Я использую модуль pystan в Windows, где многопоточность не поддерживается в Windows в модуле.Модуль pystan частично написан на C ++, и, поскольку я пытаюсь уменьшить время выполнения модуля, мне интересно, есть ли способ вручную написать многопоточный код в части C ++ модуля, чтобы уменьшить время выполнения, такЯ могу увеличить итерации?Ниже приведен код:
from __future__ import division
import pystan
import numpy as np
import os
x=np.array([453.05,453.05,453.24,453.35,453.44,453.44,453.83,454.02,454.89])
y=np.array([3232.12,3231.45,3231.90,3231.67,3231.84,3231.95,3231.89,3231.67,3231.45])
x=np.array(zip(x,y))
c=np.array([0.01,0.07,0.001,0.1,0.05,0.001,0.001,0.05,0.001])
s = np.array([454.4062631951059,3230.808656891571])
st=np.array([12,12,12,12,12,12,12,12,12])
model='''
data {
int D; //number of dimensions
int K; //number of gaussians
int N; //number of data
vector[D] y[N]; // observation data
real con[N]; //concentration
vector[D] s;//oil spill location
real st[N]; // sample time
}
parameters {
simplex[K] theta; //mixing proportions
vector[D] v[K];
vector<lower=0>[D] Dif[K];
cholesky_factor_corr[D] L[K]; //cholesky factor of correlation matrix
}
transformed parameters {
cholesky_factor_cov[D,D] cov[K,N];
vector<lower=0>[D] sigma[K,N]; // standard deviations
vector[D] mu[K,N];
real ro[K];
matrix[D,D] Omega[K];
matrix[D,D] Sigma[K,N];
vector[N] lamba;
for (k in 1:K) {
Omega[k] = multiply_lower_tri_self_transpose(L[k]);
for (n in 1:N){
sigma[k,n] = 0.05 + sqrt(2*st[n]*Dif[k]);
mu[k,n] = s+v[k]*st[n];
cov[k,n] = diag_pre_multiply(sigma[k,n],L[k]);
Sigma[k,n] = quad_form_diag(Omega[k], sigma[k,n]);
}
ro[k]=Omega[k,2,1];
}
for (i in 1 : N) {lamba[i] = 1/(theta[1]*(1./2./3.1415926/sqrt (Sigma[1,i, 1, 1])/sqrt (Sigma[1,i, 2, 2])/sqrt (1 - ro[1]*ro[1]))*exp (-1./2./(1 - ro[1]*ro[1])*(-(y[i, 1] - mu[1,i, 1])*(y[i, 1] - mu[1,i, 1])/Sigma[1, i,1, 1] - (y[i, 2] - mu[1, i,2])*(y[i, 2] - mu[1, i,2])/Sigma[1,i, 2, 2] + 2.*ro[1]*(y[i, 1] - mu[1,i, 1])*(y[i, 2] - mu[1,i, 2])/sqrt (Sigma[1, i,1, 1])/sqrt (Sigma[1,i, 2, 2]))) +
theta[2]*(1./2./3.1415926/sqrt (Sigma[2, i,1, 1])/sqrt (Sigma[2,i, 2, 2])/sqrt (1 - ro[2]*ro[2]))*exp (-1./2./(1 - ro[2]*ro[2])*(-(y[i, 1] - mu[2, i,1])*(y[i, 1] - mu[2, i,1])/Sigma[2, i,1, 1] - (y[i, 2] - mu[2,i, 2])*(y[i, 2] - mu[2, i,2])/Sigma[2,i, 2, 2] + 2.*ro[2]*(y[i, 1] - mu[2, i,1])*(y[i, 2] - mu[2, i,2])/sqrt (Sigma[2, i,1, 1])/sqrt (Sigma[2, i,2, 2]))) +
theta[3]*(1./2./3.1415926/sqrt (Sigma[3, i,1, 1])/sqrt (Sigma[3,i, 2, 2])/sqrt (1 - ro[3]*ro[3]))*exp (-1./2./(1 - ro[3]*ro[3])*(-(y[i, 1] - mu[3, i,1])*(y[i, 1] - mu[3, i,1])/Sigma[3, i,1, 1] - (y[i, 2] - mu[3,i, 2])*(y[i, 2] - mu[3, i,2])/Sigma[3,i, 2, 2] + 2.*ro[3]*(y[i, 1] - mu[3, i,1])*(y[i, 2] - mu[3, i,2])/sqrt (Sigma[3, i,1, 1])/sqrt (Sigma[3, i,2, 2]))) +
theta[4]*(1./2./3.1415926/sqrt (Sigma[4, i,1, 1])/sqrt (Sigma[4,i, 2, 2])/sqrt (1 - ro[4]*ro[4]))*exp (-1./2./(1 - ro[4]*ro[4])*(-(y[i, 1] - mu[4, i,1])*(y[i, 1] - mu[4, i,1])/Sigma[4, i,1, 1] - (y[i, 2] - mu[4,i, 2])*(y[i, 2] - mu[4, i,2])/Sigma[4,i, 2, 2] + 2.*ro[4]*(y[i, 1] - mu[4, i,1])*(y[i, 2] - mu[4, i,2])/sqrt (Sigma[4, i,1, 1])/sqrt (Sigma[4, i,2, 2]))));}
}
model {
real ps[K];
theta ~ dirichlet(rep_vector(2.0, 4));
for(k in 1:K){
v[k,1] ~ normal(0.0,4.1);// uniform(340/100,380/100);//
v[k,2] ~ normal(0.0,4.1);//uniform(3160/100,3190/100);//
Dif[k] ~ normal(0.5,0.2);//exponential(0.05);//beta(2,5);
L[k] ~ lkj_corr_cholesky(2);// contain rho
con ~ exponential(lamba);
}
for (n in 1:N){
for (k in 1:K){
ps[k] = log(theta[k])+multi_normal_cholesky_lpdf(y[n] | mu[k,N], cov[k,N]); //increment log probability of the gaussian
}
target += log_sum_exp(ps);
}
for(i in 1:N){
target += - lamba[i]*con[i]+log(lamba[i]);
}
}
'''
dat={'D':2,'K':4,'N':9,'y':x,'con':c,'s':s,'st':st}
fit = pystan.stan(model_code=model,data=dat,iter=1000,warmup=500, chains=1,init_r=0.5)
print(fit)
Я не очень хорошо разбираюсь в C ++, так как использую python, а модуль pystan требует, чтобы код был написан на C ++.Я надеюсь, что есть способ многопоточности количества итераций для разных ядер в моей Windows.