def plot_results(data, r, p):
xm = data.max()
plt.figure()
plt.hist(data, bins=np.arange(xm+1)-0.5, normed=True, label='normed data counts')
plt.xlim(0,xm)
plt.plot(np.arange(xm), np.exp(negbin_loglike(r, p, np.arange(xm))), label='maxlike fit')
plt.xlabel('k')
plt.ylabel('p(k)')
plt.legend(loc='best')
def plot_samples(data, samples):
xm = data.max()
plt.figure()
plt.hist(data, bins=np.arange(xm+1)-0.5, normed=True)
plt.xlim(0, xm)
for r, p in samples:
plt.plot(np.arange(xm), np.exp(negbin_loglike(r, p, np.arange(xm))))
plt.xlabel('k')
plt.ylabel('p(k)')
def plot_results(data, r, p):
xm = data.max()
plt.figure()
plt.hist(data, bins=np.arange(xm+1)-0.5, normed=True, label='normed data counts')
plt.xlim(0,xm)
plt.plot(np.arange(xm), np.exp(negbin_loglike(r, p, np.arange(xm))), label='maxlike fit')
plt.xlabel('k')
plt.ylabel('p(k)')
plt.legend(loc='best')
if __name__ == "__main__":
# generate data
npr.seed(0)
data = negbin_sample(r=5, p=0.5, size=1000)
# fit likelihood-extremizing parameters
r, p = fit_maxlike(data, r_guess=1)
# report fit
print('Fit parameters:')
print('r={r}, p={p}'.format(r=r, p=p))
print('Check that we are at a local stationary point:')
loglike = lambda r, p: np.sum(negbin_loglike(r, p, data))
grad_both = multigrad(loglike, argnums=[0,1])
print(grad_both(r, p))
plot_results(data, r, p)
plt.show()
