o "j4@sfddlZddlZddlmZmZddlmZddlm Z ddl m Z ddl m Z Gddde jZdS) N) check_type convert_dtype)Variable) distribution)in_dynamic_mode) multinomialc@sBeZdZdZdddZddZddZd d Zd d Zd dZ dS) Categoricala Categorical distribution is a discrete probability distribution that describes the possible results of a random variable that can take on one of K possible categories, with the probability of each category separately specified. The probability mass function (pmf) is: .. math:: pmf(k; p_i) = \prod_{i=1}^{k} p_i^{[x=i]} In the above equation: * :math:`[x=i]` : it evaluates to 1 if :math:`x==i` , 0 otherwise. Args: logits(list|tuple|numpy.ndarray|Tensor): The logits input of categorical distribution. The data type is float32 or float64. name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Categorical >>> paddle.seed(100) # on CPU device >>> x = paddle.rand([6]) >>> print(x) Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [0.55355281, 0.20714243, 0.01162981, 0.51577556, 0.36369765, 0.26091650]) >>> paddle.seed(200) # on CPU device >>> y = paddle.rand([6]) >>> print(y) Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [0.77663314, 0.90824795, 0.15685187, 0.04279523, 0.34468332, 0.79557180]) >>> cat = Categorical(x) >>> cat2 = Categorical(y) >>> paddle.seed(1000) # on CPU device >>> print(cat.sample([2,3])) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1, 5], [3, 4, 5]]) >>> print(cat.entropy()) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.77528250) >>> print(cat.kl_divergence(cat2)) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [0.07195196]) >>> value = paddle.to_tensor([2,1,3]) >>> print(cat.probs(value)) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [0.00608027, 0.10829761, 0.26965630]) >>> print(cat.log_prob(value)) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [-5.10270691, -2.22287226, -1.31060708]) NcCstst|dtjtttfd|dur|nd|_d|_| |r*||_ t |j|_n,t |tjr;t |jdvr;|j|_||d|_ |jt |j jkrVtj|j |jd|_ tj|j dd d }|j ||_dS) a1 Args: logits(list|tuple|numpy.ndarray|Tensor): The logits input of categorical distribution. The data type is float32 or float64. name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. logitsrNfloat32)r Zfloat64r)dtypeTaxiskeepdim)rrnpZndarrayrlisttuplenamer Z_validate_argsr r isinstancestrZ _to_tensorpaddlecastsum_prob)selfr rZdist_sumr`/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/distribution/categorical.py__init__[s&  zCategorical.__init__c Cs|jd}tst|dtdtt|}t|jj}t |dkr>||dd}t |jt|dd|dg}n|}|j}t | ||d}tt|}|d|d||}t j |||d S) aGenerate samples of the specified shape. Args: shape (list): Shape of the generated samples. Returns: Tensor: A tensor with prepended dimensions shape. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Categorical >>> paddle.seed(100) # on CPU device >>> x = paddle.rand([6]) >>> print(x) Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [0.55355281, 0.20714243, 0.01162981, 0.51577556, 0.36369765, 0.26091650]) >>> # doctest: +SKIP >>> cat = Categorical(x) >>> paddle.seed(1000) # on CPU device >>> print(cat.sample([2,3])) Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1, 5], [3, 4, 5]]) _sampleshapesampleNr Trr)rrrrrprodarrayr rlenrreshaperZ_logits_to_probsrangediminsertpopZ transpose) rrrZ num_samplesZ logits_shapeZ sample_shaper Z sample_indexZpermuterrrr {s&     zCategorical.samplec Cs|jd}tst|dtd|jtj|jddd}|jtj|jddd}t|}t|}tj|ddd}tj|ddd}||} tj| |t ||t |dd|d} | S)aThe KL-divergence between two Categorical distributions. Args: other (Categorical): instance of Categorical. The data type is float32. Returns: Tensor: kl-divergence between two Categorical distributions. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Categorical >>> paddle.seed(100) # on CPU device >>> x = paddle.rand([6]) >>> print(x) Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [0.55355281, 0.20714243, 0.01162981, 0.51577556, 0.36369765, 0.26091650]) >>> paddle.seed(200) # on CPU device >>> y = paddle.rand([6]) >>> print(y) Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [0.77663314, 0.90824795, 0.15685187, 0.04279523, 0.34468332, 0.79557180]) >>> cat = Categorical(x) >>> cat2 = Categorical(y) >>> print(cat.kl_divergence(cat2)) Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, [0.07195196]) Z_kl_divergenceother kl_divergencer Tr )rrr) rrrrr rmaxexprlog) rr+rr Z other_logitse_logitsZother_e_logitszZother_zprobklrrrr,s* "  zCategorical.kl_divergencecCst|jd}|jtj|jddd}t|}tj|ddd}||}tj||t|dd}tj|d|d}|S)aShannon entropy in nats. Returns: Tensor: Shannon entropy of Categorical distribution. The data type is float32. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Categorical >>> paddle.seed(100) # on CPU device >>> x = paddle.rand([6]) >>> print(x) Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [0.55355281, 0.20714243, 0.01162981, 0.51577556, 0.36369765, 0.26091650]) >>> cat = Categorical(x) >>> print(cat.entropy()) Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True, 1.77528250) Z_entropyr Tr rg)scaler)rr rr-r.rr/r5)rrr r0r1r2Z neg_entropyentropyrrrr6s  zCategorical.entropycCs|jd}t|jjdkr"tj|j|jdg|d|dj|j|dSt|jdkrDtj|jtj|t|jjddgdg|dddStj|j|ddS)aProbabilities of the given category (``value``). If ``logits`` is 2-D or higher dimension, the last dimension will be regarded as category, and the others represents the different distributions. At the same time, if ``vlaue`` is 1-D Tensor, ``value`` will be broadcast to the same number of distributions as ``logits``. If ``value`` is not 1-D Tensor, ``value`` should have the same number distributions with ``logits. That is, ``value[:-1] = logits[:-1]``. Args: value (Tensor): The input tensor represents the selected category index. Returns: Tensor: probability according to the category index. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Categorical >>> paddle.seed(100) # on CPU device >>> x = paddle.rand([6]) >>> print(x) Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [0.55355281, 0.20714243, 0.01162981, 0.51577556, 0.36369765, 0.26091650]) >>> cat = Categorical(x) >>> value = paddle.to_tensor([2,1,3]) >>> print(cat.probs(value)) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [0.00608027, 0.10829761, 0.26965630]) Z_probsr!r r"r4)rr%rrrZgatherr&Ztake_along_axisrvaluerrrrprobss$ # zCategorical.probscCs|jd}tj|||dS)aLog probabilities of the given category. Refer to ``probs`` method. Args: value (Tensor): The input tensor represents the selected category index. Returns: Tensor: Log probability. Examples: .. code-block:: python >>> import paddle >>> from paddle.distribution import Categorical >>> paddle.seed(100) # on CPU device >>> x = paddle.rand([6]) >>> print(x) Tensor(shape=[6], dtype=float32, place=Place(cpu), stop_gradient=True, [0.55355281, 0.20714243, 0.01162981, 0.51577556, 0.36369765, 0.26091650]) >>> cat = Categorical(x) >>> value = paddle.to_tensor([2,1,3]) >>> print(cat.log_prob(value)) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [-5.10270691, -2.22287226, -1.31060708]) Z _log_probr")rrr/r9r7rrrlog_probEs zCategorical.log_prob)N) __name__ __module__ __qualname____doc__rr r,r6r9r:rrrrrs A 99" 6r)numpyrrZpaddle.base.data_feederrrZpaddle.base.frameworkrZpaddle.distributionrZpaddle.frameworkrZ paddle.tensorr Distributionrrrrrs