Python poisson_like Examples

Example #1

File: disaster_model_gof.py Project: AsymmetricHuang/pymc

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)

Example #2

File: model_1_gof.py Project: wqren/pymc

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)

Example #3

File: all_cause_model.py Project: kforeman/USCOD

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)

Example #4

File: codmod.py Project: GunioRobot/codmod2

 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)

Example #5

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)

Example #6

File: mymodel.py Project: afarahi/SpringleafCode

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

Example #7

File: zip.py Project: bswaika96/pymc-source-changed

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

Example #8

File: 16_bayesian_change_detection.cw.py Project: kumarkanil/DataScienceBootcamp_GeneralAssembly

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)

Example #9

File: likelihood.py Project: ngraetz/dismod_mr

 def p_obs(value=p, pi=pi, n=n):
     return mc.poisson_like((value * n)[i_nonzero], (pi * n)[i_nonzero])

Example #10

File: likelihood.py Project: xinwen-ren/dismod_mr

 def p_obs(value=p, pi=pi, n=n):
     return mc.poisson_like((value * n)[i_nonzero], (pi * n)[i_nonzero])

Example #11

File: poisson_model.py Project: studentmicky/gbd

def obs(pi=pi):
    return mc.poisson_like(r * n, pi * n)

Example #12

File: poisson_model.py Project: studentmicky/gbd

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')

Example #13

File: test_instantiation.py Project: boomsbloom/analysis-scripts

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)

Example #14

File: schiz_forest.py Project: aflaxman/gbd

def obs(pi=pi):
    return mc.poisson_like(r*n, pi*n)

Example #15

 def y_cc(mu=mu_cc, value=np.clip(data.n_cc, 0, data.n_pop)):
     return pm.poisson_like(value[10:], mu[10:])

Example #16

File: poisson_model.py Project: aflaxman/gbd

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])

Example #17

File: test_instantiation.py Project: jhsa26/pymc

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)

Example #18

File: RW_plus_drift.py Project: kforeman/USCOD

def data_likelihood(value=data.deaths, mu=estimate):
    return mc.poisson_like(value, mu)

Example #19

 def y_symp(mu=mu_symp, value=data.n_symp):
     return pm.poisson_like(value[10:], mu[10:])