void enkf_tui_run_exp(void * enkf_main) {
const int ens_size = enkf_main_get_ensemble_size( enkf_main );
bool_vector_type * iactive = bool_vector_alloc(0,false);
state_enum init_state = ANALYZED;
int start_report = 0;
int init_step_parameters = 0;
{
char * prompt = util_alloc_sprintf("Which realizations to simulate (Ex: 1,3-5) <Enter for all> [M to return to menu] : " , ens_size);
char * select_string;
util_printf_prompt(prompt , PROMPT_LEN , '=' , "=> ");
select_string = util_alloc_stdin_line();
enkf_tui_util_sscanf_active_list( iactive , select_string , ens_size);
util_safe_free( select_string );
free( prompt );
}
if (bool_vector_count_equal(iactive , true))
enkf_main_run_exp(enkf_main , iactive , true , init_step_parameters , start_report , init_state, true);
bool_vector_free(iactive);
}
matrix_type * matrix_alloc_column_compressed_copy(const matrix_type * src, const bool_vector_type * mask) {
if (bool_vector_size( mask ) != matrix_get_columns( src ))
util_abort("%s: size mismatch. Src matrix has %d rows mask has:%d elements\n", __func__ , matrix_get_rows( src ) , bool_vector_size( mask ));
{
int target_columns = bool_vector_count_equal( mask , true );
matrix_type * target = matrix_alloc( matrix_get_rows( src ) , target_columns );
matrix_column_compressed_memcpy( target , src , mask );
return target;
}
}
void rml_enkf_common_recover_state( const matrix_type * state , matrix_type * A , const bool_vector_type * ens_mask ) {
const int ens_size = bool_vector_size( ens_mask );
const int active_size = bool_vector_count_equal( ens_mask , true );
const int rows = matrix_get_rows( state );
matrix_resize( A , rows , active_size , false );
{
int active_index = 0;
for (int iens = 0; iens < ens_size; iens++) {
if (bool_vector_iget( ens_mask , iens ))
matrix_copy_column( A , state , active_index , iens );
}
}
}
void matrix_column_compressed_memcpy(matrix_type * target, const matrix_type * src, const bool_vector_type * mask) {
if (bool_vector_count_equal( mask , true ) != matrix_get_columns( target ))
util_abort("%s: size mismatch. \n",__func__);
if (bool_vector_size( mask ) != matrix_get_columns( src))
util_abort("%s: size mismatch. \n",__func__);
{
int target_col = 0;
int src_col;
for (src_col = 0; src_col < bool_vector_size( mask ); src_col++) {
if (bool_vector_iget( mask , src_col)) {
matrix_copy_column( target , src , target_col , src_col);
target_col++;
}
}
}
}
void test_index_list() {
int_vector_type * index_list = int_vector_alloc( 0 , 0 );
int_vector_append( index_list , 10 );
int_vector_append( index_list , 20 );
int_vector_append( index_list , 30 );
{
bool_vector_type * mask = int_vector_alloc_mask( index_list );
test_assert_false( bool_vector_get_default( mask ));
test_assert_int_equal( 31 , bool_vector_size( mask ));
test_assert_true( bool_vector_iget( mask , 10 ));
test_assert_true( bool_vector_iget( mask , 20 ));
test_assert_true( bool_vector_iget( mask , 30 ));
test_assert_int_equal( 3 , bool_vector_count_equal( mask , true ));
bool_vector_free( mask );
}
int_vector_free( index_list );
}
int stepwise_get_n_active( stepwise_type * stepwise ) {
return bool_vector_count_equal( stepwise->active_set , true);
}
static double stepwise_estimate__( stepwise_type * stepwise , bool_vector_type * active_rows) {
matrix_type * X;
matrix_type * E;
matrix_type * Y;
double y_mean = 0;
int nvar = matrix_get_columns( stepwise->X0 );
int nsample = matrix_get_rows( stepwise->X0 );
nsample = bool_vector_count_equal( active_rows , true );
nvar = bool_vector_count_equal( stepwise->active_set , true );
matrix_set( stepwise->beta , 0 ); // It is essential to make sure that old finite values in the beta0 vector do not hang around.
/*
Extracting the data used for regression, and storing them in the
temporary local matrices X and Y. Selecting data is based both on
which varibles are active (stepwise->active_set) and which rows
should be used for regression, versus which should be used for
validation (@active_rows).
*/
if ((nsample < matrix_get_rows( stepwise->X0 )) || (nvar < matrix_get_columns( stepwise->X0 ))) {
X = matrix_alloc( nsample , nvar );
E = matrix_alloc( nsample , nvar );
Y = matrix_alloc( nsample , 1);
{
int icol,irow; // Running over all values.
int arow,acol; // Running over active values.
arow = 0;
for (irow = 0; irow < matrix_get_rows( stepwise->X0 ); irow++) {
if (bool_vector_iget( active_rows , irow )) {
acol = 0;
for (icol = 0; icol < matrix_get_columns( stepwise->X0 ); icol++) {
if (bool_vector_iget( stepwise->active_set , icol )) {
matrix_iset( X , arow , acol , matrix_iget( stepwise->X0 , irow , icol ));
matrix_iset( E , arow , acol , matrix_iget( stepwise->E0 , irow , icol ));
acol++;
}
}
matrix_iset( Y , arow , 0 , matrix_iget( stepwise->Y0 , irow , 0 ));
arow++;
}
}
}
} else {
X = matrix_alloc_copy( stepwise->X0 );
E = matrix_alloc_copy( stepwise->E0 );
Y = matrix_alloc_copy( stepwise->Y0 );
}
{
if (stepwise->X_mean != NULL)
matrix_free( stepwise->X_mean);
stepwise->X_mean = matrix_alloc( 1 , nvar );
if (stepwise->X_norm != NULL)
matrix_free( stepwise->X_norm);
stepwise->X_norm = matrix_alloc( 1 , nvar );
matrix_type * beta = matrix_alloc( nvar , 1); /* This is the beta vector as estimated from the OLS estimator. */
regression_augmented_OLS( X , Y , E, beta );
/*
In this code block the beta/tmp_beta vector which is dense with
fewer elements than the full model is scattered into the beta0
vector which has full size and @nvar elements.
*/
{
int ivar,avar;
avar = 0;
for (ivar = 0; ivar < matrix_get_columns( stepwise->X0 ); ivar++) {
if (bool_vector_iget( stepwise->active_set , ivar )) {
matrix_iset( stepwise->beta , ivar , 0 , matrix_iget( beta , avar , 0));
avar++;
}
}
}
matrix_free( beta );
}
matrix_free( X );
matrix_free( E );
matrix_free( Y );
return y_mean;
}
void stepwise_estimate( stepwise_type * stepwise , double deltaR2_limit , int CV_blocks) {
int nvar = matrix_get_columns( stepwise->X0 );
int nsample = matrix_get_rows( stepwise->X0 );
double currentR2 = -1;
bool_vector_type * active_rows = bool_vector_alloc( nsample , true );
/*Reset beta*/
for (int i = 0; i < nvar; i++) {
matrix_iset(stepwise->beta, i , 0 , 0.0);
}
bool_vector_set_all( stepwise->active_set , false );
double MSE_min = 10000000;
double Prev_MSE_min = MSE_min;
double minR2 = -1;
while (true) {
int best_var = 0;
Prev_MSE_min = MSE_min;
/*
Go through all the inactive variables, and calculate the
resulting prediction error IF this particular variable is added;
keep track of the variable which gives the lowest prediction error.
*/
for (int ivar = 0; ivar < nvar; ivar++) {
if (!bool_vector_iget( stepwise->active_set , ivar)) {
double newR2 = stepwise_test_var(stepwise , ivar , CV_blocks);
if ((minR2 < 0) || (newR2 < minR2)) {
minR2 = newR2;
best_var = ivar;
}
}
}
/*
If the best relative improvement in prediction error is better
than @deltaR2_limit, the corresponding variable is added to the
active set, and we return to repeat the loop one more
time. Otherwise we just exit.
*/
{
MSE_min = minR2;
double deltaR2 = MSE_min / Prev_MSE_min;
if (( currentR2 < 0) || deltaR2 < deltaR2_limit) {
bool_vector_iset( stepwise->active_set , best_var , true );
currentR2 = minR2;
bool_vector_set_all(active_rows, true);
stepwise_estimate__( stepwise , active_rows );
} else {
/* The gain in prediction error is so small that we just leave the building. */
/* NB! Need one final compuation of beta (since the test_var function does not reset the last tested beta value !) */
bool_vector_set_all(active_rows, true);
stepwise_estimate__( stepwise , active_rows );
break;
}
if (bool_vector_count_equal( stepwise->active_set , true) == matrix_get_columns( stepwise->X0 )) {
stepwise_estimate__( stepwise , active_rows );
break; /* All variables are active. */
}
}
}
stepwise_set_R2(stepwise, currentR2);
bool_vector_free( active_rows );
}
void enkf_tui_run_manual_load__( void * arg ) {
enkf_main_type * enkf_main = enkf_main_safe_cast( arg );
enkf_fs_type * fs = enkf_main_get_fs( enkf_main );
const int last_report = -1;
const int ens_size = enkf_main_get_ensemble_size( enkf_main );
int step1,step2;
bool_vector_type * iactive = bool_vector_alloc( 0 , false );
run_mode_type run_mode = ENSEMBLE_EXPERIMENT;
enkf_main_init_run(enkf_main , run_mode); /* This is ugly */
step1 = 0;
step2 = last_report; /** Observe that for the summary data it will load all the available data anyway. */
{
char * prompt = util_alloc_sprintf("Which realizations to load (Ex: 1,3-5) <Enter for all> [M to return to menu] : [ensemble size:%d] : " , ens_size);
char * select_string;
util_printf_prompt(prompt , PROMPT_LEN , '=' , "=> ");
select_string = util_alloc_stdin_line();
enkf_tui_util_sscanf_active_list( iactive , select_string , ens_size );
util_safe_free( select_string );
free( prompt );
}
if (bool_vector_count_equal( iactive , true )) {
int iens;
arg_pack_type ** arg_list = util_calloc( ens_size , sizeof * arg_list );
thread_pool_type * tp = thread_pool_alloc( 4 , true ); /* num_cpu - HARD coded. */
for (iens = 0; iens < ens_size; iens++) {
arg_pack_type * arg_pack = arg_pack_alloc();
arg_list[iens] = arg_pack;
if (bool_vector_iget(iactive , iens)) {
enkf_state_type * enkf_state = enkf_main_iget_state( enkf_main , iens );
arg_pack_append_ptr( arg_pack , enkf_state); /* 0: */
arg_pack_append_ptr( arg_pack , fs ); /* 1: */
arg_pack_append_int( arg_pack , step1 ); /* 2: This will be the load start parameter for the run_info struct. */
arg_pack_append_int( arg_pack , step1 ); /* 3: Step1 */
arg_pack_append_int( arg_pack , step2 ); /* 4: Step2 For summary data it will load the whole goddamn thing anyway.*/
arg_pack_append_bool( arg_pack , true ); /* 5: Interactive */
arg_pack_append_owned_ptr( arg_pack , stringlist_alloc_new() , stringlist_free__); /* 6: List of interactive mode messages. */
thread_pool_add_job( tp , enkf_state_load_from_forward_model_mt , arg_pack);
}
}
thread_pool_join( tp );
thread_pool_free( tp );
printf("\n");
{
qc_module_type * qc_module = enkf_main_get_qc_module( enkf_main );
runpath_list_type * runpath_list = qc_module_get_runpath_list( qc_module );
for (iens = 0; iens < ens_size; iens++) {
if (bool_vector_iget(iactive , iens)) {
const enkf_state_type * state = enkf_main_iget_state( enkf_main , iens );
runpath_list_add( runpath_list , iens , enkf_state_get_run_path( state ) , enkf_state_get_eclbase( state ));
}
}
qc_module_export_runpath_list( qc_module );
}
for (iens = 0; iens < ens_size; iens++) {
if (bool_vector_iget(iactive , iens)) {
stringlist_type * msg_list = arg_pack_iget_ptr( arg_list[iens] , 6 );
if (stringlist_get_size( msg_list ))
enkf_tui_display_load_msg( iens , msg_list );
}
}
for (iens = 0; iens < ens_size; iens++)
arg_pack_free( arg_list[iens]);
free( arg_list );
}
bool_vector_free( iactive );
}
void enkf_tui_run_manual_load__( void * arg ) {
enkf_main_type * enkf_main = enkf_main_safe_cast( arg );
const int ens_size = enkf_main_get_ensemble_size( enkf_main );
bool_vector_type * iactive = bool_vector_alloc( 0 , false );
run_mode_type run_mode = ENSEMBLE_EXPERIMENT;
int iter = 0;
enkf_main_init_run(enkf_main , iactive , run_mode , INIT_NONE); /* This is ugly */
{
char * prompt = util_alloc_sprintf("Which realizations to load (Ex: 1,3-5) <Enter for all> [M to return to menu] : [ensemble size:%d] : " , ens_size);
char * select_string;
util_printf_prompt(prompt , PROMPT_LEN , '=' , "=> ");
select_string = util_alloc_stdin_line();
enkf_tui_util_sscanf_active_list( iactive , select_string , ens_size );
util_safe_free( select_string );
free( prompt );
}
{
const model_config_type * model_config = enkf_main_get_model_config( enkf_main );
if (model_config_runpath_requires_iter( model_config )) {
const char * prompt = "Which iteration to load from [0...?) : ";
char * input;
bool OK;
util_printf_prompt(prompt , PROMPT_LEN , '=' , "=> ");
input = util_alloc_stdin_line();
if (input == NULL)
return;
OK = util_sscanf_int( input , &iter );
free( input );
if (!OK)
return;
}
}
if (bool_vector_count_equal( iactive , true )) {
stringlist_type ** realizations_msg_list = util_calloc( ens_size , sizeof * realizations_msg_list );
int iens = 0;
for (; iens < ens_size; ++iens) {
realizations_msg_list[iens] = stringlist_alloc_new();
}
enkf_main_load_from_forward_model(enkf_main, iter , iactive, realizations_msg_list);
{
qc_module_type * qc_module = enkf_main_get_qc_module( enkf_main );
runpath_list_type * runpath_list = qc_module_get_runpath_list( qc_module );
for (iens = 0; iens < ens_size; ++iens) {
if (bool_vector_iget(iactive , iens)) {
const enkf_state_type * state = enkf_main_iget_state( enkf_main , iens );
runpath_list_add( runpath_list , iens , enkf_state_get_run_path( state ) , enkf_state_get_eclbase( state ));
}
}
qc_module_export_runpath_list( qc_module );
}
for (iens = 0; iens < ens_size; ++iens) {
stringlist_type * msg_list = realizations_msg_list[iens];
if (bool_vector_iget(iactive, iens)) {
if (stringlist_get_size( msg_list )) {
enkf_tui_display_load_msg( iens , msg_list );
}
}
stringlist_free(msg_list);
}
free(realizations_msg_list);
}
bool_vector_free( iactive );
}
