2a3febefed59e906809a27c6379de2b13d3eef31,qiskit_aqua/algorithms/classical/svm/svm_classical.py,SVM_Classical,init_params,#SVM_Classical#Any#Any#,66

Before Change

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

        if algo_input.training_dataset is None:
            raise AlgorithmError("Training dataset is required! please provide it")

        is_multiclass = get_num_classes(algo_input.training_dataset) > 2
        if is_multiclass:
            multiclass_extension_params = params.get(QuantumAlgorithm.SECTION_KEY_MULTICLASS_EXTENSION)
            multiclass_extension = get_multiclass_extension_instance(multiclass_extension_params["name"])
            multiclass_extension_params["estimator_cls"] = RBF_SVC_Estimator
            multiclass_extension.init_params(multiclass_extension_params)
            logger.info("Multiclass classifcation algo:" + multiclass_extension_params["name"])
        else:

After Change

        svm_params = params.get(QuantumAlgorithm.SECTION_KEY_ALGORITHM)

        multiclass_extension = None
        multiclass_extension_params = params.get(QuantumAlgorithm.SECTION_KEY_MULTICLASS_EXTENSION)
        if multiclass_extension_params is not None:
            multiclass_extension = get_multiclass_extension_instance(multiclass_extension_params["name"])
            multiclass_extension_params["estimator_cls"] = RBF_SVC_Estimator
            multiclass_extension.init_params(multiclass_extension_params)
            logger.info("Multiclass dataset with extension: {}".format(multiclass_extension_params["name"]))

        self.init_args(algo_input.training_dataset, algo_input.test_dataset,
                       algo_input.datapoints, svm_params.get("gamma"), multiclass_extension)

    def init_args(self, training_dataset, test_dataset, datapoints, gamma, multiclass_extension=None):

Instances