Fin. LW, except for computing p of query
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main.py
165
main.py
@ -5,12 +5,15 @@
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import json
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import random
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from collections import defaultdict
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def main():
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bayes_net = import_bayes()
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print(len(bayes_net))
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print(bayes_net["0"]["prob"][0][1])
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#print(is_root("3", bayes_net))
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ev = {"0" : True, "4" : True}
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W = likelihood_weighting(1, ev, bayes_net, 10)
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print()
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for key, value in W.items():
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print(key, ' : ', value)
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#Import the BN from the json
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def import_bayes():
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@ -19,65 +22,145 @@ def import_bayes():
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json_file.close()
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return bayes_json
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#Checks if node has parents
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def is_root(node, BN):
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return (BN[node]["parents"]) == []
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#Return a list of the root nodes
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def get_root(node, BN):
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def get_root(BN):
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roots = []
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for i in range(len(BN)):
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if ((BN[node]["parents"]) == []):
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roots.append(node)
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for node in range(len(BN)):
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if ((BN[str(node)]["parents"]) == []):
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roots.append(str(node))
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return roots
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#Get parents of a node
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def get_parents(node, BN):
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return BN[str(node)]["parents"]
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#print(bayes_json["x"]): prints the information about node x (an int)
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#print(bayes_json["x"]["parents"] prints the information about node x's parents
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#class BayesianNetwork:
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"""NOTES"""
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#(bayes_json["x"]): the information about node x (an int)
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#(bayes_json["x"]["parents"] the information about node x's parents
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#bayes_json["x"]["prob"][0][0] returns the first set of truth table (0, 0), where [1][0] is the second (0,1)
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#bayes_json["x"]["prob"][parent][1] returns probability of the set evidence variable
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#E is a dict in the form of {"Node" : Value}
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#Compute the estimate of P(X|e), where X is the query variable, and e is the observed value for variables E
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def likelihood_weighting(X, e, bayes_net, samples):
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#Vector of weighted counts for each value of X, initialized to zero
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def likelihood_weighting(X, e, bayes_net, num_samples):
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W = {}
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#Init True and False probabilities
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T = 0
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F = 0
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for i in range(samples):
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x,w = weighted_sample(bayes_net, e)
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print("Hello")
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#Returns an event and a weight
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def weighted_sample(bayes_net, e):
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for i in range(num_samples):
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w = 1
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sample = {}
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#Elements in event x
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for node in bayes_net:
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#if bayes_net[e]["prob"][node] != None:
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if e[node] !=None:
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w*= bayes_net[node]["prob"][sample][1]
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sample[node] = e[node]
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#Holds all the info on the samples. EX: ~b, ~e, a, ~j, m
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samples = {}
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#Get all the roots to save traversion costs
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root_nodes = get_root(bayes_net)
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#Go through all the roots to get probabilities
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for root in root_nodes:
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#If the root is an evidence variables
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if root in e and root not in samples:
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#Just set the value to the observed value
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samples[root] = e[root]
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#Adjust the weight accordingly
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w = w * bayes_net[root]["prob"][0][1]
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else:
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#Random sample
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random_sample = random()
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sample[node] = #True or False
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#Otherwise, sample randomly
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if root not in samples:
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rand_prob = random.random()
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if rand_prob >= bayes_net[root]["prob"][0][1]:
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samples[root] = True
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else:
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samples[root] = False
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#v
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#Now go through the BN for non-root nodes
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for node in bayes_net:
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if node not in samples:
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#Get the probability, updated sample dict, and weight
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samples, prob, w = get_probability(str(node), samples, e, bayes_net, w)
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#for i in range(len(n)):
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#We now need to write to W
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#If this sample is already in W, don't add a new sample - only adjust the weight
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written = False
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for tmp in range(len(W)):
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#If sample is in W
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if samples in W[tmp].values():
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#Pull the weight that's associated with the sample
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key = list(W[tmp].items())[0][0]
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#Add the new weight to the existing weight
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new_key = key + w
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#Store it all back into W
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W[tmp] = {new_key : samples}
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#Make note that we've already written to W in this loop, so we don't write it again
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written = True
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#Given a node in the bayes net and a random sample, determine if it should be
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#classified as True or False
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def get_bool(node, rand_sample, sample_table, BN):
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table = {}
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if is_root(node, BN):
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table
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#If the sample wasn't already in W, put it in there now
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if not written:
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W[len(W)] = {w : samples}
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return W
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#Return the probability of a node and the value dict, given the current evidence and fixed evidence
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#Uses recursion to pull probabilites and values down through the network
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def get_probability(node, samples, ev, bayes_net, w):
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parents = get_parents(node, bayes_net)
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for parent in parents:
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#If we already know the value of the parent, no need to reobtain
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if str(parent) in samples:
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continue
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#If we don't know the value, then we need to get it
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else:
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gparents = get_parents(parent, bayes_net)
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#If we have all of parent's parents, then we can just get the probability
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if all(eles in samples for eles in gparents):
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samples, prob, w = translate_ev(gparents, parent, ev, samples, bayes_net, w)
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#Otherwise, we need to get values for the ancestor nodes - use recursion
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else:
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for gparent in gparents:
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if gparent not in samples:
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get_probability(gparent, samples, ev, bayes_net, w)
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samples, prob, w = translate_ev(gparents, parent, ev, samples, bayes_net, w)
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#Now that we have all the parents' values, we can get the node value, probability, and update samples
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samples, prob, w = translate_ev(parents, node, ev, samples, bayes_net, w)
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return samples, prob, w
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#Given a node and its parents, determine the node's value and it's probability
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def translate_ev(parents, node, ev, samples, bayes_net, w):
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#Sort in ascending order
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parents.sort()
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node = str(node)
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value_list = []
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for parent in parents:
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value = samples[str(parent)]
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value_list.append(value)
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#See if this is an evidence node
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if node in ev:
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samples[node] = ev[node]
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get_weight = True
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else:
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get_weight = False
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#The truth table has 2^parents entries
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for i in range(2**len(parents)):
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#If the truth table matches the value combination we have
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if bayes_net[str(node)]["prob"][i][0] == value_list:
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#Sample randomly
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rand_prob = random.random()
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table_prob = bayes_net[str(node)]["prob"][i][1]
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if rand_prob >= table_prob:
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samples[str(node)] = True
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else:
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samples[str(node)] = False
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table_prob = 1-table_prob
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if(get_weight):
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w = w * table_prob
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return samples, table_prob, w
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# for
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def gibbs_sampling():
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print("Hello")
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