大佬教程收集整理的这篇文章主要介绍了绘制scikit-learn（sklearn）SVM决策边界/曲面，大佬教程大佬觉得挺不错的，现在分享给大家，也给大家做个参考。
现在，您要问的下一个问题： 如何选择这两个功能？ 。好吧，有很多方法。您可以进行 并查看哪些功能/变量最重要。然后，您可以将这些用于绘图。此外，例如，我们可以使用 PCA 将尺寸从7减少到2 。
from sklearn.svm import SVC
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
iris = datasets.load_iris()
# SELEct 2 features / variable for the 2D plot that we are going to create.
X = iris.data[:, :2] # we only take the first two features.
y = iris.target
def make_meshgrID(x, y, h=.02):
x_min, x_max = x.min() - 1, x.max() + 1
y_min, y_max = y.min() - 1, y.max() + 1
xx, yy = np.meshgrID(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
return xx, yy
def plot_contours(ax, clf, xx, yy, **params):
Z = clf.preDict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shapE)
out = ax.contourf(xx, yy, Z, **params)
return out
model = svm.SVC(kernel='linear')
clf = model.fit(X, y)
fig, ax = plt.subplots()
# title for the plots
title = ('Decision surface of linear SVC ')
# Set-up grID for plotTing.
X0, X1 = X[:, 0], X[:, 1]
xx, yy = make_meshgrID(X0, X1)
plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, Alpha=0.8)
ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
ax.set_ylabel('y label here')
ax.set_xlabel('x label here')
ax.set_xticks(())
ax.set_yticks(())
ax.set_title(titlE)
ax.legend()
plt.show()
from sklearn.svm import SVC
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.decomposition import PCA
iris = datasets.load_iris()
X = iris.data
y = iris.target
pca = PCA(n_components=2)
Xreduced = pca.fit_transform(X)
def make_meshgrID(x, y, h=.02):
x_min, x_max = x.min() - 1, x.max() + 1
y_min, y_max = y.min() - 1, y.max() + 1
xx, yy = np.meshgrID(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
return xx, yy
def plot_contours(ax, clf, xx, yy, **params):
Z = clf.preDict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shapE)
out = ax.contourf(xx, yy, Z, **params)
return out
model = svm.SVC(kernel='linear')
clf = model.fit(Xreduced, y)
fig, ax = plt.subplots()
# title for the plots
title = ('Decision surface of linear SVC ')
# Set-up grID for plotTing.
X0, X1 = Xreduced[:, 0], Xreduced[:, 1]
xx, yy = make_meshgrID(X0, X1)
plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, Alpha=0.8)
ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
ax.set_ylabel('PC2')
ax.set_xlabel('PC1')
ax.set_xticks(())
ax.set_yticks(())
ax.set_title('Decison surface using the PCA transformed/projected features')
ax.legend()
plt.show()
from sklearn.svm import SVC
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from mpl_toolkits.mplot3d import Axes3D
iris = datasets.load_iris()
X = iris.data[:, :3] # we only take the first three features.
Y = iris.target
#make it binary classification problem
X = X[np.logical_or(Y==0,Y==1)]
Y = Y[np.logical_or(Y==0,Y==1)]
model = svm.SVC(kernel='linear')
clf = model.fit(X, Y)
# The equation of the separaTing plane is given by all x so that np.dot(svc.coef_[0], X) + b = 0.
# Solve for w3 (z)
z = lambda x,y: (-clf.intercept_[0]-clf.coef_[0][0]*x -clf.coef_[0][1]*y) / clf.coef_[0][2]
tmp = np.linspace(-5,5,30)
x,y = np.meshgrID(tmp,tmp)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot3D(X[Y==0,0], X[Y==0,1], X[Y==0,2],'ob')
ax.plot3D(X[Y==1,0], X[Y==1,1], X[Y==1,2],'sr')
ax.plot_surface(x, y, z(x,y))
ax.vIEw_init(30, 60)
plt.show()
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