Introduction :
Find-S algorithm is a basic concept learning algorithm in machine learning. Find-S algorithm finds the most specific hypothesis that fits all the positive examples. We have to note here that the algorithm considers only those positive training example. Find-S algorithm starts with the most specific hypothesis and generalizes this hypothesis each time it fails to classify an observed positive training data. Hence, Find-S algorithm moves from the most specific hypothesis to the most general hypothesis.
data.csv
#Find S by Vikas Konaparthi
import random
import csv
attributes = [['Sunny','Rainy'],
['Warm','Cold'],
['Normal','High'],
['Strong','Weak'],
['Warm','Cool'],
['Same','Change']]
num_attributes = len(attributes)
print (" \n The most general hypothesis : ['?','?','?','?','?','?']\n")
print ("\n The most specific hypothesis : ['0','0','0','0','0','0']\n")
a = []
print("\n The Given Training Data Set \n")
with open('data.csv', 'r') as csvFile:
reader = csv.reader(csvFile)
for row in reader:
a.append (row)
print(row)
print("\n The initial value of hypothesis: ")
hypothesis = ['0'] * num_attributes
print(hypothesis)
# Comparing with First Training Example
for j in range(0,num_attributes):
hypothesis[j] = a[0][j];
# Comparing with Remaining Training Examples of Given Data Set
print("\n Find S by VK\n")
for i in range(0,len(a)):
if a[i][num_attributes]=='Yes':
for j in range(0,num_attributes):
if a[i][j]!=hypothesis[j]:
hypothesis[j]='?'
else :
hypothesis[j]= a[i][j]
print(" For Training Example No :{0} the hypothesis is ".format(i),hypothesis)
print("\n The Maximally Specific Hypothesis for a given Training Examples :\n")
print(hypothesis)
Output: