def lmdif(fcn, x0, xmin, xmax, ftol=EPSILON, xtol=EPSILON, gtol=EPSILON,
maxfev=None, epsfcn=EPSILON, factor=100.0, verbose=0):
def par_at_boundary( low, val, high, tol ):
for par_min, par_val, par_max in izip( low, val, high ):
if sao_fcmp( par_val, par_min, tol ) == 0:
return True
if sao_fcmp( par_val, par_max, tol ) == 0:
return True
return False
x, xmin, xmax = _check_args(x0, xmin, xmax)
if maxfev is None:
maxfev = 256 * len(x)
def stat_cb0( pars ):
return fcn( pars )[ 0 ]
def stat_cb1( pars ):
return fcn( pars )[ 1 ]
m = numpy.asanyarray(stat_cb1(x)).size
orig_fcn = stat_cb1
error = []
def stat_cb1(x_new, iflag):
fvec = None
try:
## if _outside_limits(x_new, xmin, xmax) or _my_is_nan(x_new):
## fvec = numpy.empty((m,), x_new.dtype, numpy.isfortran(x_new))
## fvec[:] = numpy.sqrt(FUNC_MAX / m)
## else:
fvec = orig_fcn(x_new)
except:
error.append(sys.exc_info()[1])
fvec = numpy.zeros((m,), x_new.dtype, numpy.isfortran(x_new))
iflag = -1
return fvec, iflag
info, nfev, fval, covarerr = _minpack.mylmdif(stat_cb1, m, x, ftol, xtol, gtol, maxfev, epsfcn, factor, verbose, xmin, xmax)
if par_at_boundary( xmin, x, xmax, xtol ):
nm_result = neldermead( fcn, x, xmin, xmax, ftol=numpy.sqrt(ftol), maxfev=maxfev-nfev, finalsimplex=2, iquad=0, verbose=0 )
nfev += nm_result[ 4 ][ 'nfev' ]
x = nm_result[ 1 ]
fval = nm_result[ 2 ]
## if nm_result[ 2 ] < fval:
## x = nm_result[ 1 ]
## fval = nm_result[ 2 ]
## info, mynfev, fval, covarerr = _minpack.mylmdif(stat_cb1, m, x, ftol, xtol, gtol, maxfev-nfev, epsfcn, factor, verbose, xmin, xmax)
## nfev += mynfev
if error:
raise error.pop()
key = {
0: (False, 'improper input parameters'),
1: (True,
('both actual and predicted relative reductions in the sum ' +
'of squares are at most ftol=%g') % ftol),
2: (True,
('relative error between two consecutive iterates is at ' +
'most xtol=%g') % xtol),
4: (True,
('the cosine of the angle between fvec and any column of ' +
'the jacobian is at most gtol=%g in absolute value') % gtol),
5: (False,
('number of calls to function has reached or exceeded '+
'maxfev=%d') % maxfev),
6: (False,
('ftol=%g is too small; no further reduction in the sum of ' +
'squares is possible') % ftol),
7: (False,
('xtol=%g is too small; no further improvement in the ' +
'approximate solution is possible') % xtol),
8: (False,
('gtol=%g is too small; fvec is orthogonal to the columns ' +
'of the jacobian to machine precision') % gtol),
}
key[3] = (True, key[1][1] + ' and ' + key[2][1])
status, msg = key.get(info, (False, 'unknown status flag (%d)' % info))
if 0 == info:
info = 1
elif info >= 1 or info <= 4:
info = 0
else:
info = 3
status, msg = _get_saofit_msg( maxfev, info )
rv = (status, x, fval)
print_covar_err = False
if print_covar_err:
rv += (msg, {'info': info, 'nfev': nfev, 'covarerr': covarerr})
else:
rv += (msg, {'info': info, 'nfev': nfev})
return rv
def lmdif(fcn, x0, xmin, xmax, ftol=EPSILON, xtol=EPSILON, gtol=EPSILON,
maxfev=None, epsfcn=EPSILON, factor=100.0, verbose=0):
x, xmin, xmax = _check_args(x0, xmin, xmax)
if maxfev is None:
maxfev = 256 * len(x)
def stat_cb0( pars ):
return fcn( pars )[ 0 ]
def stat_cb1( pars ):
return fcn( pars )[ 1 ]
m = numpy.asanyarray(stat_cb1(x)).size
orig_fcn = stat_cb1
error = []
def stat_cb1(x_new, iflag):
fvec = None
try:
## if _outside_limits(x_new, xmin, xmax) or _my_is_nan(x_new):
## fvec = numpy.empty((m,), x_new.dtype, numpy.isfortran(x_new))
## fvec[:] = numpy.sqrt(FUNC_MAX / m)
## else:
fvec = orig_fcn(x_new)
except:
error.append(sys.exc_info()[1])
fvec = numpy.zeros((m,), x_new.dtype, numpy.isfortran(x_new))
iflag = -1
return fvec, iflag
def myfdjac( myx, myfvec, myfjac, myxmax, myiflag, myepsfcn ):
eps = numpy.sqrt( max( [ EPSILON, myepsfcn ] ) )
n = len( myx )
def fdjac( xxx, origxxx, fvec, upbound, iflag, epsilon ):
myn = len( xxx )
diffs = []
for jj in xrange( myn ):
temp = xxx[ jj ]
h = epsilon * abs( xxx[ jj ] )
if 0.0 == h:
h = eps
if xxx[ jj ] + h > upbound[ jj ]:
h = - h
xxx[ jj ] = temp + h
wa, iflag = fcn( origxxx, iflag )
xxx[ jj ] = temp
diff = ( wa - fvec ) / h
diff = numpy.append( diff, iflag )
diffs.append( diff )
return diffs
diffs = divide_run_parallel( fdjac, myx, myx, myfvec, myxmax, myiflag, eps )
for jj in range( n ):
myfjac[ 0:, jj ] = diffs[ jj ][:-1].copy()
if diffs[ jj ][ -1 ] < 0:
myiflag = diffs[ jj ][ -1 ]
return myiflag, myfjac
multicore = 0
info, nfev, fval, covarerr = _minpack.mylmdif(stat_cb1, m, x, ftol, xtol, gtol, maxfev, epsfcn, factor, verbose, xmin, xmax, multicore, myfdjac)
if error:
raise error.pop()
key = {
0: (False, 'improper input parameters'),
1: (True,
('both actual and predicted relative reductions in the sum ' +
'of squares are at most ftol=%g') % ftol),
2: (True,
('relative error between two consecutive iterates is at ' +
'most xtol=%g') % xtol),
4: (True,
('the cosine of the angle between fvec and any column of ' +
'the jacobian is at most gtol=%g in absolute value') % gtol),
5: (False,
('number of calls to function has reached or exceeded '+
'maxfev=%d') % maxfev),
6: (False,
('ftol=%g is too small; no further reduction in the sum of ' +
'squares is possible') % ftol),
7: (False,
('xtol=%g is too small; no further improvement in the ' +
'approximate solution is possible') % xtol),
8: (False,
('gtol=%g is too small; fvec is orthogonal to the columns ' +
'of the jacobian to machine precision') % gtol),
}
key[3] = (True, key[1][1] + ' and ' + key[2][1])
status, msg = key.get(info, (False, 'unknown status flag (%d)' % info))
if 0 == info:
info = 1
elif info >= 1 or info <= 4:
info = 0
else:
info = 3
status, msg = _get_saofit_msg( maxfev, info )
rv = (status, x, fval)
print_covar_err = False
if print_covar_err:
rv += (msg, {'info': info, 'nfev': nfev, 'covarerr': covarerr})
else:
rv += (msg, {'info': info, 'nfev': nfev})
return rv
def lmdif(fcn, x0, xmin, xmax, ftol=EPSILON, xtol=EPSILON, gtol=EPSILON,
maxfev=None, epsfcn=EPSILON, factor=100.0, verbose=0):
def par_at_boundary( low, val, high, tol ):
for par_min, par_val, par_max in izip( low, val, high ):
if sao_fcmp( par_val, par_min, tol ) == 0:
return True
if sao_fcmp( par_val, par_max, tol ) == 0:
return True
return False
x, xmin, xmax = _check_args(x0, xmin, xmax)
if maxfev is None:
maxfev = 256 * len(x)
def stat_cb0( pars ):
return fcn( pars )[ 0 ]
def stat_cb1( pars ):
return fcn( pars )[ 1 ]
m = numpy.asanyarray(stat_cb1(x)).size
orig_fcn = stat_cb1
error = []
def stat_cb1(x_new, iflag):
fvec = None
try:
## if _outside_limits(x_new, xmin, xmax) or _my_is_nan(x_new):
## fvec = numpy.empty((m,), x_new.dtype, numpy.isfortran(x_new))
## fvec[:] = numpy.sqrt(FUNC_MAX / m)
## else:
fvec = orig_fcn(x_new)
except:
error.append(sys.exc_info()[1])
fvec = numpy.zeros((m,), x_new.dtype, numpy.isfortran(x_new))
iflag = -1
return fvec, iflag
info, nfev, fval, covarerr = _minpack.mylmdif(stat_cb1, m, x, ftol, xtol, gtol, maxfev, epsfcn, factor, verbose, xmin, xmax)
if par_at_boundary( xmin, x, xmax, xtol ):
nm_result = neldermead( fcn, x, xmin, xmax, ftol=numpy.sqrt(ftol), maxfev=maxfev-nfev, finalsimplex=2, iquad=0, verbose=0 )
nfev += nm_result[ 4 ][ 'nfev' ]
x = nm_result[ 1 ]
fval = nm_result[ 2 ]
## if nm_result[ 2 ] < fval:
## x = nm_result[ 1 ]
## fval = nm_result[ 2 ]
## info, mynfev, fval, covarerr = _minpack.mylmdif(stat_cb1, m, x, ftol, xtol, gtol, maxfev-nfev, epsfcn, factor, verbose, xmin, xmax)
## nfev += mynfev
if error:
raise error.pop()
key = {
0: (False, 'improper input parameters'),
1: (True,
('both actual and predicted relative reductions in the sum ' +
'of squares are at most ftol=%g') % ftol),
2: (True,
('relative error between two consecutive iterates is at ' +
'most xtol=%g') % xtol),
4: (True,
('the cosine of the angle between fvec and any column of ' +
'the jacobian is at most gtol=%g in absolute value') % gtol),
5: (False,
('number of calls to function has reached or exceeded '+
'maxfev=%d') % maxfev),
6: (False,
('ftol=%g is too small; no further reduction in the sum of ' +
'squares is possible') % ftol),
7: (False,
('xtol=%g is too small; no further improvement in the ' +
'approximate solution is possible') % xtol),
8: (False,
('gtol=%g is too small; fvec is orthogonal to the columns ' +
'of the jacobian to machine precision') % gtol),
}
key[3] = (True, key[1][1] + ' and ' + key[2][1])
status, msg = key.get(info, (False, 'unknown status flag (%d)' % info))
if 0 == info:
info = 1
elif info >= 1 or info <= 4:
info = 0
else:
info = 3
status, msg = _get_saofit_msg( maxfev, info )
rv = (status, x, fval)
print_covar_err = False
if print_covar_err:
rv += (msg, {'info': info, 'nfev': nfev, 'covarerr': covarerr})
else:
rv += (msg, {'info': info, 'nfev': nfev})
return rv
