def __incremental_embedding_update(self,resource,args):
n = resource.get('n')
d = resource.get('d')
S = resource.get_list('S')
verbose = False
X = numpy.array(resource.get('X'))
X2 = numpy.array(resource.get('X2'))
# set maximum time allowed to update embedding
t_max = 1.0
epsilon = 0.00001 # a relative convergence criterion, see computeEmbeddingWithGD documentation
alpha = 1
t_start = time.time()
X,emp_loss_new,hinge_loss_new,log_loss_new,acc = utilsSTE.computeEmbeddingWithGD(X,S,alpha,max_iters=1, epsilon=epsilon,verbose=verbose)
_te = time.time()
k = 1
while (time.time()-t_start<.5*t_max) and (acc > epsilon):
# take a single gradient step
ts = time.time()
X,emp_loss_new,hinge_loss_new,log_loss_new,acc = utilsSTE.computeEmbeddingWithGD(X,S,alpha,max_iters=2**k, epsilon=epsilon,verbose=verbose)
k+=1
if verbose==True:
print "Incremental embedding time of X gradient step at iteration %s is %s"%(str(k),str(time.time()-ts))
if d==2:
X2 = X
else:
t_start = time.time()
X2,emp_loss_new,hinge_loss_new,log_loss_new,acc = utilsSTE.computeEmbeddingWithGD(X2,S,alpha,max_iters=1, epsilon=epsilon,verbose=verbose)
k = 1
while (time.time()-t_start<.5*t_max) and (acc > epsilon):
# take a single gradient step
ts = time.time()
X2,emp_loss_new,hinge_loss_new,log_loss_new,acc = utilsSTE.computeEmbeddingWithGD(X2,S,alpha,max_iters=2**k, epsilon=epsilon, verbose=verbose)
k+=1
if verbose:
print "Incremental embedding time of X2 gradient step at itration %s is %s"%(str(k),str(time.time()-ts))
t_s = time.time()
tau = utilsSTE.getSTETauDistribution(X,S,alpha)
if verbose:
print "Time to compute tau %s"%str(time.time()-t_s)
resource.set('X',X.tolist())
resource.set('X2',X2.tolist())
_ts = time.time()
tau = utilsSTE.getSTETauDistribution(X,S,alpha)
_te = time.time()
resource.set('tau',tau.tolist())
def __full_embedding_update(self,resource,args):
n = resource.get('n')
d = resource.get('d')
S = resource.get_list('S')
verbose=False
X_old = numpy.array(resource.get('X'))
X2_old = numpy.array(resource.get('X2'))
# set maximum time allowed to update embedding
t_max = 5.0
epsilon = 0.00001 # a relative convergence criterion, see computeEmbeddingWithGD documentation
alpha = 1
emp_loss_old,hinge_loss_old,log_loss_old = utilsSTE.getLoss(X_old,S,alpha)
X,tmp = utilsSTE.computeEmbeddingWithEpochSGD(n,d,S,alpha,max_num_passes=16,epsilon=0,verbose=verbose)
t_start = time.time()
X,emp_loss_new,hinge_loss_new,log_loss_new,acc = utilsSTE.computeEmbeddingWithGD(X,S,alpha,max_iters=1, epsilon=epsilon,verbose=verbose)
k = 1
while (time.time()-t_start<.5*t_max) and (acc > epsilon):
# take a single gradient step
X,emp_loss_new,hinge_loss_new,log_loss_new,acc = utilsSTE.computeEmbeddingWithGD(X,S,alpha,max_iters=2**k, epsilon=epsilon,verbose=verbose)
k += 1
emp_loss_new,hinge_loss_new,log_loss_new = utilsSTE.getLoss(X,S, alpha)
if emp_loss_old < emp_loss_new:
X = X_old
if d==2:
X2 = X
else:
emp_loss_old,hinge_loss_old,log_loss_old = utilsSTE.getLoss(X2_old,S,alpha)
X2,tmp = utilsSTE.computeEmbeddingWithEpochSGD(n,2,S,alpha,max_num_passes=16,epsilon=0,verbose=verbose)
t_start = time.time()
X2,emp_loss_new,hinge_loss_new,log_loss_new,acc = utilsSTE.computeEmbeddingWithGD(X2,S,alpha,max_iters=1, epsilon=epsilon,verbose=verbose)
k = 1
while (time.time()-t_start<.5*t_max) and (acc > epsilon):
# take a single gradient step
X2,emp_loss_new,hinge_loss_new,log_loss_new,acc = utilsSTE.computeEmbeddingWithGD(X2,S,alpha,max_iters=2**k, epsilon=epsilon,verbose=verbose)
k += 1
emp_loss_new,hinge_loss_new,log_loss_new = utilsSTE.getLoss(X2,S, alpha)
if emp_loss_old < emp_loss_new:
X2 = X2_old
_ts = time.time()
tau = utilsSTE.getSTETauDistribution(X,S,alpha)
_te = time.time()
resource.set('X',X.tolist())
resource.set('X2',X2.tolist())
resource.set('tau',tau.tolist())
def __full_embedding_update(self, resource, args):
n = resource.get('n')
d = resource.get('d')
S = resource.get_list('S')
verbose = False
X_old = numpy.array(resource.get('X'))
X2_old = numpy.array(resource.get('X2'))
# set maximum time allowed to update embedding
t_max = 5.0
epsilon = 0.00001 # a relative convergence criterion, see computeEmbeddingWithGD documentation
alpha = 1
emp_loss_old, hinge_loss_old, log_loss_old = utilsSTE.getLoss(
X_old, S, alpha)
X, tmp = utilsSTE.computeEmbeddingWithEpochSGD(n,
d,
S,
alpha,
max_num_passes=16,
epsilon=0,
verbose=verbose)
t_start = time.time()
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsSTE.computeEmbeddingWithGD(
X, S, alpha, max_iters=1, epsilon=epsilon, verbose=verbose)
k = 1
while (time.time() - t_start < .5 * t_max) and (acc > epsilon):
# take a single gradient step
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsSTE.computeEmbeddingWithGD(
X, S, alpha, max_iters=2**k, epsilon=epsilon, verbose=verbose)
k += 1
emp_loss_new, hinge_loss_new, log_loss_new = utilsSTE.getLoss(
X, S, alpha)
if emp_loss_old < emp_loss_new:
X = X_old
if d == 2:
X2 = X
else:
emp_loss_old, hinge_loss_old, log_loss_old = utilsSTE.getLoss(
X2_old, S, alpha)
X2, tmp = utilsSTE.computeEmbeddingWithEpochSGD(n,
2,
S,
alpha,
max_num_passes=16,
epsilon=0,
verbose=verbose)
t_start = time.time()
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsSTE.computeEmbeddingWithGD(
X2, S, alpha, max_iters=1, epsilon=epsilon, verbose=verbose)
k = 1
while (time.time() - t_start < .5 * t_max) and (acc > epsilon):
# take a single gradient step
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsSTE.computeEmbeddingWithGD(
X2,
S,
alpha,
max_iters=2**k,
epsilon=epsilon,
verbose=verbose)
k += 1
emp_loss_new, hinge_loss_new, log_loss_new = utilsSTE.getLoss(
X2, S, alpha)
if emp_loss_old < emp_loss_new:
X2 = X2_old
_ts = time.time()
tau = utilsSTE.getSTETauDistribution(X, S, alpha)
_te = time.time()
resource.set('X', X.tolist())
resource.set('X2', X2.tolist())
resource.set('tau', tau.tolist())
def __incremental_embedding_update(self, resource, args):
n = resource.get('n')
d = resource.get('d')
S = resource.get_list('S')
verbose = False
X = numpy.array(resource.get('X'))
X2 = numpy.array(resource.get('X2'))
# set maximum time allowed to update embedding
t_max = 1.0
epsilon = 0.00001 # a relative convergence criterion, see computeEmbeddingWithGD documentation
alpha = 1
t_start = time.time()
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsSTE.computeEmbeddingWithGD(
X, S, alpha, max_iters=1, epsilon=epsilon, verbose=verbose)
_te = time.time()
k = 1
while (time.time() - t_start < .5 * t_max) and (acc > epsilon):
# take a single gradient step
ts = time.time()
X, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsSTE.computeEmbeddingWithGD(
X, S, alpha, max_iters=2**k, epsilon=epsilon, verbose=verbose)
k += 1
if verbose == True:
print "Incremental embedding time of X gradient step at iteration %s is %s" % (
str(k), str(time.time() - ts))
if d == 2:
X2 = X
else:
t_start = time.time()
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsSTE.computeEmbeddingWithGD(
X2, S, alpha, max_iters=1, epsilon=epsilon, verbose=verbose)
k = 1
while (time.time() - t_start < .5 * t_max) and (acc > epsilon):
# take a single gradient step
ts = time.time()
X2, emp_loss_new, hinge_loss_new, log_loss_new, acc = utilsSTE.computeEmbeddingWithGD(
X2,
S,
alpha,
max_iters=2**k,
epsilon=epsilon,
verbose=verbose)
k += 1
if verbose:
print "Incremental embedding time of X2 gradient step at itration %s is %s" % (
str(k), str(time.time() - ts))
t_s = time.time()
tau = utilsSTE.getSTETauDistribution(X, S, alpha)
if verbose:
print "Time to compute tau %s" % str(time.time() - t_s)
resource.set('X', X.tolist())
resource.set('X2', X2.tolist())
_ts = time.time()
tau = utilsSTE.getSTETauDistribution(X, S, alpha)
_te = time.time()
resource.set('tau', tau.tolist())
