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'))
X2_old = numpy.array(resource.get('X2'))
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):
# 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
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())
if d == 2:
X2 = X
else:
emp_loss_old, hinge_loss_old = utilsMDS.getLoss(X2_old, S)
X2, tmp = utilsMDS.computeEmbeddingWithEpochSGD(n,
2,
S,
max_num_passes=16,
epsilon=0,
verbose=verbose)
t_start = time.time()
X2, emp_loss_new, hinge_loss_new, acc = utilsMDS.computeEmbeddingWithGD(
X2, 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
X2, emp_loss_new, hinge_loss_new, acc = utilsMDS.computeEmbeddingWithGD(
X2, S, max_iters=2**k, verbose=verbose)
k += 1
emp_loss_new, hinge_loss_new = utilsMDS.getLoss(X2, S)
if emp_loss_old < emp_loss_new:
X2 = X2_old
resource.set('X2', X2.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'))
X2_old = numpy.array(resource.get('X2'))
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):
# 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
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())
if d==2:
X2 = X
else:
emp_loss_old,hinge_loss_old = utilsMDS.getLoss(X2_old,S)
X2,tmp = utilsMDS.computeEmbeddingWithEpochSGD(n,2,S,max_num_passes=16,epsilon=0,verbose=verbose)
t_start = time.time()
X2,emp_loss_new,hinge_loss_new,acc = utilsMDS.computeEmbeddingWithGD(X2,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
X2,emp_loss_new,hinge_loss_new,acc = utilsMDS.computeEmbeddingWithGD(X2,S,max_iters=2**k,verbose=verbose)
k += 1
emp_loss_new,hinge_loss_new = utilsMDS.getLoss(X2,S)
if emp_loss_old < emp_loss_new:
X2 = X2_old
resource.set('X2',X2.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'))
X2 = numpy.array(resource.get('X2'))
# 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
if d == 2:
X2 = X
else:
# take a single gradient step
t_start = time.time()
X2, emp_loss_new, hinge_loss_new, acc = utilsMDS.computeEmbeddingWithGD(
X2, 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
X2, emp_loss_new, hinge_loss_new, acc = utilsMDS.computeEmbeddingWithGD(
X2, S, max_iters=2**k, verbose=verbose)
k += 1
resource.set('X', X.tolist())
resource.set('X2', X2.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'))
X2 = numpy.array(resource.get('X2'))
# 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
if d==2:
X2 = X
else:
# take a single gradient step
t_start = time.time()
X2,emp_loss_new,hinge_loss_new,acc = utilsMDS.computeEmbeddingWithGD(X2,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
X2,emp_loss_new,hinge_loss_new,acc = utilsMDS.computeEmbeddingWithGD(X2,S,max_iters=2**k,verbose=verbose)
k += 1
resource.set('X',X.tolist())
resource.set('X2',X2.tolist())
