Completed KNN and GNB

project3/Code/main.py

@@ -77,13 +77,23 @@ def bayes_naive(self, predictData, trainData):
             f_score.append(h.findFMeasure(tmpPrecision, tmpRecall))
         return accuracy, precision, recall, f_score
 
+    def bayes_naive_demo(self, predictData, trainData):
+        h = hp()
+        nb = bayes()
+        classPriorProbabilities = nb.findClassPriorProbability(trainData)
+        classes = nb.segregateClasses(trainData)
+        occurences, means, stdDev = nb.findDescriptorPosteriorProbabilites(classes, trainData)
+        probabilities = nb.classify_demo(predictData, classPriorProbabilities, occurences, means, stdDev)
+        for key in probabilities:
+            print("P(X|H{})*P(H{}) = {}".format(key,key,probabilities[key]))
+
     def decision_tree(self, kCrossValidation = 10):
         print("\nRunning Decision Tree Classifier ....................\n")
         from decision_tree import decisionTree
         h = hp()
         fileName = h.get_fileName()
-        # filePath = "../Data/"+fileName+".txt"
-        filePath = "CSE-601/project3/Data/"+fileName+".txt"
+        filePath = "../Data/"+fileName+".txt"
+        # filePath = "CSE-601/project3/Data/"+fileName+".txt"
         data, labels = h.readData(filePath)
         data = h.oneHotEncoding(data, labels)
         dt = decisionTree()
@@ -204,6 +214,7 @@ def random_forest(self, kCrossValidation = 10):
         predictData = h.get_file_demo(h.get_fileName(), fileType='predictData')
         accuracy, precision, recall, f_score = m.knnDemo(predictData, trainData)
         h.calculateMetricsDemo(accuracy, precision, recall, f_score)
+
     elif algorithm == 1:
         print("Enter train File name")
         trainData = h.get_file(h.get_fileName(), kCrossValidation = 10)
@@ -215,8 +226,10 @@ def random_forest(self, kCrossValidation = 10):
             predictData = h.get_file(name, fileType='predictData')
         accuracy, precision, recall, f_score = m.knn(predictData, trainData)
         h.calculateMetrics(accuracy, precision, recall, f_score)
+
     elif algorithm == 2:
         m.decision_tree()
+
     elif algorithm == 3:
         print("Enter train File name")
         trainData = h.get_file_bayes(h.get_fileName(), kCrossValidation = 10)
@@ -228,13 +241,16 @@ def random_forest(self, kCrossValidation = 10):
             predictData = h.get_file_bayes(name, fileType='predictData')
         accuracy, precision, recall, f_score = m.bayes_naive(predictData, trainData)
         h.calculateMetrics(accuracy, precision, recall, f_score)
+
     elif algorithm == 4:
         print("Enter train File name")
         trainData = h.get_file_bayes_demo(h.get_fileName())
         print("Enter test File name")
         predictData = h.get_file_bayes_demo(h.get_fileName(),fileType = 'predictData')
         m.bayes_naive_demo(predictData, trainData)
+
     elif algorithm == 5:
         m.random_forest()
+
     else:
         print("\nWrong input")

project3/Data/d4test.txt

@@ -0,0 +1 @@
+rain	hot	high	weak

project3/Data/project3_dataset4.txt

@@ -1 +1,14 @@
-rain	hot	high	weak
+sunny	hot	high	weak	0
+sunny	hot	high	strong	0
+overcast	hot	high	weak	1
+rain	mild	high	weak	1
+rain	cool	normal	weak	1
+rain	cool	normal	strong	0
+overcast	cool	normal	strong	1
+sunny	mild	high	weak	0
+sunny	cool	normal	weak	1
+rain	mild	normal	weak	1
+sunny	mild	normal	strong	1
+overcast	mild	high	strong	1
+overcast	hot	normal	weak	1
+rain	mild	high	strong	0