def _par_at_boundary(low, val, high, tol):
for par_min, par_val, par_max in zip(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
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
def test_calc_sao_fcmp(tolerance, expected):
"""
this test was created using sherpa 4.8.1 (2507414) as an oracle for the python 3 migration
py3-todo: what is the difference between sao_fcmp and gsl_fcmp?
"""
assert_array_equal(expected, _utils.sao_fcmp([0.12345, 0.54321], [0.12346, 0.54320], tolerance))
def assertEqualWithinTol(self, first, second, tol=1e-7, msg=None):
"""Check that the values are equal within an absolute tolerance.
Parameters
----------
first : number or array_like
The expected value, or values.
second : number or array_like
The value, or values, to check. If first is an array, then
second must be an array of the same size. If first is
a scalar then second can be a scalar or an array.
tol : number
The absolute tolerance used for comparison.
msg : string
The message to display if the check fails.
"""
self.assertFalse(numpy.any(sao_fcmp(first, second, tol)), msg)
def assertEqualWithinTol(self, first, second, tol=1e-7, msg=None):
"""Check that the values are equal within an absolute tolerance.
Parameters
----------
first : number or array_like
The expected value, or values.
second : number or array_like
The value, or values, to check. If first is an array, then
second must be an array of the same size. If first is
a scalar then second can be a scalar or an array.
tol : number
The absolute tolerance used for comparison.
msg : string
The message to display if the check fails.
"""
self.assertFalse(numpy.any(sao_fcmp(first, second, tol)), msg)
def myopt( myfcn, xxx, ftol, maxfev, seed, pop, xprob,
weight, factor=4.0, debug=False ):
x = xxx[ 0 ]
xmin = xxx[ 1 ]
xmax = xxx[ 2 ]
maxfev_per_iter = 512 * x.size
def random_start( xmin, xmax ):
xx = []
for ii in range( len(xmin ) ):
xx.append( random.uniform( xmin[ ii ], xmax[ ii ] ) )
return numpy.asarray( xx )
############################# NelderMead #############################
mymaxfev = min( maxfev_per_iter, maxfev )
if all( x == 0.0 ):
mystep = map( lambda fubar: 1.2 + fubar, x )
else:
mystep = map( lambda fubar: 1.2 * fubar, x )
result = neldermead( myfcn, x, xmin, xmax, maxfev=mymaxfev, ftol=ftol,
finalsimplex=9, step=mystep )
x = numpy.asarray( result[ 1 ], numpy.float_ )
nfval = result[2]
nfev = result[4].get( 'nfev' )
if verbose or False != debug:
print 'f_nm%s=%.14e in %d nfev' % ( x, nfval, nfev )
############################# NelderMead #############################
############################## nmDifEvo ##############################
xmin, xmax = _narrow_limits( 4 * factor, [x,xmin,xmax], debug=False )
mymaxfev = min( maxfev_per_iter, maxfev - nfev )
result = difevo_nm( myfcn, x, xmin, xmax, ftol=ftol, maxfev=mymaxfev,
seed=seed, population_size=pop, xprob=xprob,
weighting_factor=weight )
nfev += result[4].get( 'nfev' )
x = numpy.asarray( result[1], numpy.float_ )
nfval = result[2]
if verbose or False != debug:
print 'f_de_nm%s=%.14e in %d nfev' % ( x, result[2],
result[4].get('nfev'))
############################## nmDifEvo ##############################
ofval = FUNC_MAX
while nfev < maxfev:
xmin, xmax = _narrow_limits( factor, [x,xmin,xmax], debug=False )
############################ nmDifEvo #############################
y = random_start( xmin, xmax )
mymaxfev = min( maxfev_per_iter, maxfev - nfev )
result = difevo_nm( myfcn, y, xmin, xmax, ftol=ftol,
maxfev=mymaxfev, seed=seed,
population_size=pop, xprob=xprob,
weighting_factor=weight )
nfev += result[4].get( 'nfev' )
if result[2] < nfval:
nfval = result[2]
x = numpy.asarray( result[1], numpy.float_ )
if verbose or False != debug:
print 'f_de_nm%s=%.14e in %d nfev' % \
( x, result[2], result[4].get('nfev'))
############################ nmDifEvo #############################
if False != debug:
print 'ofval=%.14e\tnfval=%.14e\n' % (ofval, nfval)
if sao_fcmp( ofval, nfval, ftol ) <= 0:
return x, nfval, nfev
ofval = nfval
factor *= 2
return x, nfval, nfev
def myopt(myfcn,
xxx,
ftol,
maxfev,
seed,
pop,
xprob,
weight,
factor=4.0,
debug=False):
x = xxx[0]
xmin = xxx[1]
xmax = xxx[2]
maxfev_per_iter = 512 * x.size
def random_start(xmin, xmax):
xx = []
for ii in range(len(xmin)):
xx.append(random.uniform(xmin[ii], xmax[ii]))
return numpy.asarray(xx)
############################# NelderMead #############################
mymaxfev = min(maxfev_per_iter, maxfev)
if all(x == 0.0):
mystep = list(map(lambda fubar: 1.2 + fubar, x))
else:
mystep = list(map(lambda fubar: 1.2 * fubar, x))
if 1 == numcores:
result = neldermead(myfcn,
x,
xmin,
xmax,
maxfev=mymaxfev,
ftol=ftol,
finalsimplex=9,
step=mystep)
x = numpy.asarray(result[1], numpy.float_)
nfval = result[2]
nfev = result[4].get('nfev')
else:
ncores_nm = ncoresNelderMead()
nfev, nfval, x = \
ncores_nm(stat_cb0, x, xmin, xmax, ftol, mymaxfev, numcores)
if verbose or debug != False:
print('f_nm%s=%.14e in %d nfev' % (x, nfval, nfev))
############################# NelderMead #############################
############################## nmDifEvo #############################
xmin, xmax = _narrow_limits(4 * factor, [x, xmin, xmax], debug=False)
mymaxfev = min(maxfev_per_iter, maxfev - nfev)
if 1 == numcores:
result = difevo_nm(myfcn, x, xmin, xmax, ftol, mymaxfev, verbose,
seed, pop, xprob, weight)
nfev += result[4].get('nfev')
x = numpy.asarray(result[1], numpy.float_)
nfval = result[2]
else:
ncores_de = ncoresDifEvo()
mystep = None
tmp_nfev, tmp_fmin, tmp_par = \
ncores_de(stat_cb0, x, xmin, xmax, ftol, mymaxfev, mystep,
numcores, pop, seed, weight, xprob, verbose)
nfev += tmp_nfev
if tmp_fmin < nfval:
nfval = tmp_fmin
x = tmp_par
if verbose or debug != False:
print('f_de_nm%s=%.14e in %d nfev' % (x, nfval, nfev))
############################## nmDifEvo #############################
ofval = FUNC_MAX
while nfev < maxfev:
xmin, xmax = _narrow_limits(factor, [x, xmin, xmax], debug=False)
############################ nmDifEvo #############################
y = random_start(xmin, xmax)
mymaxfev = min(maxfev_per_iter, maxfev - nfev)
if 1 == numcores:
result = difevo_nm(myfcn, y, xmin, xmax, ftol, mymaxfev,
verbose, seed, pop, xprob, weight)
nfev += result[4].get('nfev')
if result[2] < nfval:
nfval = result[2]
x = numpy.asarray(result[1], numpy.float_)
if verbose or debug != False:
print('f_de_nm%s=%.14e in %d nfev' % \
(x, result[2], result[4].get('nfev')))
############################ nmDifEvo #############################
if debug != False:
print('ofval=%.14e\tnfval=%.14e\n' % (ofval, nfval))
if sao_fcmp(ofval, nfval, ftol) <= 0:
return x, nfval, nfev
ofval = nfval
factor *= 2
return x, nfval, nfev
def myopt( myfcn, xxx, ftol, maxfev, seed, pop, xprob,
weight, factor=4.0, debug=False ):
x = xxx[ 0 ]
xmin = xxx[ 1 ]
xmax = xxx[ 2 ]
maxfev_per_iter = 512 * x.size
def random_start( xmin, xmax ):
xx = []
for ii in range( len(xmin ) ):
xx.append( random.uniform( xmin[ ii ], xmax[ ii ] ) )
return numpy.asarray( xx )
############################# NelderMead #############################
mymaxfev = min( maxfev_per_iter, maxfev )
if all( x == 0.0 ):
mystep = map( lambda fubar: 1.2 + fubar, x )
else:
mystep = map( lambda fubar: 1.2 * fubar, x )
result = neldermead( myfcn, x, xmin, xmax, maxfev=mymaxfev, ftol=ftol,
finalsimplex=9, step=mystep )
x = numpy.asarray( result[ 1 ], numpy.float_ )
nfval = result[2]
nfev = result[4].get( 'nfev' )
if verbose or False != debug:
print 'f_nm%s=%.14e in %d nfev' % ( x, nfval, nfev )
############################# NelderMead #############################
############################## nmDifEvo ##############################
xmin, xmax = _narrow_limits( 4 * factor, [x,xmin,xmax], debug=False )
mymaxfev = min( maxfev_per_iter, maxfev - nfev )
result = difevo_nm( myfcn, x, xmin, xmax, ftol=ftol, maxfev=mymaxfev,
seed=seed, population_size=pop, xprob=xprob,
weighting_factor=weight )
nfev += result[4].get( 'nfev' )
x = numpy.asarray( result[1], numpy.float_ )
nfval = result[2]
if verbose or False != debug:
print 'f_de_nm%s=%.14e in %d nfev' % ( x, result[2],
result[4].get('nfev'))
############################## nmDifEvo ##############################
ofval = FUNC_MAX
while nfev < maxfev:
xmin, xmax = _narrow_limits( factor, [x,xmin,xmax], debug=False )
############################ nmDifEvo #############################
y = random_start( xmin, xmax )
mymaxfev = min( maxfev_per_iter, maxfev - nfev )
result = difevo_nm( myfcn, y, xmin, xmax, ftol=ftol,
maxfev=mymaxfev, seed=seed,
population_size=pop, xprob=xprob,
weighting_factor=weight )
nfev += result[4].get( 'nfev' )
if result[2] < nfval:
nfval = result[2]
x = numpy.asarray( result[1], numpy.float_ )
if verbose or False != debug:
print 'f_de_nm%s=%.14e in %d nfev' % \
( x, result[2], result[4].get('nfev'))
############################ nmDifEvo #############################
if False != debug:
print 'ofval=%.14e\tnfval=%.14e\n' % (ofval, nfval)
if sao_fcmp( ofval, nfval, ftol ) <= 0:
return x, nfval, nfev
ofval = nfval
factor *= 2
return x, nfval, nfev
