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R10130/lab_03_logreg/homework.py
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"""
EE-311
======
Lab 3: logistic regression and gradient descent
-----------------------------------------------
created by Francois Marelli on 05.03.2020
"""
import numpy as np
def model_prediction(data_X, beta):
"""
Compute the predicted labels for the points in data_X using a logistic regression model
Parameters:
data_X : ndarray of shape (N, M) with N the number of points and M the number of features
beta: ndarray of shape (M+1) containing the model parameters, beta[0] being the independent term
Returns:
labels: an array of shape (N) containing the model class predictions (0/1)
"""
return
def logistic_loss_derivative(data_X, data_y, beta):
"""
Compute the gradient of the averaged logistic loss function for a logistic regression model
Parameters:
data_X : ndarray of shape (N, M) with N the number of points and M the number of features
data_y: ndarray of shape (N) containing the labels (0/1)
beta: ndarray of shape (M+1) containing the model parameters, beta[0] being the independent term
Returns:
grad: an array of shape (M+1) containing the gradient of the logistic loss
function with respect to beta averaged over data_X
"""
return
def gradient_descent(data_X, data_y, beta_0, stepsize, iterates):
"""
A gradient descent algorithm, returns the trained model
DO NOT EDIT
Parameters:
data_X : ndarray of shape (N, M) with N the number of points and M the number of features
data_y: ndarray of shape (N) containing the labels (0/1)
beta: ndarray of shape (M+1) containing the initial model parameters, beta[0] being the independent term
stepsize: float, the step size of the gradient descent
iterates: number of iterations to perform
Returns:
beta: ndarray of shape (M+1) containing the trained model parameters, beta[0] being the independent term
"""
beta = beta_0
for _ in range(iterates):
dloss = logistic_loss_derivative(data_X, data_y, beta)
beta -= dloss * stepsize
return beta
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