supervised_learning.py

import numpy as np


def loss_function(prediction, ground_truth): #mean square error with batch size = 1
    cost = (ground_truth-prediction)**2
    return cost


def prediction(x,current_weights,current_bias): 
    y_predicted = np.sum((current_weights * x)) + current_bias
    return y_predicted     


def gradient_descent(x, y, current_weights, current_bias, learning_rate):
    # Making predictions
    y_predicted = prediction(x,current_weights, current_bias)
          
    # Calculationg the current cost
    current_cost = loss_function(y, y_predicted)
    
    print("current_cost ", current_cost)
    
    # Calculating the gradients
    weight_derivative = -2 * sum(x * (y-y_predicted))
    bias_derivative = -2 * sum(y-y_predicted)
          
    # Updating weights and bias
    updated_weight = current_weights - (learning_rate * weight_derivative)
    updated_bias = current_bias - (learning_rate * bias_derivative)
                  
    return updated_weight, updated_bias



""" Dataset """

v1 = np.array([1, 1, 0, 0])
v2 = np.array([1, 0, 0, 0])
v3 = np.array([0, 0, 0, 1])
v4 = np.array([0, 0, 1, 1])

inputs = [v1, v2, v3, v4]
ground_truth = [0, 0 ,1 , 1]
dataset_length = len(inputs)

print(dataset_length)


""" Training """

iteration_number = 10
learning_rate = 0.01
current_weights = [0.09, 0.2, 0.5, 0.95]
current_bias = [0.02]

for i in range(iteration_number):
    for k in range(dataset_length):
        current_weights, current_bias = gradient_descent(inputs[k], ground_truth[k], current_weights, current_bias, learning_rate)
    print("Iteration :", i, "current weights", current_weights, "current bias", current_bias)  
    
    
""" Test """

for k in range(dataset_length):
    p_ = prediction(inputs[k], current_weights, current_bias)
    print("data:", inputs[k], "prediction", p_ )