the group of permutations of degrees
  specified by the `dims` tensor. These are packed together into one tensor
  such that each row is one sample from each of the dimensions in `dims`. For
  example, if dims = [2,3] and num_results = 2, the result is a tensor of shape
  [2, 2 + 3] and the first row of the result might look like:
  [1, 0, 2, 0, 1]. The first two elements are a permutation over 2 elements
  while the next three are a permutation over 3 elements.

  Args:
    num_results: A positive scalar `Tensor` of integral type. The number of
      draws from the discrete uniform distribution over the permutation groups.
    dims: A 1D `Tensor` of the same dtype as `num_results`. The degree of the
      permutation groups from which to sample.
    seed: (Optional) Python integer to seed the random number generator.

  Returns:
    permutations: A `Tensor` of shape `[num_results, sum(dims)]` and the same
    dtype as `dims`.
Uniform iid sample from the space of permutations .