首先，載入資料，load_data（file)函式。

def load_data(file):
    s =[]

    with open(file) as f:
        for line in f.readlines():
            line = line.replace('\n','')       #追行讀取 type(line) = str
            s.append(line.split(' '))      #空格分開
    return s

其中，紅，藍分別表示好瓜和壞瓜。可以看出，用一條直線，還是不好劃分的。

這部分程式碼如下：

file = '../data/3_0a.txt'          #檔案地址
s = load_data(file)
print(type(s))
x =[]       #存好瓜的含糖率
y= []    #好瓜的密度
x1 =[]   #壞瓜含糖率
y1 = []  #壞瓜密度

for i in range(1,8):  #讀取好瓜

    for j in range(len(s[i])):
        if j == 2:
            x.append(float(s[i][j]))
        if j ==3:
            y.append(float(s[i][j]))
for i in range(8,len(s)): #壞瓜

    for j in range(len(s[i])):
        if j == 2:
            x1.append(float(s[i][j]))
        if j ==3:
            y1.append(float(s[i][j]))


  
import pylab as pl
pl.plot(x,y,'o')
pl.plot(x1,y1,'ro')
pl.show()
 

3.27公式中，yi是樣本的結果，好瓜是1，壞瓜是0.xi是樣本的屬性，我們這裡有兩個屬性。下面就是從前面讀取的資料把xi,yi讀出來。然後把這個值帶入梯度下降法中的導數項。w，b的初始值隨便設定個就行。迭代算吧。程式碼如下：

import numpy as np
import pylab as plt
import my_load_data as mld   #就是前面的那個函式，這段可以刪除，直接把上面的load_data函式放到這裡也行。




file = '../data/3_0a.txt'
s = mld.load_data(file)

x = np.mat(np.zeros((17,3)))  #why (())?     #初始化矩陣。用ndarray無法進行矩陣乘法這類運算，所以要用mat。

y = np.mat(np.zeros((17,1)))
for i in range(1,18,1):                  #yi ,xi讀取
    x[i-1] = np.mat([float(s[i][2]),float(s[i][3]),1])
    if s[i][1]=='是':
        y[i-1] = np.mat([1])
    else:
        y[i-1] =np.mat([0])

start = np.mat([[0.1],[10],[8]])  #w,b的初始化。這裡有三個數，[w1;w2;b]

i = 0
xishu =0.01
while i<2*10**5:    #一萬次差不多就可以了
    s = 0
    for j in range(17):   #3.27前面有個i=1到i=m的求和，就是這裡。
        startT =np.transpose(start)
        xT =np.transpose(x[j])
        bx = startT*xT  
        bx_1 = np.array(bx)[0][0]      
        c = -y[j]*x[j]+(np.exp(bx_1)/(1+np.exp(bx_1)))*x[j]     #導數，寫的太難看，請忽略
        s =s+c 
    s_1 = np.transpose(s)       #導數
    new = start - xishu*s_1           #梯度下降公式，這裡大家應該很熟悉
    start =new
    i=i+1
    if i%10000 ==0:
        print('no%s'%i,'start is %s'%start)
    

    
print(start)
 

迭代結果:

no10000 start is [[  2.98758124]
 [ 11.91671654]
 [ -4.21286642]]
no20000 start is [[  3.13439493]
 [ 12.43023225]
 [ -4.39732669]]
no30000 start is [[  3.15464273]
 [ 12.50721714]
 [ -4.42401375]]
no40000 start is [[  3.15776018]
 [ 12.5190382 ]
 [ -4.42811559]]
no50000 start is [[  3.15824169]
 [ 12.52086253]
 [ -4.42874883]]
no60000 start is [[  3.15831607]
 [ 12.52114431]
 [ -4.42884664]]
no70000 start is [[  3.15832756]
 [ 12.52118784]
 [ -4.42886175]]
no80000 start is [[  3.15832934]
 [ 12.52119456]
 [ -4.42886408]]
no90000 start is [[  3.15832961]
 [ 12.5211956 ]
 [ -4.42886444]]
no100000 start is [[  3.15832965]
 [ 12.52119576]
 [ -4.4288645 ]]
no110000 start is [[  3.15832966]
 [ 12.52119579]
 [ -4.42886451]]
no120000 start is [[  3.15832966]
 [ 12.52119579]
 [ -4.42886451]]
no130000 start is [[  3.15832966]
 [ 12.52119579]
 [ -4.42886451]]
no140000 start is [[  3.15832966]
 [ 12.52119579]
 [ -4.42886451]]
no150000 start is [[  3.15832966]
 [ 12.52119579]
 [ -4.42886451]]
no160000 start is [[  3.15832966]
 [ 12.52119579]
 [ -4.42886451]]
no170000 start is [[  3.15832966]
 [ 12.52119579]
 [ -4.42886451]]
no180000 start is [[  3.15832966]
 [ 12.52119579]
 [ -4.42886451]]
no190000 start is [[  3.15832966]
 [ 12.52119579]
 [ -4.42886451]]
no200000 start is [[  3.15832966]
 [ 12.52119579]
 [ -4.42886451]]
[[  3.15832966]
 [ 12.52119579]
 [ -4.42886451]]