Esempio n. 1
0
    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...")
Esempio n. 2
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    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)
Esempio n. 3
0
                 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)