使用线和偏差的权重绘制超平面（分隔线）？ （单层感知器）？

Question

我需要绘制分隔线，使用单层感知器的输出，在高度和重量上将雄性与雌性基部分开。

我有data.txt文件，其中包含两个功能（身高和体重）和性别，其中0表示男性，1表示女性

即：

|---------------------|------------------|------|
|      150.5          |     5.2          |   1  |
|---------------------|------------------|------|
|      142.8          |     4.0          |   0  | 
|---------------------|------------------|------|
|      150.5          |     5.2          |   1  |
|---------------------|------------------|------|
|      190            |     5.7          |   0  |
|---------------------|------------------|------| 



import numpy as np
from sklearn import svm
import matplotlib.pyplot as plt
from sklearn.linear_model import perceptron
from pandas import *
import fileinput

f = fileinput.input('data.txt')

#height of females and males 
X_1 = []
#weight of female and males 
X_2 = []
#labels 0 males and 1 females 
Y = []
for line in f:
    temp = line.split(",")
    if str(temp[2]) == '0\n' :
        X_1.append(round(float(temp[0]),2))
        X_2.append(round(float(temp[1]),2))
        Y.append(0)
    else:
        X_1.append(round(float(temp[0]), 2))
        X_2.append(round(float(temp[1]), 2))
        Y.append(1)

print len(X_1)
print len(Y)
inputs = DataFrame({
'Height' : X_1,
'Weight' : X_2,
'Targets' : Y
})
colormap = np.array(['r', 'b'])


net = perceptron.Perceptron(n_iter=1000, verbose=0, random_state=None, fit_intercept=True, eta0=0.002)

# Train the perceptron object (net)
net.fit(inputs[['Height','Weight']],inputs['Targets'])
# Output the values
print "Coefficient 0 " + str(net.coef_[0, 0])
print "Coefficient 1 " + str(net.coef_[0, 1])
print "Bias " + str(net.intercept_)

plt.scatter(inputs.Height,inputs.Weight, c=colormap[inputs.Targets],s=20)

# Calc the hyperplane (decision boundary)
ymin, ymax = plt.ylim()
w = net.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(ymin, ymax)
yy = a * xx - (net.intercept_[0]) / w[1]

# Plot the hyperplane
plt.plot(xx, yy, 'k-')
plt.show()

但我的图表看起来像

而我没有线的实际图形看起来像那样。我不知道我做错了什么

Answer 1

替换

ymin, ymax = plt.ylim() 

带

 xmin, xmax = plt.xlim() 

所以与计算超平面相关的部分将如下所示

# Calc the hyperplane (decision boundary)
xmin, xmax = plt.xlim()
w = net.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(xmin, xmax)
yy = a * xx - (net.intercept_[0]) / w[1]