There is a problem in function svm_predict() and the prediction cannot be implemented correctly #4
Description
Describe the bug
The problem appears on line 238 of file svm.py.In the original program, the formula for calculating the self.bias is as follows.
# Compute the bias
k = self.kernel(X_train, X_test)
SV_neg = y_train < 0
SV_pos = y_train > 0
self.bias = (-1 / 2) * (np.max(k[SV_neg[:, 0], :].T @ alpha[SV_neg]) + np.min(k[SV_pos[:, 0], :].T @ alpha[SV_pos]))
self.bias = y_train - np.sum(alpha * y_train * k, axis=1, keepdims=True)
self.bias = np.mean(self.bias)
The bias calculated in this way is incorrect and will cause errors in later predictions
Expected behavior
According to the formula I looked up, the correct calculation is as follows.
# Compute the bias
k = self.kernel(X_train, X_test)
SV_neg = y_train < 0
SV_pos = y_train > 0
kk=self.kernel(X_train, X_train)
self.bias = y_train - np.sum(alpha * y_train * kk, axis=1, keepdims=True)
self.bias = np.mean(self.bias)
Screenshots
Screenshot from the watermelon book "Machine learning" Zhou Zhihua section 6.2
