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, verbose=verbose)
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, verbose=verbose)
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())
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, verbose=verbose)
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, verbose=verbose
)
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())
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,verbose=verbose)
k = 1
while (time.time()-t_start<0.5*t_max) and (acc > epsilon):
# take a single gradient step
X,emp_loss_new,hinge_loss_new,acc = utilsMDS.computeEmbeddingWithGD(X,S,max_iters=2**k,verbose=verbose)
k += 1
resource.set('X',X.tolist())
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, verbose=verbose)
k = 1
while (time.time() - t_start < 0.5 * t_max) and (acc > epsilon):
# take a single gradient step
X, emp_loss_new, hinge_loss_new, acc = utilsMDS.computeEmbeddingWithGD(
X, S, max_iters=2**k, verbose=verbose)
k += 1
resource.set('X', X.tolist())
