# Candidate Elimination Algorithm in Python

## Python Program to Implement Candidate Elimination Algorithm to get Consistent Version Space

Exp. No. 2. For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm in python to output a description of the set of all hypotheses consistent with the training examples.

### Candidate Elimination Algorithm Machine Learning

```For each training example d, do:
If d is positive example
Remove from G any hypothesis h inconsistent with d
For each hypothesis s in S not consistent with d:
Remove s from S
Add to S all minimal generalizations of s consistent with d and having a generalization in G
Remove from S any hypothesis with a more specific h in S
If d is negative example
Remove from S any hypothesis h inconsistent with d
For each hypothesis g in G not consistent with d:
Remove g from G
Add to G all minimal specializations of g consistent with d and having a specialization in S
Remove from G any hypothesis having a more general hypothesis in G```

### Python Program to Implement and Demonstrate FIND-S Algorithm

```import numpy as np
import pandas as pd

concepts = np.array(data.iloc[:,0:-1])
print("\nInstances are:\n",concepts)
target = np.array(data.iloc[:,-1])
print("\nTarget Values are: ",target)

def learn(concepts, target):
specific_h = concepts[0].copy()
print("\nInitialization of specific_h and genearal_h")
print("\nSpecific Boundary: ", specific_h)
general_h = [["?" for i in range(len(specific_h))] for i in range(len(specific_h))]
print("\nGeneric Boundary: ",general_h)

for i, h in enumerate(concepts):
print("\nInstance", i+1 , "is ", h)
if target[i] == "yes":
print("Instance is Positive ")
for x in range(len(specific_h)):
if h[x]!= specific_h[x]:
specific_h[x] ='?'
general_h[x][x] ='?'

if target[i] == "no":
print("Instance is Negative ")
for x in range(len(specific_h)):
if h[x]!= specific_h[x]:
general_h[x][x] = specific_h[x]
else:
general_h[x][x] = '?'

print("Specific Bundary after ", i+1, "Instance is ", specific_h)
print("Generic Boundary after ", i+1, "Instance is ", general_h)
print("\n")

indices = [i for i, val in enumerate(general_h) if val == ['?', '?', '?', '?', '?', '?']]
for i in indices:
general_h.remove(['?', '?', '?', '?', '?', '?'])
return specific_h, general_h

s_final, g_final = learn(concepts, target)

print("Final Specific_h: ", s_final, sep="\n")
print("Final General_h: ", g_final, sep="\n")```

### Dataset:

EnjoySport Dataset is saved as .csv (comma separated values) file in the current working directory otherwise use the complete path of the dataset set in the program:

### Output:

Instances are:
[[‘sunny’ ‘warm’ ‘normal’ ‘strong’ ‘warm’ ‘same’]
[‘sunny’ ‘warm’ ‘high’ ‘strong’ ‘warm’ ‘same’]
[‘rainy’ ‘cold’ ‘high’ ‘strong’ ‘warm’ ‘change’]
[‘sunny’ ‘warm’ ‘high’ ‘strong’ ‘cool’ ‘change’]]

Target Values are: [‘yes’ ‘yes’ ‘no’ ‘yes’]

Initialization of specific_h and genearal_h

Specific Boundary: [‘sunny’ ‘warm’ ‘normal’ ‘strong’ ‘warm’ ‘same’]

Generic Boundary: [[‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Instance 1 is [‘sunny’ ‘warm’ ‘normal’ ‘strong’ ‘warm’ ‘same’] Instance is Positive

Specific Bundary after 1 Instance is [‘sunny’ ‘warm’ ‘normal’ ‘strong’ ‘warm’ ‘same’]

Generic Boundary after 1 Instance is [[‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Instance 2 is [‘sunny’ ‘warm’ ‘high’ ‘strong’ ‘warm’ ‘same’] Instance is Positive

Specific Bundary after 2 Instance is [‘sunny’ ‘warm’ ‘?’ ‘strong’ ‘warm’ ‘same’]

Generic Boundary after 2 Instance is [[‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Instance 3 is [‘rainy’ ‘cold’ ‘high’ ‘strong’ ‘warm’ ‘change’] Instance is Negative

Specific Bundary after 3 Instance is [‘sunny’ ‘warm’ ‘?’ ‘strong’ ‘warm’ ‘same’]

Generic Boundary after 3 Instance is [[‘sunny’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘warm’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘same’]]

Instance 4 is [‘sunny’ ‘warm’ ‘high’ ‘strong’ ‘cool’ ‘change’] Instance is Positive

Specific Bundary after 4 Instance is [‘sunny’ ‘warm’ ‘?’ ‘strong’ ‘?’ ‘?’]

Generic Boundary after 4 Instance is [[‘sunny’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘warm’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’]]

Final Specific_h: [‘sunny’ ‘warm’ ‘?’ ‘strong’ ‘?’ ‘?’]

Final General_h: [[‘sunny’, ‘?’, ‘?’, ‘?’, ‘?’, ‘?’], [‘?’, ‘warm’, ‘?’, ‘?’, ‘?’, ‘?’]]

### Solved Numerical Examples:

Candidate Elimination Algorithm Solved Example – 1

Candidate Elimination Algorithm Solved Example – 2

Candidate Elimination Algorithm Solved Example – 3

## Summary

This tutorial discusses how to Implement and demonstrate the Candidate Elimination algorithm in Python for finding the Consistent version space based on a given set of training data samples. The training data is read from a .CSV file. If you like the tutorial share with your friends. Like the Facebook page for regular updates and YouTube channel for video tutorials.