def __incremental_embedding_update(self,resource,args):
verbose = False
n = resource.get('n')
d = resource.get('d')
S = resource.get_list('S')
X = numpy.array(resource.get('X'))
# set maximum time allowed to update embedding
t_max = 1.0
epsilon = 0.01 # a relative convergence criterion, see computeEmbeddingWithGD documentation
# take a single gradient step
t_start = time.time()
X,emp_loss_new,hinge_loss_new,acc = utilsMDS.computeEmbeddingWithGD(X,S,max_iters=1)
k = 1
while (time.time()-t_start<0.5*t_max) and (acc > epsilon):
X,emp_loss_new,hinge_loss_new,acc = utilsMDS.computeEmbeddingWithGD(X,S,max_iters=2**k)
k += 1
resource.set('X',X.tolist())
def __full_embedding_update(self,resource,args):
verbose = False
n = resource.get('n')
d = resource.get('d')
S = resource.get_list('S')
X_old = numpy.array(resource.get('X'))
t_max = 5.0
epsilon = 0.01 # a relative convergence criterion, see computeEmbeddingWithGD documentation
emp_loss_old,hinge_loss_old = utilsMDS.getLoss(X_old,S)
X,tmp = utilsMDS.computeEmbeddingWithEpochSGD(n,d,S,max_num_passes=16,epsilon=0,verbose=verbose)
t_start = time.time()
X,emp_loss_new,hinge_loss_new,acc = utilsMDS.computeEmbeddingWithGD(X,S,max_iters=1)
k = 1
while (time.time()-t_start<0.5*t_max) and (acc > epsilon):
X,emp_loss_new,hinge_loss_new,acc = utilsMDS.computeEmbeddingWithGD(X,S,max_iters=2**k)
k += 1
emp_loss_new,hinge_loss_new = utilsMDS.getLoss(X,S)
if emp_loss_old < emp_loss_new:
X = X_old
resource.set('X',X.tolist())
