# tf.math.bincount

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`.

``````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`.

``````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`.

`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).

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

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