class_docstr: A joint categorical distribution.
  
  A joint categorical distribution models the probability of a vector of
  categorical values occuring without assuming that the dimensions are
  independent from each other. Essentially, it is a Categorical distribution
  without the assumption that the dimensions are independent of each other. 
  
  There are two ways to initialize this object. The first is to pass in
  the tensor of probablity parameters, at which point they can immediately be
  used. The second is to not pass in the rate parameters and then call
  either `fit` or `summary` + `from_summaries`, at which point the 
  probability parameters will be learned from data.
  
  
  Parameters
  ----------
  probs: list, numpy.ndarray, torch.tensor, or None, shape=*n_categories
          A tensor where each dimension corresponds to one column in the data
          set being modeled and the size of each dimension is the number of
          categories in that column, e.g., if the data being modeled is binary 
          and has shape (5, 4), this will be a tensor with shape (2, 2, 2, 2).
          Default is None.
  
  n_categories: list, numpy.ndarray, torch.tensor, or None, shape=(d,)
          A vector with the maximum number of categories that each column
          can have. If not given, this will be inferred from the data. Default
          is None.
  
  inertia: float, [0, 1], optional
          Indicates the proportion of the update to apply to the parameters
          during training. When the inertia is 0.0, the update is applied in
          its entirety and the previous parameters are ignored. When the
          inertia is 1.0, the update is entirely ignored and the previous
          parameters are kept, equivalently to if the parameters were frozen.
  
  pseudocount: float, optional
          A number of observations to add to each entry in the probability
          distribution during training. A higher value will smooth the 
          distributions more. Default is 0.
  
  inertia: float, [0, 1], optional
          Indicates the proportion of the update to apply to the parameters
          during training. When the inertia is 0.0, the update is applied in
          its entirety and the previous parameters are ignored. When the
          inertia is 1.0, the update is entirely ignored and the previous
          parameters are kept, equivalently to if the parameters were frozen.
  
  frozen: bool, optional
          Whether all the parameters associated with this distribution are frozen.
          If you want to freeze individual pameters, or individual values in those
          parameters, you must modify the `frozen` attribute of the tensor or
          parameter directly. Default is False.
  
  check_data: bool, optional
          Whether to check properties of the data and potentially recast it to
          torch.tensors. This does not prevent checking of parameters but can
          slightly speed up computation when you know that your inputs are valid.
          Setting this to False is also necessary for compiling.
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docstr: Extract the sufficient statistics from a batch of data.
  
  This method calculates the sufficient statistics from optionally
  weighted data and adds them to the stored cache. The examples must be
  given in a 2D format. Sample weights can either be provided as one
  value per example or as a 2D matrix of weights for each feature in
  each example.
  
  
  Parameters
  ----------
  X: list, tuple, numpy.ndarray, torch.Tensor, shape=(-1, self.d)
          A set of examples to summarize.
  
  sample_weight: list, tuple, numpy.ndarray, torch.Tensor, optional
          A set of weights for the examples. This can be either of shape
          (-1, self.d) or a vector of shape (-1,). Default is ones.
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code: def summarize(self, X, sample_weight=None):
      """Extract the sufficient statistics from a batch of data.
  
      This method calculates the sufficient statistics from optionally
      weighted data and adds them to the stored cache. The examples must be
      given in a 2D format. Sample weights can either be provided as one
      value per example or as a 2D matrix of weights for each feature in
      each example.
  
  
      Parameters
      ----------
      X: list, tuple, numpy.ndarray, torch.Tensor, shape=(-1, self.d)
              A set of examples to summarize.
  
      sample_weight: list, tuple, numpy.ndarray, torch.Tensor, optional
              A set of weights for the examples. This can be either of shape
              (-1, self.d) or a vector of shape (-1,). Default is ones.
      """
  
      if self.frozen == True:
          return
  
      X = _check_parameter(
          _cast_as_tensor(X),
          "X",
          ndim=2,
          dtypes=(torch.int32, torch.int64),
          check_parameter=self.check_data,
      )
  
      if not self._initialized:
          self._initialize(len(X[0]), torch.max(X, dim=0)[0] + 1)
  
      X = _check_parameter(
          X,
          "X",
          shape=(-1, self.d),
          value_set=tuple(range(max(self.n_categories) + 1)),
          check_parameter=self.check_data,
      )
  
      sample_weight = _reshape_weights(
          X, _cast_as_tensor(sample_weight, dtype=torch.float32)
      )[:, 0]
  
      self._w_sum += torch.sum(sample_weight, dim=0)
      for i in range(len(X)):
          self._xw_sum[tuple(X[i])] += sample_weight[i]
  
file_path: pomegranate/pomegranate/distributions/joint_categorical.py
imports: import numpy
  import torch
  from .._utils import _cast_as_tensor
  from .._utils import _cast_as_parameter
  from .._utils import _update_parameter
  from .._utils import _check_parameter
  from .._utils import _reshape_weights
  from ._distribution import Distribution
  from .categorical import Categorical
label_desc: Use image node_id 6 for calling the JointCategorical obj's underlying member method code with example usage: obj.summarize(X, sample_weight) without return types