Question

我想使用成本函数实现次梯度和随机下降，计算为数据找到理想分类器所需的迭代次数，以及权重（w）和偏差（b）。数据集是四维的

这是我的成本函数

我采用了成本函数的导数，在这里是：

我运行代码时遇到很多错误，请有人帮忙。

这是我在python中的代码

import numpy as np

learn_rate = 1
w = np.zeros((4,1))
b = 0
M = 1000

data = '/Users/labuew/Desktop/dataset.data'

#calculating the gradient

def cal_grad_w(data, w, b):
    for i in range (M):
        sample = data[i,:]
        Ym = sample[-1]
        Xm = sample[0:4]
        if -Ym[i]*(w*Xm+b) >= 0:
            tmp = 1.0
        else:
            tmp = 0
        value = Ym[i]*Xm*tmp
        sum = sum +value
    return sum
def cal_grad_b(data, w, b):
    for i in range (M):
        sample = data[i,:]
        Ym = sample[-1]
        Xm = sample[0:4]
        if -Ym*(w*Xm+b) >= 0:
            tmp = 1.0
        else:
            tmp = 0
        value = Ym[i]*x*tmp
        sum = sum +value
    return sum

if __name__ == '__main__':
    counter = 0
    while 1:
        counter +=1
        dw = cal_grad_w(data, w, b)
        db = cal_grad_b(data, w, b)
        if dw == 0 and db == 0:
            break
        w = w - learn_rate*dw
        b = b - learn_rate *dw
    print(counter,w,b)

Answer 1

您是否缺少numpy加载功能？

data = np.load('/Users/labuew/Desktop/dataset.data')

看起来您正在对字符串进行数字运算。

还

Ym = sample[-1]
Xm = sample[0:4]

也有4个维度表示Ym = Xm [3]？您的数据等级是否为2，第二等级为维度5？ [0：4]包含第四维，即

z = [1,2,3,4]
z[0:4] = [1,2,3,4]

这是我最好的猜测。我正在对您的数据格式进行一些有根据的猜测。

import numpy as np

learn_rate = 1
w = np.zeros((1,4))
b = 0
M = 1000

#Possible format
#data = np.load('/Users/labuew/Desktop/dataset.data')

#Assumed format
data = np.ones((1000,5))

#calculating the gradient

def cal_grad_w(data, w, b):
    sum = 0
    for i in range (M):
        sample = data[i,:]
        Ym = sample[-1]
        Xm = sample[0:4]
        if -1*Ym*(np.matmul(w,Xm.reshape(4,1))+b) >= 0:
            tmp = 1.0
        else:
            tmp = 0
        value = Ym*Xm*tmp
        sum = sum +value
    return sum.reshape(1,4)
def cal_grad_b(data, w, b):
    sum = 0 
    for i in range (M):
        sample = data[i,:]
        Ym = sample[-1]
        Xm = sample[0:4]
        if -1*Ym*(np.matmul(w,Xm.reshape(4,1))+b) >= 0:
            tmp = 1.0
        else:
            tmp = 0
        value = Ym*tmp
        sum = sum +value
    return sum

if __name__ == '__main__':
    counter = 0
    while 1:
        counter +=1
        dw = cal_grad_w(data, w, b)
        db = cal_grad_b(data, w, b)
        if dw.all() == 0 and db == 0:
            break
        w = w - learn_rate*dw
        b = b - learn_rate*db
        print([counter,w,b])

因为我不知道格式，所以输入了伪数据。

在python中实现子梯度随机下降

1 个答案: