使用Linerrud数据集的感知器学习

Question

我是机器学习和python编程领域的新手，我想仅使用numpy从头开始实现算法，这是linnerud数据集上的感知器学习规则。

如果感知器不收敛，则需要运行1000次迭代，并且还需要使用所有3个属性以及仅chinups结果在Linnerrud数据集上测试此算法。

矢量需要被定义为对chinup的结果的二进制类，如下所示：

if(chinups>median(chinups)) then chinups=0 else chinups=1

我需要使用这些类（0/1）来训练感知器并建立概率表。最终，我需要感知器输出20个预测值，其中每个值都是加权和（感知器权重与属性值的点积）

我得到的似乎是不正确的。如果有人可以帮助我，那将是可观的。以下是我的代码：

import numpy as np
import matplotlib.pyplot as plot 
import os
from numpy import arange
from sklearn.datasets import load_linnerud


def perceptron(linnerud):

    pathName = os.path.dirname(os.path.abspath(__file__))
    myfile = open(pathName+'\perceptron_results.txt', 'w')

    data = linnerud['data']
    target = linnerud['target']

    chinup = []

    for i in target:
        chinup.append(i[0])

    median = np.sum(chinup) / 20

    #print(median)

    binary = []

    for j in chinup:

        #print(j)
        if j > median:
            binary.append(0)

        else:
            binary.append(1)

    weights = np.zeros([3,1])

    iteration = 1000

    for i in arange(0,iteration):
        counter = 0
        converged = True

        for rowVal in data:

            predVal = np.dot(rowVal,weights)
            if predVal < 0:

                predicted = 0

            else:

                predicted = 1

            if predicted != binary[counter]:

                converged = False

                if binary[counter] == 0 :

                    weights = weights - np.expand_dims(rowVal,1)

                else:

                    weights = weights + np.expand_dims(rowVal,1)

            counter = counter + 1

        if converged == True:
            print("Error occurred")
            break


    finalPred = np.dot(data,weights)
    myfile.write(str(finalPred)+'\n')
    #print(finalPred)
    print("\nProbability values appended in gnb_result,txt file")

    plot.plot(finalPred,'bo'); 
    plot.plot([0,20],[0,0])

    plot.show()




My Output is this:
[[  2845.]
 [ -2316.]
 [ -1906.]
 [  8874.]
 [  8926.]
 [  1693.]
 [  5421.]
 [  4877.]
 [ 15905.]
 [-14406.]
 [ 13369.]
 [  2546.]
 [  5238.]
 [ -4733.]
 [  3337.]
 [   954.]
 [  2243.]
 [  7887.]
 [ 11835.]
 [   489.]]

Answer 1

在修改了代码中的一些部分后对我有用

import numpy as np
import matplotlib.pyplot as plot 
import os
from numpy import arange
from sklearn.datasets import load_linnerud
dataN = dataset.get('data')
target = dataset.get('target')


chinup = []

for i in dataN:
    chinup.append(i[0])

median = np.median(chinup)

binary = []

for j in chinup:

    #print(j)
    if j > median:
        binary.append(0)

    else:
        binary.append(1)

weights = np.zeros([3,1])

iteration = 1000

for i in arange(0,iteration):
    counter = 0
    converged = True

    for row_val in target:

        pred_val = np.dot(row_val,weights)
        if pred_val < 0:

            predicted = 0

        else:

            predicted = 1

        if predicted != binary[counter]:

            converged = False

            if binary[counter] == 0 :

                weights = weights - np.expand_dims(row_val,1)

            else:

                weights = weights + np.expand_dims(row_val,1)

        counter = counter + 1

    if converged == True:
        print("Loop broken")
        break

final_pred = np.dot(target,weights)


plot.plot(final_pred,'bo'); 
plot.plot([0,20],[0,0])
plot.show()

[output][1] [1]: https://i.stack.imgur.com/6vA5M.png

Answer 2

对于目标中的我：chinup.append（i [0]）中位数= np.median（chinup）这个中位数是176，这是错误的

应为：对于我的数据：chinup.append（i [0]）中位数= np.median（chinup）这个中位数是11.5

在load_linnerud数据集中：'target_names'：['Weight'，'Waist'，'Pulse']和'feature_names'：['Chins'，'Situps'，'Jumps']如果您想获取“ if（chinups> median（chinups）），那么chinups = 0否则chinups = 1”，您应该使用“ for i in data：”

Answer 3

median = np.sum(chinup) / 20 --> 9.45 (it is mean)
median = np.median(chinup) -->11.5 (it is median)

中位数不是指，它们是不同的

然后绘制plot.plot([0,20],[0,0])，为什么将线性回归放在这种情况下的这个位置？斜率为0？或者你只是用你的想法来画...