基於python的MySQL數據庫導入數據並進行梯度下降法迴歸方程的實現

import os
from pymysql import *
import csv
import numpy as np
import matplotlib.pyplot as plt

def mysql():

    # 建立與數據庫的連接
    cnn = connect(host="localhost", port=3306,\
                  user="root", password="root",\
                  database="test", charset="utf8")

    # 創建遊標
    cur = cnn.cursor()

    # 執行MySQL語句
    table = cur.execute("SELECT * FROM diamonds")
    diamonds = cur.fetchmany(table)

    cur.close()  # 關閉遊標
    cnn.close()  # 關閉連接
    return diamonds

def creatfile(diamonds):
    diamonds_mat = np.array(diamonds)
    if not os.path.isdir("diamonds"):
        os.mkdir("diamonds")
    if not os.path.isfile("diamonds\\diamonds.csv"):
        f = open("diamonds\\diamonds.csv", mode='w', newline='')
        csv_writer = csv.writer(f)
        for line in diamonds_mat:
            csv_writer.writerow(line)
        f.close()
    else:
        pass

def loadDataSet(filename, delim=","):
    f = open(filename)
    stringArr = [lines.strip().split(delim) for lines in f.readlines()]
    datArr = [list(map(float, line)) for line in stringArr]
    datMat = np.mat(datArr)
    m, n = np.shape(datMat)
    return datMat, m, n

diamonds = mysql()
creatfile(diamonds)
datMat, m, n = loadDataSet("diamonds\\diamonds.csv")

def regressionFunc(a, b, x):  # 求解預測值
    y_ = a + b * x
    return y_

def lossFunc(a, b, m):  # 代價函數
    y = datMat[:, 1]
    y_ = regressionFunc(a, b, datMat[:, 0])
    J = (1 / (2 * m)) * np.sum(np.square(y_ - y))
    return J

def aGrad(a, b, datMat, m):  # a的偏導數
    y = datMat[:, 1]
    a = (1 / m) * np.sum(regressionFunc(a, b, datMat[:, 0]) - y)
    return a

def bGrad(a, b, datMat, m):  # b的偏導數
    y = datMat[:, 1]
    b = (1 / m) * np.sum(np.multiply((regressionFunc(a, b, datMat[:, 0]) - y), datMat[:, 0]))
    return b

def gradientFunc(a, b, datMat, alfa, iter):  # 梯度下降法
    tempJ = np.zeros((iter, 1))
    finishIter = iter
    for i in range(iter):
        tempJ[i] = lossFunc(a, b, m)
        tempa = a - alfa * aGrad(a, b, datMat, m)
        tempb = b - alfa * bGrad(a, b, datMat, m)
        lasta = a
        lastb = b
        a = tempa
        b = tempb
        if lasta == a and lastb == b:
            finishIter = i
            J = lossFunc(a, b, m)
            break
    return a, b, tempJ, finishIter

a, b, tempJ, finishIter = gradientFunc(-2000, 40000, datMat, 0.01, 10000)
x = np.linspace(0, 2.5, 1000)
y = a + b * x
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax1.scatter(datMat[:, 0].tolist(), datMat[:, 1].tolist(), color="m", s=50)
ax1.plot(x, y, color='darkorange', linewidth=3)
plt.xlabel("x")
plt.ylabel("y")
ax2 = fig.add_subplot(122)
ax2.plot(tempJ[:finishIter], linewidth=2.5)
plt.xlabel("Iteration Times")
plt.ylabel("Cost Function")
plt.show()

應用案例：