python - How to get my custom kernel working? -
import numpy np import sklearn.svm svm here kernel function
def my_kernel(p1, p2): r = 1-np.dot(p1.t, p2) return np.exp(-r**2/4) and data
x1 = np.random.random((50,5)) x1 = x1/np.sum(x1) x1 = x1.t y1 = [0, 0, 1, 1, 1] then use sklearn svm
my_svm = svm.svc(kernel=my_kernel) my_svm.fit(x1,y1) it outputs
svc(c=1.0, cache_size=200, class_weight=none, coef0=0.0, degree=3, gamma=0.0, kernel=<function my_kernel @ 0x109891e18>, max_iter=-1, probability=false, random_state=none, shrinking=true, tol=0.001, verbose=false) but when use predict()
y_pred = my_svm.predict(x1) it says
--------------------------------------------------------------------------- valueerror traceback (most recent call last) <ipython-input-151-48e55060e495> in <module>() ----> 1 y_pred = my_svm.predict(x1) /library/frameworks/python.framework/versions/3.4/lib/python3.4/site-packages/sklearn/svm/base.py in predict(self, x) 498 class labels samples in x. 499 """ --> 500 y = super(basesvc, self).predict(x) 501 return self.classes_.take(np.asarray(y, dtype=np.intp)) 502 /library/frameworks/python.framework/versions/3.4/lib/python3.4/site-packages/sklearn/svm/base.py in predict(self, x) 290 x = self._validate_for_predict(x) 291 predict = self._sparse_predict if self._sparse else self._dense_predict --> 292 return predict(x) 293 294 def _dense_predict(self, x): /library/frameworks/python.framework/versions/3.4/lib/python3.4/site-packages/sklearn/svm/base.py in _dense_predict(self, x) 304 raise valueerror("x.shape[1] = %d should equal %d, " 305 "the number of samples @ training time" % --> 306 (x.shape[1], self.shape_fit_[0])) 307 308 svm_type = libsvm_impl.index(self._impl) valueerror: x.shape[1] = 50 should equal 5, number of samples @ training time
the problem lies in kernel function dot product should
def my_kernel(p1, p2): r = 1-np.dot(p1, p2.t) return np.exp(-r**2/4) 
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