https://towardsdatascience.com/an-equation-to-code-machine-learning-project-walk-through-in-python-part-1-linear-separable-fd0e19ed2d7

logistic_regression.py

import numpy as np
import matplotlib.pyplot as plt

# read data
data = np.loadtxt("linear_data.csv", delimiter=',', skiprows=1)
train_x = data[:, 0:2]
train_y = data[:, 2]

# initialize parameter
theta = np.random.randn(3)

# standardization
mu = train_x.mean(axis=0)
sigma = train_x.std(axis=0)
def standardizer(x):
    return (x - mu) / sigma
std_x = standardizer(train_x)

# get matrix
def to_matrix(std_x):
    return np.array([[1, x1, x2] for x1, x2 in std_x])
mat_x = to_matrix(std_x)

# dot product
def f(x):
    return np.dot(x, theta)

# sigmoid function
def f(x):
    return 1 / (1 + np.exp(-np.dot(x, theta)))

# update times
epoch = 2000

# learning rate
ETA = 1e-3

# update parameter
for _ in range(epoch):
    """
    f(mat_x) - train_y: (20,)
    mat_x: (20, 3)
    theta: (3,)
    
    dot production: (20,) x (20, 3) -> (3,)
    """
    theta = theta - ETA * np.dot(f(mat_x) - train_y, mat_x)



# plot line
x1 = np.linspace(-2, 2, 100)
x2 = - (theta[0] + x1 * theta[1]) / theta[2]

plt.plot(std_x[train_y == 1, 0], std_x[train_y == 1, 1], 'o') # train data of class 1
plt.plot(std_x[train_y == 0, 0], std_x[train_y == 0, 1], 'x') # train data of class 0
plt.plot(x1, x2, linestyle='dashed') # plot the line we learned
plt.show()