def disasters(value=disasters_array,
early_mean=early_mean,
late_mean=late_mean,
switchpoint=switchpoint):
"""Annual occurences of coal mining disasters."""
return pm.poisson_like(
value[:switchpoint], early_mean) + pm.poisson_like(value[switchpoint:], late_mean)
def disasters(value=disasters_array,
early_mean=early_mean,
late_mean=late_mean,
switchpoint=switchpoint):
"""Annual occurences of coal mining disasters."""
return pm.poisson_like(value[:switchpoint], early_mean) + pm.poisson_like(
value[switchpoint:], late_mean)
def data_likelihood(value=data.deaths, mu=predicted, alpha=omega):
if alpha >= 10**10:
return mc.poisson_like(value, mu)
else:
if mu.min() <= 0.:
mu = mu + 10.**-10
return mc.negative_binomial_like(value, mu, alpha)
def data_likelihood(value=np.round(self.training_data.cf * self.training_data.sample_size), mu=param_pred, alpha=alpha):
if alpha >= 10**10:
return mc.poisson_like(value, mu)
else:
if mu.min() <= 0.:
mu = mu + 10**-10
return mc.negative_binomial_like(value, mu, alpha)
def logpoissonlike(n,mu):
"""Likelihood function for single bin counting experiment
Args:
n - observed number of counts
mu - expected number of counts
"""
if n==None: return -1e300
return pymc.poisson_like(n,mu)
def zip(value=data, mu=mu, psi=psi):
""" Zero-inflated Poisson likelihood """
# Initialize likeihood
like = 0.0
# Loop over data
for x in value:
if not x:
# Zero values
like += np.log((1. - psi) + psi * np.exp(-mu))
else:
# Non-zero values
like += np.log(psi) + poisson_like(x, mu)
return like
def zip(value=data, mu=mu, psi=psi):
""" Zero-inflated Poisson likelihood """
# Initialize likeihood
like = 0.0
# Loop over data
for x in value:
if not x:
# Zero values
like += np.log((1. - psi) + psi * np.exp(-mu))
else:
# Non-zero values
like += np.log(psi) + poisson_like(x, mu)
return like
def arrivals(value=y, change_point=change_point,\
early_rate=early_rate, late_rate=late_rate):
return pm.poisson_like(value[:change_point], early_rate) +\
pm.poisson_like(value[change_point:], late_rate)
def p_obs(value=p, pi=pi, n=n):
return mc.poisson_like((value * n)[i_nonzero], (pi * n)[i_nonzero])
def p_obs(value=p, pi=pi, n=n):
return mc.poisson_like((value * n)[i_nonzero], (pi * n)[i_nonzero])
def obs(pi=pi):
return mc.poisson_like(r * n, pi * n)
thin = 10
results = {}
xmax = .07
### @export 'distribution-comparison'
pl.figure(**book_graphics.quarter_page_params)
ax = pl.axes([.1, .3, .85, .65])
x = pl.arange(0, n_small * pi_true * 4, .1)
# plot binomial distribution
y1 = [pl.exp(mc.binomial_like(x_i, n_small, pi_true)) for x_i in x]
pl.step(x, y1, 'k', linewidth=1, linestyle='step:', alpha=.8, label='Binomial')
# plot poisson distribution
y2 = [pl.exp(mc.poisson_like(x_i, n_small * pi_true)) for x_i in x]
pl.plot(x,
y2,
'k',
linewidth=1,
linestyle='steps--',
alpha=.8,
label='Poisson')
pl.legend(loc='upper right', fancybox=True, shadow=True)
pl.yticks([0, .05])
pl.xticks([25, 50, 75], ['', '', ''])
pl.axis([-.1, n_small * pi_true * 4, -.02, 1.1 * max(y1)])
pl.xlabel('Count')
#pl.figtext(.11, .94, 'Distribution', ha='left', va='top')
def G(value=D_array * 0.5, s=s, e=e, l=l):
"""Annual occurences of coal mining disasters."""
return poisson_like(value[:s], e) + poisson_like(value[s:], l)
def obs(pi=pi):
return mc.poisson_like(r*n, pi*n)
def y_cc(mu=mu_cc, value=np.clip(data.n_cc, 0, data.n_pop)):
return pm.poisson_like(value[10:], mu[10:])
xmax = .07
### @export 'distribution-comparison'
pl.figure(**book_graphics.quarter_page_params)
ax = pl.axes([.1, .3, .85, .65])
x = pl.arange(0, n_small*pi_true*4, .1)
# plot binomial distribution
y1 = [pl.exp(mc.binomial_like(x_i, n_small, pi_true)) for x_i in x]
pl.step(x, y1, 'k',
linewidth=1, linestyle='step:', alpha=.8,
label='Binomial')
# plot poisson distribution
y2 = [pl.exp(mc.poisson_like(x_i, n_small*pi_true)) for x_i in x]
pl.plot(x, y2, 'k',
linewidth=1, linestyle='steps--', alpha=.8,
label='Poisson')
pl.legend(loc='upper right', fancybox=True, shadow=True)
pl.yticks([0, .05])
pl.xticks([25, 50, 75], ['','',''])
pl.axis([-.1, n_small*pi_true*4, -.02, 1.1*max(y1)])
pl.xlabel('Count')
#pl.figtext(.11, .94, 'Distribution', ha='left', va='top')
ax = pl.axes([.1, .15, .85, .20])
pl.step(x, pl.array(y1)-y2, color='k')
pl.xticks([0, 25, 50, 75])
pl.yticks([-.001, .001])
def G(value=D_array * .5, s=s, e=e, l=l):
"""Annual occurences of coal mining disasters."""
return poisson_like(value[:s], e) + poisson_like(value[s:], l)
def data_likelihood(value=data.deaths, mu=estimate):
return mc.poisson_like(value, mu)
def y_symp(mu=mu_symp, value=data.n_symp):
return pm.poisson_like(value[10:], mu[10:])
