Python Implementation of Linear Regression

import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib.axes as ax from matplotlib.animation import FuncAnimation

url = 'https://media.w3wiki.org/wp-content/uploads/20240320114716/data_for_lr.csv' data = pd.read_csv(url) data # Drop the missing values data = data.dropna() # training dataset and labels train_input = np.array(data.x[0:500]).reshape(500, 1) train_output = np.array(data.y[0:500]).reshape(500, 1) # valid dataset and labels test_input = np.array(data.x[500:700]).reshape(199, 1) test_output = np.array(data.y[500:700]).reshape(199, 1)

class LinearRegression: def __init__(self): self.parameters = {} def forward_propagation(self, train_input): m = self.parameters['m'] c = self.parameters['c'] predictions = np.multiply(m, train_input) + c return predictions def cost_function(self, predictions, train_output): cost = np.mean((train_output - predictions) ** 2) return cost def backward_propagation(self, train_input, train_output, predictions): derivatives = {} df = (predictions-train_output) # dm= 2/n * mean of (predictions-actual) * input dm = 2 * np.mean(np.multiply(train_input, df)) # dc = 2/n * mean of (predictions-actual) dc = 2 * np.mean(df) derivatives['dm'] = dm derivatives['dc'] = dc return derivatives def update_parameters(self, derivatives, learning_rate): self.parameters['m'] = self.parameters['m'] - learning_rate * derivatives['dm'] self.parameters['c'] = self.parameters['c'] - learning_rate * derivatives['dc'] def train(self, train_input, train_output, learning_rate, iters): # Initialize random parameters self.parameters['m'] = np.random.uniform(0, 1) * -1 self.parameters['c'] = np.random.uniform(0, 1) * -1 # Initialize loss self.loss = [] # Initialize figure and axis for animation fig, ax = plt.subplots() x_vals = np.linspace(min(train_input), max(train_input), 100) line, = ax.plot(x_vals, self.parameters['m'] * x_vals + self.parameters['c'], color='red', label='Regression Line') ax.scatter(train_input, train_output, marker='o', color='green', label='Training Data') # Set y-axis limits to exclude negative values ax.set_ylim(0, max(train_output) + 1) def update(frame): # Forward propagation predictions = self.forward_propagation(train_input) # Cost function cost = self.cost_function(predictions, train_output) # Back propagation derivatives = self.backward_propagation( train_input, train_output, predictions) # Update parameters self.update_parameters(derivatives, learning_rate) # Update the regression line line.set_ydata(self.parameters['m'] * x_vals + self.parameters['c']) # Append loss and print self.loss.append(cost) print("Iteration = {}, Loss = {}".format(frame + 1, cost)) return line, # Create animation ani = FuncAnimation(fig, update, frames=iters, interval=200, blit=True) # Save the animation as a video file (e.g., MP4) ani.save('linear_regression_A.gif', writer='ffmpeg') plt.xlabel('Input') plt.ylabel('Output') plt.title('Linear Regression') plt.legend() plt.show() return self.parameters, self.loss

#Example usage linear_reg = LinearRegression() parameters, loss = linear_reg.train(train_input, train_output, 0.0001, 20)