AVh.& TdZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z dd l m Z dd l m Z dd l mZed g e jdddej dddfdZed dg e j$ddddej fdZddZy)z bincount ops.)dtypes)ops)tensor)tensor_conversion) array_ops) gen_math_ops)math_ops) deprecation)dispatch) tf_exportz math.bincount)v1NFc|dn|}tj|5tj|d}|tj|d}| |r t d|j j s$tj|tj}|d}|dvrt d |d tj|dkD}tj||j tj|d zz} |8tj|d |j }tj || } |8tj|d|j }tj"|| } |dk(r0|tj$|dg}tj$|dg}t'|||}tj(|| ||cdddS#1swYyxYw)aCounts 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. >>> values = tf.constant([1,1,2,3,2,4,4,5]) >>> tf.math.bincount(values) 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`. **Bin-counting with weights** >>> 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) When `weights` is specified, bins 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` (i.e. for index 1, the first two values are 1 so the first two weights, 1 and 5, are summed). There is an equivilance between bin-counting with weights and `unsorted_segement_sum` where `data` is the weights and `segment_ids` are the 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.unsorted_segment_sum(weights, values, num_segments=6).numpy() array([0, 6, 0, 1, 9, 5], dtype=int32) On GPU, `bincount` with weights is only supported when XLA is enabled (typically when a function decorated with `@tf.function(jit_compile=True)`). `unsorted_segment_sum` can be used as a workaround for the non-XLA case on GPU. **Bin-counting matrix rows independently** This example uses `axis=-1` with a 2 dimensional input and returns a `Tensor` with bincounting where axis 0 is **not** flattened, i.e. an independent bincount for each matrix row. >>> 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. If non-None, `binary_output` must be False. 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` containing the bincount values. Raises: `InvalidArgumentError` if negative values are provided as an input. Nbincountarr)nameweightszXArguments `binary_output` and `weights` are mutually exclusive. Please specify only one.r)rz$Unsupported value for argument axis=z(. Only 0 and -1 are currently supported. minlength)rdtype maxlengthr)inputsizer binary_output)r name_scoper"convert_to_tensor_v2_with_dispatch ValueErrorr is_integerr castrint32rr reduce_maxconvert_to_tensorrmaximumminimumreshapevalidate_dense_weightsdense_bincount) rrrrrraxisrarray_is_nonempty output_sizes R/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/bincount_ops.pyrrsh|$ ~~d)%  > >s OC!DD  #g} = >> 99   MM#v|| ,c | d 7 =dVD66 77"s+a/-- 1399=C 1$&K'' +SYY8i ((K@k'' +SYY8i ((K@k qy  ##GbT2   cB4 (c$S'59G  & & #  %K)%)%)%s F0GGrc t|||||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)rrrrrs r+ bincount_v1r-s< #w 9e <<c|:|rtjg|Stjg|jSt|tj s$t d|dt|j|S)z;Validates the passed weight tensor or creates an empty one.)rzTArgument `weights` must be a tf.Tensor if `values` is a tf.Tensor. Received weights=z of type: ) rconstantr isinstancerTensorrtype__name__)valuesrrs r+r&r&sy _   % 00   b 55 GV]] +  #9JtG}/E/E.F H II .r.)N)__doc__tensorflow.python.frameworkrrrrtensorflow.python.opsrrr tensorflow.python.utilr r tensorflow.python.util.tf_exportr add_dispatch_supportr rdeprecated_endpointsr-r&r.r+r>s.+.9+.*.+6 ?r" << \%#\%~  +,!!!*-ll =.-=> r.