class_docstr: An poisson distribution object.
  
  A poisson distribution models the number of occurances of events that
  happen in a fixed time span, assuming that the occurance of each event
  is independent. This distibution also asumes that each feature is
  independent of the others.
  
  There are two ways to initialize this objecct. The first is to pass in
  the tensor of lambda parameters, at which point they can immediately be
  used. The second is to not pass in the lambda parameters and then call
  either `fit` or `summary` + `from_summaries`, at which point the lambda
  parameter will be learned from data.
  
  
  Parameters
  ----------
  lambdas: list, numpy.ndarray, torch.Tensor or None, shape=(d,), optional
          The lambda parameters for each feature. 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.
  
  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.
class_docstr_tok: ["An","poisson","distribution","object",".","A","poisson","distribution","models","the","number","of","occurances","of","events","that","happen","in","a","fixed","time","span",",","assuming","that","the","occurance","of","each","event","is","independent",".","This","distibution","also","asumes","that","each","feature","is","independent","of","the","others",".","There","are","two","ways","to","initialize","this","objecct",".","The","first","is","to","pass","in","the","tensor","of","lambda","parameters",",","at","which","point","they","can","immediately","be","used",".","The","second","is","to","not","pass","in","the","lambda","parameters","and","then","call","either","`","fit","`","or","`","summary","`","+","`","from_summaries","`",",","at","which","point","the","lambda","parameter","will","be","learned","from","data",".","Parameters","--","--","--","--","--","lambdas",":","list",",","numpy.ndarray",",","torch.Tensor","or","None",",","shape=","(","d",",",")",",","optional","The","lambda","parameters","for","each","feature",".","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",".","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","."]
docstr: Initialize the probability distribution.
  
  This method is meant to only be called internally. It initializes the
  parameters of the distribution and stores its dimensionality. For more
  complex methods, this function will do more.
  
  
  Parameters
  ----------
  d: int
          The dimensionality the distribution is being initialized to.
docstr_tok: ["Initialize","the","probability","distribution",".","This","method","is","meant","to","only","be","called","internally",".","It","initializes","the","parameters","of","the","distribution","and","stores","its","dimensionality",".","For","more","complex","methods",",","this","function","will","do","more",".","Parameters","--","--","--","--","--","d",":","int","The","dimensionality","the","distribution","is","being","initialized","to","."]
code: def _initialize(self, d):
      """Initialize the probability distribution.
  
      This method is meant to only be called internally. It initializes the
      parameters of the distribution and stores its dimensionality. For more
      complex methods, this function will do more.
  
  
      Parameters
      ----------
      d: int
              The dimensionality the distribution is being initialized to.
      """
  
      self.lambdas = _cast_as_parameter(
          torch.zeros(d, dtype=self.dtype, device=self.device)
      )
  
      self._initialized = True
      super()._initialize(d)
  
file_path: pomegranate/pomegranate/distributions/poisson.py
imports: 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 ._distribution import Distribution
label_desc: Use image node_id 2 for calling the Poisson obj's underlying member method code with example usage: obj._initialize(d) without return types