perceptron_iris.py

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt

class Perceptron:
    def __init__(self, learning_rate=0.1, n_iters=10000):
        self.lr = learning_rate
        self.n_iters = n_iters
        self.weights = None
        self.bias = None

    def fit(self, X, y):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0

        # gradient descent
        for _ in range(self.n_iters):
            for idx, x_i in enumerate(X):
                linear_output = np.dot(x_i, self.weights) + self.bias
                y_predicted = self.step_function(linear_output)

                # update weights and bias
                update = self.lr * (y[idx] - y_predicted)
                self.weights += update * x_i
                self.bias += update

    def predict(self, X):
        linear_output = np.dot(X, self.weights) + self.bias
        y_predicted = self.step_function(linear_output)
        return y_predicted

    def step_function(self, x):
        return np.where(x >= 0, 1, 0)

def main():
    # Load iris dataset
    iris = load_iris()
    X = iris.data
    y = iris.target

    # Split data into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Feature scaling
    scaler = StandardScaler()
    X_train = scaler.fit_transform(X_train)
    X_test = scaler.transform(X_test)

    # Initialize and train perceptron
    perceptron = Perceptron(learning_rate=0.2, n_iters=20000)
    perceptron.fit(X_train, y_train)

    # Make predictions
    predictions = perceptron.predict(X_test)

    # Evaluate accuracy
    accuracy = np.mean(predictions == y_test)
    print("Accuracy:", accuracy)

    # Plot the data
    plt.figure(figsize=(10, 6))
    plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap='viridis', label='Training Data')
    plt.xlabel('Sepal Length (cm)')
    plt.ylabel('Sepal Width (cm)')
    plt.title('Iris Dataset - Sepal Length vs. Sepal Width')
    plt.legend()
    plt.show()

if __name__ == "__main__":
    main()