WardBrian
commented
1 year ago Huh, this seems to have uncovered another issue with profile blocks and the SoA pass. Trying to debug now.
Closed WardBrian closed 1 year ago
WardBrian
commented
1 year ago Huh, this seems to have uncovered another issue with profile blocks and the SoA pass. Trying to debug now.
mike-lawrence
commented
1 year ago We sure this is fixed? Using the binary here, and this model:
functions{
row_vector mike_dot(
int r
, int c
, vector beta
, row_vector zeros
, array[,] int index_pos_X_mat
, array[] int index_pos_X_sizes
, array[,] int col_index_into_X_first_unique_pos_X_mat
, array[] int col_index_into_X_first_unique_pos_X_sizes
, array[,] int index_of_cZ_for_each_col_in_Z_with_pos_X_mat
, array[] int index_of_cZ_for_each_col_in_Z_with_pos_X_sizes
, array[,] int index_neg_X_mat
, array[] int index_neg_X_sizes
, array[,] int col_index_into_X_first_unique_neg_X_mat
, array[] int col_index_into_X_first_unique_neg_X_sizes
, array[,] int index_of_cZ_for_each_col_in_Z_with_neg_X_mat
, array[] int index_of_cZ_for_each_col_in_Z_with_neg_X_sizes
) {
row_vector[c] out = zeros ;
for(i_r in 1:r){
out[
index_pos_X_mat[
i_r
, 1:(index_pos_X_sizes[i_r])
]
] = (
out[
col_index_into_X_first_unique_pos_X_mat[
i_r
, 1:(col_index_into_X_first_unique_pos_X_sizes[i_r])
]
]
+ beta[i_r]
)[
index_of_cZ_for_each_col_in_Z_with_pos_X_mat[
i_r
, 1:(index_of_cZ_for_each_col_in_Z_with_pos_X_sizes[i_r])
]
] ;
out[
index_neg_X_mat[
i_r
, 1:(index_neg_X_sizes[i_r])
]
] = (
out[
col_index_into_X_first_unique_neg_X_mat[
i_r
, 1:(col_index_into_X_first_unique_neg_X_sizes[i_r])
]
]
- beta[i_r]
)[
index_of_cZ_for_each_col_in_Z_with_neg_X_mat[
i_r
, 1:(index_of_cZ_for_each_col_in_Z_with_neg_X_sizes[i_r])
]
] ;
}
return(out);
}
}
data{
int r ;
int c ;
matrix[r,c] X;
row_vector[c] Y;
int index_pos_X_mat_rows ;
int index_pos_X_mat_cols ;
array[index_pos_X_mat_rows,index_pos_X_mat_cols] int index_pos_X_mat ;
array[index_pos_X_mat_rows] int index_pos_X_sizes ;
int col_index_into_X_first_unique_pos_X_mat_rows ;
int col_index_into_X_first_unique_pos_X_mat_cols ;
array[
col_index_into_X_first_unique_pos_X_mat_rows
,col_index_into_X_first_unique_pos_X_mat_cols
] int col_index_into_X_first_unique_pos_X_mat ;
array[
col_index_into_X_first_unique_pos_X_mat_rows
] int col_index_into_X_first_unique_pos_X_sizes ;
int index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows ;
int index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols ;
array[
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows
, index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols
] int index_of_cZ_for_each_col_in_Z_with_pos_X_mat ;
array[
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows
] int index_of_cZ_for_each_col_in_Z_with_pos_X_sizes ;
int index_neg_X_mat_rows ;
int index_neg_X_mat_cols ;
array[index_neg_X_mat_rows,index_neg_X_mat_cols] int index_neg_X_mat ;
array[index_neg_X_mat_rows] int index_neg_X_sizes ;
int col_index_into_X_first_unique_neg_X_mat_rows ;
int col_index_into_X_first_unique_neg_X_mat_cols ;
array[
col_index_into_X_first_unique_neg_X_mat_rows
,col_index_into_X_first_unique_neg_X_mat_cols
] int col_index_into_X_first_unique_neg_X_mat ;
array[
col_index_into_X_first_unique_neg_X_mat_rows
] int col_index_into_X_first_unique_neg_X_sizes ;
int index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows ;
int index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols ;
array[
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows
, index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols
] int index_of_cZ_for_each_col_in_Z_with_neg_X_mat ;
array[
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows
] int index_of_cZ_for_each_col_in_Z_with_neg_X_sizes ;
}
transformed data{
row_vector[c] zeros = zeros_row_vector(c) ;
}
parameters{
vector[r] beta ;
}
transformed parameters{
}
model{
row_vector[c] Z_mike ;
profile("mike"){
Z_mike = mike_dot(
r
, c
, beta
, zeros
, index_pos_X_mat
, index_pos_X_sizes
, col_index_into_X_first_unique_pos_X_mat
, col_index_into_X_first_unique_pos_X_sizes
, index_of_cZ_for_each_col_in_Z_with_pos_X_mat
, index_of_cZ_for_each_col_in_Z_with_pos_X_sizes
, index_neg_X_mat
, index_neg_X_sizes
, col_index_into_X_first_unique_neg_X_mat
, col_index_into_X_first_unique_neg_X_sizes
, index_of_cZ_for_each_col_in_Z_with_neg_X_mat
, index_of_cZ_for_each_col_in_Z_with_neg_X_sizes
) ;
}
beta ~ std_normal() ;
Y ~ normal(Z_mike,1.0) ;
}
generated quantities{
row_vector[c] Z_cdp ;
profile("cdp"){
Z_cdp = columns_dot_product(X,rep_matrix(beta,c)) ;
}
}I'm still getting the error:
/tmp/Rtmpm3ZMKi/model-297140222c35.hpp: In member function ‘stan::scalar_type_t<T2> gq_model_NA_NA_NA_none_NA_1_model_namespace::gq_model_NA_NA_NA_none_NA_1_model::log_prob_impl(VecR&, VecI&, std::ostream*) const’:
/tmp/Rtmpm3ZMKi/model-297140222c35.hpp:920:26: error: ‘inline_mike_dot_return_sym1__’ was not declared in this scope
920 | inline_mike_dot_return_sym1__, 1.0));
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
make: *** [make/program:58: /tmp/Rtmpm3ZMKi/model-297140222c35] Error 1
Error: An error occured during compilation! See the message above for more information.
In addition: Warning message:
In readLines(hpp_path) :
incomplete final line found on '/tmp/Rtmpm3ZMKi/model-297140222c35.hpp'Locally it is fixed for me. Can you check $CMDSTAN/bin/stanc --version?
Also if you are using the script you used on the forums, the rebuild of cmdstan will override any custom stanc binary
mike-lawrence
commented
1 year ago I skipped over the recompiling of cmdstan, so the new stanc was untouched. Here's the version: stanc3 ca2a7bf (Unix)
WardBrian
commented
1 year ago Can you try with the latest release?
I know cmdstanr also does some cacheing so it might be worth making some small change to the model to force it to have a different hash?
mike-lawrence
commented
1 year ago Seems to compile with that version, but with the warning:
Warning messages:
1: In readLines(hpp_path) :
incomplete final line found on '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.hpp'
2: In readLines(private$hpp_file_) :
incomplete final line found on '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.hpp'Here's the contents of that file:
// Code generated by stanc v2.31.0-68-gbd516d56
#include <stan/model/model_header.hpp>
namespace none_model_NA_NA_NA_none_NA_1_model_namespace {
using stan::model::model_base_crtp;
using namespace stan::math;
stan::math::profile_map profiles__;
static constexpr std::array<const char*, 68> locations_array__ =
{" (found before start of program)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 129, column 1 to column 17)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 138, column 12 to column 13)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 138, column 1 to column 23)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 20, column 13 to column 14)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 20, column 2 to column 29)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 22, column 3 to line 40, column 6)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 41, column 3 to line 59, column 6)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 21, column 17 to line 60, column 3)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 21, column 2 to line 60, column 3)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 61, column 2 to column 14)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 141, column 2 to line 158, column 5)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 140, column 1 to line 159, column 2)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 161, column 1 to column 22)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 162, column 1 to column 25)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 66, column 1 to column 8)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 67, column 1 to column 8)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 68, column 8 to column 9)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 68, column 10 to column 11)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 68, column 1 to column 15)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 69, column 12 to column 13)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 69, column 1 to column 17)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 71, column 1 to column 27)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 72, column 1 to column 27)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 73, column 7 to column 27)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 73, column 28 to column 48)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 73, column 1 to column 71)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 74, column 7 to column 27)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 74, column 1 to column 52)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 76, column 1 to column 51)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 77, column 1 to column 51)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 79, column 2 to column 46)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 80, column 3 to column 47)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 78, column 1 to line 81, column 48)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 83, column 2 to column 46)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 82, column 1 to line 84, column 50)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 86, column 1 to column 56)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 87, column 1 to column 56)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 89, column 2 to column 51)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 90, column 4 to column 53)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 88, column 1 to line 91, column 53)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 93, column 2 to column 51)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 92, column 1 to line 94, column 55)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 97, column 1 to column 27)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 98, column 1 to column 27)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 99, column 7 to column 27)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 99, column 28 to column 48)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 99, column 1 to column 71)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 100, column 7 to column 27)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 100, column 1 to column 52)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 102, column 1 to column 51)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 103, column 1 to column 51)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 105, column 2 to column 46)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 106, column 3 to column 47)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 104, column 1 to line 107, column 48)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 109, column 2 to column 46)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 108, column 1 to line 110, column 50)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 112, column 1 to column 56)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 113, column 1 to column 56)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 115, column 2 to column 51)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 116, column 4 to column 53)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 114, column 1 to line 117, column 53)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 119, column 2 to column 51)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 118, column 1 to line 120, column 55)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 125, column 12 to column 13)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 125, column 1 to column 44)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 129, column 8 to column 9)",
" (in '/tmp/RtmpJJA4oI/model-7c98365b5b7eb.stan', line 19, column 3 to line 62, column 2)"};
template <typename T2__, typename T3__,
stan::require_all_t<stan::is_col_vector<T2__>,
stan::is_vt_not_complex<T2__>,
stan::is_row_vector<T3__>,
stan::is_vt_not_complex<T3__>>* = nullptr>
Eigen::Matrix<stan::promote_args_t<stan::base_type_t<T2__>,
stan::base_type_t<T3__>>,1,-1>
mike_dot(const int& r, const int& c, const T2__& beta_arg__, const T3__&
zeros_arg__, const std::vector<std::vector<int>>& index_pos_X_mat,
const std::vector<int>& index_pos_X_sizes,
const std::vector<std::vector<int>>&
col_index_into_X_first_unique_pos_X_mat, const std::vector<int>&
col_index_into_X_first_unique_pos_X_sizes,
const std::vector<std::vector<int>>&
index_of_cZ_for_each_col_in_Z_with_pos_X_mat,
const std::vector<int>&
index_of_cZ_for_each_col_in_Z_with_pos_X_sizes,
const std::vector<std::vector<int>>& index_neg_X_mat,
const std::vector<int>& index_neg_X_sizes,
const std::vector<std::vector<int>>&
col_index_into_X_first_unique_neg_X_mat, const std::vector<int>&
col_index_into_X_first_unique_neg_X_sizes,
const std::vector<std::vector<int>>&
index_of_cZ_for_each_col_in_Z_with_neg_X_mat,
const std::vector<int>&
index_of_cZ_for_each_col_in_Z_with_neg_X_sizes, std::ostream*
pstream__);
template <typename T2__, typename T3__,
stan::require_all_t<stan::is_col_vector<T2__>,
stan::is_vt_not_complex<T2__>,
stan::is_row_vector<T3__>,
stan::is_vt_not_complex<T3__>>*>
Eigen::Matrix<stan::promote_args_t<stan::base_type_t<T2__>,
stan::base_type_t<T3__>>,1,-1>
mike_dot(const int& r, const int& c, const T2__& beta_arg__, const T3__&
zeros_arg__, const std::vector<std::vector<int>>& index_pos_X_mat,
const std::vector<int>& index_pos_X_sizes,
const std::vector<std::vector<int>>&
col_index_into_X_first_unique_pos_X_mat, const std::vector<int>&
col_index_into_X_first_unique_pos_X_sizes,
const std::vector<std::vector<int>>&
index_of_cZ_for_each_col_in_Z_with_pos_X_mat,
const std::vector<int>&
index_of_cZ_for_each_col_in_Z_with_pos_X_sizes,
const std::vector<std::vector<int>>& index_neg_X_mat,
const std::vector<int>& index_neg_X_sizes,
const std::vector<std::vector<int>>&
col_index_into_X_first_unique_neg_X_mat, const std::vector<int>&
col_index_into_X_first_unique_neg_X_sizes,
const std::vector<std::vector<int>>&
index_of_cZ_for_each_col_in_Z_with_neg_X_mat,
const std::vector<int>&
index_of_cZ_for_each_col_in_Z_with_neg_X_sizes, std::ostream*
pstream__) {
using local_scalar_t__ = stan::promote_args_t<stan::base_type_t<T2__>,
stan::base_type_t<T3__>>;
int current_statement__ = 0;
const auto& beta = stan::math::to_ref(beta_arg__);
const auto& zeros = stan::math::to_ref(zeros_arg__);
static constexpr bool propto__ = true;
// suppress unused var warning
(void) propto__;
local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
// suppress unused var warning
(void) DUMMY_VAR__;
try {
current_statement__ = 4;
stan::math::validate_non_negative_index("out", "c", c);
Eigen::Matrix<local_scalar_t__,1,-1> out;
current_statement__ = 5;
stan::model::assign(out, zeros, "assigning variable out");
current_statement__ = 9;
for (int i_r = 1; i_r <= r; ++i_r) {
current_statement__ = 6;
stan::model::assign(out,
stan::model::rvalue(
stan::math::add(
stan::model::deep_copy(
stan::model::rvalue(out, "out",
stan::model::index_multi(
stan::model::rvalue(
col_index_into_X_first_unique_pos_X_mat,
"col_index_into_X_first_unique_pos_X_mat",
stan::model::index_uni(i_r),
stan::model::index_min_max(1,
stan::model::rvalue(
col_index_into_X_first_unique_pos_X_sizes,
"col_index_into_X_first_unique_pos_X_sizes",
stan::model::index_uni(i_r))))))),
stan::model::rvalue(beta, "beta", stan::model::index_uni(i_r))),
"(FnDeepCopy__(out[col_index_into_X_first_unique_pos_X_mat[i_r,\n 1:col_index_into_X_first_unique_pos_X_sizes\n [i_r]]]) + \nbeta[i_r])",
stan::model::index_multi(
stan::model::rvalue(index_of_cZ_for_each_col_in_Z_with_pos_X_mat,
"index_of_cZ_for_each_col_in_Z_with_pos_X_mat",
stan::model::index_uni(i_r),
stan::model::index_min_max(1,
stan::model::rvalue(
index_of_cZ_for_each_col_in_Z_with_pos_X_sizes,
"index_of_cZ_for_each_col_in_Z_with_pos_X_sizes",
stan::model::index_uni(i_r)))))), "assigning variable out",
stan::model::index_multi(
stan::model::rvalue(index_pos_X_mat, "index_pos_X_mat",
stan::model::index_uni(i_r),
stan::model::index_min_max(1,
stan::model::rvalue(index_pos_X_sizes, "index_pos_X_sizes",
stan::model::index_uni(i_r))))));
current_statement__ = 7;
stan::model::assign(out,
stan::model::rvalue(
stan::math::subtract(
stan::model::deep_copy(
stan::model::rvalue(out, "out",
stan::model::index_multi(
stan::model::rvalue(
col_index_into_X_first_unique_neg_X_mat,
"col_index_into_X_first_unique_neg_X_mat",
stan::model::index_uni(i_r),
stan::model::index_min_max(1,
stan::model::rvalue(
col_index_into_X_first_unique_neg_X_sizes,
"col_index_into_X_first_unique_neg_X_sizes",
stan::model::index_uni(i_r))))))),
stan::model::rvalue(beta, "beta", stan::model::index_uni(i_r))),
"(FnDeepCopy__(out[col_index_into_X_first_unique_neg_X_mat[i_r,\n 1:col_index_into_X_first_unique_neg_X_sizes\n [i_r]]]) - \nbeta[i_r])",
stan::model::index_multi(
stan::model::rvalue(index_of_cZ_for_each_col_in_Z_with_neg_X_mat,
"index_of_cZ_for_each_col_in_Z_with_neg_X_mat",
stan::model::index_uni(i_r),
stan::model::index_min_max(1,
stan::model::rvalue(
index_of_cZ_for_each_col_in_Z_with_neg_X_sizes,
"index_of_cZ_for_each_col_in_Z_with_neg_X_sizes",
stan::model::index_uni(i_r)))))), "assigning variable out",
stan::model::index_multi(
stan::model::rvalue(index_neg_X_mat, "index_neg_X_mat",
stan::model::index_uni(i_r),
stan::model::index_min_max(1,
stan::model::rvalue(index_neg_X_sizes, "index_neg_X_sizes",
stan::model::index_uni(i_r))))));
}
current_statement__ = 10;
return out;
} catch (const std::exception& e) {
stan::lang::rethrow_located(e, locations_array__[current_statement__]);
}
}
class none_model_NA_NA_NA_none_NA_1_model final : public model_base_crtp<none_model_NA_NA_NA_none_NA_1_model> {
private:
int r;
int c;
Eigen::Matrix<double,-1,-1> X_data__;
Eigen::Matrix<double,1,-1> Y_data__;
int index_pos_X_mat_rows;
int index_pos_X_mat_cols;
std::vector<std::vector<int>> index_pos_X_mat;
std::vector<int> index_pos_X_sizes;
int col_index_into_X_first_unique_pos_X_mat_rows;
int col_index_into_X_first_unique_pos_X_mat_cols;
std::vector<std::vector<int>> col_index_into_X_first_unique_pos_X_mat;
std::vector<int> col_index_into_X_first_unique_pos_X_sizes;
int index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows;
int index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols;
std::vector<std::vector<int>> index_of_cZ_for_each_col_in_Z_with_pos_X_mat;
std::vector<int> index_of_cZ_for_each_col_in_Z_with_pos_X_sizes;
int index_neg_X_mat_rows;
int index_neg_X_mat_cols;
std::vector<std::vector<int>> index_neg_X_mat;
std::vector<int> index_neg_X_sizes;
int col_index_into_X_first_unique_neg_X_mat_rows;
int col_index_into_X_first_unique_neg_X_mat_cols;
std::vector<std::vector<int>> col_index_into_X_first_unique_neg_X_mat;
std::vector<int> col_index_into_X_first_unique_neg_X_sizes;
int index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows;
int index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols;
std::vector<std::vector<int>> index_of_cZ_for_each_col_in_Z_with_neg_X_mat;
std::vector<int> index_of_cZ_for_each_col_in_Z_with_neg_X_sizes;
Eigen::Matrix<double,1,-1> zeros_data__;
Eigen::Map<Eigen::Matrix<double,-1,-1>> X{nullptr, 0, 0};
Eigen::Map<Eigen::Matrix<double,1,-1>> Y{nullptr, 0};
Eigen::Map<Eigen::Matrix<double,1,-1>> zeros{nullptr, 0};
public:
~none_model_NA_NA_NA_none_NA_1_model() {}
none_model_NA_NA_NA_none_NA_1_model(stan::io::var_context& context__,
unsigned int random_seed__ = 0,
std::ostream* pstream__ = nullptr)
: model_base_crtp(0) {
int current_statement__ = 0;
using local_scalar_t__ = double;
boost::ecuyer1988 base_rng__ =
stan::services::util::create_rng(random_seed__, 0);
// suppress unused var warning
(void) base_rng__;
static constexpr const char* function__ =
"none_model_NA_NA_NA_none_NA_1_model_namespace::none_model_NA_NA_NA_none_NA_1_model";
// suppress unused var warning
(void) function__;
local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
// suppress unused var warning
(void) DUMMY_VAR__;
try {
int pos__;
pos__ = 1;
current_statement__ = 15;
context__.validate_dims("data initialization", "r", "int",
std::vector<size_t>{});
r = std::numeric_limits<int>::min();
current_statement__ = 15;
r = context__.vals_i("r")[(1 - 1)];
current_statement__ = 16;
context__.validate_dims("data initialization", "c", "int",
std::vector<size_t>{});
c = std::numeric_limits<int>::min();
current_statement__ = 16;
c = context__.vals_i("c")[(1 - 1)];
current_statement__ = 17;
stan::math::validate_non_negative_index("X", "r", r);
current_statement__ = 18;
stan::math::validate_non_negative_index("X", "c", c);
current_statement__ = 19;
context__.validate_dims("data initialization", "X", "double",
std::vector<size_t>{static_cast<size_t>(r), static_cast<size_t>(c)});
X_data__ = Eigen::Matrix<double,-1,-1>::Constant(r, c,
std::numeric_limits<double>::quiet_NaN());
new (&X) Eigen::Map<Eigen::Matrix<double,-1,-1>>(X_data__.data(), r, c);
{
std::vector<local_scalar_t__> X_flat__;
current_statement__ = 19;
X_flat__ = context__.vals_r("X");
current_statement__ = 19;
pos__ = 1;
current_statement__ = 19;
for (int sym1__ = 1; sym1__ <= c; ++sym1__) {
current_statement__ = 19;
for (int sym2__ = 1; sym2__ <= r; ++sym2__) {
current_statement__ = 19;
stan::model::assign(X, X_flat__[(pos__ - 1)],
"assigning variable X", stan::model::index_uni(sym2__),
stan::model::index_uni(sym1__));
current_statement__ = 19;
pos__ = (pos__ + 1);
}
}
}
current_statement__ = 20;
stan::math::validate_non_negative_index("Y", "c", c);
current_statement__ = 21;
context__.validate_dims("data initialization", "Y", "double",
std::vector<size_t>{static_cast<size_t>(c)});
Y_data__ = Eigen::Matrix<double,1,-1>::Constant(c,
std::numeric_limits<double>::quiet_NaN());
new (&Y) Eigen::Map<Eigen::Matrix<double,1,-1>>(Y_data__.data(), c);
{
std::vector<local_scalar_t__> Y_flat__;
current_statement__ = 21;
Y_flat__ = context__.vals_r("Y");
current_statement__ = 21;
pos__ = 1;
current_statement__ = 21;
for (int sym1__ = 1; sym1__ <= c; ++sym1__) {
current_statement__ = 21;
stan::model::assign(Y, Y_flat__[(pos__ - 1)],
"assigning variable Y", stan::model::index_uni(sym1__));
current_statement__ = 21;
pos__ = (pos__ + 1);
}
}
current_statement__ = 22;
context__.validate_dims("data initialization", "index_pos_X_mat_rows",
"int", std::vector<size_t>{});
index_pos_X_mat_rows = std::numeric_limits<int>::min();
current_statement__ = 22;
index_pos_X_mat_rows = context__.vals_i("index_pos_X_mat_rows")[(1 -
1)];
current_statement__ = 23;
context__.validate_dims("data initialization", "index_pos_X_mat_cols",
"int", std::vector<size_t>{});
index_pos_X_mat_cols = std::numeric_limits<int>::min();
current_statement__ = 23;
index_pos_X_mat_cols = context__.vals_i("index_pos_X_mat_cols")[(1 -
1)];
current_statement__ = 24;
stan::math::validate_non_negative_index("index_pos_X_mat",
"index_pos_X_mat_rows", index_pos_X_mat_rows);
current_statement__ = 25;
stan::math::validate_non_negative_index("index_pos_X_mat",
"index_pos_X_mat_cols", index_pos_X_mat_cols);
current_statement__ = 26;
context__.validate_dims("data initialization", "index_pos_X_mat",
"int",
std::vector<size_t>{static_cast<size_t>(index_pos_X_mat_rows),
static_cast<size_t>(index_pos_X_mat_cols)});
index_pos_X_mat = std::vector<std::vector<int>>(index_pos_X_mat_rows,
std::vector<int>(index_pos_X_mat_cols,
std::numeric_limits<int>::min()));
{
std::vector<int> index_pos_X_mat_flat__;
current_statement__ = 26;
index_pos_X_mat_flat__ = context__.vals_i("index_pos_X_mat");
current_statement__ = 26;
pos__ = 1;
current_statement__ = 26;
for (int sym1__ = 1; sym1__ <= index_pos_X_mat_cols; ++sym1__) {
current_statement__ = 26;
for (int sym2__ = 1; sym2__ <= index_pos_X_mat_rows; ++sym2__) {
current_statement__ = 26;
stan::model::assign(index_pos_X_mat,
index_pos_X_mat_flat__[(pos__ - 1)],
"assigning variable index_pos_X_mat",
stan::model::index_uni(sym2__), stan::model::index_uni(sym1__));
current_statement__ = 26;
pos__ = (pos__ + 1);
}
}
}
current_statement__ = 27;
stan::math::validate_non_negative_index("index_pos_X_sizes",
"index_pos_X_mat_rows", index_pos_X_mat_rows);
current_statement__ = 28;
context__.validate_dims("data initialization", "index_pos_X_sizes",
"int",
std::vector<size_t>{static_cast<size_t>(index_pos_X_mat_rows)});
index_pos_X_sizes = std::vector<int>(index_pos_X_mat_rows,
std::numeric_limits<int>::min());
current_statement__ = 28;
index_pos_X_sizes = context__.vals_i("index_pos_X_sizes");
current_statement__ = 29;
context__.validate_dims("data initialization",
"col_index_into_X_first_unique_pos_X_mat_rows", "int",
std::vector<size_t>{});
col_index_into_X_first_unique_pos_X_mat_rows = std::numeric_limits<int>::min(
);
current_statement__ = 29;
col_index_into_X_first_unique_pos_X_mat_rows = context__.vals_i("col_index_into_X_first_unique_pos_X_mat_rows")[(1
- 1)];
current_statement__ = 30;
context__.validate_dims("data initialization",
"col_index_into_X_first_unique_pos_X_mat_cols", "int",
std::vector<size_t>{});
col_index_into_X_first_unique_pos_X_mat_cols = std::numeric_limits<int>::min(
);
current_statement__ = 30;
col_index_into_X_first_unique_pos_X_mat_cols = context__.vals_i("col_index_into_X_first_unique_pos_X_mat_cols")[(1
- 1)];
current_statement__ = 31;
stan::math::validate_non_negative_index(
"col_index_into_X_first_unique_pos_X_mat",
"col_index_into_X_first_unique_pos_X_mat_rows",
col_index_into_X_first_unique_pos_X_mat_rows);
current_statement__ = 32;
stan::math::validate_non_negative_index(
"col_index_into_X_first_unique_pos_X_mat",
"col_index_into_X_first_unique_pos_X_mat_cols",
col_index_into_X_first_unique_pos_X_mat_cols);
current_statement__ = 33;
context__.validate_dims("data initialization",
"col_index_into_X_first_unique_pos_X_mat", "int",
std::vector<size_t>{static_cast<size_t>(
col_index_into_X_first_unique_pos_X_mat_rows),
static_cast<size_t>(col_index_into_X_first_unique_pos_X_mat_cols)});
col_index_into_X_first_unique_pos_X_mat = std::vector<std::vector<int>>(col_index_into_X_first_unique_pos_X_mat_rows,
std::vector<int>(col_index_into_X_first_unique_pos_X_mat_cols,
std::numeric_limits<int>::min(
)));
{
std::vector<int> col_index_into_X_first_unique_pos_X_mat_flat__;
current_statement__ = 33;
col_index_into_X_first_unique_pos_X_mat_flat__ = context__.vals_i("col_index_into_X_first_unique_pos_X_mat");
current_statement__ = 33;
pos__ = 1;
current_statement__ = 33;
for (int sym1__ = 1; sym1__ <=
col_index_into_X_first_unique_pos_X_mat_cols; ++sym1__) {
current_statement__ = 33;
for (int sym2__ = 1; sym2__ <=
col_index_into_X_first_unique_pos_X_mat_rows; ++sym2__) {
current_statement__ = 33;
stan::model::assign(col_index_into_X_first_unique_pos_X_mat,
col_index_into_X_first_unique_pos_X_mat_flat__[(pos__ - 1)],
"assigning variable col_index_into_X_first_unique_pos_X_mat",
stan::model::index_uni(sym2__), stan::model::index_uni(sym1__));
current_statement__ = 33;
pos__ = (pos__ + 1);
}
}
}
current_statement__ = 34;
stan::math::validate_non_negative_index(
"col_index_into_X_first_unique_pos_X_sizes",
"col_index_into_X_first_unique_pos_X_mat_rows",
col_index_into_X_first_unique_pos_X_mat_rows);
current_statement__ = 35;
context__.validate_dims("data initialization",
"col_index_into_X_first_unique_pos_X_sizes", "int",
std::vector<size_t>{static_cast<size_t>(
col_index_into_X_first_unique_pos_X_mat_rows)});
col_index_into_X_first_unique_pos_X_sizes = std::vector<int>(col_index_into_X_first_unique_pos_X_mat_rows,
std::numeric_limits<int>::min(
));
current_statement__ = 35;
col_index_into_X_first_unique_pos_X_sizes = context__.vals_i("col_index_into_X_first_unique_pos_X_sizes");
current_statement__ = 36;
context__.validate_dims("data initialization",
"index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows", "int",
std::vector<size_t>{});
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows = std::numeric_limits<int>::min(
);
current_statement__ = 36;
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows = context__.vals_i("index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows")[(1
- 1)];
current_statement__ = 37;
context__.validate_dims("data initialization",
"index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols", "int",
std::vector<size_t>{});
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols = std::numeric_limits<int>::min(
);
current_statement__ = 37;
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols = context__.vals_i("index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols")[(1
- 1)];
current_statement__ = 38;
stan::math::validate_non_negative_index(
"index_of_cZ_for_each_col_in_Z_with_pos_X_mat",
"index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows",
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows);
current_statement__ = 39;
stan::math::validate_non_negative_index(
"index_of_cZ_for_each_col_in_Z_with_pos_X_mat",
"index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols",
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols);
current_statement__ = 40;
context__.validate_dims("data initialization",
"index_of_cZ_for_each_col_in_Z_with_pos_X_mat", "int",
std::vector<size_t>{static_cast<size_t>(
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows),
static_cast<size_t>(
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols)});
index_of_cZ_for_each_col_in_Z_with_pos_X_mat = std::vector<
std::vector<int>>(index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows,
std::vector<int>(index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols,
std::numeric_limits<int>::min(
)));
{
std::vector<int> index_of_cZ_for_each_col_in_Z_with_pos_X_mat_flat__;
current_statement__ = 40;
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_flat__ = context__.vals_i("index_of_cZ_for_each_col_in_Z_with_pos_X_mat");
current_statement__ = 40;
pos__ = 1;
current_statement__ = 40;
for (int sym1__ = 1; sym1__ <=
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_cols; ++sym1__) {
current_statement__ = 40;
for (int sym2__ = 1; sym2__ <=
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows; ++sym2__) {
current_statement__ = 40;
stan::model::assign(index_of_cZ_for_each_col_in_Z_with_pos_X_mat,
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_flat__[(pos__ -
1)],
"assigning variable index_of_cZ_for_each_col_in_Z_with_pos_X_mat",
stan::model::index_uni(sym2__), stan::model::index_uni(sym1__));
current_statement__ = 40;
pos__ = (pos__ + 1);
}
}
}
current_statement__ = 41;
stan::math::validate_non_negative_index(
"index_of_cZ_for_each_col_in_Z_with_pos_X_sizes",
"index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows",
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows);
current_statement__ = 42;
context__.validate_dims("data initialization",
"index_of_cZ_for_each_col_in_Z_with_pos_X_sizes", "int",
std::vector<size_t>{static_cast<size_t>(
index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows)});
index_of_cZ_for_each_col_in_Z_with_pos_X_sizes = std::vector<int>(index_of_cZ_for_each_col_in_Z_with_pos_X_mat_rows,
std::numeric_limits<int>::min(
));
current_statement__ = 42;
index_of_cZ_for_each_col_in_Z_with_pos_X_sizes = context__.vals_i("index_of_cZ_for_each_col_in_Z_with_pos_X_sizes");
current_statement__ = 43;
context__.validate_dims("data initialization", "index_neg_X_mat_rows",
"int", std::vector<size_t>{});
index_neg_X_mat_rows = std::numeric_limits<int>::min();
current_statement__ = 43;
index_neg_X_mat_rows = context__.vals_i("index_neg_X_mat_rows")[(1 -
1)];
current_statement__ = 44;
context__.validate_dims("data initialization", "index_neg_X_mat_cols",
"int", std::vector<size_t>{});
index_neg_X_mat_cols = std::numeric_limits<int>::min();
current_statement__ = 44;
index_neg_X_mat_cols = context__.vals_i("index_neg_X_mat_cols")[(1 -
1)];
current_statement__ = 45;
stan::math::validate_non_negative_index("index_neg_X_mat",
"index_neg_X_mat_rows", index_neg_X_mat_rows);
current_statement__ = 46;
stan::math::validate_non_negative_index("index_neg_X_mat",
"index_neg_X_mat_cols", index_neg_X_mat_cols);
current_statement__ = 47;
context__.validate_dims("data initialization", "index_neg_X_mat",
"int",
std::vector<size_t>{static_cast<size_t>(index_neg_X_mat_rows),
static_cast<size_t>(index_neg_X_mat_cols)});
index_neg_X_mat = std::vector<std::vector<int>>(index_neg_X_mat_rows,
std::vector<int>(index_neg_X_mat_cols,
std::numeric_limits<int>::min()));
{
std::vector<int> index_neg_X_mat_flat__;
current_statement__ = 47;
index_neg_X_mat_flat__ = context__.vals_i("index_neg_X_mat");
current_statement__ = 47;
pos__ = 1;
current_statement__ = 47;
for (int sym1__ = 1; sym1__ <= index_neg_X_mat_cols; ++sym1__) {
current_statement__ = 47;
for (int sym2__ = 1; sym2__ <= index_neg_X_mat_rows; ++sym2__) {
current_statement__ = 47;
stan::model::assign(index_neg_X_mat,
index_neg_X_mat_flat__[(pos__ - 1)],
"assigning variable index_neg_X_mat",
stan::model::index_uni(sym2__), stan::model::index_uni(sym1__));
current_statement__ = 47;
pos__ = (pos__ + 1);
}
}
}
current_statement__ = 48;
stan::math::validate_non_negative_index("index_neg_X_sizes",
"index_neg_X_mat_rows", index_neg_X_mat_rows);
current_statement__ = 49;
context__.validate_dims("data initialization", "index_neg_X_sizes",
"int",
std::vector<size_t>{static_cast<size_t>(index_neg_X_mat_rows)});
index_neg_X_sizes = std::vector<int>(index_neg_X_mat_rows,
std::numeric_limits<int>::min());
current_statement__ = 49;
index_neg_X_sizes = context__.vals_i("index_neg_X_sizes");
current_statement__ = 50;
context__.validate_dims("data initialization",
"col_index_into_X_first_unique_neg_X_mat_rows", "int",
std::vector<size_t>{});
col_index_into_X_first_unique_neg_X_mat_rows = std::numeric_limits<int>::min(
);
current_statement__ = 50;
col_index_into_X_first_unique_neg_X_mat_rows = context__.vals_i("col_index_into_X_first_unique_neg_X_mat_rows")[(1
- 1)];
current_statement__ = 51;
context__.validate_dims("data initialization",
"col_index_into_X_first_unique_neg_X_mat_cols", "int",
std::vector<size_t>{});
col_index_into_X_first_unique_neg_X_mat_cols = std::numeric_limits<int>::min(
);
current_statement__ = 51;
col_index_into_X_first_unique_neg_X_mat_cols = context__.vals_i("col_index_into_X_first_unique_neg_X_mat_cols")[(1
- 1)];
current_statement__ = 52;
stan::math::validate_non_negative_index(
"col_index_into_X_first_unique_neg_X_mat",
"col_index_into_X_first_unique_neg_X_mat_rows",
col_index_into_X_first_unique_neg_X_mat_rows);
current_statement__ = 53;
stan::math::validate_non_negative_index(
"col_index_into_X_first_unique_neg_X_mat",
"col_index_into_X_first_unique_neg_X_mat_cols",
col_index_into_X_first_unique_neg_X_mat_cols);
current_statement__ = 54;
context__.validate_dims("data initialization",
"col_index_into_X_first_unique_neg_X_mat", "int",
std::vector<size_t>{static_cast<size_t>(
col_index_into_X_first_unique_neg_X_mat_rows),
static_cast<size_t>(col_index_into_X_first_unique_neg_X_mat_cols)});
col_index_into_X_first_unique_neg_X_mat = std::vector<std::vector<int>>(col_index_into_X_first_unique_neg_X_mat_rows,
std::vector<int>(col_index_into_X_first_unique_neg_X_mat_cols,
std::numeric_limits<int>::min(
)));
{
std::vector<int> col_index_into_X_first_unique_neg_X_mat_flat__;
current_statement__ = 54;
col_index_into_X_first_unique_neg_X_mat_flat__ = context__.vals_i("col_index_into_X_first_unique_neg_X_mat");
current_statement__ = 54;
pos__ = 1;
current_statement__ = 54;
for (int sym1__ = 1; sym1__ <=
col_index_into_X_first_unique_neg_X_mat_cols; ++sym1__) {
current_statement__ = 54;
for (int sym2__ = 1; sym2__ <=
col_index_into_X_first_unique_neg_X_mat_rows; ++sym2__) {
current_statement__ = 54;
stan::model::assign(col_index_into_X_first_unique_neg_X_mat,
col_index_into_X_first_unique_neg_X_mat_flat__[(pos__ - 1)],
"assigning variable col_index_into_X_first_unique_neg_X_mat",
stan::model::index_uni(sym2__), stan::model::index_uni(sym1__));
current_statement__ = 54;
pos__ = (pos__ + 1);
}
}
}
current_statement__ = 55;
stan::math::validate_non_negative_index(
"col_index_into_X_first_unique_neg_X_sizes",
"col_index_into_X_first_unique_neg_X_mat_rows",
col_index_into_X_first_unique_neg_X_mat_rows);
current_statement__ = 56;
context__.validate_dims("data initialization",
"col_index_into_X_first_unique_neg_X_sizes", "int",
std::vector<size_t>{static_cast<size_t>(
col_index_into_X_first_unique_neg_X_mat_rows)});
col_index_into_X_first_unique_neg_X_sizes = std::vector<int>(col_index_into_X_first_unique_neg_X_mat_rows,
std::numeric_limits<int>::min(
));
current_statement__ = 56;
col_index_into_X_first_unique_neg_X_sizes = context__.vals_i("col_index_into_X_first_unique_neg_X_sizes");
current_statement__ = 57;
context__.validate_dims("data initialization",
"index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows", "int",
std::vector<size_t>{});
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows = std::numeric_limits<int>::min(
);
current_statement__ = 57;
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows = context__.vals_i("index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows")[(1
- 1)];
current_statement__ = 58;
context__.validate_dims("data initialization",
"index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols", "int",
std::vector<size_t>{});
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols = std::numeric_limits<int>::min(
);
current_statement__ = 58;
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols = context__.vals_i("index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols")[(1
- 1)];
current_statement__ = 59;
stan::math::validate_non_negative_index(
"index_of_cZ_for_each_col_in_Z_with_neg_X_mat",
"index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows",
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows);
current_statement__ = 60;
stan::math::validate_non_negative_index(
"index_of_cZ_for_each_col_in_Z_with_neg_X_mat",
"index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols",
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols);
current_statement__ = 61;
context__.validate_dims("data initialization",
"index_of_cZ_for_each_col_in_Z_with_neg_X_mat", "int",
std::vector<size_t>{static_cast<size_t>(
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows),
static_cast<size_t>(
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols)});
index_of_cZ_for_each_col_in_Z_with_neg_X_mat = std::vector<
std::vector<int>>(index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows,
std::vector<int>(index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols,
std::numeric_limits<int>::min(
)));
{
std::vector<int> index_of_cZ_for_each_col_in_Z_with_neg_X_mat_flat__;
current_statement__ = 61;
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_flat__ = context__.vals_i("index_of_cZ_for_each_col_in_Z_with_neg_X_mat");
current_statement__ = 61;
pos__ = 1;
current_statement__ = 61;
for (int sym1__ = 1; sym1__ <=
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_cols; ++sym1__) {
current_statement__ = 61;
for (int sym2__ = 1; sym2__ <=
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows; ++sym2__) {
current_statement__ = 61;
stan::model::assign(index_of_cZ_for_each_col_in_Z_with_neg_X_mat,
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_flat__[(pos__ -
1)],
"assigning variable index_of_cZ_for_each_col_in_Z_with_neg_X_mat",
stan::model::index_uni(sym2__), stan::model::index_uni(sym1__));
current_statement__ = 61;
pos__ = (pos__ + 1);
}
}
}
current_statement__ = 62;
stan::math::validate_non_negative_index(
"index_of_cZ_for_each_col_in_Z_with_neg_X_sizes",
"index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows",
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows);
current_statement__ = 63;
context__.validate_dims("data initialization",
"index_of_cZ_for_each_col_in_Z_with_neg_X_sizes", "int",
std::vector<size_t>{static_cast<size_t>(
index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows)});
index_of_cZ_for_each_col_in_Z_with_neg_X_sizes = std::vector<int>(index_of_cZ_for_each_col_in_Z_with_neg_X_mat_rows,
std::numeric_limits<int>::min(
));
current_statement__ = 63;
index_of_cZ_for_each_col_in_Z_with_neg_X_sizes = context__.vals_i("index_of_cZ_for_each_col_in_Z_with_neg_X_sizes");
current_statement__ = 64;
stan::math::validate_non_negative_index("zeros", "c", c);
current_statement__ = 65;
zeros_data__ = Eigen::Matrix<double,1,-1>::Constant(c,
std::numeric_limits<double>::quiet_NaN());
new (&zeros)
Eigen::Map<Eigen::Matrix<double,1,-1>>(zeros_data__.data(), c);
current_statement__ = 65;
stan::model::assign(zeros, stan::math::zeros_row_vector(c),
"assigning variable zeros");
current_statement__ = 66;
stan::math::validate_non_negative_index("beta", "r", r);
} catch (const std::exception& e) {
stan::lang::rethrow_located(e, locations_array__[current_statement__]);
}
num_params_r__ = r;
}
inline std::string model_name() const final {
return "none_model_NA_NA_NA_none_NA_1_model";
}
inline std::vector<std::string> model_compile_info() const noexcept {
return std::vector<std::string>{"stanc_version = stanc3 v2.31.0-68-gbd516d56",
"stancflags = --O1 --name=none_model_NA_NA_NA_none_NA_1_model"};
}
template <bool propto__, bool jacobian__, typename VecR, typename VecI,
stan::require_vector_like_t<VecR>* = nullptr,
stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr>
inline stan::scalar_type_t<VecR>
log_prob_impl(VecR& params_r__, VecI& params_i__, std::ostream*
pstream__ = nullptr) const {
using T__ = stan::scalar_type_t<VecR>;
using local_scalar_t__ = T__;
T__ lp__(0.0);
stan::math::accumulator<T__> lp_accum__;
stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);
int current_statement__ = 0;
local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
// suppress unused var warning
(void) DUMMY_VAR__;
static constexpr const char* function__ =
"none_model_NA_NA_NA_none_NA_1_model_namespace::log_prob";
// suppress unused var warning
(void) function__;
try {
Eigen::Matrix<local_scalar_t__,-1,1> beta;
current_statement__ = 1;
beta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(r);
{
current_statement__ = 2;
stan::math::validate_non_negative_index("Z_mike", "c", c);
stan::conditional_var_value_t<local_scalar_t__,
Eigen::Matrix<local_scalar_t__,1,-1>> Z_mike =
stan::conditional_var_value_t<local_scalar_t__,
Eigen::Matrix<local_scalar_t__,1,-1>>(Eigen::Matrix<double,1,-1>::Constant(c,
std::numeric_limits<double>::quiet_NaN(
)));
current_statement__ = 12;
{
stan::math::profile<local_scalar_t__> profile__("mike",
const_cast<stan::math::profile_map&>(profiles__));
stan::conditional_var_value_t<local_scalar_t__,
Eigen::Matrix<local_scalar_t__,1,-1>>
inline_mike_dot_return_sym1__;
{
current_statement__ = 4;
stan::math::validate_non_negative_index("out", "c", c);
stan::conditional_var_value_t<local_scalar_t__,
Eigen::Matrix<local_scalar_t__,1,-1>>
inline_mike_dot_out_sym2__;
current_statement__ = 5;
stan::model::assign(inline_mike_dot_out_sym2__, zeros,
"assigning variable inline_mike_dot_out_sym2__");
current_statement__ = 9;
for (int inline_mike_dot_i_r_sym3__ = 1; inline_mike_dot_i_r_sym3__
<= r; ++inline_mike_dot_i_r_sym3__) {
current_statement__ = 6;
stan::model::assign(inline_mike_dot_out_sym2__,
stan::model::rvalue(
stan::math::add(
stan::model::deep_copy(
stan::model::rvalue(inline_mike_dot_out_sym2__,
"inline_mike_dot_out_sym2__",
stan::model::index_multi(
stan::model::rvalue(
col_index_into_X_first_unique_pos_X_mat,
"col_index_into_X_first_unique_pos_X_mat",
stan::model::index_uni(inline_mike_dot_i_r_sym3__),
stan::model::index_min_max(1,
col_index_into_X_first_unique_pos_X_sizes[(inline_mike_dot_i_r_sym3__
- 1)]))))), beta[(inline_mike_dot_i_r_sym3__ -
1)]),
"(FnDeepCopy__(inline_mike_dot_out_sym2__[col_index_into_X_first_unique_pos_X_mat\n [inline_mike_dot_i_r_sym3__,\n 1:col_index_into_X_first_unique_pos_X_sizes\n [inline_mike_dot_i_r_sym3__]]]) + \nbeta[inline_mike_dot_i_r_sym3__])",
stan::model::index_multi(
stan::model::rvalue(
index_of_cZ_for_each_col_in_Z_with_pos_X_mat,
"index_of_cZ_for_each_col_in_Z_with_pos_X_mat",
stan::model::index_uni(inline_mike_dot_i_r_sym3__),
stan::model::index_min_max(1,
index_of_cZ_for_each_col_in_Z_with_pos_X_sizes[(inline_mike_dot_i_r_sym3__
- 1)])))),
"assigning variable inline_mike_dot_out_sym2__",
stan::model::index_multi(
stan::model::rvalue(index_pos_X_mat, "index_pos_X_mat",
stan::model::index_uni(inline_mike_dot_i_r_sym3__),
stan::model::index_min_max(1,
index_pos_X_sizes[(inline_mike_dot_i_r_sym3__ - 1)]))));
current_statement__ = 7;
stan::model::assign(inline_mike_dot_out_sym2__,
stan::model::rvalue(
stan::math::subtract(
stan::model::deep_copy(
stan::model::rvalue(inline_mike_dot_out_sym2__,
"inline_mike_dot_out_sym2__",
stan::model::index_multi(
stan::model::rvalue(
col_index_into_X_first_unique_neg_X_mat,
"col_index_into_X_first_unique_neg_X_mat",
stan::model::index_uni(inline_mike_dot_i_r_sym3__),
stan::model::index_min_max(1,
col_index_into_X_first_unique_neg_X_sizes[(inline_mike_dot_i_r_sym3__
- 1)]))))), beta[(inline_mike_dot_i_r_sym3__ -
1)]),
"(FnDeepCopy__(inline_mike_dot_out_sym2__[col_index_into_X_first_unique_neg_X_mat\n [inline_mike_dot_i_r_sym3__,\n 1:col_index_into_X_first_unique_neg_X_sizes\n [inline_mike_dot_i_r_sym3__]]]) - \nbeta[inline_mike_dot_i_r_sym3__])",
stan::model::index_multi(
stan::model::rvalue(
index_of_cZ_for_each_col_in_Z_with_neg_X_mat,
"index_of_cZ_for_each_col_in_Z_with_neg_X_mat",
stan::model::index_uni(inline_mike_dot_i_r_sym3__),
stan::model::index_min_max(1,
index_of_cZ_for_each_col_in_Z_with_neg_X_sizes[(inline_mike_dot_i_r_sym3__
- 1)])))),
"assigning variable inline_mike_dot_out_sym2__",
stan::model::index_multi(
stan::model::rvalue(index_neg_X_mat, "index_neg_X_mat",
stan::model::index_uni(inline_mike_dot_i_r_sym3__),
stan::model::index_min_max(1,
index_neg_X_sizes[(inline_mike_dot_i_r_sym3__ - 1)]))));
}
current_statement__ = 10;
stan::model::assign(inline_mike_dot_return_sym1__,
inline_mike_dot_out_sym2__,
"assigning variable inline_mike_dot_return_sym1__");
}
stan::model::assign(Z_mike, inline_mike_dot_return_sym1__,
"assigning variable Z_mike");
}
current_statement__ = 13;
lp_accum__.add(stan::math::std_normal_lpdf<propto__>(beta));
current_statement__ = 14;
lp_accum__.add(stan::math::normal_lpdf<propto__>(Y, Z_mike, 1.0));
}
} catch (const std::exception& e) {
stan::lang::rethrow_located(e, locations_array__[current_statement__]);
}
lp_accum__.add(lp__);
return lp_accum__.sum();
}
template <typename RNG, typename VecR, typename VecI, typename VecVar,
stan::require_vector_like_vt<std::is_floating_point,
VecR>* = nullptr, stan::require_vector_like_vt<std::is_integral,
VecI>* = nullptr, stan::require_vector_vt<std::is_floating_point,
VecVar>* = nullptr>
inline void
write_array_impl(RNG& base_rng__, VecR& params_r__, VecI& params_i__,
VecVar& vars__, const bool
emit_transformed_parameters__ = true, const bool
emit_generated_quantities__ = true, std::ostream*
pstream__ = nullptr) const {
using local_scalar_t__ = double;
stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);
stan::io::serializer<local_scalar_t__> out__(vars__);
static constexpr bool propto__ = true;
// suppress unused var warning
(void) propto__;
double lp__ = 0.0;
// suppress unused var warning
(void) lp__;
int current_statement__ = 0;
stan::math::accumulator<double> lp_accum__;
local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
// suppress unused var warning
(void) DUMMY_VAR__;
constexpr bool jacobian__ = false;
static constexpr const char* function__ =
"none_model_NA_NA_NA_none_NA_1_model_namespace::write_array";
// suppress unused var warning
(void) function__;
try {
Eigen::Matrix<double,-1,1> beta;
current_statement__ = 1;
beta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(r);
out__.write(beta);
if (stan::math::logical_negation(
(stan::math::primitive_value(emit_transformed_parameters__) ||
stan::math::primitive_value(emit_generated_quantities__)))) {
return ;
}
if (stan::math::logical_negation(emit_generated_quantities__)) {
return ;
}
} catch (const std::exception& e) {
stan::lang::rethrow_located(e, locations_array__[current_statement__]);
}
}
template <typename VecVar, typename VecI,
stan::require_vector_t<VecVar>* = nullptr,
stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr>
inline void
transform_inits_impl(VecVar& params_r__, VecI& params_i__, VecVar& vars__,
std::ostream* pstream__ = nullptr) const {
using local_scalar_t__ = double;
stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);
stan::io::serializer<local_scalar_t__> out__(vars__);
int current_statement__ = 0;
local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
// suppress unused var warning
(void) DUMMY_VAR__;
try {
int pos__;
pos__ = 1;
Eigen::Matrix<local_scalar_t__,-1,1> beta =
Eigen::Matrix<local_scalar_t__,-1,1>::Constant(r, DUMMY_VAR__);
for (int sym1__ = 1; sym1__ <= r; ++sym1__) {
stan::model::assign(beta, in__.read<local_scalar_t__>(),
"assigning variable beta", stan::model::index_uni(sym1__));
}
out__.write(beta);
} catch (const std::exception& e) {
stan::lang::rethrow_located(e, locations_array__[current_statement__]);
}
}
inline void get_param_names(std::vector<std::string>& names__) const {
names__ = std::vector<std::string>{"beta"};
}
inline void get_dims(std::vector<std::vector<size_t>>& dimss__) const {
dimss__ = std::vector<std::vector<size_t>>{std::vector<size_t>{static_cast<
size_t>(r)}};
}
inline void
constrained_param_names(std::vector<std::string>& param_names__, bool
emit_transformed_parameters__ = true, bool
emit_generated_quantities__ = true) const final {
for (int sym5__ = 1; sym5__ <= r; ++sym5__) {
param_names__.emplace_back(std::string() + "beta" + '.' +
std::to_string(sym5__));
}
if (emit_transformed_parameters__) {}
if (emit_generated_quantities__) {}
}
inline void
unconstrained_param_names(std::vector<std::string>& param_names__, bool
emit_transformed_parameters__ = true, bool
emit_generated_quantities__ = true) const final {
for (int sym5__ = 1; sym5__ <= r; ++sym5__) {
param_names__.emplace_back(std::string() + "beta" + '.' +
std::to_string(sym5__));
}
if (emit_transformed_parameters__) {}
if (emit_generated_quantities__) {}
}
inline std::string get_constrained_sizedtypes() const {
return std::string("[{\"name\":\"beta\",\"type\":{\"name\":\"vector\",\"length\":" + std::to_string(r) + "},\"block\":\"parameters\"}]");
}
inline std::string get_unconstrained_sizedtypes() const {
return std::string("[{\"name\":\"beta\",\"type\":{\"name\":\"vector\",\"length\":" + std::to_string(r) + "},\"block\":\"parameters\"}]");
}
// Begin method overload boilerplate
template <typename RNG> inline void
write_array(RNG& base_rng, Eigen::Matrix<double,-1,1>& params_r,
Eigen::Matrix<double,-1,1>& vars, const bool
emit_transformed_parameters = true, const bool
emit_generated_quantities = true, std::ostream*
pstream = nullptr) const {
const size_t num_params__ = r;
const size_t num_transformed = emit_transformed_parameters * (0);
const size_t num_gen_quantities = emit_generated_quantities * (0);
const size_t num_to_write = num_params__ + num_transformed +
num_gen_quantities;
std::vector<int> params_i;
vars = Eigen::Matrix<double,-1,1>::Constant(num_to_write,
std::numeric_limits<double>::quiet_NaN());
write_array_impl(base_rng, params_r, params_i, vars,
emit_transformed_parameters, emit_generated_quantities, pstream);
}
template <typename RNG> inline void
write_array(RNG& base_rng, std::vector<double>& params_r, std::vector<int>&
params_i, std::vector<double>& vars, bool
emit_transformed_parameters = true, bool
emit_generated_quantities = true, std::ostream*
pstream = nullptr) const {
const size_t num_params__ = r;
const size_t num_transformed = emit_transformed_parameters * (0);
const size_t num_gen_quantities = emit_generated_quantities * (0);
const size_t num_to_write = num_params__ + num_transformed +
num_gen_quantities;
vars = std::vector<double>(num_to_write,
std::numeric_limits<double>::quiet_NaN());
write_array_impl(base_rng, params_r, params_i, vars,
emit_transformed_parameters, emit_generated_quantities, pstream);
}
template <bool propto__, bool jacobian__, typename T_> inline T_
log_prob(Eigen::Matrix<T_,-1,1>& params_r, std::ostream* pstream = nullptr) const {
Eigen::Matrix<int,-1,1> params_i;
return log_prob_impl<propto__, jacobian__>(params_r, params_i, pstream);
}
template <bool propto__, bool jacobian__, typename T_> inline T_
log_prob(std::vector<T_>& params_r, std::vector<int>& params_i,
std::ostream* pstream = nullptr) const {
return log_prob_impl<propto__, jacobian__>(params_r, params_i, pstream);
}
inline void
transform_inits(const stan::io::var_context& context,
Eigen::Matrix<double,-1,1>& params_r, std::ostream*
pstream = nullptr) const final {
std::vector<double> params_r_vec(params_r.size());
std::vector<int> params_i;
transform_inits(context, params_i, params_r_vec, pstream);
params_r = Eigen::Map<Eigen::Matrix<double,-1,1>>(params_r_vec.data(),
params_r_vec.size());
}
inline void
transform_inits(const stan::io::var_context& context, std::vector<int>&
params_i, std::vector<double>& vars, std::ostream*
pstream__ = nullptr) const {
constexpr std::array<const char*, 1> names__{"beta"};
const std::array<Eigen::Index, 1> constrain_param_sizes__{r};
const auto num_constrained_params__ =
std::accumulate(constrain_param_sizes__.begin(),
constrain_param_sizes__.end(), 0);
std::vector<double> params_r_flat__(num_constrained_params__);
Eigen::Index size_iter__ = 0;
Eigen::Index flat_iter__ = 0;
for (auto&& param_name__: names__) {
const auto param_vec__ = context.vals_r(param_name__);
for (Eigen::Index i = 0; i < constrain_param_sizes__[size_iter__]; ++i) {
params_r_flat__[flat_iter__] = param_vec__[i];
++flat_iter__;
}
++size_iter__;
}
vars.resize(num_params_r__);
transform_inits_impl(params_r_flat__, params_i, vars, pstream__);
}
};
}
using stan_model = none_model_NA_NA_NA_none_NA_1_model_namespace::none_model_NA_NA_NA_none_NA_1_model;
#ifndef USING_R
// Boilerplate
stan::model::model_base&
new_model(stan::io::var_context& data_context, unsigned int seed,
std::ostream* msg_stream) {
stan_model* m = new stan_model(data_context, seed, msg_stream);
return *m;
}
stan::math::profile_map& get_stan_profile_data() {
return none_model_NA_NA_NA_none_NA_1_model_namespace::profiles__;
}
#endifIf it compiles and runs then it seems like that warning is due to an implementation detail of cmdstanr
mike-lawrence
commented
1 year ago Just realized that reading/pasting might not have kept the incomplete line the warning refers to, so attaching the file (with a txt extension to get past the file format filter) model-7c98365b5b7eb.txt
mike-lawrence
commented
1 year ago oh, and yes, it samples without error too.
This is related to #1280 but is probably not the cause. It was also reported in this forum thread by @mike-lawrence
Submission Checklist
Release notes
Fixes an issue where
profileblocks could generate uncompilable C++ when used with--O1.Copyright and Licensing
By submitting this pull request, the copyright holder is agreeing to license the submitted work under the BSD 3-clause license (https://opensource.org/licenses/BSD-3-Clause)