def decision_function(self, X):
"""
predicted values for the points which account for both the labels and the confidence
Args:
X (numpy.ndarray): input points
"""
alphas = self._ret['svm']['alphas']
bias = self._ret['svm']['bias']
SVMs = self._ret['svm']['support_vectors']
yin = self._ret['svm']['yin']
kernel_matrix = kernel_join(X, SVMs, self.entangler_map,
self.coupling_map, self.initial_layout,
self.shots, self._random_seed,
self.num_of_qubits, self._backend)
total_num_points = len(X)
Lsign = np.zeros(total_num_points)
for tin in range(total_num_points):
Ltot = 0
for sin in range(len(SVMs)):
L = yin[sin] * alphas[sin] * kernel_matrix[tin][sin]
Ltot += L
Lsign[tin] = Ltot + bias
return Lsign
def predict(self, test_points):
"""
predict using the svm
Args:
test_points (numpy.ndarray): the points
"""
alphas = self.ret['svm']['alphas']
bias = self.ret['svm']['bias']
SVMs = self.ret['svm']['support_vectors']
yin = self.ret['svm']['yin']
kernel_matrix = kernel_join(test_points, SVMs, self.entangler_map, self.coupling_map,
self.initial_layout, self.shots, self._random_seed,
self.num_of_qubits, self._backend)
self.ret['kernel_matrix_prediction'] = kernel_matrix
total_num_points = len(test_points)
Lsign = np.zeros(total_num_points)
for tin in range(total_num_points):
Ltot = 0
for sin in range(len(SVMs)):
L = yin[sin] * alphas[sin] * kernel_matrix[tin][sin]
Ltot += L
Lsign[tin] = np.int(np.sign(Ltot + bias))
return Lsign
def train(self, training_input, class_labels):
"""
train the svm
Args:
training_input (dict): dictionary which maps each class to the points in the class
class_labels (list): list of classes. For example: ['A', 'B']
"""
training_points, training_points_labels, label_to_class = get_points_and_labels(training_input, class_labels)
kernel_matrix = kernel_join(training_points, training_points, self.entangler_map,
self.coupling_map, self.initial_layout, self.shots,
self._random_seed, self.num_of_qubits, self._backend)
self.ret['kernel_matrix_training'] = kernel_matrix
[alpha, b, support] = optimize_SVM(kernel_matrix, training_points_labels)
alphas = np.array([])
SVMs = np.array([])
yin = np.array([])
for alphindex in range(len(support)):
if support[alphindex]:
alphas = np.vstack([alphas, alpha[alphindex]]) if alphas.size else alpha[alphindex]
SVMs = np.vstack([SVMs, training_points[alphindex]]) if SVMs.size else training_points[alphindex]
yin = np.vstack([yin, training_points_labels[alphindex]]
) if yin.size else training_points_labels[alphindex]
self.ret['svm'] = {}
self.ret['svm']['alphas'] = alphas
self.ret['svm']['bias'] = b
self.ret['svm']['support_vectors'] = SVMs
self.ret['svm']['yin'] = yin
def test(self, test_input, class_labels):
"""
test the svm
Args:
test_input (dict): dictionary which maps each class to the points in the class
class_labels (list): list of classes. For example: ['A', 'B']
"""
test_points, test_points_labels, label_to_labelclass = get_points_and_labels(test_input, class_labels)
alphas = self.ret['svm']['alphas']
bias = self.ret['svm']['bias']
SVMs = self.ret['svm']['support_vectors']
yin = self.ret['svm']['yin']
kernel_matrix = kernel_join(test_points, SVMs, self.entangler_map, self.coupling_map,
self.initial_layout, self.shots, self._random_seed,
self.num_of_qubits, self._backend)
self.ret['kernel_matrix_testing'] = kernel_matrix
success_ratio = 0
L = 0
total_num_points = len(test_points)
Lsign = np.zeros(total_num_points)
for tin in range(total_num_points):
Ltot = 0
for sin in range(len(SVMs)):
L = yin[sin] * alphas[sin] * kernel_matrix[tin][sin]
Ltot += L
Lsign[tin] = np.sign(Ltot + bias)
logger.debug("\n=============================================")
logger.debug('classifying' + str(test_points[tin]))
logger.debug('Label should be ' + str(label_to_labelclass[np.int(test_points_labels[tin])]))
logger.debug('Predicted label is ' + str(label_to_labelclass[np.int(Lsign[tin])]))
if np.int(test_points_labels[tin]) == np.int(Lsign[tin]):
logger.debug('CORRECT')
else:
logger.debug('INCORRECT')
if Lsign[tin] == test_points_labels[tin]:
success_ratio += 1
final_success_ratio = success_ratio / total_num_points
logger.debug('Classification success for this set is %s %% \n' % (100 * final_success_ratio))
return final_success_ratio
def fit(self, X, y):
"""
fit values for the points and the labels
Args:
X (numpy.ndarray): input points
y (numpy.ndarray): input labels
"""
y = y.astype(float)
self.class_labels = np.unique(y)
if len(self.class_labels) == 1:
raise ValueError(" can not be fit when only one"
" class is present.")
self.num_of_qubits = X.shape[1]
self.entangler_map = entangler_map_creator(self.num_of_qubits)
self.coupling_map = None
self.initial_layout = None
if self._backend is None:
self._backend = 'local_qasm_simulator'
if self.shots is None:
self.shots = 1000
kernel_matrix = kernel_join(X, X, self.entangler_map,
self.coupling_map, self.initial_layout,
self.shots, self._random_seed,
self.num_of_qubits, self._backend)
[alpha, b, support] = optimize_SVM(kernel_matrix, y)
alphas = np.array([])
SVMs = np.array([])
yin = np.array([])
for alphindex in range(len(support)):
if support[alphindex]:
alphas = np.vstack([alphas, alpha[alphindex]
]) if alphas.size else alpha[alphindex]
SVMs = np.vstack([SVMs, X[alphindex]
]) if SVMs.size else X[alphindex]
yin = np.vstack([yin, y[alphindex]
]) if yin.size else y[alphindex]
self._ret['svm'] = {}
self._ret['svm']['alphas'] = alphas
self._ret['svm']['bias'] = b
self._ret['svm']['support_vectors'] = SVMs
self._ret['svm']['yin'] = yin