Python:实现linear regression线性回归算法(附完整源码)

import numpy as np
import requests


def collect_dataset():
    """Collect dataset of CSGO
    The dataset contains ADR vs Rating of a Player
    :return : dataset obtained from the link, as matrix
    """
    response = requests.get(
        "https://"
        + "The_Math_of_Intelligence/master/Week1/ADRvs"
        + "Rating.csv"
    )
    lines = response.text.splitlines()
    data = []
    for item in lines:
        item = item.split(",")
        data.append(item)
    data.pop(0)  # This is for removing the labels from the list
    dataset = np.matrix(data)
    return dataset


def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
  
    n = len_data

    prod = np.dot(theta, data_x.transpose())
    prod -= data_y.transpose()
    sum_grad = np.dot(prod, data_x)
    theta = theta - (alpha / n) * sum_grad
    return theta


def sum_of_square_error(data_x, data_y, len_data, theta):
   
    prod = np.dot(theta, data_x.transpose())
    prod -= data_y.transpose()
    sum_elem = np.sum(np.square(prod))
    error = sum_elem / (2 * len_data)
    return error


def run_linear_regression(data_x, data_y):

    iterations = 100000
    alpha = 0.0001550

    no_features = data_x.shape[1]
    len_data = data_x.shape[0] - 1

    theta = np.zeros((1, no_features))

    for i in range(0, iterations):
        theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
        error = sum_of_square_error(data_x, data_y, len_data, theta)
        print("At Iteration %d - Error is %.5f " % (i + 1, error))

    return theta


def main():
    """Driver function"""
    data = collect_dataset()

    len_data = data.shape[0]
    data_x = np.c_[np.ones(len_data), data[:, :-1]].astype(float)
    data_y = data[:, -1].astype(float)

    theta = run_linear_regression(data_x, data_y)
    len_result = theta.shape[1]
    print("Resultant Feature vector : ")
    for i in range(0, len_result):
        print(f"{theta[0, i]:.5f}")


if __name__ == "__main__":
    main()

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