import numpy as np import pandas as pd from pgmpy.models import MarkovModel from pgmpy.estimators import MaximumLikelihoodEstimator # Generating some random data raw_data = np.random.randint(low=0, high=2, size=(100, 2)) raw_data data = pd.DataFrame(raw_data, columns=['A', 'B']) data # Markov Model as stated in Fig. 6.5 markov_model = MarkovModel([('A', 'B')]) markov_model.fit(data, estimator=MaximumLikelihoodEstimator) factors = coin_model.get_factors() print(factors[0])
import numpy as np import pandas as pd from pgmpy.models import MarkovModel from pgmpy.estimators import PseudoMomentMatchingEstimator # Generating some random data raw_data = np.random.randint(low=0, high=2, size=(100, 4)) raw_data data = pd.DataFrame(raw_data, columns=['A', 'B', 'C', 'D']) data # Diamond shaped Markov Model as stated in Fig. 6.1 markov_model = MarkovModel([('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'A')]) markov_model.fit(data, estimator=PseudoMomentMatchingEstimator) factors = coin_model.get_factors() factors
" array([[1, 1], " " [1, 1], " " [0, 1], " " ...... " " [0, 0]])" data = pd.DataFrame(raw_data, columns=['A', 'B']) print(data) # Two coins toss result " X Y " "0 1 1 " " ......." "98 0 0 " # Markov Model markov_model = MarkovModel([('A','B')]) markov_model.fit(data, estimator=MaximumLikelihoodEstimator) factors = markov_model.get_factors() print(factors[0]) " A B phi(A,B) " " A_0 B_0 0.100 " " A_0 B_1 0.200 " " .......................... " -2- "Approximate Inference - <Belief Propagation and pseudo-moment matching> " import numpy as np import pandas as pd
import numpy as np import pandas as pd from pgmpy.models import MarkovModel from pgmpy.estimators import BayesianEstimator # Generating random data raw_data = np.random.randint(low=0, high=2, size=(1000, 2)) data = pd.DataFrame(raw_data, columns=['X', 'Y']) model = MarkovModel() model.fit(data, estimator=BayesianEstimator) model.get_factors() model.get_nodes() model.get_edges()