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
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()
