Source code for pgmpy.models.NoisyOrModel

#!/usr/bin/env python3
from itertools import chain

import numpy as np
import networkx as nx

from pgmpy.extern.six.moves import zip
from pgmpy.extern import six


[docs]class NoisyOrModel(nx.DiGraph): """ Base class for Noisy-Or models. This is an implementation of generalized Noisy-Or models and is not limited to Boolean variables and also any arbitrary function can be used instead of the boolean OR function. Reference: http://xenon.stanford.edu/~srinivas/research/6-UAI93-Srinivas-Generalization-of-Noisy-Or.pdf """ def __init__(self, variables, cardinality, inhibitor_probability): # TODO: Accept values of each state so that it could be # put into F to compute the final state values of the output """ Init method for NoisyOrModel. Parameters ---------- variables: list, tuple, dict (array like) array containing names of the variables. cardinality: list, tuple, dict (array like) array containing integers representing the cardinality of the variables. inhibitor_probability: list, tuple, dict (array_like) array containing the inhibitor probabilities of each variable. Examples -------- >>> from pgmpy.models import NoisyOrModel >>> model = NoisyOrModel(['x1', 'x2', 'x3'], [2, 3, 2], [[0.6, 0.4], ... [0.2, 0.4, 0.7], ... [0.1, 0.4]]) """ self.variables = np.array([]) self.cardinality = np.array([], dtype=np.int) self.inhibitor_probability = [] self.add_variables(variables, cardinality, inhibitor_probability)
[docs] def add_variables(self, variables, cardinality, inhibitor_probability): """ Adds variables to the NoisyOrModel. Parameters ---------- variables: list, tuple, dict (array like) array containing names of the variables that are to be added. cardinality: list, tuple, dict (array like) array containing integers representing the cardinality of the variables. inhibitor_probability: list, tuple, dict (array_like) array containing the inhibitor probabilities corresponding to each variable. Examples -------- >>> from pgmpy.models import NoisyOrModel >>> model = NoisyOrModel(['x1', 'x2', 'x3'], [2, 3, 2], [[0.6, 0.4], ... [0.2, 0.4, 0.7], ... [0.1, 0. 4]]) >>> model.add_variables(['x4'], [3], [0.1, 0.4, 0.2]) """ if len(variables) == 1: if not isinstance(inhibitor_probability[0], (list, tuple)): inhibitor_probability = [inhibitor_probability] if len(variables) != len(cardinality): raise ValueError("Size of variables and cardinality should be same") elif any(cardinal != len(prob_array) for prob_array, cardinal in zip(inhibitor_probability, cardinality)) or \ len(cardinality) != len(inhibitor_probability): raise ValueError("Size of variables and inhibitor_probability should be same") elif not all(0 <= item <= 1 for item in chain.from_iterable(inhibitor_probability)): raise ValueError("Probability values should be between 0 and 1(both inclusive).") else: self.variables = np.concatenate((self.variables, variables)) self.cardinality = np.concatenate((self.cardinality, cardinality)) self.inhibitor_probability.extend(inhibitor_probability)
[docs] def del_variables(self, variables): """ Deletes variables from the NoisyOrModel. Parameters ---------- variables: list, tuple, dict (array like) list of variables to be deleted. Examples -------- >>> from pgmpy.models import NoisyOrModel >>> model = NoisyOrModel(['x1', 'x2', 'x3'], [2, 3, 2], [[0.6, 0.4], ... [0.2, 0.4, 0.7], ... [0.1, 0. 4]]) >>> model.del_variables(['x1']) """ variables = [variables] if isinstance(variables, six.string_types) else set(variables) indices = [index for index, variable in enumerate(self.variables) if variable in variables] self.variables = np.delete(self.variables, indices, 0) self.cardinality = np.delete(self.cardinality, indices, 0) self.inhibitor_probability = [prob_array for index, prob_array in enumerate(self.inhibitor_probability) if index not in indices]
# # def out_prob(self, func): # """ # Compute the conditional probability of output variable # given all other variables [P(X|U)] where X is the output # variable and U is the set of input variables. # # Parameters # ---------- # func: function # The deterministic function which maps input to the # output. # # Returns # ------- # List of tuples. Each tuple is of the form (state, probability). # """ # states = [] # from itertools import product # for u in product([(values(var)) for var in self.variables]): # for state in product([(values(var) for var in self.variables)]):