def find_most_violated_constraint_margin(F, y, model, sparm):
"""Return ybar associated with x's most violated constraint.
The find most violated constraint function for margin rescaling.
The default behavior is that this returns the value from the
general find_most_violated_constraint function."""
if len(y) != 2:
raise Exception('y should be a pair (states,orients)')
data_weights, T = diagonal.unpack_weights(list(model.w))
states, orients = y
A = path.compute_loss_augmented_terms(F, data_weights, states, path.L2)
ybar = diagonal.solve(A, T)
if len(ybar) != 2:
raise Exception('ybar should be a pair (states,orients)')
print '\nFinding most violated constraint'
print ' w: ', list(model.w)
print ' data w: ', data_weights
print ' transition:\n', T
print ' true y: ', y
print ' classified ybar: ', ybar
print ' feature(true y): ', path.compute_path_features(F, y)
print ' feature(ybar): ', path.compute_path_features(F, ybar)
print ' loss: ', path.compute_loss(y[0], ybar[0], path.L2)
return ybar
def find_most_violated_constraint_margin(problem, gt, model, sparm):
"""Return ybar associated with x's most violated constraint.
The find most violated constraint function for margin rescaling.
The default behavior is that this returns the value from the
general find_most_violated_constraint function."""
assert(isinstance(problem, manhattan_utils.ManhattanProblem))
assert(isinstance(gt, manhattan_utils.ManhattanSolution))
data_weights,T = diagonal.unpack_weights(list(model.w))
data_terms = path.compute_data_terms(problem.F, data_weights)
loss_terms = gt.compute_loss_terms(LossFunc)
A = data_terms + loss_terms
est_states,est_orients = diagonal.solve(A, T)
hyp = manhattan_utils.ManhattanSolution(problem, est_states, est_orients)
print '\nFinding most violated constraint'
print ' w: ',list(model.w)
print ' data w: ',data_weights
print ' transition:\n',T
print ' true y: ',gt.ys
print ' classified ybar: ',hyp.ys
print ' feature(true y): ',path.compute_path_features(problem.F, gt.pair)
print ' feature(ybar): ',path.compute_path_features(problem.F, hyp.pair)
print ' loss: ',gt.compute_loss(hyp, LossFunc)
return hyp
def find_most_violated_constraint_margin(problem, gt, model, sparm):
"""Return ybar associated with x's most violated constraint.
The find most violated constraint function for margin rescaling.
The default behavior is that this returns the value from the
general find_most_violated_constraint function."""
assert (isinstance(problem, manhattan_utils.ManhattanProblem))
assert (isinstance(gt, manhattan_utils.ManhattanSolution))
data_weights, T = diagonal.unpack_weights(list(model.w))
data_terms = path.compute_data_terms(problem.F, data_weights)
loss_terms = gt.compute_loss_terms(LossFunc)
A = data_terms + loss_terms
est_states, est_orients = diagonal.solve(A, T)
hyp = manhattan_utils.ManhattanSolution(problem, est_states, est_orients)
print '\nFinding most violated constraint'
print ' w: ', list(model.w)
print ' data w: ', data_weights
print ' transition:\n', T
print ' true y: ', gt.ys
print ' classified ybar: ', hyp.ys
print ' feature(true y): ', path.compute_path_features(problem.F, gt.pair)
print ' feature(ybar): ', path.compute_path_features(problem.F, hyp.pair)
print ' loss: ', gt.compute_loss(hyp, LossFunc)
return hyp
def find_most_violated_constraint_margin(F, y, model, sparm):
"""Return ybar associated with x's most violated constraint.
The find most violated constraint function for margin rescaling.
The default behavior is that this returns the value from the
general find_most_violated_constraint function."""
if len(y) != 2:
raise Exception('y should be a pair (states,orients)')
data_weights,T = diagonal.unpack_weights(list(model.w))
states,orients = y
A = path.compute_loss_augmented_terms(F, data_weights, states, path.L2)
ybar = diagonal.solve(A,T)
if len(ybar) != 2:
raise Exception('ybar should be a pair (states,orients)')
print '\nFinding most violated constraint'
print ' w: ',list(model.w)
print ' data w: ',data_weights
print ' transition:\n',T
print ' true y: ',y
print ' classified ybar: ',ybar
print ' feature(true y): ',path.compute_path_features(F,y)
print ' feature(ybar): ',path.compute_path_features(F,ybar)
print ' loss: ',path.compute_loss(y[0], ybar[0], path.L2)
return ybar
def psi(problem, hyp, model, sparm):
"""Return a feature vector representing pattern x and label y.
This is the combined feature function, which this returns either a
svmapi.Sparse object, or sequence of svmapi.Sparse objects (useful
during kernel evaluations, as all components undergo kernel
evaluation separately). There is no default behavior."""
assert(isinstance(problem, manhattan_utils.ManhattanProblem))
assert(isinstance(hyp, manhattan_utils.ManhattanSolution))
return svmapi.Sparse(path.compute_path_features(problem.F, hyp.pair))
def psi(problem, hyp, model, sparm):
"""Return a feature vector representing pattern x and label y.
This is the combined feature function, which this returns either a
svmapi.Sparse object, or sequence of svmapi.Sparse objects (useful
during kernel evaluations, as all components undergo kernel
evaluation separately). There is no default behavior."""
assert (isinstance(problem, manhattan_utils.ManhattanProblem))
assert (isinstance(hyp, manhattan_utils.ManhattanSolution))
return svmapi.Sparse(path.compute_path_features(problem.F, hyp.pair))
def psi(F, y, model, sparm):
"""Return a feature vector representing pattern x and label y.
This is the combined feature function, which this returns either a
svmapi.Sparse object, or sequence of svmapi.Sparse objects (useful
during kernel evaluations, as all components undergo kernel
evaluation separately). There is no default behavior."""
if len(y) != 2:
raise Exception('y should be a pair (states,orients)')
return svmapi.Sparse(path.compute_path_features(F, y))
def psi(F, y, model, sparm):
"""Return a feature vector representing pattern x and label y.
This is the combined feature function, which this returns either a
svmapi.Sparse object, or sequence of svmapi.Sparse objects (useful
during kernel evaluations, as all components undergo kernel
evaluation separately). There is no default behavior."""
if len(y) != 2:
raise Exception('y should be a pair (states,orients)')
return svmapi.Sparse(path.compute_path_features(F, y))
def init_model(sample, model, sparm):
"""Initializes the learning model.
Initialize the structure model model. The model.size_psi must be set to
the number of features. The ancillary purpose is to add any
information to model that is necessary from the user code
perspective. This function returns nothing."""
# The weights are not simple the length of the data features: they
# also include terms for the transition matrix
gt_ftr = path.compute_path_features(sample[0][0].F, sample[0][1].pair)
assert(np.ndim(gt_ftr) == 1)
model.size_psi = len(gt_ftr)
def init_model(sample, model, sparm):
"""Initializes the learning model.
Initialize the structure model model. The model.size_psi must be set to
the number of features. The ancillary purpose is to add any
information to model that is necessary from the user code
perspective. This function returns nothing."""
# The weights are not simple the length of the data features: they
# also include terms for the transition matrix
gt_ftr = path.compute_path_features(sample[0][0].F, sample[0][1].pair)
assert (np.ndim(gt_ftr) == 1)
model.size_psi = len(gt_ftr)