import math
import random
import csv
def encode_class(mydata):
classes = []
for i in range(len(mydata)):
if mydata[i][-1] not in classes:
classes.append(mydata[i][-1])
for i in range(len(classes)):
for j in range(len(mydata)):
if mydata[j][-1] == classes[i]:
mydata[j][-1] = i
return mydata
def splitting(mydata, ratio):
train_num = int(len(mydata) * ratio)
train = []
test = list(mydata)
while len(train) < train_num:
index = random.randrange(len(test))
train.append(test.pop(index))
return train, test
1
def groupUnderClass(mydata):
dict = {}
for i in range(len(mydata)):
if mydata[i][-1] not in dict:
dict[mydata[i][-1]] = []
dict[mydata[i][-1]].append(mydata[i])
return dict
def mean(numbers):
return sum(numbers) / float(len(numbers))
def std_dev(numbers):
if len(numbers) <= 1:
return 0
avg = mean(numbers)
variance = sum([pow(x - avg, 2) for x in numbers]) / float(len(numbers) - 1)
return math.sqrt(variance)
def MeanAndStdDev(mydata):
info = [(mean(attribute), std_dev(attribute)) for attribute in zip(*mydata)]
del info[-1]
return info
def MeanAndStdDevForClass(mydata):
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info = {}
dict = groupUnderClass(mydata)
for classValue, instances in dict.items():
info[classValue] = MeanAndStdDev(instances)
return info
def calculateGaussianProbability(x, mean, stdev):
expo = math.exp(-(math.pow(x - mean, 2) / (2 * math.pow(stdev, 2))))
return (1 / (math.sqrt(2 * math.pi) * stdev)) * expo
def calculateClassProbabilities(info, test):
probabilities = {}
for classValue, classSummaries in info.items():
probabilities[classValue] = 1
for i in range(len(classSummaries)):
mean, std_dev = classSummaries[i]
x = test[i]
probabilities[classValue] *= calculateGaussianProbability(x, mean, std_dev)
return probabilities
def predict(info, test):
probabilities = calculateClassProbabilities(info, test)
bestLabel, bestProb = None, -1
for classValue, probability in probabilities.items():
3
if bestLabel is None or probability > bestProb:
bestProb = probability
bestLabel = classValue
return bestLabel
def getPredictions(info, test):
predictions = []
for i in range(len(test)):
result = predict(info, test[i])
predictions.append(result)
return predictions
def accuracy_rate(test, predictions):
correct = 0
for i in range(len(test)):
if test[i][-1] == predictions[i]:
correct += 1
return (correct / float(len(test))) * 100.0
filename = r'C:\Users\DBMS LAB\Downloads\test.csv '
mydata = csv.reader(open(filename, "rt"))
mydata = list(mydata)
mydata = encode_class(mydata)
for i in range(len(mydata)):
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mydata[i] = [float(x) for x in mydata[i]]
ratio = 0.7
train_data, test_data = splitting(mydata, ratio)
print('Total number of examples are: ', len(mydata))
print('Out of these, training examples are: ', len(train_data))
print("Test examples are: ", len(test_data))
info = MeanAndStdDevForClass(train_data)
predictions = getPredictions(info, test_data)
accuracy = accuracy_rate(test_data, predictions)
print("Accuracy of your model is: ", accuracy)
OUTPUT :
Total number of examples are: 6
Out of these, training examples are: 4
Test examples are: 2
Accuracy of your model is: 0.0
