9c72c3b0e95c663f64a09c092c69174794245133,qiskit_aqua/algorithms/many_sample/qsvm/svm_qkernel.py,SVM_QKernel,init_params,#SVM_QKernel#Any#Any#,58

Before Change

        else:
            logger.debug("We will apply the binary classifcation and ignore all options related to the multiclass")
            self.instance = SVM_QKernel_Binary()
        self.instance.init_args(algo_input.training_dataset, algo_input.test_dataset, algo_input.datapoints, SVMQK_params.get("multiclass_alg"), self._backend, self._execute_config["shots"], self._random_seed)

    def run(self):
        self.instance.run()

After Change

    def init_params(self, params, algo_input):
        svmqk_params = params.get(QuantumAlgorithm.SECTION_KEY_ALGORITHM)

        fea_map_params = params.get(QuantumAlgorithm.SECTION_KEY_FEATURE_MAP)
        feature_map = get_feature_map_instance(fea_map_params["name"])
        num_qubits = self._check_feature_dim(algo_input.training_dataset)
        fea_map_params["num_qubits"] = num_qubits
        feature_map.init_params(fea_map_params)

        is_multiclass = self._check_num_classes(algo_input.training_dataset) > 2

        if is_multiclass:

Instances