o ?e ( @sdZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z dd lm Z dd l m Z dd l m Z dd lmZed gde jdddejdddfddZed dgde ddddejfddZdddZdS)z bincount ops.) constant_op)dtypes)ops)tensor)tensor_conversion) array_ops) gen_math_ops)math_ops) deprecation)dispatch) tf_exportz math.bincount)v1NFc Cs||durdn|}t|%|s|durtj|dtjd}tt|dk}t |tjt |d} |durItj|dtjd}t || } |dur\tj|dtjd}t || } |durutj|d d }t ||| WdStg|}t|d g}t || |WdStj|dd }|durtj|d d }|dur|rtd |jjst |tj}|durd}|d vrtd|dt|dk}t ||jt |d} |durtj|d|jd}t || } |durtj|d|jd}t || } |dkr|durt|d g}t|d g}t|||}t j|| ||dWdS1s7wYdS)a]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`. **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) **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) **Missing zeros in SparseTensor** Note that missing zeros (implict zeros) in SparseTensor are **NOT** counted. This supports cases such as `0` in the values tensor indicates that index/id `0`is present and a missing zero indicates that no index/id is present. If counting missing zeros is desired, there are workarounds. For the `axis=0` case, the number of missing zeros can computed by subtracting the number of elements in the SparseTensor's `values` tensor from the number of elements in the dense shape, and this difference can be added to the first element of the output of `bincount`. For all cases, the SparseTensor can be converted to a dense Tensor with `tf.sparse.to_dense` before calling `tf.math.bincount`. Args: 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. 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. Nbincountarr)namedtyper minlength maxlengthweights)rzXArguments `binary_output` and `weights` are mutually exclusive. Please specify only one.)rrz$Unsupported value for argument axis=z(. Only 0 and -1 are currently supported.)inputsizer binary_output)rZ name_scopeZconvert_to_tensorrint32r Z reduce_prodrshapecastZ reduce_maxrmaximumminimumZunsorted_segment_sumrconstantZreshaperrZ"convert_to_tensor_v2_with_dispatch ValueErrorr is_integerrvalidate_dense_weightsZdense_bincount) rrrrrrZaxisrZarray_is_nonemptyZ output_sizer#c/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/ops/bincount_ops.pyrse                 &rcCst|||||S)a&Counts the number of occurrences of each value in an integer array. If `minlength` and `maxlength` are not given, returns a vector with length `tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise. If `weights` are non-None, then index `i` of the output stores the sum of the value in `weights` at each index where the corresponding value in `arr` is `i`. Args: arr: An int32 tensor of non-negative values. weights: If non-None, must be the same shape as arr. For each value in `arr`, the bin will be incremented by the corresponding weight instead of 1. minlength: If given, ensures the output has length at least `minlength`, padding with zeros at the end if necessary. maxlength: If given, skips values in `arr` that are equal or greater than `maxlength`, ensuring that the output has length at most `maxlength`. dtype: If `weights` is None, determines the type of the output bins. Returns: A vector with the same dtype as `weights` or the given `dtype`. The bin values. )r)rrrrrr#r#r$ bincount_v1sr%cCsT|dur|r tjg|dStjg|jdSt|tjs(td|dt|j|S)z;Validates the passed weight tensor or creates an empty one.N)rzTArgument `weights` must be a tf.Tensor if `values` is a tf.Tensor. Received weights=z of type: ) rrr isinstancerZTensorr type__name__)valuesrrr#r#r$r"s r")N)__doc__Ztensorflow.python.frameworkrrrrrZtensorflow.python.opsrrr Ztensorflow.python.utilr r Z tensorflow.python.util.tf_exportr Zadd_dispatch_supportrrZdeprecated_endpointsr%r"r#r#r#r$s>             (