#!/usr/bin/env python3
import sys
import numpy as np
import utils
def normal_equations(xs, ys, m, n):
"""
Perform normal equations on training set
xs : Training x values
ys : Training y values
m : Number of training examples
n : Number of features
"""
xs = utils.insert_zeroth_feature(xs, m)
return (np.linalg.inv((xs.transpose()).dot(xs))).dot(xs.transpose()).dot(ys)
if __name__ == "__main__":
path = sys.argv[1]
training_xs, training_ys = utils.training_data_from_csv_file(path)
print("Training set loaded")
print("Computing normal equations...")
utils.print_thetas(normal_equations(training_xs, training_ys, len(training_xs), len(training_xs[0])))
"""
Implementation of logistic regression using gradient descent
"""
import numpy as np
import sys
import utils
import math
def h(thetas, xi):
"""
Sigmoid hypothesis function for logistic regression
thetas : Theta values
xi : Feature vector
"""
return 1.0 / (1.0 + math.exp(-thetas.dot(xi)))
if __name__ == "__main__":
path = sys.argv[1]
training_xs, training_ys = utils.training_data_from_csv_file(path)
thetas = utils.gradient_descent(h,
training_xs,
training_ys,
0.001,
4000)
utils.print_thetas(thetas)
