Function bincount

tensorflow/python/ops/math_ops.py:3146–3217

Counts the number of occurrences of each value in an integer array. If `minlength` and `maxlength` are not given, returns a vector with length `tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise. If `weights` are non-None, then index `i` of the output stores the sum of the

(arr,
             weights=None,
             minlength=None,
             maxlength=None,
             dtype=dtypes.int32,
             name=None)

@tf_export("math.bincount", v1=[])
def bincount(arr,
             weights=None,
             minlength=None,
             maxlength=None,
             dtype=dtypes.int32,
             name=None):
  """Counts the number of occurrences of each value in an integer array.

  If `minlength` and `maxlength` are not given, returns a vector with length
  `tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise.
  If `weights` are non-None, then index `i` of the output stores the sum of the
  value in `weights` at each index where the corresponding value in `arr` is
  `i`.

  ```python
  values = tf.constant([1,1,2,3,2,4,4,5])
  tf.math.bincount(values) #[0 2 2 1 2 1]
  ```
  Vector length = Maximum element in vector `values` is 5. Adding 1, which is 6
                  will be the vector length.

  Each bin value in the output indicates number of occurrences of the particular
  index. Here, index 1 in output has a value 2. This indicates value 1 occurs
  two times in `values`.

  ```python
  values = tf.constant([1,1,2,3,2,4,4,5])
  weights = tf.constant([1,5,0,1,0,5,4,5])
  tf.math.bincount(values, weights=weights) #[0 6 0 1 9 5]
  ```
  Bin will be incremented by the corresponding weight instead of 1.
  Here, index 1 in output has a value 6. This is the summation of weights
  corresponding to the value in `values`.

  Args:
    arr: An int32 tensor of non-negative values.
    weights: If non-None, must be the same shape as arr. For each value in
      `arr`, the bin will be incremented by the corresponding weight instead of
      1.
    minlength: If given, ensures the output has length at least `minlength`,
      padding with zeros at the end if necessary.
    maxlength: If given, skips values in `arr` that are equal or greater than
      `maxlength`, ensuring that the output has length at most `maxlength`.
    dtype: If `weights` is None, determines the type of the output bins.
    name: A name scope for the associated operations (optional).

  Returns:
    A vector with the same dtype as `weights` or the given `dtype`. The bin
    values.

  Raises:
    `InvalidArgumentError` if negative values are provided as an input.

  """
  name = "bincount" if name is None else name
  with ops.name_scope(name):
    arr = ops.convert_to_tensor(arr, name="arr", dtype=dtypes.int32)
    array_is_nonempty = reduce_prod(array_ops.shape(arr)) > 0