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/*
PX-Bayesian Low-rank Graph Regression Model
* main.cpp
* Eunjuee Lee
* C++ implementation by Andre Zapico
*/
#
include
<
iostream
>
#
include
<
armadillo
>
#
include
"
px_blgrm.h
"
#
include
"
px_blgrm_helper.h
"
#
include
"
mcmc_para.h
"
#
include
"
random_generator.h
"
using
namespace
std
;
using
namespace
arma
;
int
main
(
int
argc,
char
*argv[]){
try
{
mat L_temp; L_temp.
load
(argv[
1
], csv_ascii);
mat X; X.
load
(argv[
2
], csv_ascii);
string R_in = argv[
3
];
string V_in = argv[
4
];
string niter_in = argv[
5
];
string burnin_in = argv[
6
];
string slice_niter_in = argv[
7
];
string slice_burnin_in = argv[
8
];
int
R =
stoi
(R_in);
int
V =
stoi
(V_in);
int
n = X.
n_rows
;
int
p = X.
n_cols
;
//
load in L
cube L; L.
zeros
(V, V, n);
for
(
int
i =
0
; i < n; ++i){
L.
slice
(i) =
reshape
(L_temp.
row
(i), V, V);
}
//
set parameters
mcmc_para parameters;
parameters.
set_niter
(
stoi
(niter_in));
parameters.
set_burnin
(
stoi
(burnin_in));
parameters.
set_B
(V, R);
parameters.
set_Lambda
(n, R);
parameters.
set_Gamma
(p, R);
parameters.
set_sigma
(
1
);
parameters.
set_sig_gam
(
1
);
parameters.
set_b1
(.
01
);
parameters.
set_b2
(.
01
);
parameters.
set_c1
(.
01
);
parameters.
set_c2
(.
01
);
parameters.
set_va
(.
5
);
parameters.
set_vb
(.
5
);
parameters.
set_slice_niter
(
stoi
(slice_niter_in));
parameters.
set_slice_burnin
(
stoi
(slice_burnin_in));
parameters.
set_slice_width
(
10
);
cout <<
size
(L) <<
"
\n
"
;
cout <<
size
(X) <<
"
\n
"
;
px_blgrm
(L, X, R, parameters);
}
catch
(
const
std::exception& e){
cout <<
"
Error in inputs. Please correct inputs, or uncomment line 164 in main.cpp to run simulation
\n
"
;
srand
(
1
);
//
fix random seed
//
initialize all parameters, according to coni3_test.m
int
R =
10
;
int
V =
50
;
int
n =
100
;
int
p =
2
;
double
sigma_0 =
1.0
;
//
Fix variance of Lambda prior for identifiability
mcmc_para parameters;
parameters.
set_niter
(
5500
);
parameters.
set_B
(V, R);
parameters.
set_burnin
(
500
);
parameters.
set_Lambda
(n, R);
parameters.
set_Gamma
(p, R);
//
p as in the simulation
//
init to defaults
parameters.
set_sigma
(
1
);
parameters.
set_sig_gam
(
1
);
parameters.
set_b1
(.
01
);
parameters.
set_b2
(.
01
);
parameters.
set_c1
(.
01
);
parameters.
set_c2
(.
01
);
parameters.
set_va
(.
5
);
parameters.
set_vb
(.
5
);
//
iterations for metrop/slice
parameters.
set_slice_niter
(
25
);
parameters.
set_slice_burnin
(
10
);
parameters.
set_slice_width
(
10
);
//
now simulate data, L and X
double
sigma_error =
1.0
;
mat
B
(V, R);
for
(
int
i =
0
; i < R; ++i){
B.
col
(i) =
n_norm
(V);
}
//
simulate data
cube L; L.
zeros
(V, V, n);
cube tL; tL.
zeros
(V, V, n);
cube Lambda; Lambda.
zeros
(R, R, n);
vec x; x =
n_norm
(n) +
0.5
;
//
init x to 100 std norm, mean .5
mat A; vec
temp
(R*(R+
1
)/
2
); temp =
n_norm
(R*(R+
1
)/
2
);
mat Lamb; Lamb.
zeros
(R, R);
mat
AA
;
AA
.
zeros
(V, V); vec a;
mat noise; noise.
zeros
(
50
,
50
);
for
(
int
i =
0
; i < n; ++i){
A.
zeros
(R, R);
vec_2_uptri
(A,
n_norm
(R * (R +
1
) /
2
) * (
1
/
sqrt
(
1
)) +
1
, R);
A
(
0
,
1
) =
A
(
0
,
1
) +
x
(i) *
4
;
A
(
1
,
2
) =
A
(
1
,
2
) +
x
(i) *
4
;
Lamb = A + A.
t
() -
2
*
diagmat
(A.
diag
());
Lamb.
diag
() =
n_norm
(R) +
1
;
//
3 norm(1,1) RV
Lambda.
slice
(i) = Lamb;
//
need V*(V+1)/2 RB
a =
n_norm_musig
(V * (V +
1
) /
2
,
1
, sigma_error /
sqrt
(
2
));
vec_2_uptri
(
AA
, a, V);
noise =
AA
+
AA
.
t
() -
2
*
diagmat
(
AA
.
diag
());
noise.
diag
() =
n_norm_musig
(V,
0
, sigma_error);
tL.
slice
(i) = B * Lambda.
slice
(i) * B.
t
();
L.
slice
(i) = B * Lambda.
slice
(i) * B.
t
() + noise;
}
//
set up design matrix
mat X; X.
ones
(n,
2
);
X.
col
(
1
) = x;
a =
n_norm_musig
(V * (V +
1
) /
2
,
1
, sigma_error);
//
write the actual B, Lambda, for testing
ofstream outf;
outf.
open
(
"
B_true.txt
"
);
outf << B;
outf.
close
();
//
begin loop and testing helper functions
//
px_blgrm(L, X, R, parameters);
}
return
0
;
}
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