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 classify_example(problem, model, sparm):
"""Given a pattern x, return the predicted label."""
data_weights,T = diagonal.unpack_weights(list(model.w))
A = path.compute_data_terms(problem.F, data_weights)
hyp_path,hyp_orients = diagonal.solve(A, T)
return manhattan_utils.ManhattanSolution(problem, hyp_path, hyp_orients)
def classify_example(problem, model, sparm):
"""Given a pattern x, return the predicted label."""
data_weights, T = diagonal.unpack_weights(list(model.w))
A = path.compute_data_terms(problem.F, data_weights)
hyp_path, hyp_orients = diagonal.solve(A, T)
return manhattan_utils.ManhattanSolution(problem, hyp_path, hyp_orients)
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(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 classify_example(F, model, sparm):
"""Given a pattern x, return the predicted label."""
data_weights, T = diagonal.unpack_weights(list(model.w))
A = path.compute_data_terms(F, data_weights)
return diagonal.solve(A, T)
def classify_example(F, model, sparm):
"""Given a pattern x, return the predicted label."""
data_weights,T = diagonal.unpack_weights(list(model.w))
A = path.compute_data_terms(F, data_weights)
return diagonal.solve(A, T)
