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mscentipede.pxd
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mscentipede.pxd
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import numpy as np
cimport numpy as np
from cpython cimport bool
cdef class Data:
cdef public long N, L, R, J
cdef public dict valueA, valueB, total
cdef transform_to_multiscale(self, np.ndarray[np.float64_t, ndim=3] reads)
cdef class Zeta:
cdef public long N
cdef public np.ndarray total, prior_log_odds, \
footprint_log_likelihood_ratio, total_log_likelihood_ratio, \
posterior_log_odds, estim
cdef update(self, Data data, np.ndarray[np.float64_t, ndim=2] scores, \
Pi pi, Tau tau, Alpha alpha, Beta beta, Omega omega, \
Pi pi_null, Tau tau_null, str model)
cdef infer(self, Data data, np.ndarray[np.float64_t, ndim=2] scores, \
Pi pi, Tau tau, Alpha alpha, Beta beta, Omega omega, \
Pi pi_null, Tau tau_null, str model)
cdef class Pi:
cdef public long J
cdef public dict value
cpdef tuple pi_function_gradient(np.ndarray[np.float64_t, ndim=1] x, dict args)
cpdef tuple pi_function_gradient_hessian(np.ndarray[np.float64_t, ndim=1] x, dict args)
cdef class Tau:
cdef public long J
cdef public np.ndarray estim
cpdef tuple tau_function_gradient(np.ndarray[np.float64_t, ndim=1] x, dict args)
cpdef tuple tau_function_gradient_hessian(np.ndarray[np.float64_t, ndim=1] x, dict args)
cdef class Alpha:
cdef public long R
cdef public np.ndarray estim
cpdef tuple alpha_function_gradient(np.ndarray[np.float64_t, ndim=1] x, dict args)
cpdef tuple alpha_function_gradient_hessian(np.ndarray[np.float64_t, ndim=1] x, dict args)
cdef class Omega:
cdef public long R
cdef public np.ndarray estim
cdef update(self, Zeta zeta, Alpha alpha)
cdef class Beta:
cdef public long S
cdef public np.ndarray estim
cpdef tuple beta_function_gradient(np.ndarray[np.float64_t, ndim=1] x, dict args)
cpdef tuple beta_function_gradient_hessian(np.ndarray[np.float64_t, ndim=1] x, dict args)
cdef tuple compute_footprint_likelihood(Data data, Pi pi, Tau tau, Pi pi_null, Tau tau_null, str model)
cdef double likelihood(Data data, np.ndarray[np.float64_t, ndim=2] scores, \
Zeta zeta, Pi pi, Tau tau, Alpha alpha, Beta beta, \
Omega omega, Pi pi_null, Tau tau_null, str model)
cdef EM(Data data, np.ndarray[np.float64_t, ndim=2] scores, \
Zeta zeta, Pi pi, Tau tau, Alpha alpha, Beta beta, \
Omega omega, Pi pi_null, Tau tau_null, str model)
cdef square_EM(Data data, np.ndarray[np.float64_t, ndim=2] scores, \
Zeta zeta, Pi pi, Tau tau, Alpha alpha, Beta beta, \
Omega omega, Pi pi_null, Tau tau_null, str model)