class_docstr: Class used to compute parameters for a model using Maximum Likelihood Estimation.
  
  Parameters
  ----------
  model: A pgmpy.models.BayesianNetwork instance
  
  data: pandas DataFrame object
      DataFrame object with column names identical to the variable names of the network.
      (If some values in the data are missing the data cells should be set to `numpy.NaN`.
      Note that pandas converts each column containing `numpy.NaN`s to dtype `float`.)
  
  state_names: dict (optional)
      A dict indicating, for each variable, the discrete set of states
      that the variable can take. If unspecified, the observed values
      in the data set are taken to be the only possible states.
  
  Examples
  --------
  >>> import numpy as np
  >>> import pandas as pd
  >>> from pgmpy.models import BayesianNetwork
  >>> from pgmpy.estimators import MaximumLikelihoodEstimator
  >>> data = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 5)),
  ...                       columns=['A', 'B', 'C', 'D', 'E'])
  >>> model = BayesianNetwork([('A', 'B'), ('C', 'B'), ('C', 'D'), ('B', 'E')])
  >>> estimator = MaximumLikelihoodEstimator(model, data)
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docstr: Method to estimate the CPD for a given variable.
  
  Parameters
  ----------
  node: int, string (any hashable python object)
      The name of the variable for which the CPD is to be estimated.
  
  weighted: bool
      If weighted=True, the data must contain a `_weight` column specifying the
      weight of each datapoint (row). If False, assigns an equal weight to each
      datapoint.
  
  Returns
  -------
  Estimated CPD: pgmpy.factors.discrete.TabularCPD
      Estimated CPD for `node`.
  
  Examples
  --------
  >>> import pandas as pd
  >>> from pgmpy.models import BayesianNetwork
  >>> from pgmpy.estimators import MaximumLikelihoodEstimator
  >>> data = pd.DataFrame(data={'A': [0, 0, 1], 'B': [0, 1, 0], 'C': [1, 1, 0]})
  >>> model = BayesianNetwork([('A', 'C'), ('B', 'C')])
  >>> cpd_A = MaximumLikelihoodEstimator(model, data).estimate_cpd('A')
  >>> print(cpd_A)
  ╒══════╤══════════╕
  │ A(0) │ 0.666667 │
  ├──────┼──────────┤
  │ A(1) │ 0.333333 │
  ╘══════╧══════════╛
  >>> cpd_C = MaximumLikelihoodEstimator(model, data).estimate_cpd('C')
  >>> print(cpd_C)
  ╒══════╤══════╤══════╤══════╤══════╕
  │ A    │ A(0) │ A(0) │ A(1) │ A(1) │
  ├──────┼──────┼──────┼──────┼──────┤
  │ B    │ B(0) │ B(1) │ B(0) │ B(1) │
  ├──────┼──────┼──────┼──────┼──────┤
  │ C(0) │ 0.0  │ 0.0  │ 1.0  │ 0.5  │
  ├──────┼──────┼──────┼──────┼──────┤
  │ C(1) │ 1.0  │ 1.0  │ 0.0  │ 0.5  │
  ╘══════╧══════╧══════╧══════╧══════╛
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code: def estimate_cpd(self, node, weighted=False):
      """
      Method to estimate the CPD for a given variable.
  
      Parameters
      ----------
      node: int, string (any hashable python object)
          The name of the variable for which the CPD is to be estimated.
  
      weighted: bool
          If weighted=True, the data must contain a `_weight` column specifying the
          weight of each datapoint (row). If False, assigns an equal weight to each
          datapoint.
  
      Returns
      -------
      Estimated CPD: pgmpy.factors.discrete.TabularCPD
          Estimated CPD for `node`.
  
      Examples
      --------
      >>> import pandas as pd
      >>> from pgmpy.models import BayesianNetwork
      >>> from pgmpy.estimators import MaximumLikelihoodEstimator
      >>> data = pd.DataFrame(data={'A': [0, 0, 1], 'B': [0, 1, 0], 'C': [1, 1, 0]})
      >>> model = BayesianNetwork([('A', 'C'), ('B', 'C')])
      >>> cpd_A = MaximumLikelihoodEstimator(model, data).estimate_cpd('A')
      >>> print(cpd_A)
      ╒══════╤══════════╕
      │ A(0) │ 0.666667 │
      ├──────┼──────────┤
      │ A(1) │ 0.333333 │
      ╘══════╧══════════╛
      >>> cpd_C = MaximumLikelihoodEstimator(model, data).estimate_cpd('C')
      >>> print(cpd_C)
      ╒══════╤══════╤══════╤══════╤══════╕
      │ A    │ A(0) │ A(0) │ A(1) │ A(1) │
      ├──────┼──────┼──────┼──────┼──────┤
      │ B    │ B(0) │ B(1) │ B(0) │ B(1) │
      ├──────┼──────┼──────┼──────┼──────┤
      │ C(0) │ 0.0  │ 0.0  │ 1.0  │ 0.5  │
      ├──────┼──────┼──────┼──────┼──────┤
      │ C(1) │ 1.0  │ 1.0  │ 0.0  │ 0.5  │
      ╘══════╧══════╧══════╧══════╧══════╛
      """
  
      state_counts = self.state_counts(node, weighted=weighted)
  
      # if a column contains only `0`s (no states observed for some configuration
      # of parents' states) fill that column uniformly instead
      state_counts.iloc[:, (state_counts.values == 0).all(axis=0)] = 1.0
  
      parents = sorted(self.model.get_parents(node))
      parents_cardinalities = [
          len(self.state_names[parent]) for parent in parents
      ]
      node_cardinality = len(self.state_names[node])
  
      # Get the state names for the CPD
      state_names = {node: list(state_counts.index)}
      if parents:
          state_names.update(
              {
                  state_counts.columns.names[i]: list(
                      state_counts.columns.levels[i]
                  )
                  for i in range(len(parents))
              }
          )
  
      cpd = TabularCPD(
          node,
          node_cardinality,
          np.array(state_counts),
          evidence=parents,
          evidence_card=parents_cardinalities,
          state_names={
              var: self.state_names[var]
              for var in chain([node], parents)
          },
      )
      cpd.normalize()
      return cpd
  
file_path: pgmpy/pgmpy/estimators/MLE.py
imports: from itertools import chain
  import numpy
  from joblib import Parallel, delayed
  from pgmpy.estimators import ParameterEstimator
  from pgmpy.factors.discrete import TabularCPD
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 3 for calling the MaximumLikelihoodEstimator obj's underlying member method code with example usage: obj.estimate_cpd(node, weighted) and returns: cpd