Naive Bayes Algo Code

Former-commit-id: 8df1f6cc17134b294e885f2395adaefd8a7c1a98

Author: akashzcoder

AlgorithmCode/NaiveBayes.py

@@ -0,0 +1,98 @@
+import csv
+import random
+import math
+
+def loadCsv (filename):
+   lines = csv.reader(open(filename,""))
+   dataset = list(lines)
+   for i in range(len(dataset)):
+     dataset[i]= [float(x) for x in dataset[i]]
+   return dataset
+
+def splitDataset (dataset,splitRatio):
+   trainSize = into(len(dataset)*splitRatio)
+   trainSet = []
+   copy = list(dataset)
+   whilelen(trainset)<trainSize:
+       index = random.randrange(len(copy))
+       trainSet.append(copy.pop(index))
+   return[trainset,copy]
+
+def separateByClass(dataset):
+   separated = {}
+   for i in range(len(dataset)):
+       vector= dataset[i]
+       if(vector[-1] not in separated):
+           separated[vector[-1]]= []
+       separated[vector[-1]].append(vector)
+   return separated
+def mean(numbers):
+   return sum(numbers)/float(len(numbers))
+   
+def stdev(numbers)
+   avg = mean(numbers)
+   variance = sum([pow(x-avg,2) for x in numbers])/float(len(numbers-1))
+   
+def summarize(dataset):
+    summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)]
+    del summaries[-1]
+    return summaries
+
+def summarizeByClass(dataset):
+    separated = separateByClass(dataset)
+    summaries = {}
+    for classValue, instances in separated.iteritems():
+        summaries[classValue] = summarize(instances)
+    return summaries
+
+def calculateProbability(x, mean, stdev):
+    exponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))
+    return (1 / (math.sqrt(2*math.pi) * stdev)) * exponent
+
+def calculateClassProbabilities(summaries, inputVector):
+    probabilities = {}
+    for classValue, classSummaries in summaries.iteritems():
+        probabilities[classValue] = 1
+        for i in range(len(classSummaries)):
+            mean, stdev = classSummaries[i]
+            x = inputVector[i]
+            probabilities[classValue] *= calculateProbability(x, mean, stdev)
+    return probabilities
+            
+def predict(summaries, inputVector):
+    probabilities = calculateClassProbabilities(summaries, inputVector)
+    bestLabel, bestProb = None, -1
+    for classValue, probability in probabilities.iteritems():
+        if bestLabel is None or probability > bestProb:
+            bestProb = probability
+            bestLabel = classValue
+    return bestLabel
+
+def getPredictions(summaries, testSet):
+    predictions = []
+    for i in range(len(testSet)):
+        result = predict(summaries, testSet[i])
+        predictions.append(result)
+    return predictions
+
+def getAccuracy(testSet, predictions):
+    correct = 0
+    for i in range(len(testSet)):
+        if testSet[i][-1] == predictions[i]:
+            correct += 1
+    return (correct/float(len(testSet))) * 100.0
+
+def main():
+    filename = ''
+    splitRatio = 0.67
+    dataset = loadCsv(filename)
+    trainingSet, testSet = splitDataset(dataset, splitRatio)
+    print('Split {0} rows into train={1} and test={2} rows').format(len(dataset), len(trainingSet), len(testSet))
+    # prepare model
+    summaries = summarizeByClass(trainingSet)
+    # test model
+    predictions = getPredictions(summaries, testSet)
+    accuracy = getAccuracy(testSet, predictions)
+    print('Accuracy: {0}%').format(accuracy)
+
+main()