int CFeasibilityMap::count_x_out_fn(CData &Data,int i_tau, int i_original,int n_simul, Uniform &randUnif) {
// double case2_count_out = 0;
int case2_count_out = 0; // Changed by Hang on 5/16/2015
ColumnVector s_i = tau_to_s_fn( i_tau, Data.n_var );
ColumnVector item_by_joint = Data.copy_non_balance_edit(s_i);
ColumnVector tilde_y_i = Data.log_D_Observed.row(i_original).t();
for (int i_simul=1; i_simul<=n_simul; i_simul++){
//Generate from uniform distribution
ColumnVector y_q = tilde_y_i;
for ( int temp_j=1; temp_j<=Data.n_var; temp_j++ ){
if ( item_by_joint(temp_j)==1 ){
y_q(temp_j) = Data.logB_L(temp_j)+Data.logB_U_L(temp_j)*randUnif.Next();
}
}
ColumnVector x_q = exp_ColumnVector(y_q) ;
Data.update_full_x_for_balance_edit(x_q);
// if (!Data.PassEdits(x_q)) { case2_count_out += 1.0;}
if (!Data.PassEdits(x_q)) { case2_count_out += 1;} // Changed by Hang on 5/16/2015
}
if (case2_count_out ==0) {
case2_count_out = 1;
}
return case2_count_out; // ADDED by Hang on 5/16/2015
}
void CParam::S2_add(Uniform &randUnif,CData &Data) {
int n_needtoupdate = 0;
for (int i_faulty=1; i_faulty<=Data.n_faulty; i_faulty++){
int i_original = Data.Faulty2Original[i_faulty-1];
ColumnVector item_by_bal;
ColumnVector s_i = S_Mat.column(i_faulty);
ColumnVector item_by_rnorm = Data.get_item_by_norm_indicator(s_i,item_by_bal);
//Generate from normal distribution
if ( item_by_rnorm.sum() >= 1 ) { // if no random number, other values by balanc edits remain same
n_needtoupdate++;
ColumnVector mu_z_i = Mu.column(z_in(i_original)) ;
ColumnVector tilde_y_i = Data.log_D_Observed.row(i_original).t();
ColumnVector s_1_compact = Data.get_compact_vector(item_by_rnorm);
ColumnVector Mu_1i = subvector(mu_z_i,s_1_compact);
LowerTriangularMatrix LSigma_1i_i;
ColumnVector y_q(n_var);
double log_cond_norm_q = calculate_log_cond_norm(Data, i_original, item_by_rnorm, tilde_y_i, y_q, true, LSigma_1i_i, s_i); // MODIFIED 2015/02/16
ColumnVector y_i = (Y_in.row(i_original)).t() ;
// ColumnVector y_part_i = subvector(y_i,item_by_rnorm);
// Put values from balance edits
ColumnVector x_q = exp_ColumnVector(y_q) ;
Data.set_balance_edit_values_for_x_q(s_i, x_q, item_by_bal); // CHANGED by Hang, 2014/12/29
// double log_cond_norm_i = log_MVN_fn(y_part_i,Mu_1i,LSigma_1i_i);
double log_cond_norm_i = calculate_log_cond_norm(Data, i_original, item_by_rnorm, tilde_y_i, y_q, false, LSigma_1i_i, s_i); // CHANGED 2015/01/27 , // MODIFIED 2015/02/16
// Acceptance/Rejection
if (Data.PassEdits(x_q)) { // Check constraints
y_q = log_ColumnVector(x_q) ;
ColumnVector y_compact_q = Data.get_compact_vector(y_q);
ColumnVector y_compact_i = Data.get_compact_vector(y_i);
double log_full_norm_q = log_MVN_fn(y_compact_q,mu_z_i,LSIGMA_i[z_in(i_original)-1],logdet_and_more(z_in(i_original)));
double log_full_norm_i = log_MVN_fn(y_compact_i,mu_z_i,LSIGMA_i[z_in(i_original)-1],logdet_and_more(z_in(i_original)));
// Calculate acceptance ratio
double logNum = log_full_norm_q - log_cond_norm_q;
double logDen = log_full_norm_i - log_cond_norm_i;
accept_rate(2) = exp( logNum - logDen );
if (randUnif.Next() < accept_rate(2)){
Y_in.row(i_original) = y_q.t();
is_accept(2)++;
}
}
}
}
is_accept(2) = is_accept(2) / n_needtoupdate;
}
void CFeasibilityMap::Simulate_logUnif_case2(int n_simul, Uniform &randUnif, CData &Data) {
if (useMap) {return;}
if (feasibleMap.nrows() == 0 || feasibleMap.maximum()==0) {
Rprintf( "Feasibility Map need to be set or computed first\n");
return;
}
Data.logUnif_case2 =Matrix(Data.n_faulty,Data.n_tau); Data.logUnif_case2 = 0;
for (int i_original=1; i_original<=Data.n_sample; i_original++){
if (Data.is_case(i_original,2)) {
int i_faulty = Data.Original2Faulty[i_original-1];
ColumnVector tilde_y_i = Data.log_D_Observed.row(i_original).t();
for (int i_tau=1; i_tau<=Data.n_tau; i_tau++){
if ( feasibleMap(i_tau,i_faulty)==1 ){
double case2_count_out = 0;
ColumnVector s_i = tau_to_s_fn( i_tau, Data.n_var );
ColumnVector item_by_joint = Data.copy_non_balance_edit(s_i);
for (int i_simul=1; i_simul<=n_simul; i_simul++){
//Generate from uniform distribution
ColumnVector y_q = tilde_y_i;
for ( int temp_j=1; temp_j<=Data.n_var; temp_j++ ){
if ( item_by_joint(temp_j)==1 ){
y_q(temp_j) = Data.logB_L(temp_j)+Data.logB_U_L(temp_j)*randUnif.Next();
}
}
ColumnVector x_q = exp_ColumnVector(y_q) ;
Data.update_full_x_for_balance_edit(x_q);
if (!Data.PassEdits(x_q)) { case2_count_out += 1.0;}
}
double Area = 1.0;
for ( int temp_j=1; temp_j<=Data.n_var; temp_j++ ){
if ( item_by_joint(temp_j)==1 ){
Area = Area * Data.logB_U_L(temp_j) ;
}
}
Area = Area * case2_count_out / n_simul ;
Data.set_logUnif_case2(i_original, i_tau, -log(Area));
}
}
if ( ((1.0*i_original/100)==(floor(1.0*i_original/100))) ){
Rprintf( "logUnif_y_tilde for i_sample= %d\n",i_original);
}
}
}
}
Individual * Population::Emigrate() {
int nMales = _mpMales.size();
int nFemales = _mpFemales.size();
int nTotal = nMales + nFemales;
bool bWhichSex = (UniformGen.Next() <= (double)nMales / (double)nTotal)? true:false;
vector< Individual *> * pIndividuals = bWhichSex? &_mpMales:&_mpFemales;
if (pIndividuals->size() == 0) { // no more individuals to return
return NULL;
}
int nEmigrant = fnGetRandIndex(pIndividuals->size());
//printf("1\n");
Individual * pEmigrant = pIndividuals->at(nEmigrant);
//printf("2\n");
pIndividuals->erase(pIndividuals->begin() + nEmigrant);
//printf("3\n");
return pEmigrant;
}
void CParam::S1(int iter, Uniform &randUnif, CData &Data, CFeasibilityMap &FM, int n_simul) {
is_accept(1) = 0;
for (int i_faulty=1; i_faulty<=Data.n_faulty; i_faulty++) {
if (FM.useMap) {FM.pmm->iter = iter;}
int i_original = Data.Faulty2Original[i_faulty-1];
double g_option_q, g_mode_q;
int what_type_move;
ColumnVector s_i = S_Mat.column(i_faulty);
int tau_q = FM.EvaluateMove(i_original, Data, s_i, iter, what_type_move, g_option_q, g_mode_q, true);
// calculate g_mode_i & g_option_i
ColumnVector s_q = CFeasibilityMap::tau_to_s_fn(tau_q, n_var);
double g_option_i, g_mode_i;
FM.EvaluateMove(i_original, Data, s_q, iter, what_type_move, g_option_i, g_mode_i, false);
// calculate g_s_q & g_s_i
double g_s_q = g_mode_q * g_option_q;
double g_s_i = g_mode_i * g_option_i;
// whether drawn by item_by_rnorm and balance edits
ColumnVector item_by_bal;
ColumnVector item_by_rnorm;
if (Data.is_case(i_original,1)) {
item_by_rnorm = Data.get_item_by_norm_indicator(s_q,item_by_bal);
} else {
item_by_rnorm = Data.copy_non_balance_edit(s_q);
}
//Generate from normal distribution
ColumnVector mu_z_i = Mu.column(z_in(i_original)) ;
ColumnVector tilde_y_i = Data.log_D_Observed.row(i_original).t();
ColumnVector y_q(n_var);
LowerTriangularMatrix LSigma_i;
double log_cond_norm_q = calculate_log_cond_norm(Data, i_original, item_by_rnorm, tilde_y_i, y_q, true, LSigma_i, s_q); // MODIFIED 2015/02/16
// Put values from balance edits
ColumnVector x_q = exp_ColumnVector(y_q);
if (Data.is_case(i_original,1)) {
Data.set_balance_edit_values_for_x_q(s_q, x_q, item_by_bal); // CHANGED by Hang, 2014/12/29
} else {
Data.update_full_x_for_balance_edit(x_q);
}
// Acceptance/Rejection
if (Data.PassEdits(x_q)) { // Check constraints
ColumnVector item_i_by_rnorm;
if (Data.is_case(i_original,1)) {
item_i_by_rnorm = Data.get_item_by_norm_indicator(s_i,item_by_bal);
} else {
item_i_by_rnorm = Data.copy_non_balance_edit(s_i);
}
double log_cond_norm_i = calculate_log_cond_norm(Data, i_original, item_i_by_rnorm, tilde_y_i, y_q, false, LSigma_i, s_i); // MODIFIED 2015/02/16
y_q = log_ColumnVector(x_q) ;
ColumnVector y_i = (Y_in.row(i_original)).t();
// Calculate acceptance ratio
ColumnVector y_compact_q = Data.get_compact_vector(y_q);
ColumnVector y_compact_i = Data.get_compact_vector(y_i);
double log_full_norm_q = log_MVN_fn(y_compact_q,mu_z_i,LSIGMA_i[z_in(i_original)-1],logdet_and_more(z_in(i_original)));
double log_full_norm_i = log_MVN_fn(y_compact_i,mu_z_i,LSIGMA_i[z_in(i_original)-1],logdet_and_more(z_in(i_original)));
double log_f_y_tilde_q = 0;
if (FM.useMap && Data.is_case(i_original,2)) {
log_f_y_tilde_q = FM.Simulate_logUnif_case2(CFeasibilityMap::s_to_tau_fn(s_q),i_original,n_simul,randUnif);
} else {
log_f_y_tilde_q = Data.get_logUnif_y_tilde(s_q, CFeasibilityMap::s_to_tau_fn(s_q),i_original);
}
double logNum = log_full_norm_q + log_f_y_tilde_q - log_cond_norm_q - log(g_s_q);
double logDen = log_full_norm_i + logUnif_y_tilde(i_faulty) - log_cond_norm_i - log(g_s_i);
accept_rate(1) = exp( logNum - logDen ) ;
if ( randUnif.Next() < accept_rate(1) ){
Y_in.row(i_original) = y_q.t() ;
S_Mat.column(i_faulty) = s_q;
logUnif_y_tilde(i_faulty) = log_f_y_tilde_q;
is_accept(1)++;
}
}
}
is_accept(1) = is_accept(1) / Data.n_faulty;
}
void test1(double N)
{
Uniform U;
double sum = 0.0, sumsq = 0.0, ar1 = 0.0, last = 0.0;
double j;
Array<double> chi0(0,15);
Array<double> chi1(0,255);
Array<double> chi1x(0,255);
Array<double> chi2(0,65535);
Array<double> chi2x(0,65535);
chi0 = 0; chi1 = 0; chi1x = 0; chi2 = 0; chi2x = 0;
Array<double> crawl7(0,127);
Array<double> crawl8(0,255);
Array<double> crawl15(0,32767);
Array<double> crawl16(0,65535);
crawl7 = 0;
crawl8 = 0;
crawl15 = 0;
crawl16 = 0;
unsigned long crawler = 0;
int m_bits = (int)(log(N) / 0.693 - 0.471); // number of bits in sparse monkey test
unsigned long M = 1; M <<= (m_bits - 3); // 2**m_bits / 8
String Seen(M, (char)0); // to accumulate results
unsigned long mask1 = (M - 1);
for (j = 0; j < N; ++j)
{
double u = U.Next();
if (u == 1.0) { cout << "Reject value == 1" << endl; continue; }
double v = u - 0.5;
sum += v;
sumsq += v * v;
ar1 += v * (last - 0.5);
int k = (int)floor(u * 256); ++chi1(k);
int m = (int)floor(u * 65536); ++chi2(m);
int a = (int)floor(u * 16); ++chi0(a);
if (j > 0)
{
int b = (int)floor(last * 16);
++chi1x(a + 16 * b);
int l = (int)floor(last * 256); ++chi2x(k + 256 * l);
}
last = u;
crawler <<= 1; if (v >= 0) ++crawler;
if (j >= 6) ++crawl7(crawler & 0x7F);
if (j >= 7) ++crawl8(crawler & 0xFF);
if (j >= 14) ++crawl15(crawler & 0x7FFF);
if (j >= 15) ++crawl16(crawler & 0xFFFF);
if ( j >= (unsigned int)(m_bits-1) )
{
unsigned char mask2 = 1; mask2 <<= crawler & 7;
Seen[(crawler >> 3) & mask1] |= mask2;
}
}
void test3(int n)
{
cout << endl;
// Do chi-squared tests to discrete data
cout << "ChiSquared tests for discrete data" << endl;
cout << "chisq should be less than 95% point in most cases" << endl;
cout << " and 99% point in almost all cases" << endl << endl;
{
Real p[] = { 0.05, 0.10, 0.05, 0.5, 0.01, 0.01, 0.03, 0.20, 0.05 };
TestDiscreteGen(9, p, n);
}
{
Real p[] = { 0.4, 0.2, 0.1, 0.05, 0.025, 0.0125, 0.00625, 0.00625, 0.2 };
TestDiscreteGen(9, p, n);
}
TestNegativeBinomial(200.3, 0.05, n);
TestNegativeBinomial(150.3, 0.15, n);
TestNegativeBinomial(100.8, 0.18, n);
TestNegativeBinomial(100.8, 1.22, n);
TestNegativeBinomial(100.8, 9.0, n);
TestNegativeBinomial(10.5, 0.18, n);
TestNegativeBinomial(10.5, 1.22, n);
TestNegativeBinomial(10.5, 9.0, n);
TestNegativeBinomial(0.35, 0.18, n);
TestNegativeBinomial(0.35, 1.22, n);
TestNegativeBinomial(0.35, 9.0, n);
TestBinomial(100, 0.45, n);
TestBinomial(100, 0.25, n);
TestBinomial(100, 0.02, n);
TestBinomial(100, 0.01, n);
TestBinomial(49, 0.60, n);
TestBinomial(21, 0.70, n);
TestBinomial(10, 0.90, n);
TestBinomial(10, 0.25, n);
TestBinomial(10, 0.10, n);
TestPoisson(0.75, n);
TestPoisson(4.3, n);
TestPoisson(10, n);
TestPoisson(100, n);
Real* data = new Real[n];
if (!data) Throw(Bad_alloc());
// Apply KS test to a variety of continuous distributions
// - use cdf transform to convert to uniform
cout << endl;
cout << "Kolmogorov-Smirnoff tests for continuous distributions" << endl;
cout << "25%, 5%, 1%, .1% upper points are 1.019, 1.358, 1.628, 1.950"
<< endl;
cout << "5% lower point is 0.520" << endl;
cout << "Values should be mostly less than 5% upper point" << endl;
cout << " and less than 1% point almost always" << endl << endl;
{
ChiSq X(1, 1.44);
for (int i = 0; i < n; i++)
{
Real x = sqrt(X.Next());
data[i] = NormalDF(x - 1.2) - NormalDF(-x - 1.2);
}
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
ChiSq X(4);
for (int i = 0; i < n; i++)
{ Real x = 0.5 * X.Next(); data[i] = (1+x)*exp(-x); }
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
ChiSq X(2);
for (int i = 0; i < n; i++) data[i] = exp(-0.5 * X.Next());
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Pareto X(0.5);
for (int i = 0; i < n; i++)
{ Real x = X.Next(); data[i] = 1.0 / sqrt(x); }
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Pareto X(1.5);
for (int i = 0; i < n; i++)
{ Real x = X.Next(); data[i] = 1.0 / (x * sqrt(x)); }
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Normal X;
for (int i = 0; i < n; i++)
{ Real x = X.Next(); data[i] = NormalDF(x); }
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Normal N; SumRandom X = 10 + 5 * N;
for (int i = 0; i < n; i++)
{ Real x = X.Next(); data[i] = NormalDF((x-10)/5); }
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Normal N; Cauchy C; MixedRandom X = N(0.9) + C(0.1);
for (int i = 0; i < n; i++)
{
Real x = X.Next();
data[i] = 0.9*NormalDF(x)+0.1*(atan(x)/3.141592654 + 0.5);
}
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Normal N; MixedRandom X = N(0.9) + (10*N)(0.1);
for (int i = 0; i < n; i++)
{
Real x = X.Next();
data[i] = 0.9*NormalDF(x)+0.1*NormalDF(x/10);
}
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Normal X0; SumRandom X = X0 * 0.6 + X0 * 0.8;
for (int i = 0; i < n; i++)
{ Real x = X.Next(); data[i] = NormalDF(x); }
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Normal X1;
MixedRandom X = X1(0.2) + (X1 * 2.5 + 1.1)(0.35) + (X1 + 2.3)(0.45);
for (int i = 0; i < n; i++)
{
Real x = X.Next();
data[i] = 0.20 * NormalDF(x)
+ 0.35 * NormalDF((x - 1.1) / 2.5)
+ 0.45 * NormalDF(x - 2.3);
}
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Gamma X(0.5);
for (int i = 0; i < n; i++)
{ Real x = X.Next(); data[i] = 2.0 * NormalDF(-sqrt(2 * x)); }
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Gamma X(3);
for (int i = 0; i < n; i++)
{ Real x = X.Next(); data[i] = (1+x+0.5*x*x)*exp(-x); }
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Gamma X1(0.85); Gamma X2(2.15); SumRandom X = X1 + X2;
for (int i = 0; i < n; i++)
{ Real x = X.Next(); data[i] = (1+x+0.5*x*x)*exp(-x); }
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Gamma X1(0.75); Gamma X2(0.25); SumRandom X = X1 + X2;
for (int i = 0; i < n; i++) data[i] = exp(-X.Next());
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Gamma X(2);
for (int i = 0; i < n; i++)
{ Real x = X.Next(); data[i] = (1+x)*exp(-x); }
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Exponential X;
for (int i = 0; i < n; i++) data[i] = exp(-X.Next());
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Cauchy X;
for (int i = 0; i < n; i++) data[i] = atan(X.Next())/3.141592654 + 0.5;
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Cauchy X0; SumRandom X = X0 * 0.3 + X0 * 0.7;
for (int i = 0; i < n; i++) data[i] = atan(X.Next())/3.141592654 + 0.5;
cout << X.Name() << ": " << KS(data, n) << endl;
}
{
Uniform X;
for (int i = 0; i < n; i++) data[i] = X.Next();
cout << X.Name() << ": " << KS(data, n) << endl;
}
delete [] data;
}
void Population::FreqDependentNaturalSelection() {
int nCurrGen = SimulConfig.GetCurrGen();
bool bIgnoreGlobalRules = SimulConfig.pNaturalSelConfig->IgnoreGlobalRules(nCurrGen);
string sPop = this->_sPopName;
map< int, map<string , list< pair<Parser *, int> > > > * mppqParsers = SimulConfig.pNaturalSelConfig->GetFreqDependentFormulaeAllCPUs();
//list< vector<string> > * pqvCourterSymbols = SimulConfig.pNaturalSelConfig->GetFormulaSymbolStringsCourter( sPop);
list< vector<string> > * pqvSelfSymbols = SimulConfig.pNaturalSelConfig->GetFormulaSymbolStringsSelf( sPop);
list< vector<string> > * pqvPopSymbols = SimulConfig.pNaturalSelConfig->GetFormulaSymbolStringsPop( sPop);
list< vector<string> > * pqvPopCourterSymbols = SimulConfig.pNaturalSelConfig->GetFormulaSymbolStringsPopCourter( sPop);
list< vector<string> > * pqvPopChooserSymbols = SimulConfig.pNaturalSelConfig->GetFormulaSymbolStringsPopChooser( sPop);
//set population level parameters
for(int nCPU=0;nCPU<nTotalCPUCore;nCPU++) { //set global current population parameters for all the CPUs
list< pair< Parser *, int > > * pqParsers = &(*mppqParsers)[nCPU][this->_sPopName];
list< vector<string> >::iterator itPopSymbols = pqvPopSymbols->begin();
list< vector<string> >::iterator itPopCourterSymbols = pqvPopCourterSymbols->begin();
list< vector<string> >::iterator itPopChooserSymbols = pqvPopChooserSymbols->begin();
for (list< pair< Parser *, int> >::iterator itParser= pqParsers->begin(); itParser != pqParsers->end() ; ++itParser) {
vector<string> vSymbolsPop = *itPopSymbols;
vector<string> vSymbolsPopCourter = *itPopCourterSymbols;
vector<string> vSymbolsPopChooser = *itPopChooserSymbols;
Parser * pParser = itParser->first;
//set population level symbols
for(vector<string>::iterator itSymbol=vSymbolsPop.begin();itSymbol!=vSymbolsPop.end();++itSymbol)
{
string sSymbol = (*itSymbol).substr(4);
string sType = (*itSymbol).substr(0, 3); //either Avg or Std
if (sType != "Avg" && sType != "Std") {
throw new Exception("Population parameter type unknown!");
}
if (this->_mpSumPhenotype.find(sSymbol) == _mpSumPhenotype.end()) {
throw new Exception("Unable to find population symbol");
}
pParser->symbols_[string("Pop_Avg_"+sSymbol)] = this->_mpSumPhenotype[sSymbol].first;
pParser->symbols_[string("Pop_Std_"+sSymbol)] = this->_mpSumPhenotype[sSymbol].second;
}
for(vector<string>::iterator itSymbol=vSymbolsPopCourter.begin();itSymbol!=vSymbolsPopCourter.end();++itSymbol)
{
string sSymbol = (*itSymbol).substr(4);
string sType = (*itSymbol).substr(0, 3); //either Avg or Std
if (sType != "Avg" && sType != "Std") {
throw new Exception("Population parameter type unknown!");
}
if (this->_mpSumPhenotypeMale.find(sSymbol) == _mpSumPhenotypeMale.end()) {
throw new Exception("Unable to find population symbol: male");
}
pParser->symbols_[string("PopCourter_Avg_"+sSymbol)] = this->_mpSumPhenotypeMale[sSymbol].first;
pParser->symbols_[string("PopCourter_Std_"+sSymbol)] = this->_mpSumPhenotypeMale[sSymbol].second;
}
for(vector<string>::iterator itSymbol=vSymbolsPopChooser.begin();itSymbol!=vSymbolsPopChooser.end();++itSymbol)
{
string sSymbol = (*itSymbol).substr(4);
string sType = (*itSymbol).substr(0, 3); //either Avg or Std
if (sType != "Avg" && sType != "Std") {
throw new Exception("Population parameter type unknown!");
}
if (this->_mpSumPhenotypeFemale.find(sSymbol) == _mpSumPhenotypeFemale.end()) {
throw new Exception("Unable to find population symbol: female");
}
pParser->symbols_[string("PopChooser_Avg_"+sSymbol)] = this->_mpSumPhenotypeFemale[sSymbol].first;
pParser->symbols_[string("PopChooser_Std_"+sSymbol)] = this->_mpSumPhenotypeFemale[sSymbol].second;
}
++itPopSymbols;
++itPopCourterSymbols;
++itPopChooserSymbols;
}
}
//now go through each individual
vector<int>::size_type nMale = this->_mpMales.size();
vector<int>::size_type nFemale = this->_mpFemales.size();
#pragma omp parallel
{
#pragma omp for
//for (vector< Individual * >::size_type i=0; i< nMale; i++ ) {
for (signed long i=0; i< nMale; i++ ) {
Individual * pInd = _mpMales[i];
#ifdef _OPENMP
int nCPU = omp_get_thread_num();
#else
int nCPU = 0;
#endif
list< pair< Parser *, int > > * pqParsers = &(*mppqParsers)[nCPU][this->_sPopName];
list< vector<string> >::iterator itSelfSymbols = pqvSelfSymbols->begin();
for (list< pair< Parser *, int> >::iterator itParser= pqParsers->begin(); itParser != pqParsers->end() ; ++itParser) {
vector<string> vSymbolsSelf = *itSelfSymbols;
Parser * pParser = itParser->first;
int nGen = itParser->second;
if ((nGen ==-1 && !bIgnoreGlobalRules) || (bIgnoreGlobalRules && nGen == nCurrGen) ) {
for(vector<string>::iterator itSymbol=vSymbolsSelf.begin();itSymbol!=vSymbolsSelf.end();++itSymbol)
{
//#pragma omp critical
//{
pParser->symbols_[string("My_"+(*itSymbol))] = pInd->GetPhenotype(*itSymbol);
pParser->symbols_[*itSymbol] = pInd->GetPhenotype(*itSymbol); //set both variables
//}
}
bool bLive = true;
//#pragma omp critical
//{
bLive = (UniformGen.Next() <= pParser->Evaluate())? true : false;
//}
if (!bLive) {
#pragma omp critical
{
delete pInd; // uhoh, dead!!
_mpMales[i] = NULL; //mark it
//_mpMales.erase(_mpMales.begin() + i);
}
break;
}
}
++itSelfSymbols;
}
}
#pragma omp for
//for (vector< Individual * >::size_type i=0; i< nFemale; i++ ) {
for (signed long i=0; i< nFemale; i++ ) {
Individual * pInd = _mpFemales[i];
#ifdef _OPENMP
int nCPU = omp_get_thread_num();
#else
int nCPU = 0;
#endif
list< pair< Parser *, int > > * pqParsers = &(*mppqParsers)[nCPU][this->_sPopName];
list< vector<string> >::iterator itSelfSymbols = pqvSelfSymbols->begin();
for (list< pair< Parser *, int> >::iterator itParser= pqParsers->begin(); itParser != pqParsers->end() ; ++itParser) {
vector<string> vSymbolsSelf = *itSelfSymbols;
Parser * pParser = itParser->first;
int nGen = itParser->second;
if ((nGen ==-1 && !bIgnoreGlobalRules) || (bIgnoreGlobalRules && nGen == nCurrGen) ) {
for(vector<string>::iterator itSymbol=vSymbolsSelf.begin();itSymbol!=vSymbolsSelf.end();++itSymbol)
{
//#pragma omp critical
//{
pParser->symbols_[string("My_"+(*itSymbol))] = pInd->GetPhenotype(*itSymbol);
pParser->symbols_[*itSymbol] = pInd->GetPhenotype(*itSymbol); //set both variables
//}
}
bool bLive = true;
//#pragma omp critical
//{
bLive = (UniformGen.Next() <= pParser->Evaluate())? true : false;
//}
if (!bLive) {
#pragma omp critical
{
delete pInd; // uhoh, dead!!
_mpFemales[i] = NULL;
//_mpFemales.erase(_mpFemales.begin() + i);
}
break;
}
}
++itSelfSymbols;
}
}
} //end parallel block
//clear NULL pointers from the arrays:
for (vector< Individual * >::size_type i=0; i< nMale; i++ ) {
if (! _mpMales[i]) {
_mpMales.erase(_mpMales.begin() + i);
nMale--; // readjust upper bound
i--;
}
}
for (vector< Individual * >::size_type i=0; i< nFemale; i++ ) {
if (! _mpFemales[i]) {
_mpFemales.erase(_mpFemales.begin() + i);
nFemale--; // readjust upper bound
i--;
}
}
}
bool Population::ImmigrateConfirm(bool bForceExistingDie) {
int nCacheMaleSize = _mpImCacheMales.size();
int nCacheFemaleSize = _mpImCacheFemales.size();
int nTotalCached = nCacheMaleSize + nCacheFemaleSize;
int nTotalToAdd;
int nCurrPopSize = this->GetPopSize(4);
int nRoomLeft = _nPopMaxSize-nCurrPopSize >=0? _nPopMaxSize-nCurrPopSize : 0;
double nProbToAdd;
if (bForceExistingDie) {
nTotalToAdd = nTotalCached < _nPopMaxSize? nTotalCached : _nPopMaxSize;
nProbToAdd = (double)nTotalToAdd / (double)nTotalCached;
} else {
if (nRoomLeft == 0) {
return false; //no room to add any immigrants
}
nTotalToAdd = nTotalCached < nRoomLeft? nTotalCached : nRoomLeft;
nProbToAdd = (double)nTotalToAdd / (double)nTotalCached;
}
//printf("MaxPopSize: %d \n", _nPopMaxSize );
printf("Candidate immigrating males %d\n", nCacheMaleSize);
printf("Candidate immigrating females %d\n", nCacheFemaleSize);
int nAddedMales=0;
int nAddedFemales=0;
if (bForceExistingDie && nRoomLeft < nTotalToAdd) {
int nMakeMoreRoom = nTotalToAdd - nRoomLeft;
for(int i=0;i< nMakeMoreRoom;i++) {
Individual * pVictim = this->Emigrate();
if (!pVictim) {
} else {
delete pVictim;
}
}
}
for(vector< Individual * >::iterator it=this->_mpImCacheMales.begin(); it!=this->_mpImCacheMales.end();++it) {
if (UniformGen.Next() < nProbToAdd) {
//add
_mpMales.push_back( *it );
nAddedMales++;
}
}
for(vector< Individual * >::iterator it=this->_mpImCacheFemales.begin(); it!=this->_mpImCacheFemales.end();++it) {
if (UniformGen.Next() < nProbToAdd) {
//add
_mpFemales.push_back( *it );
nAddedFemales++;
}
}
printf("Added immigrating males %d\n", nAddedMales);
printf("Added immigrating females %d\n", nAddedFemales);
//do final check on pop size:
nCurrPopSize = this->GetPopSize(4);
if (nCurrPopSize > _nPopMaxSize) {
//randomly remove some individuals
while(this->GetPopSize(4) > _nPopMaxSize) {
Individual * pVictim = this->Emigrate();
if (!pVictim) {
} else {
delete pVictim;
}
}
}
}
