logistic-regression.py

from numpy import log, dot, e
from numpy.random import rand

class LogisticRegression:
    
    def sigmoid(self, z): return 1 / (1 + e**(-z))
    
    def cost_function(self, X, y, weights):                 
        z = dot(X, weights)
        predict_1 = y * log(self.sigmoid(z))
        predict_0 = (1 - y) * log(1 - self.sigmoid(z))
        return -sum(predict_1 + predict_0) / len(X)
    
    def fit(self, X, y, epochs=25, lr=0.05):        
        loss = []
        weights = rand(X.shape[1])
        N = len(X)
                 
        for _ in range(epochs):        
            # Gradient Descent
            y_hat = self.sigmoid(dot(X, weights))
            weights -= lr * dot(X.T,  y_hat - y) / N            
            # Saving Progress
            loss.append(self.cost_function(X, y, weights)) 
            
        self.weights = weights
        self.loss = loss
    
    def predict(self, X):        
        # Predicting with sigmoid function
        z = dot(X, self.weights)
        # Returning binary result
        return [1 if i > 0.5 else 0 for i in self.sigmoid(z)]