Programming Machine Learning

Classification in Action

Here’s our final binary classification code, in all its glory. It loads Roberto’s data file, learns from it, and then comes up with a bunch of classifications:

05_discerning/classifier.py

import numpy as np


def sigmoid(z):
    return 1 / (1 + np.exp(-z))


def forward(X, w):
    weighted_sum = np.matmul(X, w)
    return sigmoid(weighted_sum)


def classify(X, w):
    return np.round(forward(X, w))


def loss(X, Y, w):
    y_hat = forward(X, w)
    first_term = Y * np.log(y_hat)
    second_term = (1 - Y) * np.log(1 - y_hat)
    return -np.average(first_term + second_term)


def gradient(X, Y, w):
    return np.matmul(X.T, (forward(X, w) - Y)) / X.shape[0]


def train(X, Y, iterations, ...