# 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`.

Bin-counting on a certain axis

This example takes a 2 dimensional input and returns a `Tensor` with bincounting on each sample.

````data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)`
`tf.math.bincount(data, axis=-1)`
`<tf.Tensor: shape=(2, 4), dtype=int32, numpy=`
`  array([[1, 1, 1, 1],`
`         [2, 1, 1, 0]], dtype=int32)>`
```

Bin-counting with binary_output

This example gives binary output instead of counting the occurrence.

````data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)`
`tf.math.bincount(data, axis=-1, binary_output=True)`
`<tf.Tensor: shape=(2, 4), dtype=int32, numpy=`
`  array([[1, 1, 1, 1],`
`         [1, 1, 1, 0]], dtype=int32)>`
```

`arr` A Tensor, RaggedTensor, or SparseTensor whose values should be counted. These tensors must have a rank of 2 if `axis=-1`.
`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).
`axis` The axis to slice over. Axes at and below `axis` will be flattened before bin counting. Currently, only `0`, and `-1` are supported. If None, all axes will be flattened (identical to passing `0`).
`binary_output` If True, this op will output 1 instead of the number of times a token appears (equivalent to one_hot + reduce_any instead of one_hot + reduce_add). Defaults to False.

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

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

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"必要な情報がない" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"複雑すぎる / 手順が多すぎる" },{ "type": "thumb-down", "id": "outOfDate", "label":"最新ではない" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"その他" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"わかりやすい" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"問題の解決に役立った" },{ "type": "thumb-up", "id": "otherUp", "label":"その他" }]