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cp_probability_model.py
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cp_probability_model.py
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# Python 3.5.2 |Anaconda 4.2.0 (64-bit)|
# -*- coding: utf-8 -*-
"""
Last edited: 2017-09-13
Author: Jeremias Knoblauch (J.Knoblauch@warwick.ac.uk)
Forked by: Luke Shirley (L.Shirley@warwick.ac.uk)
Description: Implements class CpModel, the Changepoint model used by the
Bayesian Online CP detection. The objects of this class store the prior
information on the number of CPs as well as the prior distribution on the
location of the CPs.
Traditionally, both are contained in a single prior run-length
distribution that implicitly specifies them, see Adams & MacKay (2007)
"""
from scipy import stats
class CpModel:
"""A model for location and number of the CPs in a (spatio-)temporal model.
See Adams & MacKay (2007), or Fearnhead & Liu (2007) for details.
The implemented model here is the geometric distribution
Attributes:
intensity: float >0; specifying the CP intensity rate
Later additions:
boundary: (optional) boolean; specifying if we want steady state
probability (True) or if we assume that there is a CP at
0 (False), with the latter being the default
g: generic pmf (not necessarily geometric distribution) for CP
g_0: generic pmf corresponding to boundary condition
EPS: (static), with which precision we want to calculate the
resulting cdfs G and G_0 from g, g_0
"""
def __init__(self, intensity):
"""Initialize the CP object by specifying intensity and indicating if
you wish to use the steady state/advanced boundary conditions"""
self.intensity = intensity
self.cp_pmf = self.create_distribution()
def create_distribution(self):
"""Creates the CP distribution using the object properties.
NOTE: At this point, it just operates with the geometric distribution.
Once g and g_0 are allowed as input, this function handles the more
general case, too."""
return stats.geom(self.intensity)
def pmf(self, k):
"""Returns the changepoint pmf for having k time periods passing
between two CPs.
NOTE: At this point, it just operates with the geometric distribution.
Once g and g_0 are allowed as input, this function handles the more
general case, too."""
return self.cp_pmf.pmf(k)
def pmf_0(self, k):
"""Returns the cp pmf for the very first CP (i.e., we do not assume
that there is a CP at time point 0 with probability one)
NOTE: At this point, it just operates with the geometric distribution.
Once g and g_0 are allowed as input, this function handles the more
general case, too."""
if k ==0:
return 1
else:
return 0
def hazard(self, k):
"""Returns the changepoint hazard for r_t = k|r_{t-1} = k-1.
NOTE: At this point, it just operates with the geometric distribution.
Once g and g_0 are allowed as input, this function handles the more
general case, too."""
return 1.0/self.intensity #memorylessness of geometric distribution
def hazard_vector(self, k, m):
"""Returns the changepoint hazard for for r_t = l|r_{t-1} = l-1,
with l = k, k+1, k+2, ... m between two CPs. In the geometric case,
the hazard will be the same for all l.
NOTE: At this point, it just operates with the geometric distribution.
Once g and g_0 are allowed as input, this function handles the more
general case, too."""
return 1.0/self.intensity #memorylessness of geometric distribution