FukuML/ProbabilisticSVM.py
def svm_score(self, x):

        x = x[1:]

        '''
        original_X = self.svm_processor.train_X[:, 1:]
        score = 0
        for i in range(len(self.svm_processor.sv_alpha)):
            score += self.svm_processor.sv_alpha[i] * self.svm_processor.sv_Y[i] * utility.Kernel.gaussian_kernel(self, original_X[self.svm_processor.sv_index[i]], x)
        score = score + self.svm_processor.sv_avg_b
        '''

        score = np.sum(self.svm_processor.sv_alpha * self.svm_processor.sv_Y * utility.Kernel.kernel_matrix_xX(self, x, self.svm_processor.sv_X)) + self.svm_processor.sv_avg_b

        return score
def svm_score(self, x):

        x = x[1:]

        '''
        original_X = self.svm_processor.train_X[:, 1:]
        score = 0
        for i in range(len(self.svm_processor.sv_alpha)):
            score += self.svm_processor.sv_alpha[i] * self.svm_processor.sv_Y[i] * utility.Kernel.gaussian_kernel(self, original_X[self.svm_processor.sv_index[i]], x)
        score = score + self.svm_processor.sv_avg_b
        '''

        score = np.sum(self.svm_processor.sv_alpha * self.svm_processor.sv_Y * utility.Kernel.kernel_matrix_xX(self, x, self.svm_processor.sv_X)) + self.svm_processor.sv_avg_b

        return score
original_X = self.svm_processor.train_X[:, 1:]
        score = 0
        for i in range(len(self.svm_processor.sv_alpha)):
            score += self.svm_processor.sv_alpha[i] * self.svm_processor.sv_Y[i] * utility.Kernel.gaussian_kernel(self, original_X[self.svm_processor.sv_index[i]], x)
        score = score + self.svm_processor.sv_avg_b