def test1a(self):
print("====test1a============================")
nn = 100
x = numpy.zeros(nn, dtype=float)
ym = 0.2 + 0.5 * x
nf = 1.0
nf = 0.1
numpy.random.seed(2345)
noise = numpy.random.randn(nn)
y = ym + nf * noise
limits = [-20, 20]
pm = PolynomialModel(0)
bf = Fitter(x, pm)
pars = bf.fit(y)
logz0 = bf.getLogZ(limits=limits)
logl0 = bf.logLikelihood
print("pars ", fmt(pars))
print("stdv ", fmt(bf.stdevs))
print("logZ ", fmt(logz0), " logl ", fmt(logl0))
errdis = GaussErrorDistribution()
problem = ClassicProblem(pm, xdata=x, ydata=y)
logz1, maxll = plotErrdis(errdis,
problem,
limits=limits,
max=0,
plot=self.doplot)
print("logZ ", fmt(logz1))
model = PolynomialModel(0)
model.setLimits(lowLimits=limits[0], highLimits=limits[1])
ns = NestedSampler(x, model, y, verbose=0)
logE = ns.sample()
par2 = ns.parameters
stdv = ns.stdevs
logz2 = ns.logZ
dlz2 = ns.logZprecision
print("pars ", fmt(par2))
print("stdv ", fmt(stdv))
print("logZ ", fmt(logz2), " +- ", fmt(dlz2))
self.assertTrue(abs(logz2 - logz0) < 2 * dlz2)
samples = ns.samples
parevo = samples.getParameterEvolution()
llevo = samples.getLogLikelihoodEvolution()
lwevo = samples.getLogWeightEvolution()
assertAAE(numpy.sum(numpy.exp(lwevo)), 1.0)
def test6_0(self):
print("====test6_0 Laplace ================")
plot = self.doplot
nn = 20
x = numpy.linspace(0, 2, nn, dtype=float)
ym = 0.3 + 0.0 * x
nf = 0.9
numpy.random.seed(2345)
noise = numpy.random.laplace(size=nn)
y = ym + nf * noise
limits = [-1, 2]
if plot:
plt.plot(x, ym, 'k-')
plt.plot(x, y, 'r.')
model = PolynomialModel(0)
model.setLimits(lowLimits=limits[0], highLimits=limits[1])
bf = PowellFitter(x, model, errdis="laplace")
pars = bf.fit(y, tolerance=1e-20)
print("pars ", pars)
print("stdv ", fmt(bf.stdevs))
logz0 = bf.getLogZ(limits=limits)
logl0 = bf.logLikelihood
print("logZ ", fmt(logz0), " logL ", fmt(logl0))
errdis = LaplaceErrorDistribution(scale=nf)
problem = ClassicProblem(model, xdata=x, ydata=y)
logz1, logl1 = plotErrdis(errdis,
problem,
limits=limits,
max=0,
plot=plot)
if plot:
plt.plot(pars[0], logl1, 'k.')
print("logZ ", fmt(logz1), " logL ", fmt(logl1))
model = PolynomialModel(0)
model.setLimits(lowLimits=limits[0], highLimits=limits[1])
ns = NestedSampler(x, model, y, distribution='laplace', verbose=0)
logE = ns.sample()
par2 = ns.parameters
stdv = ns.stdevs
logE = ns.logZ
dlz2 = ns.logZprecision
logz2 = ns.logZ
print("pars ", fmt(par2))
print("stdv ", fmt(stdv))
print("logZ ", fmt(logz2), " +- ", fmt(dlz2))
# self.assertTrue( abs( logz2 - logz0 ) < dlz2 )
print(logz1 - logz2)
samples = ns.samples
parevo = samples.getParameterEvolution()
llevo = samples.getLogLikelihoodEvolution()
lwevo = samples.getLogWeightEvolution()
assertAAE(numpy.sum(numpy.exp(lwevo)), 1.0)
if plot:
plt.plot(parevo, numpy.exp(llevo), 'r,')
mxl = numpy.exp(numpy.max(llevo)) * 1.2
plt.plot([pars, pars], [0.0, mxl], 'b-')
plt.plot([par2, par2], [0.0, mxl], 'r-')
plt.plot([par2, par2] + stdv, [0.0, mxl], 'g-')
plt.plot([par2, par2] - stdv, [0.0, mxl], 'g-')
plt.show()
def test1( self, plot=False ) :
print( "====test1============================" )
nn = 100
x = numpy.zeros( nn, dtype=float )
ym = 0.2 + 0.5 * x
nf = 1.0
nf = 0.1
numpy.random.seed( 2345 )
noise = numpy.random.randn( nn )
y = ym + nf * noise
limits = [-2,2]
pm = PolynomialModel( 0 )
bf = Fitter( x, pm )
pars = bf.fit( y )
logz0 = bf.getLogZ( limits=limits )
logl0 = bf.logLikelihood
print( "pars ", fmt( pars ) )
print( "stdv ", fmt( bf.stdevs ) )
print( "logZ ", fmt( logz0 ) )
print( "logl ", fmt( logl0 ) )
errdis = GaussErrorDistribution ( x, y )
logz1, maxll = plotErrdis( errdis, pm, limits=limits,
max=0, plot=plot )
print( "logZ ", fmt( logz1 ) )
model = PolynomialModel( 0 )
model.setLimits( lowLimits=limits[0], highLimits=limits[1] )
ns = NestedSampler( x, model, y )
yfit = ns.sample()
par2 = ns.parameters
stdv = ns.stdevs
logz2 = ns.logZ
dlz2 = ns.logZprecision
print( "pars ", fmt( par2 ) )
print( "stdv ", fmt( stdv ) )
print( "logZ ", fmt( logz2 ), " +- ", fmt( dlz2 ) )
self.assertTrue( abs( logz2 - logz0 ) < dlz2 )
samples = ns.samples
parevo =samples.getParameterEvolution()
llevo = samples.getLogLikelihoodEvolution()
lwevo = samples.getLogWeightEvolution()
assertAAE( numpy.sum( numpy.exp( lwevo ) ), 1.0 )
if plot :
plt.plot( parevo, numpy.exp( llevo ), 'r,' )
mxl = numpy.exp( numpy.max( llevo ) ) * 1.2
plt.plot( [pars,pars], [0.0,mxl], 'b-' )
plt.plot( [par2,par2], [0.0,mxl], 'r-' )
plt.plot( [par2,par2]+stdv, [0.0,mxl], 'g-' )
plt.plot( [par2,par2]-stdv, [0.0,mxl], 'g-' )
plt.show()