AvoidingMatrixExponential/JMJP_inverse_2S.py at master · LabPresse/AvoidingMatrixExponential · GitHub

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import numpy as np
import pandas as pd
import smn
from basis import *

from models import lam_2S as get_lam


@njit
def rho_Bk(rho_init,B,k_all,w_all,N_RNA):
    k_all_indices = np.argsort(k_all)
    sorted_k_all = k_all[k_all_indices]
    sorted_w_all = w_all[k_all_indices]

    rho = rho_init
    k = 0

    rhoBk = np.zeros(k_all.size)

    for (ind,k_t,w_t) in zip(k_all_indices,sorted_k_all,sorted_w_all):
        if k<k_t:
            for i in range(k_t-k):
                rho = smn.dot(rho,B)
                k+=1
        rhoBk[ind] = rho[w_t] + rho[w_t+N_RNA]
    return rhoBk

class params:
    def __init__(self,theta,N_RNA):
        N_DNA = 2
        ingestion,d,l01,l10 = 1.0*theta
        self.value=theta

        rho = make_initial(0,0,N_RNA*N_DNA)##this creates on inactive state
        self.rho = rho

        self.N_RNA = N_RNA
        self.N = N_RNA*N_DNA
        self.lam = get_lam(ingestion,d,l01,l10,N_RNA,N_DNA)
        self.B,self.omega = smn.get_B(self.lam)

        self.log_prior = lprior(theta) #fix when applying to real data

    def sample_k(self,T_all):
        return np.random.poisson(self.omega*T_all)

    def loglike_k(self,k_all,T_all):
        return loglike_poisson(k_all,self.omega*T_all)

    def loglike_w_k(self,w_all,k_all):
        return np.log(rho_Bk(self.rho,self.B,k_all,w_all,self.N_RNA))


def update_k(llw,llk,k_all,w_all,T_all,th):
    k_all_prop = th.sample_k(T_all)

    llw_prop = th.loglike_w_k(w_all,k_all_prop)

    #llk = th.loglike_k(k_all,T_all)
    #llk_prop = th.loglike_k(k_all_prop,T_all)

    update = np.log(np.random.rand(w_all.size)) < llw_prop - llw

    res_k = arr_replace(k_all,k_all_prop,update)
    res_llw = arr_replace(llw,llw_prop,update)
    res_llk = th.loglike_k(res_k,T_all)

    return res_llw,res_llk,res_k

def update_S(th_list):
    return (np.cov(np.log(th_list).T,bias=True) + np.eye(4)*1e-12)*((2.38/np.sqrt(4))**2)


def update_th(llw,llk,k_all,w_all,T_all,th,S_prop):
    value_prop = np.exp(np.random.multivariate_normal(np.log(th.value),S_prop))
    th_prop = params(value_prop,th.N_RNA)
    llw_prop = th_prop.loglike_w_k(w_all,k_all)
    llk_prop = th_prop.loglike_k(k_all,T_all)

    update = np.log(np.random.rand()) < llw_prop.sum() + llk_prop.sum() + th_prop.log_prior -llw.sum() - llk.sum() -th.log_prior
    if update:
        return llw_prop,llk_prop,th_prop
    return llw,llk,th


def save(llw_list,llk_list,th_list,beta_gt,burnin=False):
    llw_pd = np.stack(llw_list)
    llk_pd = np.stack(llk_list)
    th_pd = np.stack(th_list)

    df = pd.DataFrame(th_pd,columns=['birth rate','death rate','activation rate','deactivtion rate'])
    df['log p(w|k,th)'] = llw_pd
    df['log p(k|th)'] = llk_pd

    filename = '2S_IEU_inference_beta={}.csv'.format(beta_gt)
    folder ='inference/'
    if burnin:
        folder='burn/'

    df.to_csv(folder+filename,index=False)