class FourParamIM(object):
def __init__(self, ts, loci_20, loci_11, loci_02):
self.ts = ts
self.lik = Likelihood(ts, loci_20, loci_11, loci_02)
def log_likelihood(self, c1, c2, m12, m21):
'''Compute the log likelihood for the four parameter model.'''
c1s = [c1] * len(self.ts)
c2s = [c2] * len(self.ts)
m12s = [m12] * len(self.ts)
m21s = [m21] * len(self.ts)
return self.lik.log_likelihood(c1s, c2s, m12s, m21s)
def __call__(self, c1, c2, m12, m21):
return self.log_likelihood(c1, c2, m12, m21)
def mle_parameters(self, c1, c2, m12, m21):
''' Estimate MLE parameters. Provided parameters are used as start values.'''
def function(x):
c1, c2, m12, m21 = x
return -self.log_likelihood(c1, c2, m12, m21)
return fmin(function, [c1, c2, m12, m21])
class FourParamIM(object):
def __init__(self, ts, loci_20, loci_11, loci_02):
self.ts = ts
self.lik = Likelihood(ts, loci_20, loci_11, loci_02)
def log_likelihood(self, c1, c2, m12, m21):
'''Compute the log likelihood for the four parameter model.'''
c1s = [c1] * len(self.ts)
c2s = [c2] * len(self.ts)
m12s = [m12] * len(self.ts)
m21s = [m21] * len(self.ts)
return self.lik.log_likelihood(c1s, c2s, m12s, m21s)
def __call__(self, c1, c2, m12, m21):
return self.log_likelihood(c1, c2, m12, m21)
def mle_parameters(self, c1, c2, m12, m21):
''' Estimate MLE parameters. Provided parameters are used as start values.'''
def function(x):
c1, c2, m12, m21 = x
return -self.log_likelihood(c1, c2, m12, m21)
return fmin(function, [c1,c2,m12,m21])
class EpochsIM(object):
def __init__(self, epsilon, tsplits, n, loci_20, loci_11, loci_02):
''' Build a model with n intervals per epochs, epochs separated by tsplits.'''
self.no_epochs = len(tsplits)
self.no_intervals = n
ts = list(linspace(epsilon, tsplits[0], num=n))
for i in xrange(1,len(tsplits)):
ts.extend(linspace(tsplits[i-1]+epsilon, tsplits[i], num=n))
self.lik = Likelihood(ts, loci_20, loci_11, loci_02)
def log_likelihood(self, c1s, c2s, m12s, m21s):
'''Compute the log likelihood for the four parameter model.'''
assert len(c1s) == self.no_epochs
assert len(c2s) == self.no_epochs
assert len(m12s) == self.no_epochs
assert len(m21s) == self.no_epochs
def rep(lst, n):
result = []
for elm in lst:
result.extend([elm] * n)
return result
IM_c1s = rep(c1s, self.no_intervals)
IM_c2s = rep(c2s, self.no_intervals)
IM_m12s = rep(m12s, self.no_intervals)
IM_m21s = rep(m21s, self.no_intervals)
return self.lik.log_likelihood(IM_c1s, IM_c2s, IM_m12s, IM_m21s)
def __call__(self, c1s, c2s, m12s, m21s):
return self.log_likelihood(c1s, c2s, m12s, m21s)
def _wrap_params(self, c1s, c2s, m12s, m21s):
return c1s + c2s + m12s + m21s
def _unwrap_params(self, x):
c1s = x[:self.no_epochs]
c2s = x[self.no_epochs:2*self.no_epochs]
m12s = x[2*self.no_epochs:3*self.no_epochs]
m21s = x[3*self.no_epochs:]
return c1s, c2s, m12s, m21s
def mle_parameters(self, c1s, c2s, m12s, m21s):
''' Estimate MLE parameters. Provided parameters are used as start values.'''
def function(x):
if min(x) < 0:
return float('Inf')
c1, c2, m12, m21 = self._unwrap_params(x)
return -self.log_likelihood(c1, c2, m12, m21)
def callback(x):
c1s,c2s,m12s,m21s = self._unwrap_params(x)
print 'c1s:', c1s
print 'c2s:', c2s
print 'm12s:', m12s
print 'm21s:', m21s
print
return self._unwrap_params(fmin(function,
self._wrap_params(c1s,c2s,m12s,m21s),
maxiter=1000, callback=callback))
class EpochsIM(object):
def __init__(self, epsilon, tsplits, n, loci_20, loci_11, loci_02):
''' Build a model with n intervals per epochs, epochs separated by tsplits.'''
self.no_epochs = len(tsplits)
self.no_intervals = n
ts = list(linspace(epsilon, tsplits[0], num=n))
for i in xrange(1, len(tsplits)):
ts.extend(linspace(tsplits[i - 1] + epsilon, tsplits[i], num=n))
self.lik = Likelihood(ts, loci_20, loci_11, loci_02)
def log_likelihood(self, c1s, c2s, m12s, m21s):
'''Compute the log likelihood for the four parameter model.'''
assert len(c1s) == self.no_epochs
assert len(c2s) == self.no_epochs
assert len(m12s) == self.no_epochs
assert len(m21s) == self.no_epochs
def rep(lst, n):
result = []
for elm in lst:
result.extend([elm] * n)
return result
IM_c1s = rep(c1s, self.no_intervals)
IM_c2s = rep(c2s, self.no_intervals)
IM_m12s = rep(m12s, self.no_intervals)
IM_m21s = rep(m21s, self.no_intervals)
return self.lik.log_likelihood(IM_c1s, IM_c2s, IM_m12s, IM_m21s)
def __call__(self, c1s, c2s, m12s, m21s):
return self.log_likelihood(c1s, c2s, m12s, m21s)
def _wrap_params(self, c1s, c2s, m12s, m21s):
return c1s + c2s + m12s + m21s
def _unwrap_params(self, x):
c1s = x[:self.no_epochs]
c2s = x[self.no_epochs:2 * self.no_epochs]
m12s = x[2 * self.no_epochs:3 * self.no_epochs]
m21s = x[3 * self.no_epochs:]
return c1s, c2s, m12s, m21s
def mle_parameters(self, c1s, c2s, m12s, m21s):
''' Estimate MLE parameters. Provided parameters are used as start values.'''
def function(x):
if min(x) < 0:
return float('Inf')
c1, c2, m12, m21 = self._unwrap_params(x)
return -self.log_likelihood(c1, c2, m12, m21)
def callback(x):
c1s, c2s, m12s, m21s = self._unwrap_params(x)
print 'c1s:', c1s
print 'c2s:', c2s
print 'm12s:', m12s
print 'm21s:', m21s
print
return self._unwrap_params(
fmin(function,
self._wrap_params(c1s, c2s, m12s, m21s),
maxiter=1000,
callback=callback))