Added Candidate-Elimination Algorithm

Program-2/Candidate-Elimination Algorithm.py

@@ -0,0 +1,81 @@
+"""
+Candidate-Elimination Algorithm:
+1. Load data set
+2. G <-maximally general hypotheses in H
+3. S <- maximally specific hypotheses in H
+4. For each training example d=<x,c(x)>
+Case 1 : If d is a positive example
+Remove from G any hypothesis that is inconsistent with d
+For each hypothesis s in S that is not consistent with d
+• Remove s from S.
+• Add to S all minimal generalizations h of s such that
+• h consistent with d
+• Some member of G is more general than h
+• Remove from S any hypothesis that is more general than another hypothesis in S
+Case 2: If d is a negative example
+Remove from S any hypothesis that is inconsistent with d
+For each hypothesis g in G that is not consistent with d
+*Remove g from G.
+*Add to G all minimal specializations h of g such that
+o h consistent with d
+o Some member of S is more specific than h
+• Remove from G any hypothesis that is less general than another hypothesis in G 
+"""
+
+import numpy as np
+import pandas as pd
+data = pd.DataFrame(data=pd.read_csv('finds1.csv'))
+concepts = np.array(data.iloc[:,0:-1])
+target = np.array(data.iloc[:,-1])
+def learn(concepts, target):
+ specific_h = concepts[0].copy()
+ print("initialization of specific_h and general_h")
+ print(specific_h)
+ general_h = [["?" for i in range(len(specific_h))] for i in range(len(specific_h))]
+ print(general_h)
+ for i, h in enumerate(concepts):
+ if target[i] == "Yes":
+ for x in range(len(specific_h)):
+ if h[x] != specific_h[x]:
+ specific_h[x] = '?'
+general_h[x][x] = '?'
+ if target[i] == "No":
+ for x in range(len(specific_h)):
+ if h[x] != specific_h[x]:
+ general_h[x][x] = specific_h[x]
+ else:
+ general_h[x][x] = '?'
+ print(" steps of Candidate Elimination Algorithm",i+1)
+ print("Specific_h ",i+1,"\n ")
+ print(specific_h)
+ print("general_h ", i+1, "\n ")
+ print(general_h)
+
+ indices = [i for i, val in enumerate(general_h) if val == ['?', '?', '?', '?', '?', '?']]
+ for i in indices:
+ general_h.remove(['?', '?', '?', '?', '?', '?'])
+
+ return specific_h, general_h
+s_final, g_final = learn(concepts, target)
+print("Final Specific_h:", s_final, sep="\n")
+print("Final General_h:", g_final, sep="\n")
+
+"""
+
+OUTPUT
+initialization of specific_h and general_h
+['Cloudy' 'Cold' 'High' 'Strong' 'Warm' 'Change']
+[['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?',
+'?', '?', '?'], ['?', '?', '?', '?', '?', '?']]
+steps of Candidate Elimination Algorithm 8
+Specific_h 8
+['?' '?' '?' 'Strong' '?' '?']
+general_h 8
+[['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?'], ['?', '?', '?', 'Strong', '?', '?'], ['?',
+'?', '?', '?', '?', '?'], ['?', '?', '?', '?', '?', '?']]
+Final Specific_h:
+['?' '?' '?' 'Strong' '?' '?']
+Final General_h:
+[['?', '?', '?', 'Strong', '?', '?']]
+
+"""

Program-7/program7.py

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+import numpy as np
+from urllib.request import urlopen
+import urllib
+import pandas as pd
+from pgmpy.inference import VariableElimination
+from pgmpy.models import BayesianModel
+from pgmpy.estimators import MaximumLikelihoodEstimator, BayesianEstimator
+names = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca','thal', 'heartdisease']
+heartDisease = pd.read_csv('heart.csv', names = names)
+heartDisease = heartDisease.replace('?', np.nan)
+model = BayesianModel([('age', 'trestbps'), ('age', 'fbs'), ('sex', 'trestbps'), ('exang','trestbps'),('trestbps','heartdisease'),('fbs','heartdisease'),('heartdisease','restecg'),('heartdisease','thalach'), ('heartdisease','chol')]) model.fit(heartDisease, estimator=MaximumLikelihoodEstimator)
+from pgmpy.inference import VariableElimination
+HeartDisease_infer = VariableElimination(model)
+q = HeartDisease_infer.query(variables=['heartdisease'], evidence={'age': 37, 'sex' :0})
+print(q['heartdisease'])