【机器学习】作业 Exam1

线性回归预测

唉，研0了，得学机器学习了。当然还是听的吴恩达老师的课，虽然全是英文，但是，怎么评价呢，讲得既专业又通俗易懂，初学者(像我这样的菜鸡)都值得一看！！

根据人口预测利润输入变量只有一个特征人口，输出变量为利润

很基础的东西，跟着老师来，lab里面都已经给你写好了。

import pandas as pd
from matplotlib import pyplot as plt

# 损失函数
def compute_loss(x, y, w, b):
    m = x.shape[0]
    sum = 0.
    for i in range(m):
        sum += (w * x[i] + b - y[i]) ** 2
    return sum / m

# 梯度下降
def gradient_descent(x, y, w, b, eta, iterations):
    m = x.shape[0]
    loss_history = []
    for _ in range(iterations):
        sum_w = 0.
        sum_b = 0.
        for i in range(m):
            sum_w += (w * x[i] + b - y[i]) * x[i]
            sum_b += (w * x[i] + b - y[i])
        new_w = w - eta * sum_w / m
        new_b = b - eta * sum_b / m
        w = new_w
        b = new_b
        loss_history.append(compute_loss(x, y, w, b))
    return w, b, loss_history

if __name__ == '__main__':
    data = pd.read_csv(r'D:\BaiduNetdiskDownload\data_sets\ex1data1.txt', names=["x", "y"])
    x = data['x']
    y = data['y']

    w, b, loss_history = gradient_descent(x, y, 0, 0, 0.01, 1000)
    epochs = range(len(loss_history))
    print(w, b)
    # 打印图标
    plt.plot(epochs, loss_history, color='red', label='loss')
    # plt.plot(x, w * x + b, color='red')
    # plt.scatter(x, y, color='blue')
    plt.show()