def fit(self, feats, labels):
self.X_tr = feats
self.y_train = labels
feats = feats.astype(np.float64)
feat = RealFeatures(feats.T)
self.metric_model = shogun_LMNN(
feat, MulticlassLabels(labels.astype(np.float64)), self.k)
self.metric_model.set_maxiter(self.max_iter)
self.metric_model.set_regularization(self.regularization)
self.metric_model.set_obj_threshold(self.convergence_tol)
self.metric_model.set_stepsize(self.learn_rate)
self.metric_model.train()
stats = self.metric_model.get_statistics()
#pdb.set_trace()
#plt.plot(stats.obj.get())
#plt.grid(True)
#plt.show()
self.linear_transform = self.metric_model.get_linear_transform()
#self.projected_data = np.dot(self.linear_transform, feats.T)
#norms = np.linalg.norm(self.projected_data, axis=0)
#self.projected_data /= norms
# Fit the data with PCA first.
# pdb.set_trace()
return self
def fit(self, feats, labels):
self.eigenvecs, self.space = self.space_model.fit(feats, labels)
feat = RealFeatures(self.space)
self.metric_model = shogun_LMNN(
feat, MulticlassLabels(labels.astype(np.float64)), self.k)
self.metric_model.set_maxiter(1000)
self.metric_model.set_regularization(0.50)
self.metric_model.set_obj_threshold(0.001)
self.metric_model.set_stepsize(1e-7)
#pdb.set_trace()
L = np.eye(self.space.shape[1])
self.metric_model.train(L)
stats = self.metric_model.get_statistics()
#plt.plot(stats.obj.get())
#plt.grid(True)
#plt.show()
self.linear_transform = self.metric_model.get_linear_transform()
self.projected_data = np.dot(self.linear_transform, self.space)
norms = np.linalg.norm(self.projected_data, axis=0)
self.projected_data /= norms
# Fit the data with PCA first.
# pdb.set_trace()
return self.eigenvecs, self.projected_data
def fit(self, X, y):
self.X_, y = check_X_y(X, y, dtype=float)
labels = MulticlassLabels(y)
self._lmnn = shogun_LMNN(RealFeatures(self.X_.T), labels, self.k)
self._lmnn.set_maxiter(self.max_iter)
self._lmnn.set_obj_threshold(self.convergence_tol)
self._lmnn.set_regularization(self.regularization)
self._lmnn.set_stepsize(self.learn_rate)
if self.use_pca:
self._lmnn.train()
else:
self._lmnn.train(np.eye(X.shape[1]))
self.L_ = self._lmnn.get_linear_transform()
return self
def fit(self, X, y):
self.X_, y = check_X_y(X, y, dtype=float)
labels = MulticlassLabels(y)
self._lmnn = shogun_LMNN(RealFeatures(self.X_.T), labels, self.k)
self._lmnn.set_maxiter(self.max_iter)
self._lmnn.set_obj_threshold(self.convergence_tol)
self._lmnn.set_regularization(self.regularization)
self._lmnn.set_stepsize(self.learn_rate)
if self.use_pca:
self._lmnn.train()
else:
self._lmnn.train(np.eye(X.shape[1]))
self.L_ = self._lmnn.get_linear_transform()
return self
def fit(self, X, labels, verbose=False):
self.X = X
self.L = np.eye(X.shape[1])
labels = MulticlassLabels(labels.astype(np.float64))
self._lmnn = shogun_LMNN(RealFeatures(X.T), labels, self.params['k'])
self._lmnn.set_maxiter(self.params['max_iter'])
self._lmnn.set_obj_threshold(self.params['convergence_tol'])
self._lmnn.set_regularization(self.params['regularization'])
self._lmnn.set_stepsize(self.params['learn_rate'])
if self.params['use_pca']:
self._lmnn.train()
else:
self._lmnn.train(self.L)
self.L = self._lmnn.get_linear_transform()
return self
def fit(self, X, y):
X, y = self._prepare_inputs(X, y, dtype=float,
ensure_min_samples=2)
labels = MulticlassLabels(y)
self._lmnn = shogun_LMNN(RealFeatures(X.T), labels, self.k)
self._lmnn.set_maxiter(self.max_iter)
self._lmnn.set_obj_threshold(self.convergence_tol)
self._lmnn.set_regularization(self.regularization)
self._lmnn.set_stepsize(self.learn_rate)
if self.use_pca:
self._lmnn.train()
else:
self._lmnn.train(np.eye(X.shape[1]))
self.transformer_ = self._lmnn.get_linear_transform(X)
return self
def fit(self, X, labels):
self.X = X
self.L = np.eye(X.shape[1])
labels = MulticlassLabels(labels.astype(np.float64))
self._lmnn = shogun_LMNN(RealFeatures(X.T), labels, self.params['k'])
self._lmnn.set_maxiter(self.params['max_iter'])
self._lmnn.set_obj_threshold(self.params['convergence_tol'])
self._lmnn.set_regularization(self.params['regularization'])
self._lmnn.set_stepsize(self.params['learn_rate'])
if self.params['use_pca']:
self._lmnn.train()
else:
self._lmnn.train(self.L)
self.L = self._lmnn.get_linear_transform()
return self
def fit(self, X, y):
X, y = self._prepare_inputs(X, y, dtype=float,
ensure_min_samples=2)
labels = MulticlassLabels(y)
self._lmnn = shogun_LMNN(RealFeatures(X.T), labels, self.k)
self._lmnn.set_maxiter(self.max_iter)
self._lmnn.set_obj_threshold(self.convergence_tol)
self._lmnn.set_regularization(self.regularization)
self._lmnn.set_stepsize(self.learn_rate)
if self.use_pca:
self._lmnn.train()
else:
self._lmnn.train(np.eye(X.shape[1]))
self.transformer_ = self._lmnn.get_linear_transform(X)
return self
def __init__(self, X, labels, k=3): self.X = X self.L = np.eye(X.shape[1]) labels = MulticlassLabels(labels.astype(np.float64)) self._lmnn = shogun_LMNN(RealFeatures(X.T), labels, k)
