def test_nested_concatenate(self):
aa = ch.arange(3)
bb = ch.arange(4)
cc = ch.arange(5)
result = ch.concatenate((ch.concatenate((aa,bb)),cc))
self.assertTrue(result.m0 is aa)
self.assertTrue(result.m1 is bb)
self.assertTrue(result.m2 is cc)
self.assertTrue(result.dr_wrt(aa).nnz > 0)
self.assertTrue(result.dr_wrt(bb).nnz > 0)
self.assertTrue(result.dr_wrt(cc).nnz > 0)
def test_nested_concatenate(self):
aa = ch.arange(3)
bb = ch.arange(4)
cc = ch.arange(5)
result = ch.concatenate((ch.concatenate((aa, bb)), cc))
self.assertTrue(result.m0 is aa)
self.assertTrue(result.m1 is bb)
self.assertTrue(result.m2 is cc)
self.assertTrue(result.dr_wrt(aa).nnz > 0)
self.assertTrue(result.dr_wrt(bb).nnz > 0)
self.assertTrue(result.dr_wrt(cc).nnz > 0)
def setup_objective(obj,
free_variables,
on_step=None,
disp=True,
make_dense=False):
'''
obj here can be a list of ch objects or a dict of label: ch objects. Either way, the ch
objects will be merged into one objective using a ChInputsStacked. The labels are just used
for printing out values per objective with each iteration. If make_dense is True, the
resulting object with return a desne Jacobian
'''
# Validate free variables
num_unique_ids = len(
np.unique(np.array([id(freevar) for freevar in free_variables])))
if num_unique_ids != len(free_variables):
raise Exception(
'The "free_variables" param contains duplicate variables.')
# Extract labels
labels = {}
if isinstance(obj, list) or isinstance(obj, tuple):
obj = ch.concatenate([f.ravel() for f in obj])
elif isinstance(obj, dict):
labels = obj
obj = ch.concatenate([f.ravel() for f in obj.values()])
# build objective
x = np.concatenate([freevar.r.ravel() for freevar in free_variables])
obj = ChInputsStacked(obj=obj,
free_variables=free_variables,
x=x,
make_dense=make_dense)
# build callback
def callback():
if on_step is not None:
on_step(obj)
if disp:
report_line = ['%.2e' % (np.sum(obj.r**2), )]
for label, objective in sorted(labels.items(), key=lambda x: x[0]):
report_line.append('%s: %.2e' %
(label, np.sum(objective.r**2)))
report_line = " | ".join(report_line) + '\n'
sys.stderr.write(report_line)
return obj, callback
def slogdet(*args):
n = len(args)
if n == 1:
r2 = LogAbsDet(x=args[0])
r1 = SignLogAbsDet(r2)
return r1, r2
else:
r2 = [LogAbsDet(x=arg) for arg in args]
r1 = [SignLogAbsDet(r) for r in r2]
r2 = ch.concatenate(r2)
return r1, r2
def minimize(fun, x0, method='dogleg', bounds=None, constraints=(), tol=None, callback=None, options=None):
if method == 'dogleg':
if options is None: options = {}
return _minimize_dogleg(fun, free_variables=x0, on_step=callback, **options)
if isinstance(fun, list) or isinstance(fun, tuple):
fun = ch.concatenate([f.ravel() for f in fun])
if isinstance(fun, dict):
fun = ch.concatenate([f.ravel() for f in fun.values()])
obj = fun
free_variables = x0
from ch import SumOfSquares
hessp = None
hess = None
if obj.size == 1:
obj_scalar = obj
else:
obj_scalar = SumOfSquares(obj)
def hessp(vs, p,obj, obj_scalar, free_variables):
changevars(vs,obj,obj_scalar,free_variables)
if not hasattr(hessp, 'vs'):
hessp.vs = vs*0+1e16
if np.max(np.abs(vs-hessp.vs)) > 0:
J = ns_jacfunc(vs,obj,obj_scalar,free_variables)
hessp.J = J
hessp.H = 2. * J.T.dot(J)
hessp.vs = vs
return np.array(hessp.H.dot(p)).ravel()
#return 2*np.array(hessp.J.T.dot(hessp.J.dot(p))).ravel()
if method.lower() != 'newton-cg':
def hess(vs, obj, obj_scalar, free_variables):
changevars(vs,obj,obj_scalar,free_variables)
if not hasattr(hessp, 'vs'):
hessp.vs = vs*0+1e16
if np.max(np.abs(vs-hessp.vs)) > 0:
J = ns_jacfunc(vs,obj,obj_scalar,free_variables)
hessp.H = 2. * J.T.dot(J)
return hessp.H
def changevars(vs, obj, obj_scalar, free_variables):
cur = 0
changed = False
for idx, freevar in enumerate(free_variables):
sz = freevar.r.size
newvals = vs[cur:cur+sz].copy().reshape(free_variables[idx].shape)
if np.max(np.abs(newvals-free_variables[idx]).ravel()) > 0:
free_variables[idx][:] = newvals
changed = True
cur += sz
methods_without_callback = ('anneal', 'powell', 'cobyla', 'slsqp')
if callback is not None and changed and method.lower() in methods_without_callback:
callback(None)
return changed
def residuals(vs,obj, obj_scalar, free_variables):
changevars(vs, obj, obj_scalar, free_variables)
residuals = obj_scalar.r.ravel()[0]
return residuals
def scalar_jacfunc(vs,obj, obj_scalar, free_variables):
if not hasattr(scalar_jacfunc, 'vs'):
scalar_jacfunc.vs = vs*0+1e16
if np.max(np.abs(vs-scalar_jacfunc.vs)) == 0:
return scalar_jacfunc.J
changevars(vs, obj, obj_scalar, free_variables)
if True: # faster, at least on some problems
result = np.concatenate([np.array(obj_scalar.lop(wrt, np.array([[1]]))).ravel() for wrt in free_variables])
else:
jacs = [obj_scalar.dr_wrt(wrt) for wrt in free_variables]
for idx, jac in enumerate(jacs):
if sp.issparse(jac):
jacs[idx] = jacs[idx].todense()
result = np.concatenate([jac.ravel() for jac in jacs])
scalar_jacfunc.J = result
scalar_jacfunc.vs = vs
return result.ravel()
def ns_jacfunc(vs,obj, obj_scalar, free_variables):
if not hasattr(ns_jacfunc, 'vs'):
ns_jacfunc.vs = vs*0+1e16
if np.max(np.abs(vs-ns_jacfunc.vs)) == 0:
return ns_jacfunc.J
changevars(vs, obj, obj_scalar, free_variables)
jacs = [obj.dr_wrt(wrt) for wrt in free_variables]
result = hstack(jacs)
ns_jacfunc.J = result
ns_jacfunc.vs = vs
return result
x1 = scipy.optimize.minimize(
method=method,
fun=residuals,
callback=callback,
x0=np.concatenate([free_variable.r.ravel() for free_variable in free_variables]),
jac=scalar_jacfunc,
hessp=hessp, hess=hess, args=(obj, obj_scalar, free_variables),
bounds=bounds, constraints=constraints, tol=tol, options=options).x
changevars(x1, obj, obj_scalar, free_variables)
return free_variables
def _minimize_dogleg(obj, free_variables, on_step=None,
maxiter=200, max_fevals=np.inf, sparse_solver='spsolve',
disp=False, show_residuals=None, e_1=1e-15, e_2=1e-15, e_3=0., delta_0=None):
""""Nonlinear optimization using Powell's dogleg method.
See Lourakis et al, 2005, ICCV '05, "Is Levenberg-Marquardt
the Most Efficient Optimization for Implementing Bundle
Adjustment?":
http://www.ics.forth.gr/cvrl/publications/conferences/0201-P0401-lourakis-levenberg.pdf
"""
if show_residuals is not None:
import warnings
warnings.warn('minimize_dogleg: show_residuals parm is deprecaed, pass a dict instead.')
labels = {}
if isinstance(obj, list) or isinstance(obj, tuple):
obj = ch.concatenate([f.ravel() for f in obj])
elif isinstance(obj, dict):
labels = obj
obj = ch.concatenate([f.ravel() for f in obj.values()])
niters = maxiter
verbose = disp
num_unique_ids = len(np.unique(np.array([id(freevar) for freevar in free_variables])))
if num_unique_ids != len(free_variables):
raise Exception('The "free_variables" param contains duplicate variables.')
obj = ChInputsStacked(obj=obj, free_variables=free_variables, x=np.concatenate([freevar.r.ravel() for freevar in free_variables]))
def call_cb():
if on_step is not None:
on_step(obj)
report_line = ""
if len(labels) > 0:
report_line += '%.2e | ' % (np.sum(obj.r**2),)
for label in sorted(labels.keys()):
objective = labels[label]
report_line += '%s: %.2e | ' % (label, np.sum(objective.r**2))
if len(labels) > 0:
report_line += '\n'
sys.stderr.write(report_line)
call_cb()
# pif = print-if-verbose.
# can't use "print" because it's a statement, not a fn
pif = lambda x: sys.stdout.write(x + '\n') if verbose else 0
if callable(sparse_solver):
solve = sparse_solver
elif isinstance(sparse_solver, str) and sparse_solver in _solver_fns.keys():
solve = _solver_fns[sparse_solver]
else:
raise Exception('sparse_solver argument must be either a string in the set (%s) or have the api of scipy.sparse.linalg.spsolve.' % ''.join(_solver_fns.keys(), ' '))
# optimization parms
k_max = niters
fevals = 0
k = 0
delta = delta_0
p = col(obj.x.r)
fevals += 1
tm = time.time()
pif('computing Jacobian...')
J = obj.J
if sp.issparse(J):
assert(J.nnz > 0)
pif('Jacobian (%dx%d) computed in %.2fs' % (J.shape[0], J.shape[1], time.time() - tm))
if J.shape[1] != p.size:
import pdb; pdb.set_trace()
assert(J.shape[1] == p.size)
tm = time.time()
pif('updating A and g...')
A = J.T.dot(J)
r = col(obj.r.copy())
g = col(J.T.dot(-r))
pif('A and g updated in %.2fs' % (time.time() - tm))
stop = norm(g, np.inf) < e_1
while (not stop) and (k < k_max) and (fevals < max_fevals):
k += 1
pif('beginning iteration %d' % (k,))
d_sd = col((sqnorm(g)) / (sqnorm (J.dot(g))) * g)
GNcomputed = False
while True:
# if the Cauchy point is outside the trust region,
# take that direction but only to the edge of the trust region
if delta is not None and norm(d_sd) >= delta:
pif('PROGRESS: Using stunted cauchy')
d_dl = np.array(col(delta/norm(d_sd) * d_sd))
else:
if not GNcomputed:
tm = time.time()
if scipy.sparse.issparse(A):
A.eliminate_zeros()
pif('sparse solve...sparsity infill is %.3f%% (hessian %dx%d), J infill %.3f%%' % (
100. * A.nnz / (A.shape[0] * A.shape[1]),
A.shape[0],
A.shape[1],
100. * J.nnz / (J.shape[0] * J.shape[1])))
d_gn = col(solve(A, g)) if g.size>1 else np.atleast_1d(g.ravel()[0]/A[0,0])
pif('sparse solve...done in %.2fs' % (time.time() - tm))
else:
pif('dense solve...')
try:
d_gn = col(np.linalg.solve(A, g))
except Exception:
d_gn = col(np.linalg.lstsq(A, g)[0])
pif('dense solve...done in %.2fs' % (time.time() - tm))
GNcomputed = True
# if the gauss-newton solution is within the trust region, use it
if delta is None or norm(d_gn) <= delta:
pif('PROGRESS: Using gauss-newton solution')
d_dl = np.array(d_gn)
if delta is None:
delta = norm(d_gn)
else: # between cauchy step and gauss-newton step
pif('PROGRESS: between cauchy and gauss-newton')
# compute beta multiplier
delta_sq = delta**2
pnow = (
(d_gn-d_sd).T.dot(d_gn-d_sd)*delta_sq
+ d_gn.T.dot(d_sd)**2
- sqnorm(d_gn) * (sqnorm(d_sd)))
B = delta_sq - sqnorm(d_sd)
B /= ((d_gn-d_sd).T.dot(d_sd) + math.sqrt(pnow))
# apply step
d_dl = np.array(d_sd + float(B) * (d_gn - d_sd))
#assert(math.fabs(norm(d_dl) - delta) < 1e-12)
if norm(d_dl) <= e_2*norm(p):
pif('stopping because of small step size (norm_dl < %.2e)' % (e_2*norm(p)))
stop = True
else:
p_new = p + d_dl
tm_residuals = time.time()
obj.x = p_new
fevals += 1
r_trial = obj.r.copy()
tm_residuals = time.time() - tm
# rho is the ratio of...
# (improvement in SSE) / (predicted improvement in SSE)
# slower
#rho = norm(e_p)**2 - norm(e_p_trial)**2
#rho = rho / (L(d_dl*0, e_p, J) - L(d_dl, e_p, J))
# faster
sqnorm_ep = sqnorm(r)
rho = sqnorm_ep - norm(r_trial)**2
if rho > 0:
rho /= predicted_improvement(d_dl, -r, J, sqnorm_ep, A, g)
improvement_occurred = rho > 0
# if the objective function improved, update input parameter estimate.
# Note that the obj.x already has the new parms,
# and we should not set them again to the same (or we'll bust the cache)
if improvement_occurred:
p = col(p_new)
call_cb()
if (sqnorm_ep - norm(r_trial)**2) / sqnorm_ep < e_3:
stop = True
pif('stopping because improvement < %.1e%%' % (100*e_3,))
else: # Put the old parms back
obj.x = ch.Ch(p)
obj.on_changed('x') # copies from flat vector to free variables
# if the objective function improved and we're not done,
# get ready for the next iteration
if improvement_occurred and not stop:
tm_jac = time.time()
pif('computing Jacobian...')
J = obj.J.copy()
tm_jac = time.time() - tm_jac
pif('Jacobian (%dx%d) computed in %.2fs' % (J.shape[0], J.shape[1], tm_jac))
pif('Residuals+Jac computed in %.2fs' % (tm_jac + tm_residuals,))
tm = time.time()
pif('updating A and g...')
A = J.T.dot(J)
r = col(r_trial)
g = col(J.T.dot(-r))
pif('A and g updated in %.2fs' % (time.time() - tm))
if norm(g, np.inf) < e_1:
stop = True
pif('stopping because norm(g, np.inf) < %.2e' % (e_1))
# update our trust region
delta = updateRadius(rho, delta, d_dl)
if delta <= e_2*norm(p):
stop = True
pif('stopping because trust region is too small')
# the following "collect" is very expensive.
# please contact matt if you find situations where it actually helps things.
#import gc; gc.collect()
if stop or improvement_occurred or (fevals >= max_fevals):
break
if k >= k_max:
pif('stopping because max number of user-specified iterations (%d) has been met' % (k_max,))
elif fevals >= max_fevals:
pif('stopping because max number of user-specified func evals (%d) has been met' % (max_fevals,))
return obj.free_variables
def minimize(fun,
x0,
method='dogleg',
bounds=None,
constraints=(),
tol=None,
callback=None,
options=None):
if method == 'dogleg':
if options is None: options = {}
return minimize_dogleg(fun,
free_variables=x0,
on_step=callback,
**options)
if isinstance(fun, list) or isinstance(fun, tuple):
fun = ch.concatenate([f.ravel() for f in fun])
if isinstance(fun, dict):
fun = ch.concatenate([f.ravel() for f in fun.values()])
obj = fun
free_variables = x0
from ch import SumOfSquares
hessp = None
hess = None
if obj.size == 1:
obj_scalar = obj
else:
obj_scalar = SumOfSquares(obj)
def hessp(vs, p, obj, obj_scalar, free_variables):
changevars(vs, obj, obj_scalar, free_variables)
if not hasattr(hessp, 'vs'):
hessp.vs = vs * 0 + 1e16
if np.max(np.abs(vs - hessp.vs)) > 0:
J = ns_jacfunc(vs, obj, obj_scalar, free_variables)
hessp.J = J
hessp.H = 2. * J.T.dot(J)
hessp.vs = vs
return np.array(hessp.H.dot(p)).ravel()
#return 2*np.array(hessp.J.T.dot(hessp.J.dot(p))).ravel()
if method.lower() != 'newton-cg':
def hess(vs, obj, obj_scalar, free_variables):
changevars(vs, obj, obj_scalar, free_variables)
if not hasattr(hessp, 'vs'):
hessp.vs = vs * 0 + 1e16
if np.max(np.abs(vs - hessp.vs)) > 0:
J = ns_jacfunc(vs, obj, obj_scalar, free_variables)
hessp.H = 2. * J.T.dot(J)
return hessp.H
def changevars(vs, obj, obj_scalar, free_variables):
cur = 0
changed = False
for idx, freevar in enumerate(free_variables):
sz = freevar.r.size
newvals = vs[cur:cur + sz].copy().reshape(
free_variables[idx].shape)
if np.max(np.abs(newvals - free_variables[idx]).ravel()) > 0:
free_variables[idx][:] = newvals
changed = True
cur += sz
methods_without_callback = ('anneal', 'powell', 'cobyla', 'slsqp')
if callback is not None and changed and method.lower(
) in methods_without_callback:
callback(None)
return changed
def residuals(vs, obj, obj_scalar, free_variables):
changevars(vs, obj, obj_scalar, free_variables)
residuals = obj_scalar.r.ravel()[0]
return residuals
def scalar_jacfunc(vs, obj, obj_scalar, free_variables):
if not hasattr(scalar_jacfunc, 'vs'):
scalar_jacfunc.vs = vs * 0 + 1e16
if np.max(np.abs(vs - scalar_jacfunc.vs)) == 0:
return scalar_jacfunc.J
changevars(vs, obj, obj_scalar, free_variables)
if True: # faster, at least on some problems
result = np.concatenate([
np.array(obj_scalar.lop(wrt, np.array([[1]]))).ravel()
for wrt in free_variables
])
else:
jacs = [obj_scalar.dr_wrt(wrt) for wrt in free_variables]
for idx, jac in enumerate(jacs):
if sp.issparse(jac):
jacs[idx] = jacs[idx].todense()
result = np.concatenate([jac.ravel() for jac in jacs])
scalar_jacfunc.J = result
scalar_jacfunc.vs = vs
return result.ravel()
def ns_jacfunc(vs, obj, obj_scalar, free_variables):
if not hasattr(ns_jacfunc, 'vs'):
ns_jacfunc.vs = vs * 0 + 1e16
if np.max(np.abs(vs - ns_jacfunc.vs)) == 0:
return ns_jacfunc.J
changevars(vs, obj, obj_scalar, free_variables)
jacs = [obj.dr_wrt(wrt) for wrt in free_variables]
result = hstack(jacs)
ns_jacfunc.J = result
ns_jacfunc.vs = vs
return result
x1 = scipy.optimize.minimize(method=method,
fun=residuals,
callback=callback,
x0=np.concatenate([
free_variable.r.ravel()
for free_variable in free_variables
]),
jac=scalar_jacfunc,
hessp=hessp,
hess=hess,
args=(obj, obj_scalar, free_variables),
bounds=bounds,
constraints=constraints,
tol=tol,
options=options).x
changevars(x1, obj, obj_scalar, free_variables)
return free_variables
def _minimize_dogleg(obj, free_variables, on_step=None,
maxiter=200, max_fevals=np.inf, sparse_solver='spsolve',
disp=False, show_residuals=None, e_1=1e-15, e_2=1e-15, e_3=0., delta_0=None):
""""Nonlinear optimization using Powell's dogleg method.
See Lourakis et al, 2005, ICCV '05, "Is Levenberg-Marquardt
the Most Efficient Optimization for Implementing Bundle
Adjustment?":
http://www.ics.forth.gr/cvrl/publications/conferences/0201-P0401-lourakis-levenberg.pdf
"""
import warnings
if show_residuals is not None:
import warnings
warnings.warn('minimize_dogleg: show_residuals parm is deprecaed, pass a dict instead.')
labels = {}
if isinstance(obj, list) or isinstance(obj, tuple):
obj = ch.concatenate([f.ravel() for f in obj])
elif isinstance(obj, dict):
labels = obj
obj = ch.concatenate([f.ravel() for f in obj.values()])
niters = maxiter
verbose = disp
num_unique_ids = len(np.unique(np.array([id(freevar) for freevar in free_variables])))
if num_unique_ids != len(free_variables):
raise Exception('The "free_variables" param contains duplicate variables.')
obj = ChInputsStacked(obj=obj, free_variables=free_variables, x=np.concatenate([freevar.r.ravel() for freevar in free_variables]))
def call_cb():
if on_step is not None:
on_step(obj)
report_line = ""
if len(labels) > 0:
report_line += '%.2e | ' % (np.sum(obj.r**2),)
for label in sorted(labels.keys()):
objective = labels[label]
report_line += '%s: %.2e | ' % (label, np.sum(objective.r**2))
if len(labels) > 0:
report_line += '\n'
sys.stderr.write(report_line)
call_cb()
# pif = print-if-verbose.
# can't use "print" because it's a statement, not a fn
pif = lambda x: sys.stdout.write(x + '\n') if verbose else 0
if callable(sparse_solver):
solve = sparse_solver
elif isinstance(sparse_solver, str) and sparse_solver in _solver_fns.keys():
solve = _solver_fns[sparse_solver]
else:
raise Exception('sparse_solver argument must be either a string in the set (%s) or have the api of scipy.sparse.linalg.spsolve.' % ''.join(_solver_fns.keys(), ' '))
# optimization parms
k_max = niters
fevals = 0
k = 0
delta = delta_0
p = col(obj.x.r)
fevals += 1
tm = time.time()
pif('computing Jacobian...')
J = obj.J
if sp.issparse(J):
assert(J.nnz > 0)
pif('Jacobian (%dx%d) computed in %.2fs' % (J.shape[0], J.shape[1], time.time() - tm))
if J.shape[1] != p.size:
import pdb; pdb.set_trace()
assert(J.shape[1] == p.size)
tm = time.time()
pif('updating A and g...')
A = J.T.dot(J)
r = col(obj.r.copy())
g = col(J.T.dot(-r))
pif('A and g updated in %.2fs' % (time.time() - tm))
stop = norm(g, np.inf) < e_1
while (not stop) and (k < k_max) and (fevals < max_fevals):
k += 1
pif('beginning iteration %d' % (k,))
d_sd = col((sqnorm(g)) / (sqnorm (J.dot(g))) * g)
GNcomputed = False
while True:
# if the Cauchy point is outside the trust region,
# take that direction but only to the edge of the trust region
if delta is not None and norm(d_sd) >= delta:
pif('PROGRESS: Using stunted cauchy')
d_dl = np.array(col(delta/norm(d_sd) * d_sd))
else:
if not GNcomputed:
tm = time.time()
if scipy.sparse.issparse(A):
A.eliminate_zeros()
pif('sparse solve...sparsity infill is %.3f%% (hessian %dx%d), J infill %.3f%%' % (
100. * A.nnz / (A.shape[0] * A.shape[1]),
A.shape[0],
A.shape[1],
100. * J.nnz / (J.shape[0] * J.shape[1])))
if g.size > 1:
d_gn = col(solve(A, g))
if np.any(np.isnan(d_gn)) or np.any(np.isinf(d_gn)):
from scipy.sparse.linalg import lsqr
d_gn = col(lsqr(A, g)[0])
else:
d_gn = np.atleast_1d(g.ravel()[0]/A[0,0])
pif('sparse solve...done in %.2fs' % (time.time() - tm))
else:
pif('dense solve...')
try:
d_gn = col(np.linalg.solve(A, g))
except Exception:
d_gn = col(np.linalg.lstsq(A, g)[0])
pif('dense solve...done in %.2fs' % (time.time() - tm))
GNcomputed = True
# if the gauss-newton solution is within the trust region, use it
if delta is None or norm(d_gn) <= delta:
pif('PROGRESS: Using gauss-newton solution')
d_dl = np.array(d_gn)
if delta is None:
delta = norm(d_gn)
else: # between cauchy step and gauss-newton step
pif('PROGRESS: between cauchy and gauss-newton')
# compute beta multiplier
delta_sq = delta**2
pnow = (
(d_gn-d_sd).T.dot(d_gn-d_sd)*delta_sq
+ d_gn.T.dot(d_sd)**2
- sqnorm(d_gn) * (sqnorm(d_sd)))
B = delta_sq - sqnorm(d_sd)
B /= ((d_gn-d_sd).T.dot(d_sd) + math.sqrt(pnow))
# apply step
d_dl = np.array(d_sd + float(B) * (d_gn - d_sd))
#assert(math.fabs(norm(d_dl) - delta) < 1e-12)
if norm(d_dl) <= e_2*norm(p):
pif('stopping because of small step size (norm_dl < %.2e)' % (e_2*norm(p)))
stop = True
else:
p_new = p + d_dl
tm_residuals = time.time()
obj.x = p_new
fevals += 1
r_trial = obj.r.copy()
tm_residuals = time.time() - tm
# rho is the ratio of...
# (improvement in SSE) / (predicted improvement in SSE)
# slower
#rho = norm(e_p)**2 - norm(e_p_trial)**2
#rho = rho / (L(d_dl*0, e_p, J) - L(d_dl, e_p, J))
# faster
sqnorm_ep = sqnorm(r)
rho = sqnorm_ep - norm(r_trial)**2
with warnings.catch_warnings():
warnings.filterwarnings('ignore',category=RuntimeWarning)
if rho > 0:
rho /= predicted_improvement(d_dl, -r, J, sqnorm_ep, A, g)
improvement_occurred = rho > 0
# if the objective function improved, update input parameter estimate.
# Note that the obj.x already has the new parms,
# and we should not set them again to the same (or we'll bust the cache)
if improvement_occurred:
p = col(p_new)
call_cb()
if (sqnorm_ep - norm(r_trial)**2) / sqnorm_ep < e_3:
stop = True
pif('stopping because improvement < %.1e%%' % (100*e_3,))
else: # Put the old parms back
obj.x = ch.Ch(p)
obj.on_changed('x') # copies from flat vector to free variables
# if the objective function improved and we're not done,
# get ready for the next iteration
if improvement_occurred and not stop:
tm_jac = time.time()
pif('computing Jacobian...')
J = obj.J.copy()
tm_jac = time.time() - tm_jac
pif('Jacobian (%dx%d) computed in %.2fs' % (J.shape[0], J.shape[1], tm_jac))
pif('Residuals+Jac computed in %.2fs' % (tm_jac + tm_residuals,))
tm = time.time()
pif('updating A and g...')
A = J.T.dot(J)
r = col(r_trial)
g = col(J.T.dot(-r))
pif('A and g updated in %.2fs' % (time.time() - tm))
if norm(g, np.inf) < e_1:
stop = True
pif('stopping because norm(g, np.inf) < %.2e' % (e_1))
# update our trust region
delta = updateRadius(rho, delta, d_dl)
if delta <= e_2*norm(p):
stop = True
pif('stopping because trust region is too small')
# the following "collect" is very expensive.
# please contact matt if you find situations where it actually helps things.
#import gc; gc.collect()
if stop or improvement_occurred or (fevals >= max_fevals):
break
if k >= k_max:
pif('stopping because max number of user-specified iterations (%d) has been met' % (k_max,))
elif fevals >= max_fevals:
pif('stopping because max number of user-specified func evals (%d) has been met' % (max_fevals,))
return obj.free_variables
