int give_calHunters()
{
int message = 0, cal = 0, hunters=0, hero=-1,cal_per=0;
int_array clan_list;
init_int_array (&clan_list);
// Recivir partida de cazadores
START_INFOHUNTER_MESSAGE_LOOP
hunters += infoHunter_message->hunters;
add_int(&clan_list,infoHunter_message->clans[0]);
add_int(&clan_list,infoHunter_message->clans[1]);
message ++;
FINISH_INFOHUNTER_MESSAGE_LOOP
if (message > 0)
{
// Calcular las calorias obtenidas segun la cantidad de guanacos cazados
cal =60000 *(rand()%get_adultos());
// Eliminar a los guanacos cazados
// Determinar quien acaba con mas guanacos de forma aleatoria
hero = rand()%clan_list.size;
// Repartir calorias obtenidas por personas. Despues cada partida
// calcula segun su num de miembros, las cal que les coresponden
cal_per = cal / hunters;
add_repCal_message(get_x(),get_y(),cal_per);
}
free_int_array (&clan_list);
return 0;
}
/*
* allocates memory for storing all the computed intermediate values, that could be reused
*/
int alloc_Accelerator_Memory_tri_min(int value_count){
int bytes = 0;
e_tri_min = init_dbl_array(0, value_count - 1, 0);
bytes += value_count * sizeof(double);
e_returned_tri_min = init_int_array(0, value_count - 1, 0);
bytes += value_count * sizeof(int);
return bytes;
}
SEXP TASCB_min(SEXP vect, SEXP tau, SEXP numberofSamples, SEXP sigma){
int value_count, sigma_count;
mgs_result mgs_res;
quant_result q_res;
dbl_array *vector, *vect_sorted, *s;
dbl_matrix *H_Mat1, *H_Mat2;
calc_V_result_tri v_res;
final_result_tri f_res;
SEXP result, binarized_vector, threshold1, threshold2, p_value, other_results, names;
SEXP smoothed, zerocrossing, deriv, steps, H1, H2, index, v_vec, meanlist, smoothedX;
value_count = length(vect);
sigma_count = length(sigma);
PROTECT(result = allocVector(VECSXP, 5));
PROTECT(names = allocVector(VECSXP, 5));
SET_VECTOR_ELT(names,0, mkChar("binarized_vector"));
SET_VECTOR_ELT(names,1, mkChar("threshold1"));
SET_VECTOR_ELT(names,2, mkChar("threshold2"));
SET_VECTOR_ELT(names,3, mkChar("p_value"));
SET_VECTOR_ELT(names,4, mkChar("other_results"));
setAttrib(result, R_NamesSymbol, names);
UNPROTECT(1);
PROTECT(other_results = allocVector(VECSXP, 10));
PROTECT(names = allocVector(VECSXP, 10));
SET_VECTOR_ELT(names,0, mkChar("smoothed"));
SET_VECTOR_ELT(names,1, mkChar("zerocrossing"));
SET_VECTOR_ELT(names,2, mkChar("deriv"));
SET_VECTOR_ELT(names,3, mkChar("steps"));
SET_VECTOR_ELT(names,4, mkChar("H_Mat1"));
SET_VECTOR_ELT(names,5, mkChar("H_Mat2"));
SET_VECTOR_ELT(names,6, mkChar("index"));
SET_VECTOR_ELT(names,7, mkChar("v_vec"));
SET_VECTOR_ELT(names,8, mkChar("smoothedX"));
SET_VECTOR_ELT(names,9, mkChar("meanlist"));
setAttrib(other_results, R_NamesSymbol, names);
UNPROTECT(1);
PROTECT(smoothed = allocMatrix(REALSXP, value_count - 1, sigma_count));
PROTECT(zerocrossing = allocMatrix(INTSXP, (int)ceil((double)value_count / 2.0), sigma_count));
PROTECT(deriv = allocVector(REALSXP, value_count - 1));
mgs_res.smoothed = init_dbl_matrix(REAL(smoothed), sigma_count, value_count - 1, 0);
mgs_res.zerocrossing = init_int_matrix(INTEGER(zerocrossing), sigma_count, (int)ceil((double)value_count / 2.0), 0);
mgs_res.deriv = init_dbl_array(REAL(deriv), value_count - 1, 0);
vector = init_dbl_array(REAL(vect), value_count, 1);
vect_sorted = init_dbl_array(0, value_count, 0);
s = init_dbl_array(REAL(sigma), sigma_count, 1);
b = init_dbl_matrix(0, sigma_count, mgs_res.deriv->length, 0);
b_returned = init_int_matrix(0, sigma_count, mgs_res.deriv->length, 0);
memcpy(vect_sorted->values, REAL(vect), vect_sorted->length * sizeof(double));
qsort(vect_sorted->values, vect_sorted->length, sizeof(double), comp);
mgs(&mgs_res, vect_sorted, s);
q_res.steps = init_int_matrix(0, sigma_count, (int)ceil((double)value_count / 2.0), 0);
q_res.index = init_int_array(0, sigma_count, 0);
q_res.greatest_index_ind = 0;
q_res.greatest_steps_col = 0;
q_res.greatest_steps_row = 0;
getQuantizations(&q_res, &mgs_res);
PROTECT(steps = allocMatrix(INTSXP, q_res.greatest_steps_col, q_res.greatest_steps_row));
PROTECT(H1 = allocMatrix(REALSXP, q_res.greatest_steps_col, q_res.greatest_steps_row));
PROTECT(H2 = allocMatrix(REALSXP, q_res.greatest_steps_col, q_res.greatest_steps_row));
PROTECT(index = allocVector(INTSXP, q_res.greatest_index_ind));
cut_int_matrix(q_res.steps, INTEGER(steps), 0, q_res.greatest_steps_row - 1, 0, q_res.greatest_steps_col - 1);
cut_int_array(q_res.index, INTEGER(index), 0, q_res.greatest_index_ind - 1);
H_Mat1 = init_dbl_matrix(REAL(H1), q_res.greatest_steps_row, q_res.greatest_steps_col, 0);
H_Mat2 = init_dbl_matrix(REAL(H2), q_res.greatest_steps_row, q_res.greatest_steps_col, 0);
PROTECT(v_vec = allocMatrix(INTSXP, q_res.index->length, 2));
PROTECT(smoothedX = allocMatrix(REALSXP, mgs_res.smoothed->cols + 1, q_res.index->length));
PROTECT(meanlist = allocMatrix(REALSXP, vect_sorted->length, q_res.index->length + 1));
v_res.v = init_int_matrix(INTEGER(v_vec), q_res.index->length, 2, 0);
v_res.meanlist = init_dbl_matrix(REAL(meanlist), q_res.index->length + 1, vect_sorted->length, 0);
v_res.smoothedX = init_dbl_matrix(REAL(smoothedX), q_res.index->length, mgs_res.smoothed->cols + 1, 0);
calc_V_Scalespace_tri_min(&v_res, &mgs_res, &q_res, H_Mat1, H_Mat2, vect_sorted);
PROTECT(binarized_vector = allocVector(INTSXP, value_count));
PROTECT(threshold1 = allocVector(REALSXP, 1));
PROTECT(threshold2 = allocVector(REALSXP, 1));
PROTECT(p_value = allocVector(REALSXP, 1));
f_res.binarized_vector = init_int_array(INTEGER(binarized_vector), value_count, 0);
f_res.p = REAL(p_value);
f_res.threshold1 = REAL(threshold1);
f_res.threshold2 = REAL(threshold2);
calc_final_results_tri_min(&f_res, v_res.v, vector, vect_sorted, *REAL(tau), *INTEGER(numberofSamples));
SET_VECTOR_ELT(other_results, 0, smoothed);
SET_VECTOR_ELT(other_results, 1, zerocrossing);
SET_VECTOR_ELT(other_results, 2, deriv);
SET_VECTOR_ELT(other_results, 3, steps);
SET_VECTOR_ELT(other_results, 4, H1);
SET_VECTOR_ELT(other_results, 5, H2);
SET_VECTOR_ELT(other_results, 6, index);
SET_VECTOR_ELT(other_results, 7, v_vec);
SET_VECTOR_ELT(other_results, 8, smoothedX);
SET_VECTOR_ELT(other_results, 9, meanlist);
SET_VECTOR_ELT(result, 0, binarized_vector);
SET_VECTOR_ELT(result, 1, threshold1);
SET_VECTOR_ELT(result, 2, threshold2);
SET_VECTOR_ELT(result, 3, p_value);
SET_VECTOR_ELT(result, 4, other_results);
destroy_dbl_matrix(mgs_res.smoothed);
destroy_int_matrix(mgs_res.zerocrossing);
destroy_dbl_array(mgs_res.deriv);
destroy_dbl_array(vector);
destroy_dbl_array(vect_sorted);
destroy_dbl_array(s);
destroy_dbl_matrix(b);
destroy_int_matrix(b_returned);
b = 0;
b_returned = 0;
destroy_dbl_matrix(H_Mat1);
destroy_dbl_matrix(H_Mat2);
destroy_int_matrix(q_res.steps);
destroy_int_array(q_res.index);
destroy_int_matrix(v_res.v);
destroy_dbl_matrix(v_res.meanlist);
destroy_dbl_matrix(v_res.smoothedX);
destroy_int_array(f_res.binarized_vector);
UNPROTECT(16);
return result;
}
SEXP TASCA_min(SEXP vect, SEXP tau, SEXP numberofsamples){
//get the lengths
//int i,j,sum,sum_tot;
//int bytes = 0;
int value_count = length(vect);
int vc_m1 = value_count - 1;
int vc_m2 = vc_m1 - 1;
dbl_array *vector, *vect_sorted, *Q_Max_vec;
dbl_matrix *Cc_Mat, *Q_Mat, *H_Mat1, *H_Mat2;
int_matrix *Ind_Mat, *P_Mat, *v_vec;
final_result_tri f_res;
//sort the vect into vect_sorted
vector = init_dbl_array(REAL(vect), value_count, 1);
vect_sorted = init_dbl_array(0, value_count, 0);
memcpy(vect_sorted->values, vector->values, vect_sorted->length * sizeof(double));
qsort(vect_sorted->values, vect_sorted->length, sizeof(double), comp);
//name the required SEXP Objects
SEXP result, binarized_vector, threshold1, threshold2, p_value, other_results, Cc, Ind, P, Q, H1, H2, Q_max, v, Names;
//allocate memory for saving calculated values
alloc_Accelerator_Memory(value_count);
alloc_Accelerator_Memory_tri_min(value_count);
//allocate the final result and set the names of the entries
PROTECT(result = allocVector(VECSXP, 5));
PROTECT(Names = allocVector(VECSXP, 5));
SET_VECTOR_ELT(Names,0, mkChar("binarized_vector"));
SET_VECTOR_ELT(Names,1, mkChar("threshold1"));
SET_VECTOR_ELT(Names,2, mkChar("threshold2"));
SET_VECTOR_ELT(Names,3, mkChar("p_value"));
SET_VECTOR_ELT(Names,4, mkChar("other_results"));
setAttrib(result, R_NamesSymbol, Names);
UNPROTECT(1);
PROTECT(other_results = allocVector(VECSXP, 8));
PROTECT(Names = allocVector(VECSXP, 8));
SET_VECTOR_ELT(Names,0, mkChar("Cc"));
SET_VECTOR_ELT(Names,1, mkChar("Ind"));
SET_VECTOR_ELT(Names,2, mkChar("P_Mat"));
SET_VECTOR_ELT(Names,3, mkChar("Q_Mat"));
SET_VECTOR_ELT(Names,4, mkChar("H_Mat1"));
SET_VECTOR_ELT(Names,5, mkChar("H_Mat2"));
SET_VECTOR_ELT(Names,6, mkChar("maximal_Qs"));
SET_VECTOR_ELT(Names,7, mkChar("v_vec"));
setAttrib(other_results, R_NamesSymbol, Names);
UNPROTECT(1);
//allocate memory for result matrices and vectors and set the matrix values to zero (because they aren't
//all overwritten by the functions)
PROTECT(binarized_vector = allocVector(INTSXP, value_count));
PROTECT(threshold1 = allocVector(REALSXP, 1));
PROTECT(threshold2 = allocVector(REALSXP, 1));
PROTECT(p_value = allocVector(REALSXP, 1));
PROTECT(Cc = allocMatrix(REALSXP, vc_m1, vc_m1));
PROTECT(Ind = allocMatrix(INTSXP, vc_m1, vc_m2));
PROTECT(P = allocMatrix(INTSXP, vc_m2, vc_m2));
PROTECT(Q = allocMatrix(REALSXP, vc_m2, vc_m2));
PROTECT(H1 = allocMatrix(REALSXP, vc_m2, vc_m2));
PROTECT(H2 = allocMatrix(REALSXP, vc_m2, vc_m2));
PROTECT(Q_max = allocVector(REALSXP, vc_m2));
PROTECT(v = allocMatrix(INTSXP, vc_m2, 2));
Cc_Mat = init_dbl_matrix(REAL(Cc), vc_m1, vc_m1, 0);
Ind_Mat = init_int_matrix(INTEGER(Ind), vc_m2, vc_m1, 0);
P_Mat = init_int_matrix(INTEGER(P), vc_m2, vc_m2, 0);
v_vec = init_int_matrix(INTEGER(v), vc_m2, 2, 0);
Q_Max_vec = init_dbl_array(REAL(Q_max), vc_m2, 0);
Q_Mat = init_dbl_matrix(REAL(Q), vc_m2, vc_m2, 0);
H_Mat1 = init_dbl_matrix(REAL(H1), vc_m2, vc_m2, 0);
H_Mat2 = init_dbl_matrix(REAL(H2), vc_m2, vc_m2, 0);
f_res.binarized_vector = init_int_array(INTEGER(binarized_vector), value_count, 0);
f_res.p = REAL(p_value);
f_res.threshold1 = REAL(threshold1);
f_res.threshold2 = REAL(threshold2);
//start the computation of the entries of all matrices
calc_First_Cost_Matrix_Line(Cc_Mat, vect_sorted);
calc_RestCc_and_Ind_Matrices(Cc_Mat, Ind_Mat, vect_sorted);
calc_P_Matrix(P_Mat, Ind_Mat);
calc_V_tri_min(v_vec, Q_Max_vec, Q_Mat, H_Mat1, H_Mat2, P_Mat, vect_sorted);
//free the memory for calculated values
free_Accelerator_Memory();
free_Accelerator_Memory_tri_min();
//calculate the final three results
calc_final_results_tri_min(&f_res, v_vec, vector, vect_sorted, *REAL(tau), *INTEGER(numberofsamples));
//free(vect_sorted);
destroy_dbl_array(vector);
destroy_dbl_array(vect_sorted);
destroy_dbl_matrix(Cc_Mat);
destroy_int_matrix(Ind_Mat);
destroy_int_matrix(P_Mat);
destroy_int_matrix(v_vec);
destroy_dbl_array(Q_Max_vec);
destroy_dbl_matrix(Q_Mat);
destroy_dbl_matrix(H_Mat1);
destroy_dbl_matrix(H_Mat2);
destroy_int_array(f_res.binarized_vector);
//assign the computed elements to the final result
SET_VECTOR_ELT(other_results,0, Cc);
SET_VECTOR_ELT(other_results,1, Ind);
SET_VECTOR_ELT(other_results,2, P);
SET_VECTOR_ELT(other_results,3, Q);
SET_VECTOR_ELT(other_results,4, H1);
SET_VECTOR_ELT(other_results,5, H2);
SET_VECTOR_ELT(other_results,6, Q_max);
SET_VECTOR_ELT(other_results,7, v);
SET_VECTOR_ELT(result,0, binarized_vector);
SET_VECTOR_ELT(result,1, threshold1);
SET_VECTOR_ELT(result,2, threshold2);
SET_VECTOR_ELT(result,3, p_value);
SET_VECTOR_ELT(result,4, other_results);
UNPROTECT(14);
return result;
}
/*
* Calculates the final three results (binarized_vector, threshold, p-value).
*/
void calc_final_results_tri_min(final_result_tri* result, int_matrix* v, dbl_array* vect, dbl_array* vect_sorted, double tau, int numberofsamples)
{
int i, k, c;
int_array* samples, * v_col1, * v_col2;
double nom, nom1, nom2, t_zero, t_star, mdm, mdm1, mdm2;
v_col1 = init_int_array(0, v->rows-1, 0);
v_col2 = init_int_array(0, v->rows-1, 0);
for(c = 1; c < v->rows; c++){
v_col1->values[c-1] = v->values[c][0];
v_col2->values[c-1] = v->values[c][1];
}
//calculate the threshold and the binarized vector:
i = (int)floor(median(v_col1));
*(result->threshold1) = (vect_sorted->values[i] + vect_sorted->values[i-1]) * 0.5;
k = (int)floor(median(v_col2));
*(result->threshold2) = (vect_sorted->values[k] + vect_sorted->values[k-1]) * 0.5;
for(i = 0; i < result->binarized_vector->length; i++)
{
result->binarized_vector->values[i] = (int)(vect->values[i] > *(result->threshold1));
if(vect->values[i] > *(result->threshold2)){
result->binarized_vector->values[i] = 2;
}
}
//calculate the statistics:
samples = init_int_array(0, v->rows-1, 0);
//nom = MDM(v')
nom1 = normabsmedian(v_col1, vect_sorted);
nom2 = normabsmedian(v_col2, vect_sorted);
nom = (nom1 + nom2) / 2;
// print_int_array(v_col1, "col1");
// print_int_array(v_col2, "col2");
// printf("norm med1: %f ; norm med2: %f ; nom: %f\n", nom1, nom2, nom);
t_zero = tau - nom;
*(result->p) = 1.0;
if(v->rows < 3)
{
warning("Too few members in the vector of strongest discontinuities of the optimal step functions. The computed p-value may not be reliable.");
}
#if !DEBUG_MODE
GetRNGstate();
#endif
for(i = 0; i < numberofsamples; i++)
{
//resample the values and calc t(v*) = MDM(v') - MDM(v*)
blockwiseboot(samples, v_col1);
mdm1 = normabsmedian(samples, vect_sorted);
blockwiseboot(samples, v_col2);
mdm2 = normabsmedian(samples, vect_sorted);
mdm = (mdm1 + mdm2)/2;
// printf("norm block med1: %f ; norm block med2: %f ; mdm: %f\n", mdm1, mdm2, mdm);
t_star = nom - mdm;
//sum up the number of t(v*) >= t0
*(result->p) += (double)(t_star >= t_zero);
}
#if !DEBUG_MODE
PutRNGstate();
#endif
//divide p by the number of samples, which is the maximal possible value for p
//so p is in interval [0,1]
*(result->p) /= (((double)numberofsamples) +1.0);
destroy_int_array(samples);
destroy_int_array(v_col1);
destroy_int_array(v_col2);
}
