1. AI AND MACHINE LEARNING VTU LAB | READ NOW

MACHINE LEARNING VTU LAB

1. Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a . CSV file.

Program Code – lab1.py

import csv
hypo = ['%','%','%','%','%','%'];

with open('trainingdata.csv') as csv_file:
    readcsv = csv.reader(csv_file, delimiter=',')
    print(readcsv)
    
    data = []
    print("\nThe given training examples are:")
    for row in readcsv:
        print(row)
        if row[len(row)-1].upper() == "YES":
            data.append(row)

print("\nThe positive examples are:");
for x in data:
    print(x);
print("\n");

TotalExamples = len(data);
i=0;
j=0;
k=0;
print("The steps of the Find-s algorithm are :\n",hypo);
list = [];
p=0;
d=len(data[p])-1;
for j in range(d):
    list.append(data[i][j]);
hypo=list;
i=1;
for i in range(TotalExamples):
    for k in range(d):
        if hypo[k]!=data[i][k]:
            hypo[k]='?';
            k=k+1;        
        else:
            hypo[k];
    print(hypo);
i=i+1;

print("\nThe maximally specific Find-s hypothesis for the given training examples is :");
list=[];
for i in range(d):
    list.append(hypo[i]);
print(list);

MACHINE LEARNING Program Execution – LAB1.ipynb

Jupyter Notebook program execution.

import csv
hypo = ['%','%','%','%','%','%'];

with open('trainingdata.csv') as csv_file:
    readcsv = csv.reader(csv_file, delimiter=',')
    print(readcsv)
    
    data = []
    print("\nThe given training examples are:")
    for row in readcsv:
        print(row)
        if row[len(row)-1].upper() == "YES":
            data.append(row)

The given training examples are:

[‘sky’, ‘airTemp’, ‘humidity’, ‘wind’, ‘water’, ‘forecast’, ‘enjoySport’]

[‘Sunny’, ‘Warm’, ‘Normal’, ‘Strong’, ‘Warm’, ‘Same’, ‘Yes’]

[‘Sunny’, ‘Warm’, ‘High’, ‘Strong’, ‘Warm’, ‘Same’, ‘Yes’]

[‘Rainy’, ‘Cold’, ‘High’, ‘Strong’, ‘Warm’, ‘Change’, ‘No’]

[‘Sunny’, ‘Warm’, ‘High’, ‘Strong’, ‘Cool’, ‘Change’, ‘Yes’]

print("\nThe positive examples are:");
for x in data:
    print(x);
print("\n");

The positive examples are:

[‘Sunny’, ‘Warm’, ‘Normal’, ‘Strong’, ‘Warm’, ‘Same’, ‘Yes’]

[‘Sunny’, ‘Warm’, ‘High’, ‘Strong’, ‘Warm’, ‘Same’, ‘Yes’]

[‘Sunny’, ‘Warm’, ‘High’, ‘Strong’, ‘Cool’, ‘Change’, ‘Yes’]

TotalExamples = len(data);
i=0;
j=0;
k=0;
print("The steps of the Find-s algorithm are :\n",hypo);
list = [];
p=0;
d=len(data[p])-1;
for j in range(d):
    list.append(data[i][j]);
hypo=list;
i=1;
for i in range(TotalExamples):
    for k in range(d):
        if hypo[k]!=data[i][k]:
            hypo[k]='?';
            k=k+1;        
        else:
            hypo[k];
    print(hypo);
i=i+1;

The steps of the Find-s algorithm are :

[‘%’, ‘%’, ‘%’, ‘%’, ‘%’, ‘%’]

[‘Sunny’, ‘Warm’, ‘Normal’, ‘Strong’, ‘Warm’, ‘Same’]

[‘Sunny’, ‘Warm’, ‘?’, ‘Strong’, ‘Warm’, ‘Same’]

[‘Sunny’, ‘Warm’, ‘?’, ‘Strong’, ‘?’, ‘?’]

print("\nThe maximally specific Find-s hypothesis for the given training examples is :");
list=[];
for i in range(d):
    list.append(hypo[i]);
print(list);

The maximally specific Find-s hypothesis for the given training examples is :

[‘Sunny’, ‘Warm’, ‘?’, ‘Strong’, ‘?’, ‘?’]

Download the Dataset