There is a problem in function svm_predict() and the prediction cannot be implemented correctly

[Cuisine4]

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

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