def fit_model(filename):
numpy.random.seed(SEED)
p =load_problem([os.path.join(example_dir(),filename)])
#x.fx = RLFit(p).solve(steps=1000, burn=99)
x,fx = DEFit(p).solve(steps=200, pop=10)
#x,fx = PTFit(p).solve(steps=100,burn=400)
#x.fx = BFGSFit(p).solve(steps=200)
chisq = p(x)
print "chisq=",chisq
if chisq>2:
raise RuntimeError("Fit did not converge")
p.plot()
pylab.show()
def fit_model(filename):
#import sys; print >>sys.stderr, "in plot with",filename, example_dir()
numpy.random.seed(SEED)
p =load_problem([os.path.join(example_dir(),filename)])
#x.fx = RLFit(p).solve(steps=1000, burn=99)
x,fx = DEFit(p).solve(steps=200, pop=10)
#x,fx = PTFit(p).solve(steps=100,burn=400)
#x.fx = BFGSFit(p).solve(steps=200)
chisq = p(x)
print("chisq=%g"%chisq)
if chisq>2:
raise RuntimeError("Fit did not converge")
p.plot()
pylab.show()
def plot_model(filename):
numpy.random.seed(SEED)
p = load_problem([os.path.join(example_dir(), filename)])
p.plot()
pylab.show()
def plot_model(filename):
#import sys; print >>sys.stderr, "in plot with",filename, example_dir()
numpy.random.seed(SEED)
p = load_problem([os.path.join(example_dir(), filename)])
p.plot()
pylab.show()
