def __init__(self, transition_rate_matrix):
self._rateMatrix = transition_rate_matrix
self._expDists = []
self._empiricalDists = []
for i, row in enumerate(transition_rate_matrix):
# find sum of rates out of this state
rate_out = out_rate(row, i)
# if the rate is 0, put None as the exponential and empirical distributions
if rate_out > 0:
# create an exponential distribution with rate equal to sum of rates out of this state
self._expDists.append(RVG.Exponential(scale=1 / rate_out))
# find the transition rates to other states
# assume that the rate into this state is 0
rates = []
for j, v in enumerate(row):
if i == j:
rates.append(0)
else:
rates.append(v)
# calculate the probability of each event (prob_j = rate_j / (sum over j of rate_j)
probs = np.array(rates) / rate_out
# create an empirical distribution over the future states from this state
self._empiricalDists.append(RVG.Empirical(probs))
else: # if the sum of rates out of this state is 0
self._expDists.append(None)
self._empiricalDists.append(None)
def test_exponential(rnd, scale, loc=0):
# exponential random variate generator
exp_dist = RVGs.Exponential(scale, loc)
# obtain samples
samples = get_samples(exp_dist, rnd)
# report mean and variance
print_test_results('Exponential',
samples,
expectation=scale + loc,
variance=scale**2)
import SimPy.FittingProbDist_ML as Fit
import numpy as np
import SimPy.RandomVariantGenerators as RVGs
from tests.ProbDistributions.RVGtests import get_samples
import scipy.stats as scs
# simulate some data
np.random.seed(1)
# 1 fitting a exponential distribution
dist = RVGs.Exponential(5, 1)
dat_exp = np.array(get_samples(dist, np.random)) # generate data
dictResults = Fit.fit_exp(dat_exp, 'Data', fixed_location=1) # fit
print("Fitting Exponential:", dictResults)
# 2 fitting a beta distribution
dist = RVGs.Beta(2, 3, loc=1, scale=2)
dat_beta = np.array(get_samples(dist, np.random)) # generate data
dictResults = Fit.fit_beta(dat_beta, 'Data', minimum=1, maximum=3) # fit
print("Fitting Beta:", dictResults)
# 3 fitting a beta-binomial distribution
dist = RVGs.BetaBinomial(100, 2, 3, loc=1, scale=2) # n, a, b
dat_betabin = np.array(get_samples(dist, np.random))
dictResults = Fit.fit_beta_binomial(dat_betabin,
'Data',
fixed_location=1,
fixed_scale=2) # fit
print("Fitting BetaBinomial:", dictResults)
# 4 Binomial
