def loadResources(self):
AbstractIterativeLearner.loadResources(self)
AbstractSupervisedLearner.loadResources(self)
#Y = self.resource_pool[data_sources.TRAIN_LABELS]
#self.setLabels(Y)
X = self.resource_pool[data_sources.TRAIN_FEATURES]
self.X = csc_matrix(X.T)
if self.resource_pool.has_key('bias'):
self.bias = float(self.resource_pool['bias'])
if self.bias != 0.:
bias_slice = sqrt(self.bias)*np.mat(ones((1,self.X.shape[1]),dtype=np.float64))
self.X = sparse.vstack([self.X,bias_slice]).tocsc()
else:
self.bias = 0.
self.X_csr = self.X.tocsr()
if (data_sources.VALIDATION_FEATURES in self.resource_pool) and (data_sources.VALIDATION_LABELS in self.resource_pool):
validation_X = self.resource_pool[data_sources.VALIDATION_FEATURES]
validation_Y = self.resource_pool[data_sources.VALIDATION_LABELS]
self.callbackfun = EarlyStopCB(validation_X, validation_Y)
def loadResources(self):
"""
Loads the resources from the previously set resource pool.
@raise Exception: when some of the resources required by the learner is not available in the ResourcePool object.
"""
AbstractIterativeLearner.loadResources(self)
AbstractSupervisedLearner.loadResources(self)
self.Y = Y
#Number of training examples
self.size = Y.shape[0]
if not Y.shape[1] == 1:
raise Exception('GreedyRLS currently supports only one output at a time. The output matrix is now of shape ' + str(Y.shape) + '.')
X = self.resource_pool['train_features']
if isinstance(X, scipy.sparse.base.spmatrix):
self.X = X.todense()
else:
self.X = X
def loadResources(self):
"""
Loads the resources from the previously set resource pool.
@raise Exception: when some of the resources required by the learner is not available in the ResourcePool object.
"""
AbstractIterativeLearner.loadResources(self)
AbstractSupervisedLearner.loadResources(self)
self.Y = Y
# Number of training examples
self.size = Y.shape[0]
if not Y.shape[1] == 1:
raise Exception(
"GreedyRLS currently supports only one output at a time. The output matrix is now of shape "
+ str(Y.shape)
+ "."
)
X = self.resource_pool["train_features"]
if isinstance(X, scipy.sparse.base.spmatrix):
self.X = X.todense()
else:
self.X = X
