在我的代码中,X和y是训练数据:
from sklearn.svm import SVC
clf = SVC(kernel=lambda x,y:gauss_kernel(x, y, 100) )
print(X.shape[0])
print(X.shape[1])
print(X.shape)
clf.fit(X, y)
我收到以下错误:
211
2
(211, 2)
/Users/mona/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:547: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
y = column_or_1d(y, warn=True)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-23-1f163ab380a5> in <module>()
8 print(X.shape)
9
---> 10 clf.fit(X, y)
11 plot_data()
12 plot_boundary(svm,-.5,.3,-.8,.6)
~/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py in fit(self, X, y, sample_weight)
185
186 seed = rnd.randint(np.iinfo('i').max)
--> 187 fit(X, y, sample_weight, solver_type, kernel, random_seed=seed)
188 # see comment on the other call to np.iinfo in this file
189
~/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py in _dense_fit(self, X, y, sample_weight, solver_type, kernel, random_seed)
226 X = self._compute_kernel(X)
227
--> 228 if X.shape[0] != X.shape[1]:
229 raise ValueError("X.shape[0] should be equal to X.shape[1]")
230
IndexError:元组索引超出范围
这是我写的定制高斯内核:
import math
def gauss_kernel(x1, x2, gamma):
sigma = math.sqrt(gamma)
return np.exp(-np.sum((x1-x2)**2)/(2*sigma**2))
我该如何解决这个问题?当我在sklearn中查看SVM示例时,它们基本上做同样的事情。我相信我忽略了一些小事,但在与sklearn示例匹配时无法解决问题。
答案 0 :(得分:1)
请确保自定义内核的输出是方阵。
目前,gauss_kernel
的实现将返回一个数字,而不是数组。所以调用shape [0]或shape [1]会使&#34;元组索引超出范围错误&#34;。
所以解决这个问题:
import math
def gauss_kernel(x1, x2):
sigma = math.sqrt(100)
return np.array([np.exp(-np.sum((x1-x2)**2)/(2*sigma**2))])
然后使用您的代码。
注意:这只是将单个数字包装到数组的解决方法。您应该检查原始gauss_kernel
的错误是否返回单个数字。
答案 1 :(得分:0)
from sklearn import svm
def gauss_kernel(x1, x2, gamma):
x1 = x1.flatten()
x2 = x2.flatten()
sigma = math.sqrt(gamma)
return np.exp(-np.sum((x1-x2)**2)/(2*sigma**2))
# from @lejlot http://stackoverflow.com/a/26962861/583834
def gaussianKernelGramMatrix(X1, X2, K_function=gauss_kernel, gamma=0.1):
"""(Pre)calculates Gram Matrix K"""
gram_matrix = np.zeros((X1.shape[0], X2.shape[0]))
for i, x1 in enumerate(X1):
for j, x2 in enumerate(X2):
gram_matrix[i, j] = K_function(x1, x2, gamma)
return gram_matrix
gamma=0.1
y = y.flatten()
clf = svm.SVC(kernel="precomputed", verbose=2, C=2.0, probability=True)
clf.fit(gaussianKernelGramMatrix(X,X, gauss_kernel, gamma=gamma), y)
答案 2 :(得分:0)
今天我正在做课程家庭作业ex6,我也遇到同样的问题。现在我解决了。 sklearn使用自定义内核请求内核函数返回新的[m * m]矩阵,其代码如下:
def _compute_kernel(self, X):
"""Return the data transformed by a callable kernel"""
if callable(self.kernel):
# in the case of precomputed kernel given as a function, we
# have to compute explicitly the kernel matrix
kernel = self.kernel(X, self.__Xfit)
if sp.issparse(kernel):
kernel = kernel.toarray()
X = np.asarray(kernel, dtype=np.float64, order='C')
return X
因此我定义了内核函数返回矩阵,它可以计算x1 = [m,n]和x2 = [h,n]欧几里得距离,然后使用exp计算返回值。
def gaussianKernel(x1: ndarray, x2: ndarray, sigma):
# RBFKERNEL returns a radial basis function kernel between x1 and x2
# sim = gaussianKernel(x1, x2) returns a gaussian kernel between x1 and x2
# and returns the value in sim
# Ensure that x1 and x2 are column vectors
m = size(x1, 0)
n = size(x2, 0)
# You need to return the following variables correctly.
sim = 0
# ====================== YOUR CODE HERE ======================
# Instructions: Fill in this function to return the similarity between x1
# and x2 computed using a Gaussian kernel with bandwidth
# sigma
#
# Note: use the matrix compute the distence
M = x1@x2.T
H1 = sum(square(mat(x1)), 1) # [m,1]
H2 = sum(square(mat(x2)), 1) # [n,1]
D = H1+H2.T-2*M
sim = exp(-D/(2*sigma*sigma))
# =============================================================
return sim
现在在主函数中添加以下行代码:
def mykernel(x1, x2): return gaussianKernel(x1, x2, sigma)
model = svm.SVC(C, kernel=mykernel) # type:SVC
model.fit(X, y.ravel())
visualizeBoundary(X, y, model)
完成剧情: visualizeBoundary