【发布时间】：2021-12-13 13:52:15
【问题描述】：
我正在尝试将此 sklearn 模块用于二进制分类问题，并且我的数据显然是线性可分的。 我不明白为什么我的情节的绿色区域不包括五个红色圆圈。
我尝试将迭代次数参数（max_iter）从 100 更改为 10000，但没有任何区别。
这是我的代码：
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import Perceptron
def learn_and_display_Perceptron(datafile):
#taking data reading this from the above functions
data = np.loadtxt(datafile)
n,d = data.shape
x = data[:,0:2]
y = data[:,2]
clf = Perceptron(max_iter=10000)
clf.fit(x, y)
sv = np.zeros(n,dtype=bool) ## all False array
notsv = np.logical_not(sv) # all True array
# Determine the x1- and x2- limits of the plot
x1min = min(x[:,0]) - 1
x1max = max(x[:,0]) + 1
x2min = min(x[:,1]) - 1
x2max = max(x[:,1]) + 1
plt.xlim(x1min,x1max)
plt.ylim(x2min,x2max)
# Plot the data points, enlarging those that are support vectors
plt.plot(x[(y==1)*notsv,0], x[(y==1)*notsv,1], 'ro')
plt.plot(x[(y==1)*sv,0], x[(y==1)*sv,1], 'ro', markersize=10)
plt.plot(x[(y==-1)*notsv,0], x[(y==-1)*notsv,1], 'k^')
plt.plot(x[(y==-1)*sv,0], x[(y==-1)*sv,1], 'k^', markersize=10)
# Construct a grid of points and evaluate classifier at each grid points
grid_spacing = 0.05
xx1, xx2 = np.meshgrid(np.arange(x1min, x1max, grid_spacing), np.arange(x2min, x2max, grid_spacing))
grid = np.c_[xx1.ravel(), xx2.ravel()]
Z = clf.predict(grid)
# Quantize the values to -1, -0.5, 0, 0.5, 1 for display purposes
for i in range(len(Z)):
Z[i] = min(Z[i],1.0)
Z[i] = max(Z[i],-1.0)
if (Z[i] > 0.0) and (Z[i] < 1.0):
Z[i] = 0.5
if (Z[i] < 0.0) and (Z[i] > -1.0):
Z[i] = -0.5
# Show boundary and margin using a color plot
Z = Z.reshape(xx1.shape)
plt.pcolormesh(xx1, xx2, Z, cmap=plt.cm.PRGn, vmin=-2, vmax=2, shading='auto')
plt.show()
我的数据文件，data_1.txt 可以在这里找到，https://github.com/bluetail14/MyCourserapractice/tree/main/Edx
我可以在我的代码中进行哪些更改来调整绿色/紫色边界线以包含五个红色圆圈？
【问题讨论】：
标签： python-3.x numpy matplotlib scikit-learn perceptron