Python scipy.optimize.fmin_ncg() Examples
The following are 11
code examples of scipy.optimize.fmin_ncg().
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Example #1
| Source File: optimizer.py From vnpy_crypto with MIT License | 5 votes |
|
def _fit_ncg(f, score, start_params, fargs, kwargs, disp=True,
maxiter=100, callback=None, retall=False,
full_output=True, hess=None):
fhess_p = kwargs.setdefault('fhess_p', None)
avextol = kwargs.setdefault('avextol', 1.0000000000000001e-05)
epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08)
retvals = optimize.fmin_ncg(f, start_params, score, fhess_p=fhess_p,
fhess=hess, args=fargs, avextol=avextol,
epsilon=epsilon, maxiter=maxiter,
full_output=full_output, disp=disp,
retall=retall, callback=callback)
if full_output:
if not retall:
xopt, fopt, fcalls, gcalls, hcalls, warnflag = retvals
else:
xopt, fopt, fcalls, gcalls, hcalls, warnflag, allvecs =\
retvals
converged = not warnflag
retvals = {'fopt': fopt, 'fcalls': fcalls, 'gcalls': gcalls,
'hcalls': hcalls, 'warnflag': warnflag,
'converged': converged}
if retall:
retvals.update({'allvecs': allvecs})
else:
xopt = retvals
retvals = None
return xopt, retvals
Example #2
| Source File: gmm.py From vnpy_crypto with MIT License | 5 votes |
|
def fitgmm_cu(self, start, optim_method='bfgs', optim_args=None):
'''estimate parameters using continuously updating GMM
Parameters
----------
start : array_like
starting values for minimization
Returns
-------
paramest : array
estimated parameters
Notes
-----
todo: add fixed parameter option, not here ???
uses scipy.optimize.fmin
'''
## if not fixed is None: #fixed not defined in this version
## raise NotImplementedError
if optim_args is None:
optim_args = {}
if optim_method == 'nm':
optimizer = optimize.fmin
elif optim_method == 'bfgs':
optimizer = optimize.fmin_bfgs
optim_args['fprime'] = self.score_cu
elif optim_method == 'ncg':
optimizer = optimize.fmin_ncg
else:
raise ValueError('optimizer method not available')
#TODO: add other optimization options and results
return optimizer(self.gmmobjective_cu, start, args=(), **optim_args)
Example #3
| Source File: test_optimize.py From Computable with MIT License | 5 votes |
|
def test_ncg(self, use_wrapper=False):
""" line-search Newton conjugate gradient optimization routine
"""
if use_wrapper:
opts = {'maxiter': self.maxiter, 'disp': False,
'return_all': False}
retval = optimize.minimize(self.func, self.startparams,
method='Newton-CG', jac=self.grad,
args=(), options=opts)['x']
else:
retval = optimize.fmin_ncg(self.func, self.startparams, self.grad,
args=(), maxiter=self.maxiter,
full_output=False, disp=False,
retall=False)
params = retval
assert_allclose(self.func(params), self.func(self.solution),
atol=1e-6)
# Ensure that function call counts are 'known good'; these are from
# Scipy 0.7.0. Don't allow them to increase.
assert_(self.funccalls == 7, self.funccalls)
assert_(self.gradcalls <= 22, self.gradcalls) # 0.13.0
#assert_(self.gradcalls <= 18, self.gradcalls) # 0.9.0
#assert_(self.gradcalls == 18, self.gradcalls) # 0.8.0
#assert_(self.gradcalls == 22, self.gradcalls) # 0.7.0
# Ensure that the function behaves the same; this is from Scipy 0.7.0
assert_allclose(self.trace[3:5],
[[-4.35700753e-07, -5.24869435e-01, 4.87527480e-01],
[-4.35700753e-07, -5.24869401e-01, 4.87527774e-01]],
atol=1e-6, rtol=1e-7)
Example #4
| Source File: test_optimize.py From Computable with MIT License | 5 votes |
|
def test_ncg_hess(self, use_wrapper=False):
""" Newton conjugate gradient with Hessian """
if use_wrapper:
opts = {'maxiter': self.maxiter, 'disp': False,
'return_all': False}
retval = optimize.minimize(self.func, self.startparams,
method='Newton-CG', jac=self.grad,
hess=self.hess,
args=(), options=opts)['x']
else:
retval = optimize.fmin_ncg(self.func, self.startparams, self.grad,
fhess=self.hess,
args=(), maxiter=self.maxiter,
full_output=False, disp=False,
retall=False)
params = retval
assert_allclose(self.func(params), self.func(self.solution),
atol=1e-6)
# Ensure that function call counts are 'known good'; these are from
# Scipy 0.7.0. Don't allow them to increase.
assert_(self.funccalls == 7, self.funccalls)
assert_(self.gradcalls <= 18, self.gradcalls) # 0.9.0
# assert_(self.gradcalls == 18, self.gradcalls) # 0.8.0
# assert_(self.gradcalls == 22, self.gradcalls) # 0.7.0
# Ensure that the function behaves the same; this is from Scipy 0.7.0
assert_allclose(self.trace[3:5],
[[-4.35700753e-07, -5.24869435e-01, 4.87527480e-01],
[-4.35700753e-07, -5.24869401e-01, 4.87527774e-01]],
atol=1e-6, rtol=1e-7)
Example #5
| Source File: test_optimize.py From Computable with MIT License | 5 votes |
|
def test_ncg_hessp(self, use_wrapper=False):
""" Newton conjugate gradient with Hessian times a vector p """
if use_wrapper:
opts = {'maxiter': self.maxiter, 'disp': False,
'return_all': False}
retval = optimize.minimize(self.func, self.startparams,
method='Newton-CG', jac=self.grad,
hessp=self.hessp,
args=(), options=opts)['x']
else:
retval = optimize.fmin_ncg(self.func, self.startparams, self.grad,
fhess_p=self.hessp,
args=(), maxiter=self.maxiter,
full_output=False, disp=False,
retall=False)
params = retval
assert_allclose(self.func(params), self.func(self.solution),
atol=1e-6)
# Ensure that function call counts are 'known good'; these are from
# Scipy 0.7.0. Don't allow them to increase.
assert_(self.funccalls == 7, self.funccalls)
assert_(self.gradcalls <= 18, self.gradcalls) # 0.9.0
# assert_(self.gradcalls == 18, self.gradcalls) # 0.8.0
# assert_(self.gradcalls == 22, self.gradcalls) # 0.7.0
# Ensure that the function behaves the same; this is from Scipy 0.7.0
assert_allclose(self.trace[3:5],
[[-4.35700753e-07, -5.24869435e-01, 4.87527480e-01],
[-4.35700753e-07, -5.24869401e-01, 4.87527774e-01]],
atol=1e-6, rtol=1e-7)
Example #6
| Source File: test_optimize.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_ncg(self):
# line-search Newton conjugate gradient optimization routine
if self.use_wrapper:
opts = {'maxiter': self.maxiter, 'disp': self.disp,
'return_all': False}
retval = optimize.minimize(self.func, self.startparams,
method='Newton-CG', jac=self.grad,
args=(), options=opts)['x']
else:
retval = optimize.fmin_ncg(self.func, self.startparams, self.grad,
args=(), maxiter=self.maxiter,
full_output=False, disp=self.disp,
retall=False)
params = retval
assert_allclose(self.func(params), self.func(self.solution),
atol=1e-6)
# Ensure that function call counts are 'known good'; these are from
# Scipy 0.7.0. Don't allow them to increase.
assert_(self.funccalls == 7, self.funccalls)
assert_(self.gradcalls <= 22, self.gradcalls) # 0.13.0
#assert_(self.gradcalls <= 18, self.gradcalls) # 0.9.0
#assert_(self.gradcalls == 18, self.gradcalls) # 0.8.0
#assert_(self.gradcalls == 22, self.gradcalls) # 0.7.0
# Ensure that the function behaves the same; this is from Scipy 0.7.0
assert_allclose(self.trace[3:5],
[[-4.35700753e-07, -5.24869435e-01, 4.87527480e-01],
[-4.35700753e-07, -5.24869401e-01, 4.87527774e-01]],
atol=1e-6, rtol=1e-7)
Example #7
| Source File: test_optimize.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_ncg_hess(self):
# Newton conjugate gradient with Hessian
if self.use_wrapper:
opts = {'maxiter': self.maxiter, 'disp': self.disp,
'return_all': False}
retval = optimize.minimize(self.func, self.startparams,
method='Newton-CG', jac=self.grad,
hess=self.hess,
args=(), options=opts)['x']
else:
retval = optimize.fmin_ncg(self.func, self.startparams, self.grad,
fhess=self.hess,
args=(), maxiter=self.maxiter,
full_output=False, disp=self.disp,
retall=False)
params = retval
assert_allclose(self.func(params), self.func(self.solution),
atol=1e-6)
# Ensure that function call counts are 'known good'; these are from
# Scipy 0.7.0. Don't allow them to increase.
assert_(self.funccalls == 7, self.funccalls)
assert_(self.gradcalls <= 18, self.gradcalls) # 0.9.0
# assert_(self.gradcalls == 18, self.gradcalls) # 0.8.0
# assert_(self.gradcalls == 22, self.gradcalls) # 0.7.0
# Ensure that the function behaves the same; this is from Scipy 0.7.0
assert_allclose(self.trace[3:5],
[[-4.35700753e-07, -5.24869435e-01, 4.87527480e-01],
[-4.35700753e-07, -5.24869401e-01, 4.87527774e-01]],
atol=1e-6, rtol=1e-7)
Example #8
| Source File: test_optimize.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def test_ncg_hessp(self):
# Newton conjugate gradient with Hessian times a vector p.
if self.use_wrapper:
opts = {'maxiter': self.maxiter, 'disp': self.disp,
'return_all': False}
retval = optimize.minimize(self.func, self.startparams,
method='Newton-CG', jac=self.grad,
hessp=self.hessp,
args=(), options=opts)['x']
else:
retval = optimize.fmin_ncg(self.func, self.startparams, self.grad,
fhess_p=self.hessp,
args=(), maxiter=self.maxiter,
full_output=False, disp=self.disp,
retall=False)
params = retval
assert_allclose(self.func(params), self.func(self.solution),
atol=1e-6)
# Ensure that function call counts are 'known good'; these are from
# Scipy 0.7.0. Don't allow them to increase.
assert_(self.funccalls == 7, self.funccalls)
assert_(self.gradcalls <= 18, self.gradcalls) # 0.9.0
# assert_(self.gradcalls == 18, self.gradcalls) # 0.8.0
# assert_(self.gradcalls == 22, self.gradcalls) # 0.7.0
# Ensure that the function behaves the same; this is from Scipy 0.7.0
assert_allclose(self.trace[3:5],
[[-4.35700753e-07, -5.24869435e-01, 4.87527480e-01],
[-4.35700753e-07, -5.24869401e-01, 4.87527774e-01]],
atol=1e-6, rtol=1e-7)
Example #9
| Source File: optimizer.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def _fit_ncg(f, score, start_params, fargs, kwargs, disp=True,
maxiter=100, callback=None, retall=False,
full_output=True, hess=None):
fhess_p = kwargs.setdefault('fhess_p', None)
avextol = kwargs.setdefault('avextol', 1.0000000000000001e-05)
epsilon = kwargs.setdefault('epsilon', 1.4901161193847656e-08)
retvals = optimize.fmin_ncg(f, start_params, score, fhess_p=fhess_p,
fhess=hess, args=fargs, avextol=avextol,
epsilon=epsilon, maxiter=maxiter,
full_output=full_output, disp=disp,
retall=retall, callback=callback)
if full_output:
if not retall:
xopt, fopt, fcalls, gcalls, hcalls, warnflag = retvals
else:
xopt, fopt, fcalls, gcalls, hcalls, warnflag, allvecs =\
retvals
converged = not warnflag
retvals = {'fopt': fopt, 'fcalls': fcalls, 'gcalls': gcalls,
'hcalls': hcalls, 'warnflag': warnflag,
'converged': converged}
if retall:
retvals.update({'allvecs': allvecs})
else:
xopt = retvals
retvals = None
return xopt, retvals
Example #10
| Source File: gmm.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def fitgmm_cu(self, start, optim_method='bfgs', optim_args=None):
'''estimate parameters using continuously updating GMM
Parameters
----------
start : array_like
starting values for minimization
Returns
-------
paramest : array
estimated parameters
Notes
-----
todo: add fixed parameter option, not here ???
uses scipy.optimize.fmin
'''
## if not fixed is None: #fixed not defined in this version
## raise NotImplementedError
if optim_args is None:
optim_args = {}
if optim_method == 'nm':
optimizer = optimize.fmin
elif optim_method == 'bfgs':
optimizer = optimize.fmin_bfgs
optim_args['fprime'] = self.score_cu
elif optim_method == 'ncg':
optimizer = optimize.fmin_ncg
else:
raise ValueError('optimizer method not available')
#TODO: add other optimization options and results
return optimizer(self.gmmobjective_cu, start, args=(), **optim_args)
Example #11
| Source File: test_optimize.py From GraphicDesignPatternByPython with MIT License | 4 votes |
|
def test_minimize_callback_copies_array(self, method):
# Check that arrays passed to callbacks are not modified
# inplace by the optimizer afterward
if method in ('fmin_tnc', 'fmin_l_bfgs_b'):
func = lambda x: (optimize.rosen(x), optimize.rosen_der(x))
else:
func = optimize.rosen
jac = optimize.rosen_der
hess = optimize.rosen_hess
x0 = np.zeros(10)
# Set options
kwargs = {}
if method.startswith('fmin'):
routine = getattr(optimize, method)
if method == 'fmin_slsqp':
kwargs['iter'] = 5
elif method == 'fmin_tnc':
kwargs['maxfun'] = 100
else:
kwargs['maxiter'] = 5
else:
def routine(*a, **kw):
kw['method'] = method
return optimize.minimize(*a, **kw)
if method == 'TNC':
kwargs['options'] = dict(maxiter=100)
else:
kwargs['options'] = dict(maxiter=5)
if method in ('fmin_ncg',):
kwargs['fprime'] = jac
elif method in ('Newton-CG',):
kwargs['jac'] = jac
elif method in ('trust-krylov', 'trust-exact', 'trust-ncg', 'dogleg',
'trust-constr'):
kwargs['jac'] = jac
kwargs['hess'] = hess
# Run with callback
results = []
def callback(x, *args, **kwargs):
results.append((x, np.copy(x)))
sol = routine(func, x0, callback=callback, **kwargs)
# Check returned arrays coincide with their copies and have no memory overlap
assert_(len(results) > 2)
assert_(all(np.all(x == y) for x, y in results))
assert_(not any(np.may_share_memory(x[0], y[0]) for x, y in itertools.combinations(results, 2)))
