solution = solver.Solution()
return solution, stepmon
if __name__ == '__main__':
target = [1., 45., -10., 20., 1., 0.1, 120.]
from mystic.models.lorentzian import gendata, histogram
npts = 4000
binwidth = 0.1
N = npts * binwidth
xmin, xmax = 0.0, 3.0
pdf = F(target)
print("pdf(1): %s" % pdf(1))
data = gendata(target, xmin, xmax, npts)
plt.plot(data[1:int(N)], 0 * data[1:int(N)], 'k.')
plt.title('Samples drawn from density to be estimated.')
try:
show()
except ImportError:
plt.show()
plt.clf()
binsc, histo = histogram(data, binwidth, xmin, xmax)
print("binsc: %s" % binsc)
print("count: %s" % histo)
print("ncount: %s" % (histo // N))
print("exact : %s" % pdf(binsc))
print("now with DE...")
solution = solver.Solution()
return solution, stepmon
if __name__ == '__main__':
#NOTE: we will calculate the norm, not solve for it as a parameter
target = [1., 45., -10., 20., 1., 0.1, 1.0] # norm set to 1.0
from mystic.models.lorentzian import gendata, histogram
npts = 4000; binwidth = 0.1
N = npts * binwidth
xmin, xmax = 0.0, 3.0
pdf = F(target) # normalized
print "pdf(1): ", pdf(1)
data = gendata(target, xmin, xmax, npts) # data is 'unnormalized'
#pylab.plot(data[1:N],0*data[1:N],'k.')
#pylab.title('Samples drawn from density to be estimated.')
#show()
#pylab.clf()
binsc, histo = histogram(data, binwidth, xmin,xmax)
print "binsc: ", binsc
print "count: ", histo
print "ncount: ", histo/N
print "exact : ", pdf(binsc)
print "now with DE..."
from mystic.forward_model import CostFactory
CF = CostFactory()
CF.addModel(F, 'lorentz', ND)
sigint_callback = plot_sol(solver))
solution = solver.Solution()
return solution, stepmon
if __name__ == '__main__':
target = [1., 45., -10., 20., 1., 0.1, 120.]
from mystic.models.lorentzian import gendata, histogram
npts = 4000; binwidth = 0.1
N = npts * binwidth
xmin, xmax = 0.0, 3.0
pdf = F(target)
print("pdf(1): %s" % pdf(1))
data = gendata(target, xmin, xmax, npts)
pylab.plot(data[1:int(N)],0*data[1:int(N)],'k.')
pylab.title('Samples drawn from density to be estimated.')
try: show()
except ImportError: pylab.show()
pylab.clf()
binsc, histo = histogram(data, binwidth, xmin,xmax)
print("binsc: %s" % binsc)
print("count: %s" % histo)
print("ncount: %s" % (histo//N))
print("exact : %s" % pdf(binsc))
print("now with DE...")
myCF = lorentzian.CostFactory2(binsc, histo//N, ND)
sol, steps = de_solve(myCF)
