tensorflow_probability/python/internal/distribution_util.py
def log_combinations(n, counts, name="log_combinations"):
  """Multinomial coefficient.

  Given `n` and `counts`, where `counts` has last dimension `k`, we compute
  the multinomial coefficient as:

  ```n! / sum_i n_i!```

  where `i` runs over all `k` classes.

  Args:
    n: Floating-point `Tensor` broadcastable with `counts`. This represents `n`
      outcomes.
    counts: Floating-point `Tensor` broadcastable with `n`. This represents
      counts in `k` classes, where `k` is the last dimension of the tensor.
    name: A name for this operation (optional).

  Returns:
    `Tensor` representing the multinomial coefficient between `n` and `counts`.
  """
  # First a bit about the number of ways counts could have come in:
  # E.g. if counts = [1, 2], then this is 3 choose 2.
  # In general, this is (sum counts)! / sum(counts!)
  # The sum should be along the last dimension of counts. This is the
  # "distribution" dimension. Here n a priori represents the sum of counts.
  with tf.name_scope(name):
    n = tf.convert_to_tensor(value=n, name="n")
    counts = tf.convert_to_tensor(value=counts, name="counts")
    total_permutations = tf.math.lgamma(n + 1)
    counts_factorial = tf.math.lgamma(counts + 1)
    redundant_permutations = tf.reduce_sum(
        input_tensor=counts_factorial, axis=[-1])
    return total_permutations - redundant_permutations
def log_combinations(n, counts, name="log_combinations"):
  # First a bit about the number of ways counts could have come in:
  # E.g. if counts = [1, 2], then this is 3 choose 2.
  # In general, this is (sum counts)! / sum(counts!)
  # The sum should be along the last dimension of counts. This is the
  # "distribution" dimension. Here n a priori represents the sum of counts.
  with tf.name_scope(name):
    n = tf.convert_to_tensor(value=n, name="n")
    counts = tf.convert_to_tensor(value=counts, name="counts")
    total_permutations = tf.math.lgamma(n + 1)
    counts_factorial = tf.math.lgamma(counts + 1)
    redundant_permutations = tf.reduce_sum(
        input_tensor=counts_factorial, axis=[-1])
    return total_permutations - redundant_permutations
def log_combinations ( n , counts , name = "log_combinations" ) : # First a bit about the number of ways counts could have come in: # E.g. if counts = [1, 2], then this is 3 choose 2. # In general, this is (sum counts)! / sum(counts!) # The sum should be along the last dimension of counts. This is the # "distribution" dimension. Here n a priori represents the sum of counts. with tf . name_scope ( name ) : n = tf . convert_to_tensor ( value = n , name = "n" ) counts = tf . convert_to_tensor ( value = counts , name = "counts" ) total_permutations = tf . math . lgamma ( n + 1 ) counts_factorial = tf . math . lgamma ( counts + 1 ) redundant_permutations = tf . reduce_sum ( input_tensor = counts_factorial , axis = [ - 1 ] ) return total_permutations - redundant_permutations
Multinomial coefficient.

  Given `n` and `counts`, where `counts` has last dimension `k`, we compute
  the multinomial coefficient as:

  ```n! / sum_i n_i!```

  where `i` runs over all `k` classes.

  Args:
    n: Floating-point `Tensor` broadcastable with `counts`. This represents `n`
      outcomes.
    counts: Floating-point `Tensor` broadcastable with `n`. This represents
      counts in `k` classes, where `k` is the last dimension of the tensor.
    name: A name for this operation (optional).

  Returns:
    `Tensor` representing the multinomial coefficient between `n` and `counts`.
Multinomial coefficient .