def _run(self): self.train(self._training_dataset[0], self._training_dataset[1]) if self._test_dataset is not None: self.test(self._test_dataset[0], self._test_dataset[1]) if self._datapoints is not None: predicted_probs, predicted_labels = self.predict(self._datapoints) self._ret['predicted_classes'] = map_label_to_class_name( predicted_labels, self._label_to_class) return self._ret
def _run(self): self.train(self._training_dataset[0], self._training_dataset[1]) if self._test_dataset is not None: self.test(self._test_dataset[0], self._test_dataset[1]) if self._datapoints is not None: _, predicted_labels = self.predict(self._datapoints) self._ret['predicted_classes'] = map_label_to_class_name(predicted_labels, self._label_to_class) self.cleanup_parameterized_circuits() return self._ret
def run(self): """ put the train, test, predict together """ self.train(self._qalgo.training_dataset[0], self._qalgo.training_dataset[1]) if self._qalgo.test_dataset is not None: self.test(self._qalgo.test_dataset[0], self._qalgo.test_dataset[1]) if self._qalgo.datapoints is not None: predicted_labels = self.predict(self._qalgo.datapoints) predicted_classes = map_label_to_class_name(predicted_labels, self._qalgo.label_to_class) self._ret['predicted_classes'] = predicted_classes return self._ret
reps=2, entanglement='linear') qsvm = QSVM(feature_map, training_input, test_input, datapoints[0]) backend = BasicAer.get_backend('qasm_simulator') quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed) result = qsvm.run(quantum_instance) print("testing success ratio: {}".format(result['testing_accuracy'])) print("preduction of datapoints:") print("ground truth: {}".format( map_label_to_class_name(datapoints[1], qsvm.label_to_class))) print("prediction: {}".format(result['predicted_classes'])) print("kernel matrix during the training:") kernel_matrix = result['kernel_matrix_training'] img = plt.imshow(np.asmatrix(kernel_matrix), interpolation='nearest', origin='upper', cmap='bone_r') plt.show() sample_Total, training_input, test_input, class_labels = breast_cancer( training_size=20, test_size=10, n=2, plot_data=True) seed = 10598
training_size=training_dataset_size, test_size=testing_dataset_size, n=feature_dim, gap=0.3, plot_data=True) datapoints, class_to_label = split_dataset_to_data_and_labels(test_input) print(class_to_label) backend = BasicAer.get_backend('qasm_simulator') optimizer = SPSA(max_trials=100, c0=4.0, skip_calibration=True) optimizer.set_options(save_steps=1) feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=2) var_form = TwoLocal(feature_dim, ['ry', 'rz'], 'cz', reps=3) vqc = VQC(optimizer, feature_map, var_form, training_input, test_input) quantum_instance = QuantumInstance(backend, shots=shots, seed_simulator=random_seed, seed_transpiler=random_seed) result = vqc.run(quantum_instance) print("testing success ratio: ", result['testing_accuracy']) predicted_probs, predicted_labels = vqc.predict(datapoints[0]) predicted_classes = map_label_to_class_name(predicted_labels, vqc.label_to_class) print("prediction: {}".format(predicted_labels)) # testing success ratio: 1.0 # sumber = https://github.com/qiskit-community/qiskit-community-tutorials/blob/master/machine_learning/vqc.ipynb
print("testing success ratio: ", result['testing_accuracy']) print("predicted classes:", result['predicted_classes']) sample_Total, training_input, test_input, class_labels = breast_cancer( training_size=20, test_size=10, n=2, plot_data=True ) # n =2 is the dimension of each data point datapoints, class_to_label = split_dataset_to_data_and_labels(test_input) label_to_class = {label:class_name for class_name, label in class_to_label.items()} print(class_to_label, label_to_class) result = SklearnSVM(training_input, test_input, datapoints[0]).run() print("kernel matrix during the training:") kernel_matrix = result['kernel_matrix_training'] img = plt.imshow(np.asmatrix(kernel_matrix),interpolation='nearest',origin='upper',cmap='bone_r') plt.show() print("testing success ratio: ", result['testing_accuracy']) print("ground truth: {}".format(map_label_to_class_name(datapoints[1], label_to_class))) print("predicted: {}".format(result['predicted_classes'])) # testing success ratio: 0.85 # sumber = https://github.com/qiskit-community/qiskit-community-tutorials/blob/master/machine_learning/svm_classical.ipynb
vqc = VQC(optimizer, feature_map, var_form, train_input, test_input, datapoints[0]) backend = BasicAer.get_backend('qasm_simulator') quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed) result = vqc.run(quantum_instance) print('Prediction from datapoints set:') print( f' ground truth: {map_label_to_class_name(datapoints[1], vqc.label_to_class)}' ) print(f' prediction: {result["predicted_classes"]}') a = map_label_to_class_name(datapoints[1], vqc.label_to_class) b = result["predicted_classes"] r = len(a) result = {} for i in range(r): if a[i] == b[i]: if a[i] not in result: result[a[i]] = 1 else: result[a[i]] += 1 a1 = (result['A'] / a.count('A')) * 100 b1 = (result['B'] / a.count('B')) * 100 print( f'The background rejection:{round(a1,3)}%.The signal efficiency:{round(b1,3)}%'