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Practical 2

The document outlines the implementation of the Candidate-Elimination algorithm using Python to learn hypotheses from training data stored in a CSV file. It includes code for loading data, separating features and targets, and defining the learning function that updates specific and general hypotheses based on the training examples. The final output consists of the specific and general hypotheses after processing the data.

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manishjaat321
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36 views2 pages

Practical 2

The document outlines the implementation of the Candidate-Elimination algorithm using Python to learn hypotheses from training data stored in a CSV file. It includes code for loading data, separating features and targets, and defining the learning function that updates specific and general hypotheses based on the training examples. The final output consists of the specific and general hypotheses after processing the data.

Uploaded by

manishjaat321
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Practical 2

For a given set of training data examples stored in a .CSV file, implement and demonstrate the
Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent
with the training examples.

Code

import numpy as np
import pandas as pd

# Loading Data from a CSV File


data = pd.read_csv("tennis.csv")
print(data)

# Separating concept features from Target


concepts = np.array(data.iloc[:,0:-1])
print(concepts)

# Isolating target into a separate DataFrame


# copying last column to target array
target = np.array(data.iloc[:,-1])
print(target)

def learn(concepts, target):

'''
learn() function implements the learning method of the Candidate elimination algorithm.
Arguments:
concepts - a data frame with all the features
target - a data frame with corresponding output values
'''

# Initialise S0 with the first instance from concepts


# .copy() makes sure a new list is created instead of just pointing to the same memory
location
specific_h = concepts[0].copy()
print("\nInitialization of specific_h and general_h")
print(specific_h)
#h=["#" for i in range(0,5)]
#print(h)

general_h = [["?" for i in range(len(specific_h))] for i in range(len(specific_h))]


print(general_h)
# The learning iterations
for i, h in enumerate(concepts):

# Checking if the hypothesis has a positive target


if target[i] == "Yes":
for x in range(len(specific_h)):

# Change values in S & G only if values change


if h[x] != specific_h[x]:
specific_h[x] = '?'
general_h[x][x] = '?'

# Checking if the hypothesis has a positive target


if target[i] == "No":
for x in range(len(specific_h)):
# For negative hyposthesis change values only in G
if h[x] != specific_h[x]:
general_h[x][x] = specific_h[x]
else:
general_h[x][x] = '?'

print("\nSteps of Candidate Elimination Algorithm",i+1)


print(specific_h)
print(general_h)

# find indices where we have empty rows, meaning those that are unchanged
indices = [i for i, val in enumerate(general_h) if val == ['?', '?', '?', '?', '?', '?']]
for i in indices:
# remove those rows from general_h
general_h.remove(['?', '?', '?', '?', '?', '?'])
# Return final values
return specific_h, general_h

s_final, g_final = learn(concepts, target)


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

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