fuzzymamdanirl 1.0.3
Fuzzy Mamdani RL Implementaion

# Note:- 
# 1) For Trapizoidal membership function kindly use "trapmf" instead of trimf which is triangular membership function.
# 2) trimf() takes 4 parameters and trimf() takes 3

# 1) Centroid method

import matplotlib.pyplot as plt
import numpy as np
import skfuzzy as fuzz

# Define the triangular membership function
x = np.arange(0, 41)
mfx = fuzz.trimf(x, [10, 20, 30])

# For Trapizoidal membership function
# mfx = fuzz.trapmf(x, [10, 20, 30, 40])

# Plot the membership function
plt.plot(x, mfx, 'b', linewidth=1.5, label='Fuzzy set')

# Calculate the centroid using skfuzzy
centroid = fuzz.defuzz(x, mfx, 'centroid')

# Plot the centroid
plt.axvline(x=centroid, color='k', linestyle='--')
# plt.fill_betweenx([0, np.interp(centroid, x, mfx)], [0, centroid], alpha=0.1)

# Add labels and legends
plt.title("Centroid Method")
plt.xlabel('Temperature (Celsius)')
plt.ylabel('Membership degree')
plt.legend()

# Show the plot
plt.show()
print('Centroid: {:.2f}'.format(centroid))

# 2) Weighted Average Method

import numpy as np
import skfuzzy as fuzz
import matplotlib.pyplot as plt

# Define the input variable
x = np.arange(0, 41)

# Define the membership function
mfx = fuzz.trimf(x, [10, 20, 30])

# For Trapizoidal membership function
# mfx = fuzz.trapmf(x, [10, 20, 30, 40])

# Apply the weighted average defuzzification method
centroid_wa = np.sum(x * mfx) / np.sum(mfx)

# Plot the membership function
fig, ax = plt.subplots(figsize=(8, 3))
ax.plot(x, mfx, 'b', linewidth=1.5, label='Membership Function')
ax.fill_between(x, 0, mfx, alpha=0.1)

# Plot the centroid
ax.axvline(x=centroid_wa, color='r', linestyle='--', linewidth=1.5, label='Centroid (WA)')
ax.legend()

# Set plot attributes
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.set_title('Weighted Average Defuzzification')
ax.set_xlabel('Input Variable (x)')
ax.set_ylabel('Membership Degree')
plt.show()


# 3) Center of Sum method

import numpy as np
import skfuzzy as fuzz
import matplotlib.pyplot as plt

# Define the input variable
x = np.arange(0, 41)

# Define the membership function for traingular function
# mfx = fuzz.trimf(x, [10, 20, 30])

# For Trapizoidal membership function
mfx = fuzz.trapmf(x, [10, 20, 30, 40])

# Apply the center of sum defuzzification method
centroid_cos = np.sum(mfx * (x + np.random.normal(size=x.size))) / np.sum(mfx)

# Plot the membership function
fig, ax = plt.subplots(figsize=(8, 3))
ax.plot(x, mfx, 'b', linewidth=1.5, label='Membership Function')
ax.fill_between(x, 0, mfx, alpha=0.1)

# Plot the centroid
ax.axvline(x=centroid_cos, color='r', linestyle='--', linewidth=1.5, label='Centroid (COS)')
ax.legend()

# Set plot attributes
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.set_title('Center of Sum Defuzzification')
ax.set_xlabel('Input Variable (x)')
ax.set_ylabel('Membership Degree')
plt.show()


# 4) Mean of Maximum method

import numpy as np
import skfuzzy as fuzz
import matplotlib.pyplot as plt

# Define the input variable
x = np.arange(0, 41)

# Define the membership function
# mfx = fuzz.trimf(x, [10, 20, 30])

# For Trapizoidal membership function
mfx = fuzz.trapmf(x, [10, 20, 30, 40])

# Apply the mean of maximum defuzzification method
centroid_mom = fuzz.defuzz(x, mfx, 'mom')

# Plot the membership function
fig, ax = plt.subplots(figsize=(8, 3))
ax.plot(x, mfx, 'b', linewidth=1.5, label='Membership Function')
ax.fill_between(x, 0, mfx, alpha=0.1)

# Plot the centroid
ax.axvline(x=centroid_mom, color='r', linestyle='--', linewidth=1.5, label='Centroid (MOM)')
ax.legend()

# Set plot attributes
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.set_title('Mean of Maximum Defuzzification')
ax.set_xlabel('Input Variable (x)')
ax.set_ylabel('Membership Degree')
plt.show()


# Test Only:- 
# (defuzification method used is default one which is "Centroid method")

import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as ctrl
import matplotlib.pyplot as plt


# Define fuzzy variables
temp = ctrl.Antecedent(np.arange(16, 41, 1), 'Temperature')
humidity = ctrl.Antecedent(np.arange(0, 101, 1), 'Humidity')
cooling = ctrl.Consequent(np.arange(16, 31, 1), 'Cooling')

# Define membership functions for temperature
temp['cold'] = fuzz.trimf(temp.universe, [16, 16, 24])
temp['cool'] = fuzz.trimf(temp.universe, [18, 22, 26])
temp['comfortable'] = fuzz.trimf(temp.universe, [24, 26, 28])
temp['warm'] = fuzz.trimf(temp.universe, [26, 30, 34])
temp['hot'] = fuzz.trimf(temp.universe, [30, 40, 40])

# Define membership functions for humidity
humidity['dry'] = fuzz.trimf(humidity.universe, [0, 0, 50])
humidity['comfortable'] = fuzz.trimf(humidity.universe, [40, 50, 60])
humidity['humid'] = fuzz.trimf(humidity.universe, [50, 100, 100])

# Define membership functions for cooling
cooling['low'] = fuzz.trimf(cooling.universe, [16, 16, 23])
cooling['medium'] = fuzz.trimf(cooling.universe, [20, 24, 28])
cooling['high'] = fuzz.trimf(cooling.universe, [25, 30, 30])

# Define fuzzy rules
rule1 = ctrl.Rule(temp['cold'] | humidity['humid'], cooling['low'])
rule2 = ctrl.Rule(temp['cool'] & humidity['dry'], cooling['medium'])
rule3 = ctrl.Rule(temp['comfortable'] & humidity['comfortable'], cooling['medium'])
rule4 = ctrl.Rule(temp['warm'] & humidity['dry'], cooling['medium'])
rule5 = ctrl.Rule(temp['hot'] & humidity['dry'], cooling['high'])
rule6 = ctrl.Rule(temp['hot'] & humidity['humid'], cooling['low'])

# Create control system and simulate
ac_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5, rule6])
ac = ctrl.ControlSystemSimulation(ac_ctrl)

# Set input values
ac.input['Temperature'] = 28
ac.input['Humidity'] = 65

# Compute output
ac.compute()


# Plot membership functions
temp.view()
humidity.view()
cooling.view()

plt.show()

# Print output value
print("AC Temperature: ", ac.output['Cooling'])

cooling.view(sim = ac)
#Q.1) Design a fuzzy logic based decision modelling for an AC consider Room temperature and Humidity as Input ans AC temperature as output. Use 4 descriptor each for input and output. Derive proper set of rules and using appropriate defuzzyfication technique to get exact AC temperature. The Design should be supported by appropriate figures?

# Note:- 
# 1) For Trapizoidal membership function kindly use "trapmf" instead of trimf which is triangular membership function.
# 2) trimf() takes 4 parameters and trimf() takes 3

# 1) AC:- (defuzification method used is default one which is "Centroid method")

import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as ctrl

# Define input and output variables and their fuzzy sets
temperature = ctrl.Antecedent(np.arange(0, 31, 1), 'temperature')
humidity = ctrl.Antecedent(np.arange(0, 31, 1), 'humidity')
AC_temperature = ctrl.Consequent(np.arange(0, 31, 1), 'AC_temperature')

# Define membership functions for input and output variables
temperature['cold'] = fuzz.trimf(temperature.universe, [0, 0, 20])
temperature['cool'] = fuzz.trimf(temperature.universe, [0, 20, 25])
temperature['warm'] = fuzz.trimf(temperature.universe, [20, 25, 30])
temperature['hot'] = fuzz.trimf(temperature.universe, [25, 30, 30])

humidity['dry'] = fuzz.trimf(humidity.universe, [0, 0, 20])
humidity['moderate'] = fuzz.trimf(humidity.universe, [0, 20, 25])
humidity['humid'] = fuzz.trimf(humidity.universe, [20, 25, 30])
humidity['very humid'] = fuzz.trimf(humidity.universe, [25, 30, 30])

AC_temperature['very cold'] = fuzz.trimf(AC_temperature.universe, [0, 0, 20])
AC_temperature['cold'] = fuzz.trimf(AC_temperature.universe, [0, 20, 25])
AC_temperature['warm'] = fuzz.trimf(AC_temperature.universe, [20, 25, 30])
AC_temperature['hot'] = fuzz.trimf(AC_temperature.universe, [25, 30, 30])

# Define fuzzy rules
rule1 = ctrl.Rule(temperature['cold'] & humidity['dry'], AC_temperature['very cold'])
rule2 = ctrl.Rule(temperature['cold'] & humidity['moderate'], AC_temperature['cold'])
rule3 = ctrl.Rule(temperature['cold'] & humidity['humid'], AC_temperature['cold'])
rule4 = ctrl.Rule(temperature['cold'] & humidity['very humid'], AC_temperature['warm'])
rule5 = ctrl.Rule(temperature['cool'] & humidity['dry'], AC_temperature['cold'])
rule6 = ctrl.Rule(temperature['cool'] & humidity['moderate'], AC_temperature['cold'])
rule7 = ctrl.Rule(temperature['cool'] & humidity['humid'], AC_temperature['warm'])
rule8 = ctrl.Rule(temperature['cool'] & humidity['very humid'], AC_temperature['warm'])
rule9 = ctrl.Rule(temperature['warm'] & humidity['dry'], AC_temperature['warm'])
rule10 = ctrl.Rule(temperature['warm'] & humidity['moderate'], AC_temperature['warm'])
rule11 = ctrl.Rule(temperature['warm'] & humidity['humid'], AC_temperature['hot'])
rule12 = ctrl.Rule(temperature['warm'] & humidity['very humid'], AC_temperature['hot'])
rule13 = ctrl.Rule(temperature['hot'] & humidity['dry'], AC_temperature['warm'])
rule14 = ctrl.Rule(temperature['hot'] & humidity['moderate'], AC_temperature['warm'])
rule15 = ctrl.Rule(temperature['hot'] & humidity['humid'], AC_temperature['hot'])
rule16 = ctrl.Rule(temperature['hot'] & humidity['very humid'], AC_temperature['hot'])

# # Define control system
# Define control system
AC_ctrl = ctrl.ControlSystem(
    [rule1, rule2, rule3, rule4, rule5, rule6, rule7, rule8,
     rule9, rule10, rule11, rule12, rule13, rule14, rule15, rule16])

# Create a simulation to test the system
AC_sim = ctrl.ControlSystemSimulation(AC_ctrl)

# # Set input values for temperature and humidity
AC_sim.input['temperature'] = 24 # 35
AC_sim.input['humidity'] = 24 # 80

# Compute the result
AC_sim.compute()

# Get the output value
AC_output = AC_sim.output['AC_temperature']

# Print the output value
print("AC Temperature:", AC_output)

# Plot the membership functions
temperature.view()
humidity.view()
AC_temperature.view()

AC_temperature.view(sim = AC_sim)



# Q.2) Design fuzzy logic controller to determine the washtime of a domestic Washing Machine. Assume that input is "dirt" and "grease" on the clothes. Use 3 discriptor for each of this 2 input variables and 5 discriptor variables for output.Device a set of rules for control action and defuzzyfication. Design should be supported by figures whereever possible.

# 2) For washing machine. (defuzification method used is default one which is "Centroid method")

import numpy as np
import matplotlib.pyplot as plt
import skfuzzy as fuzz
from skfuzzy import control as ctrl

# Defining input and output variable and also discriptor.
# Inputs are below:- 
dirt = ctrl.Antecedent(np.arange(0, 101, 1), "Dirt")
grease = ctrl.Antecedent(np.arange(0, 101, 1), "Grease")

# Outputs are below:-
wash_time = ctrl.Consequent(np.arange(0, 61, 1), "Wash Time")

# Defining the membership function for dirt
dirt["small_dirt"] = fuzz.trimf(dirt.universe, [0, 0, 50])
dirt["medium_dirt"] = fuzz.trimf(dirt.universe, [0, 50, 100])
dirt["large_dirt"] = fuzz.trimf(dirt.universe, [50, 100, 100])

dirt.view()

# Defining the membership function for grease
grease["small_grease"] = fuzz.trimf(grease.universe, [0, 0, 50])
grease["medium_grease"] = fuzz.trimf(grease.universe, [0, 50, 100])
grease["large_grease"] = fuzz.trimf(grease.universe, [50, 100, 100])

grease.view()

# Defining the membership function for wash_time
wash_time["very_small"] = fuzz.trimf(wash_time.universe, [0, 0, 10])
wash_time["small"] = fuzz.trimf(wash_time.universe, [0, 10, 25])
wash_time["medium"] = fuzz.trimf(wash_time.universe, [10, 25, 40])
wash_time["large"] = fuzz.trimf(wash_time.universe, [25, 40, 60])
wash_time["very_large"] = fuzz.trimf(wash_time.universe, [40, 60, 60])

wash_time.view()

# Defining fuzzy rules
rule1 = ctrl.Rule(dirt["small_dirt"] & grease["small_grease"], wash_time["very_small"])
rule2 = ctrl.Rule(dirt["small_dirt"] & grease["medium_grease"], wash_time["medium"])
rule3 = ctrl.Rule(dirt["small_dirt"] & grease["large_grease"], wash_time["large"])

rule4 = ctrl.Rule(dirt["medium_dirt"] & grease["small_grease"], wash_time["small"])
rule5 = ctrl.Rule(dirt["medium_dirt"] & grease["medium_grease"], wash_time["medium"])
rule6 = ctrl.Rule(dirt["medium_dirt"] & grease["large_grease"], wash_time["large"])

rule7 = ctrl.Rule(dirt["large_dirt"] & grease["small_grease"], wash_time["medium"])
rule8 = ctrl.Rule(dirt["large_dirt"] & grease["medium_grease"], wash_time["large"])
rule9 = ctrl.Rule(dirt["large_dirt"] & grease["large_grease"], wash_time["very_large"])



# Creating control system and simulating
wt_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5, rule6, rule7, rule8, rule9])
wt = ctrl.ControlSystemSimulation(wt_ctrl)

# Now, Setting the input values
wt.input["Dirt"] = 60
wt.input["Grease"] = 70

plt.show()
# Compute output
wt.compute()

print("Wash Time: ", wt.output["Wash Time"])

wash_time.view(sim = wt)



#Q.3) Design a fuzzy controller for a train approching or leaving a station. The inputs are distance from the station and speed of the train. The output is amount of brake power used. Use 4 discriptor each for input and output and design using mamdani fuzzy model. Derive set of rules for control action and defuzzyfication. The design should be supported by appropriate figures?


# 3) For Train. (defuzification method used is default one which is "Centroid method")

import numpy as np
import matplotlib.pyplot as plt
import skfuzzy as fuzz
from skfuzzy import control as ctrl

# Defining input and output variable and also discriptor.
# Inputs are below:- 
Distance = ctrl.Antecedent(np.arange(0, 101, 1), "Distance")
Speed = ctrl.Antecedent(np.arange(0, 101, 1), "Speed")

# Outputs are below:-
BrakePower = ctrl.Consequent(np.arange(0, 101, 1), "BrakePower")

# Defining the membership function for Distance
Distance["close"] = fuzz.trimf(Distance.universe, [0, 0, 25])
Distance["near"] = fuzz.trimf(Distance.universe, [0, 25, 75])
Distance["far"] = fuzz.trimf(Distance.universe, [25, 75, 100])
Distance["very_far"] = fuzz.trimf(Distance.universe, [75, 100, 100])


Distance.view()

# Defining the membership function for Speed
Speed["very_slow"] = fuzz.trimf(Speed.universe, [0, 0, 25])
Speed["slow"] = fuzz.trimf(Speed.universe, [0, 25, 75])
Speed["fast"] = fuzz.trimf(Speed.universe, [25, 75, 100])
Speed["very_fast"] = fuzz.trimf(Speed.universe, [75, 100, 100])


Speed.view()

# Defining the membership function for BrakePower
BrakePower["very_low"] = fuzz.trimf(BrakePower.universe, [0, 0, 25])
BrakePower["low"] = fuzz.trimf(BrakePower.universe, [0, 25, 75])
BrakePower["medium"] = fuzz.trimf(BrakePower.universe, [25, 75, 100])
BrakePower["very_high"] = fuzz.trimf(BrakePower.universe, [75, 100, 100])

BrakePower.view()

# Defining fuzzy rules
rule1 = ctrl.Rule(Distance["close"] & Speed["very_slow"], BrakePower["very_low"])
rule2 = ctrl.Rule(Distance["close"] & Speed["slow"], BrakePower["low"])
rule3 = ctrl.Rule(Distance["close"] & Speed["fast"], BrakePower["medium"])
rule4 = ctrl.Rule(Distance["close"] & Speed["very_fast"], BrakePower["very_high"])


rule5 = ctrl.Rule(Distance["near"] & Speed["very_slow"], BrakePower["very_low"])
rule6 = ctrl.Rule(Distance["near"] & Speed["slow"], BrakePower["low"])
rule7 = ctrl.Rule(Distance["near"] & Speed["fast"], BrakePower["medium"])
rule8 = ctrl.Rule(Distance["near"] & Speed["very_fast"], BrakePower["very_high"])


rule9 = ctrl.Rule(Distance["far"] & Speed["very_slow"], BrakePower["very_low"])
rule10 = ctrl.Rule(Distance["far"] & Speed["slow"], BrakePower["very_low"])
rule11 = ctrl.Rule(Distance["far"] & Speed["fast"], BrakePower["medium"])
rule12 = ctrl.Rule(Distance["far"] & Speed["very_fast"], BrakePower["medium"])

rule13 = ctrl.Rule(Distance["very_far"] & Speed["very_slow"], BrakePower["very_low"])
rule14 = ctrl.Rule(Distance["very_far"] & Speed["slow"], BrakePower["very_low"])
rule15 = ctrl.Rule(Distance["very_far"] & Speed["fast"], BrakePower["medium"])
rule16 = ctrl.Rule(Distance["very_far"] & Speed["very_fast"], BrakePower["very_high"])



# Creating control system and simulating
t_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5, rule6, rule7, rule8, rule9, rule10, rule11, rule12, rule13, rule14, rule15, rule16])
t = ctrl.ControlSystemSimulation(t_ctrl)

# Now, Setting the input values
t.input["Distance"] = 20
t.input["Speed"] = 90

plt.show()
# Compute output
t.compute()

print("Brake Power: ", t.output["BrakePower"])

BrakePower.view(sim = t)


#GRADIENT DESCENT 
import numpy as np

def gradient_descent(X, y, learning_rate, num_iterations):
    num_samples, num_features = X.shape
    theta = np.zeros(num_features)  # Initialize the parameter vector with zeros
    
    for iteration in range(num_iterations):
        # Calculate the predicted values
        y_pred = np.dot(X, theta)
        
        # Calculate the gradient
        gradient = (1/num_samples) * np.dot(X.T, y_pred - y)
        
        # Update the parameters
        theta = theta - learning_rate * gradient
    
    return theta

# Example usage
# Suppose you have a dataset X and the corresponding target values y
X = np.array([[1, 2, 3],
              [4, 5, 6],
              [7, 8, 9]])
y = np.array([4, 8, 12])

learning_rate = 0.01
num_iterations = 1000

# Call the gradient descent function
theta = gradient_descent(X, y, learning_rate, num_iterations)
print("Optimized theta:", theta)


# DECISION TREE FROM SCRATCH

import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score

# Reading the Iris.csv file
data = load_iris()

# Extracting Attributes / Features
X = data.data

# Extracting Target / Class Labels
y = data.target

# Import Library for splitting data
from sklearn.model_selection import train_test_split

# Creating Train and Test datasets
X_train, X_test, y_train, y_test = train_test_split(X,y, random_state = 50, test_size = 0.25)

# Creating Decision Tree Classifier
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
clf.fit(X_train,y_train)

# Predict Accuracy Score
y_pred = clf.predict(X_test)
print("Train data accuracy:",accuracy_score(y_true = y_train, y_pred=clf.predict(X_train)))
print("Test data accuracy:",accuracy_score(y_true = y_test, y_pred=y_pred))