我使用自己定义的内核函数创建了一个 SVM 实例。当我尝试对创建的模型运行交叉验证时，出现以下错误:
ValueError: X should be a square kernel matrix
Traceback:
scores = cross_val_score(model, X, y, cv=10)
File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\cross_validation.py", line 1152, in cross_val_score
for train, test in cv)
File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\externals\joblib\parallel.py", line 517, in call
self.dispatch(function, args, kwargs)
File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\externals\joblib\parallel.py", line 312, in dispatch
job = ImmediateApply(func, args, kwargs)
File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7- win32.egg\sklearn\externals\joblib\parallel.py", line 136, in init
self.results = func(*args, **kwargs)
File "C:\Python27\lib\site-packages\scikit_learn-0.14.1-py2.7-win32.egg\sklearn\cross_validation.py", line 1047, in _cross_val_score
raise ValueError("X should be a square kernel matrix")
这是我的代码:
def hist_intersection(x, y):
return np.sum(np.array([min(xi,yi) for xi,yi in zip(x,y)]))
model = svm.SVC(kernel = hist_intersection)
scores = cross_val_score(model, X, y, cv=10)
最佳答案
我快速浏览了一下，SVC 类(和交叉验证工具)似乎都希望内核可调用项从全数据矩阵立即计算整个内核矩阵(我同意这使得此功能非常有限)。请查看测试以获取更多详细信息:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/svm/tests/test_svm.py#L124
关于python - 错误 - 在 scikit-learn 中为 SVM 使用自定义内核，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/22329893/