我的问题与此问题相同：
Can I plot the SVM decision boundary for 3 features(in 3D space) using Scikit learn?
其中一个答案是：
举个例子 scikit-learn.org/stable/auto_examples/svm/plot_iris.html。如果你 有3个功能，你所要做的就是为你的z添加一个z坐标 meshgrid的定义与x和y类似，并将其传递给您的预测 方法和contourf方法。
所以我尝试这样做，将2D示例转换为3D：
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :3] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
y = iris.target
h = .02 # step size in the mesh
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0 # SVM regularization parameter
svc = svm.SVC(kernel='linear', C=C).fit(X, y)
rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)
poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, y)
lin_svc = svm.LinearSVC(C=C).fit(X, y)
# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
z_min, z_max = X[:, 2].min() - 1, X[:, 2].max() + 1
xx, yy, zz = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h),
np.arange(z_min, z_max, h))
# title for the plots
titles = ['SVC with linear kernel',
'LinearSVC (linear kernel)',
'SVC with RBF kernel',
'SVC with polynomial (degree 3) kernel']
for i, clf in enumerate((svc, lin_svc, rbf_svc, poly_svc)):
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
plt.subplot(2, 2, i + 1, projection='3d')
plt.subplots_adjust(wspace=0.4, hspace=0.4)
pred = clf.predict(np.c_[xx.ravel(), yy.ravel(), zz.ravel()])
# Put the result into a color plot
pred = pred.reshape(xx.shape)
plt.contourf(xx, yy, zz, pred, cmap=plt.cm.coolwarm, alpha=0.8)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.coolwarm)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.zlim(zz.min(), zz.max())
plt.xticks(())
plt.yticks(())
plt.zticks(())
plt.title(titles[i])
plt.show()
但无论我如何调整它，我每次都会得到同样的错误：
=========================
3D surface (checkerboard)
=========================
Demonstrates plotting a 3D surface colored in a checkerboard pattern.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-54-b750fe83f0a6> in <module>()
46 # Put the result into a color plot
47 pred = pred.reshape(xx.shape)
---> 48 plt.contourf(xx, yy, zz, pred, cmap=plt.cm.coolwarm, alpha=0.8)
49
50 # Plot also the training points
C:\Users\BNielson\Anaconda3\envs\PythonMachineLearning\lib\site-packages\matplotlib\pyplot.py in contourf(*args, **kwargs)
2873 mplDeprecation)
2874 try:
-> 2875 ret = ax.contourf(*args, **kwargs)
2876 finally:
2877 ax._hold = washold
C:\Users\BNielson\Anaconda3\envs\PythonMachineLearning\lib\site-packages\mpl_toolkits\mplot3d\axes3d.py in contourf(self, X, Y, Z, *args, **kwargs)
2194
2195 jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir)
-> 2196 cset = Axes.contourf(self, jX, jY, jZ, *args, **kwargs)
2197 self.add_contourf_set(cset, zdir, offset)
2198
C:\Users\BNielson\Anaconda3\envs\PythonMachineLearning\lib\site-packages\matplotlib\__init__.py in inner(ax, *args, **kwargs)
1889 warnings.warn(msg % (label_namer, func.__name__),
1890 RuntimeWarning, stacklevel=2)
-> 1891 return func(ax, *args, **kwargs)
1892 pre_doc = inner.__doc__
1893 if pre_doc is None:
C:\Users\BNielson\Anaconda3\envs\PythonMachineLearning\lib\site-packages\matplotlib\axes\_axes.py in contourf(self, *args, **kwargs)
5827 self.cla()
5828 kwargs['filled'] = True
-> 5829 contours = mcontour.QuadContourSet(self, *args, **kwargs)
5830 self.autoscale_view()
5831 return contours
C:\Users\BNielson\Anaconda3\envs\PythonMachineLearning\lib\site-packages\matplotlib\contour.py in __init__(self, ax, *args, **kwargs)
862 self._transform = kwargs.get('transform', None)
863
--> 864 self._process_args(*args, **kwargs)
865 self._process_levels()
866
C:\Users\BNielson\Anaconda3\envs\PythonMachineLearning\lib\site-packages\matplotlib\contour.py in _process_args(self, *args, **kwargs)
1427 self._corner_mask = mpl.rcParams['contour.corner_mask']
1428
-> 1429 x, y, z = self._contour_args(args, kwargs)
1430
1431 _mask = ma.getmask(z)
C:\Users\BNielson\Anaconda3\envs\PythonMachineLearning\lib\site-packages\matplotlib\contour.py in _contour_args(self, args, kwargs)
1506 args = args[1:]
1507 elif Nargs <= 4:
-> 1508 x, y, z = self._check_xyz(args[:3], kwargs)
1509 args = args[3:]
1510 else:
C:\Users\BNielson\Anaconda3\envs\PythonMachineLearning\lib\site-packages\matplotlib\contour.py in _check_xyz(self, args, kwargs)
1540
1541 if z.ndim != 2:
-> 1542 raise TypeError("Input z must be a 2D array.")
1543 else:
1544 Ny, Nx = z.shape
TypeError: Input z must be a 2D array.