def train(self, data, labels):
"""
train the svm
Args:
data (dict): dictionary which maps each class to the points in the class
class_labels (list): list of classes. For example: ['A', 'B']
"""
labels = labels.astype(np.float)
labels = labels * 2. - 1.
kernel_matrix = self.construct_kernel_matrix(data, data, self.gamma)
self._ret['kernel_matrix_training'] = kernel_matrix
[alpha, b, support] = optimize_svm(kernel_matrix, 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, data[alphindex]
]) if svms.size else data[alphindex]
yin = np.vstack([yin, labels[alphindex]
]) if yin.size else 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 train(self, data, labels):
"""
train the svm
Args:
data (numpy.ndarray): NxD array, where N is the number of data,
D is the feature dimension.
labels (numpy.ndarray): Nx1 array, where N is the number of data
"""
scaling = 1.0 if QuantumAlgorithm.is_statevector_backend(
self.qalgo.backend) else None
kernel_matrix = self.construct_kernel_matrix(data)
labels = labels * 2 - 1 # map label from 0 --> -1 and 1 --> 1
labels = labels.astype(np.float)
[alpha, b, support] = optimize_svm(kernel_matrix,
labels,
scaling=scaling)
support_index = np.where(support)
alphas = alpha[support_index]
svms = data[support_index]
yin = labels[support_index]
self._ret['kernel_matrix_training'] = kernel_matrix
self._ret['svm'] = {}
self._ret['svm']['alphas'] = alphas
self._ret['svm']['bias'] = b
self._ret['svm']['support_vectors'] = svms
self._ret['svm']['yin'] = yin
