/**
* Return the calculated value stored in this derived quantity
* for the parameter year. If the year does not exist as a standard
* value we'll calculate how many years to go back in to
* the initialisation phase values.
*
* Note: We cannot go back more than 1 phase. If this condition
* is triggered we return the first value from the phase instead
* of going back.
*
* @param year The year to get the derived quantity value for.
* @return The derived quantity value
*/
Double DerivedQuantity::GetValue(unsigned year) {
LOG_FINEST() << "get value for year: " << year;
if (override_values_.find(year) != override_values_.end()) {
values_[year] = override_values_[year];
return override_values_[year];
}
if (values_.find(year) != values_.end())
return values_[year];
if (initialisation_values_.size() == 0)
return 0.0;
// Calculate how many years to go back. At this point
// either we're in the init phases or we're going back
// in to the init phases.
unsigned years_to_go_back = model_->start_year() - year;
Double result = 0.0;
if (years_to_go_back == 0) {
LOG_WARNING() << "Years to go back is 0 in derived quantity " << label_ << " when it shouldn't be";
result = (*initialisation_values_.rbegin()->rbegin());
} else if (initialisation_values_.rbegin()->size() > years_to_go_back) {
result = initialisation_values_.rbegin()->at(initialisation_values_.rbegin()->size() - years_to_go_back);
} else if (initialisation_values_.size() == 1) {
result = (*initialisation_values_.rbegin()->begin()); // first value of last init phase
} else {
result = (*(initialisation_values_.rbegin() + 1)->begin()); // first value of last init phase
}
// Make an exception for intialisation phases such as derived which only requires to go back one year
if (model_->b0_initialised(label_)) {
result = (*initialisation_values_.rbegin()->rbegin());
}
LOG_FINEST() << "years_to_go_back: " << years_to_go_back
<< "; year: " << year
<< "; result: " << result
<< "; .begin(): " << (*initialisation_values_.rbegin()->rbegin())
<< ": .size(): " << initialisation_values_.rbegin()->size();
return result;
}
void MortalityEventBiomass::DoExecute() {
if (catch_years_[model_->current_year()] == 0)
return;
LOG_TRACE();
/**
* Work our how much of the stock is vulnerable
*/
Double vulnerable = 0.0;
unsigned i = 0;
for (auto categories : partition_) {
categories->UpdateMeanWeightData();
unsigned offset = 0;
for (Double& data : categories->data_) {
Double temp = data * selectivities_[i]->GetResult(categories->min_age_ + offset, categories->age_length_);
vulnerable += temp * categories->mean_weight_per_[categories->min_age_ + offset];
++offset;
}
++i;
}
/**
* Work out the exploitation rate to remove (catch/vulnerable)
*/
Double exploitation = catch_years_[model_->current_year()] / utilities::doublecompare::ZeroFun(vulnerable);
if (exploitation > u_max_) {
exploitation = u_max_;
if (penalty_)
penalty_->Trigger(label_, catch_years_[model_->current_year()], vulnerable * u_max_);
} else if (exploitation < 0.0) {
exploitation = 0.0;
}
LOG_FINEST() << "year: " << model_->current_year() << "; exploitation: " << AS_DOUBLE(exploitation);
/**
* Remove the stock now. The amount to remove is
* vulnerable * exploitation
*/
i = 0;
for (auto categories : partition_) {
unsigned offset = 0;
for (Double& data : categories->data_) {
data -= data * selectivities_[i]->GetResult(categories->min_age_ + offset, categories->age_length_) * exploitation;
++offset;
}
++i;
}
}
/**
* Execute any reports that have the year and
* time step label as their execution parameters.
* Note: All these reports are only in the execute phase.
*
* @param year The current year for the model
* @param time_step_label The last time step to be completed
*/
void Manager::Execute(unsigned year, const string& time_step_label) {
LOG_TRACE();
LOG_FINEST() << "year: " << year << "; time_step_label: " << time_step_label << "; reports: " << time_step_reports_[time_step_label].size();
RunMode::Type run_mode = model_->run_mode();
bool tabular = model_->global_configuration().print_tabular();
for(auto report : time_step_reports_[time_step_label]) {
if ( (RunMode::Type)(report->run_mode() & run_mode) != run_mode) {
LOG_FINEST() << "Skipping report: " << report->label() << " because run mode is not right";
continue;
}
if (!report->HasYear(year)) {
LOG_FINEST() << "Skipping report: " << report->label() << " because it does not have year " << year;
continue;
}
if (tabular)
report->ExecuteTabular();
else
report->Execute();
}
}
/**
* This method is called at the start of the targetted
* time step for this observation.
*
* At this point we need to build our cache for the partition
* structure to use with any interpolation
*/
void ProportionsMigrating::PreExecute() {
cached_partition_->BuildCache();
LOG_FINEST() << "Entering observation " << label_;
if (cached_partition_->Size() != proportions_[model_->current_year()].size()) {
LOG_MEDIUM() << "Cached size " << cached_partition_->Size() << " partition size = " << proportions_[model_->current_year()].size();
LOG_CODE_ERROR() << "cached_partition_->Size() != proportions_[model->current_year()].size()";
}
if (partition_->Size() != proportions_[model_->current_year()].size())
LOG_CODE_ERROR() << "partition_->Size() != proportions_[model->current_year()].size()";
}
/**
* Store the value from our addressable for this year
*/
void Project::StoreValue(unsigned current_year) {
if (addressable_ != nullptr)
stored_values_[current_year] = *addressable_;
else if (addressable_map_ != nullptr)
stored_values_[current_year] = (*addressable_map_)[current_year];
else if (addressable_vector_ != nullptr) {
unsigned index = current_year - model_->start_year();
if (index >= addressable_vector_->size()) {
LOG_CODE_ERROR() << "Could not store value for @project parameter " << parameter_ << " in year "
<< current_year << " because index exceeded size of vector " << index << " : " << addressable_vector_->size();
}
stored_values_[current_year] = addressable_vector_->at(index);
}
LOG_FINEST() << "Storing value = " << stored_values_[current_year];
}
void SumToOne::DoBuild() {
LOG_TRACE();
for (auto& estimate_label : estimate_labels_) {
Estimate* estimate = model_->managers().estimate()->GetEstimateByLabel(estimate_label);
if (estimate == nullptr) {
LOG_ERROR_P(PARAM_ESTIMATE_LABELS) << "Estimate " << estimate_label << " could not be found. Have you defined it?";
return;
} else {
LOG_FINE() << "transform with objective = " << transform_with_jacobian_ << " estimate transform " << estimate->transform_for_objective() << " together = " << !transform_with_jacobian_ && !estimate->transform_for_objective();
if (!transform_with_jacobian_ && !estimate->transform_for_objective()) {
LOG_ERROR_P(PARAM_LABEL) << "You have specified a transformation that does not contribute a jacobian, and the prior parameters do not refer to the transformed estimate, in the @estimate" << estimate_label_ << ". This is not advised, and may cause bias estimation. Please address the user manual if you need help";
}
if (estimate->transform_with_jacobian_is_defined()) {
if (transform_with_jacobian_ != estimate->transform_with_jacobian()) {
LOG_ERROR_P(PARAM_LABEL) << "This parameter is not consistent with the equivalent parameter in the @estimate block " << estimate_label_ << ". please make sure these are both true or both false.";
}
}
estimates_.push_back(estimate);
}
}
// Validate that the parameters sum to one.
Double total = 0.0;
for (auto& estimate : estimates_) {
LOG_FINEST() << "transformation value = " << estimate->value();
total += estimate->value();
}
if (total != 1.0)
LOG_ERROR_P(PARAM_ESTIMATE_LABELS) << "The estiamtes you supplied to not sum to 1.0, they sum to " << total << ", please check initial values of these parameters";
// Check that the bounds are sensible
if (parameters_.Get(PARAM_UPPER_BOUND)->has_been_defined() & parameters_.Get(PARAM_LOWER_BOUND)->has_been_defined()) {
for (unsigned i = 0; i < estimates_.size(); ++i) {
if (estimates_[i]->lower_bound() < 0.0 || estimates_[i]->lower_bound() > 1.0)
LOG_ERROR_P(PARAM_LOWER_BOUND) << "You cannot specify a lower bound less than 0 and greater than 1.0";
if (estimates_[i]->upper_bound() < 0.0 || estimates_[i]->upper_bound() > 1.0)
LOG_ERROR_P(PARAM_UPPER_BOUND) << "You cannot specify a upper bound less than 0 and greater than 1.0";
}
}
LOG_MEDIUM() << "total = " << total;
// Turn off the last estimate
LOG_FINE() << "Turning off parameter, this won't be estimated, and will be an outcome of other parameters " << estimates_[estimates_.size() - 1]->parameter() << " in the estimation";
estimates_[estimates_.size() - 1]->set_estimated(false);
LOG_MEDIUM() << "flagged estimated = " << estimates_[estimates_.size() - 1]->estimated();
}
void Manager::Prepare() {
LOG_TRACE();
RunMode::Type run_mode = model_->run_mode();
bool tabular = model_->global_configuration().print_tabular();
for (auto report : objects_) {
if ( (RunMode::Type)(report->run_mode() & run_mode) != run_mode) {
LOG_FINEST() << "Skipping report: " << report->label() << " because run mode is not right";
continue;
}
if (tabular)
report->PrepareTabular();
else
report->Prepare();
}
}
/**
* Build our reports then
* organise the reports stored in our
* object list into different containers
* based on their type.
*/
void Manager::Build() {
LOG_FINEST() << "objects_.size(): " << objects_.size();
for (auto report : objects_) {
report->Build();
if ((RunMode::Type)(report->run_mode() & RunMode::kInvalid) == RunMode::kInvalid)
LOG_CODE_ERROR() << "Report: " << report->label() << " has not been properly configured to have a run mode";
if (report->model_state() != State::kExecute) {
LOG_FINE() << "Adding report " << report->label() << " to state reports";
state_reports_[report->model_state()].push_back(report);
} else {
LOG_FINE() << "Adding report " << report->label() << " to time step reports";
time_step_reports_[report->time_step()].push_back(report);
}
}
}
void Manager::Validate(Model* model) {
LOG_TRACE();
base::Manager<niwa::timesteps::Manager, niwa::TimeStep>::Validate();
model_ = model;
// Order our time steps based on the parameter given to the model
vector<string> time_steps = model->time_steps();
for(string time_step_label : time_steps) {
for(auto time_step : objects_) {
if (time_step->label() == time_step_label) {
ordered_time_steps_.push_back(time_step);
break;
}
}
}
LOG_FINEST() << "ordered_time_steps_.size(): " << ordered_time_steps_.size();
}
/**
* This method will take the current age population for this category stored
* in this->data_ and populate this->length_data_ by using the age length
* proportions generated and stored against the Partition class. The age
* length proportions are generated during the build phase.
*
* @parameter selectivity The selectivity to apply to the age data
*/
void Category::PopulateAgeLengthMatrix(Selectivity* selectivity) {
LOG_FINEST() << "About to populate the length data for category " << name_ << " in year " << model_->current_year();
if (selectivity == nullptr)
LOG_CODE_ERROR() << "selectivity == nullptr";
if (age_length_ == nullptr)
LOG_CODE_ERROR() << "In category " << name_ << " there is no age length object to have calculated the age length proportions";
if (age_length_matrix_.size() == 0)
LOG_CODE_ERROR() << "No memory has been allocated for the age_length_matrix for category " << name_;
auto& age_length_proportions = model_->partition().age_length_proportions(name_);
unsigned year = model_->current_year() - model_->start_year();
vector<unsigned> length_bins = model_->length_bins();
unsigned time_step_index = model_->managers().time_step()->current_time_step();
LOG_FINEST() << "Year: " << year << "; time_step: " << time_step_index << "; length_bins: " << length_bins.size();
LOG_FINEST() << "Years in proportions: " << age_length_proportions.size();
LOG_FINEST() << "Timesteps in current year: " << age_length_proportions[year].size();
if (year > age_length_proportions.size())
LOG_CODE_ERROR() << "year > age_length_proportions.size()";
if (time_step_index > age_length_proportions[year].size())
LOG_CODE_ERROR() << "time_step_index > age_length_proportions[year].size()";
vector<vector<Double>>& proportions_for_now = age_length_proportions[year][time_step_index];
unsigned size = model_->length_plus() == true ? model_->length_bins().size() : model_->length_bins().size() - 1;
LOG_FINEST() << "Calculating age length data";
for (unsigned age = min_age_; age <= max_age_; ++age) {
unsigned i = age - min_age_;
if (i >= proportions_for_now.size())
LOG_CODE_ERROR() << "i >= proportions_for_now.size()";
if (i >= data_.size())
LOG_CODE_ERROR() << "i >= data_.size()";
if (i >= age_length_matrix_.size())
LOG_CODE_ERROR() << "(i >= age_length_matrix_.size())";
vector<Double>& ages_at_length = proportions_for_now[i];
for (unsigned bin = 0; bin < size; ++bin) {
if (bin >= age_length_matrix_[i].size())
LOG_CODE_ERROR() << "bin (" << bin << ") >= age_length_matrix_[i].size(" << age_length_matrix_[i].size() << ")";
if (bin >= ages_at_length.size())
LOG_CODE_ERROR() << "bin >= ages_at_length.size()";
age_length_matrix_[i][bin] = selectivity->GetAgeResult(age, age_length_) * data_[i] * ages_at_length[bin];
}
}
LOG_FINEST() << "Finished populating the length data for category " << name_ << " in year " << model_->current_year();
}
/**
* Build our partition structure now. This involves getting
* the category information and building the raw structure.
*
* We're not interested in the range of years that each
* category has because this will be addressed with the
* accessor objects.
*/
void Partition::Build() {
Categories* categories = model_->categories();
vector<string> category_names = categories->category_names();
for(string category : category_names) {
LOG_FINEST() << "Adding category " << category << " to the partition";
partition::Category* new_category = new partition::Category(model_);
new_category->name_ = category;
new_category->min_age_ = categories->min_age(category);
new_category->max_age_ = categories->max_age(category);
new_category->years_ = categories->years(category);
new_category->age_length_ = categories->age_length(category);
unsigned age_spread = (categories->max_age(category) - categories->min_age(category)) + 1;
new_category->data_.resize(age_spread, 0.0);
partition_[category] = new_category;
}
}
/**
* Build any runtime relationships
* - Build the partition accessor
* - Build our list of selectivities
* - Build our ratios for the number of time steps
*/
void TagLoss::DoBuild() {
partition_.Init(category_labels_);
for (string label : selectivity_names_) {
Selectivity* selectivity = model_->managers().selectivity()->GetSelectivity(label);
if (!selectivity)
LOG_ERROR_P(PARAM_SELECTIVITIES) << ": selectivity " << label << " does not exist. Have you defined it?";
selectivities_.push_back(selectivity);
}
/**
* Organise our time step ratios. Each time step can
* apply a different ratio of M so here we want to verify
* we have enough and re-scale them to 1.0
*/
vector<TimeStep*> time_steps = model_->managers().time_step()->ordered_time_steps();
LOG_FINEST() << "time_steps.size(): " << time_steps.size();
vector<unsigned> active_time_steps;
for (unsigned i = 0; i < time_steps.size(); ++i) {
if (time_steps[i]->HasProcess(label_))
active_time_steps.push_back(i);
}
if (ratios_.size() == 0) {
for (unsigned i : active_time_steps)
time_step_ratios_[i] = 1.0;
} else {
if (ratios_.size() != active_time_steps.size())
LOG_FATAL_P(PARAM_TIME_STEP_RATIO) << " length (" << ratios_.size()
<< ") does not match the number of time steps this process has been assigned to (" << active_time_steps.size() << ")";
for (Double value : ratios_) {
if (value < 0.0 || value > 1.0)
LOG_ERROR_P(PARAM_TIME_STEP_RATIO) << " value (" << value << ") must be between 0.0 (inclusive) and 1.0 (inclusive)";
}
for (unsigned i = 0; i < ratios_.size(); ++i)
time_step_ratios_[active_time_steps[i]] = ratios_[i];
}
}
/**
* Execute our maturation rate process.
*/
void TransitionCategory::DoExecute() {
LOG_TRACE();
auto from_iter = from_partition_.begin();
auto to_iter = to_partition_.begin();
Double amount = 0.0;
LOG_FINEST() << "transition_rates_.size(): " << transition_rates_.size() << "; from_partition_.size(): " << from_partition_.size()
<< "; to_partition_.size(): " << to_partition_.size();
if (from_partition_.size() != to_partition_.size()) {
LOG_FATAL() << "The list of categories for the Transition Category process are not of equal size in year " << model_->current_year()
<< ". We have " << from_partition_.size() << " and " << to_partition_.size() << " categories to transition between";
}
if (transition_rates_.size() != from_partition_.size()) {
LOG_FINE() << "Re-building the transition rates because the partition size has changed";
transition_rates_.resize(from_partition_.size());
for (unsigned i = 0; i < transition_rates_.size(); ++i) {
Double proportion = proportions_.size() > 1 ? proportions_[i] : proportions_[0];
unsigned min_age = (*from_iter)->min_age_;
for (unsigned j = 0; j < (*from_iter)->data_.size(); ++j) {
transition_rates_[i].push_back(proportion * selectivities_[i]->GetResult(min_age + j, (*from_iter)->age_length_));
if (selectivities_[i]->GetResult(min_age + j, (*from_iter)->age_length_) > 1.0)
LOG_ERROR() << " Selectivity result is greater than 1.0, check selectivity";
}
}
}
for (unsigned i = 0; from_iter != from_partition_.end() && to_iter != to_partition_.end(); ++from_iter, ++to_iter, ++i) {
for (unsigned offset = 0; offset < (*from_iter)->data_.size(); ++offset) {
amount = transition_rates_[i][offset] * (*from_iter)->data_[offset];
(*from_iter)->data_[offset] -= amount;
(*to_iter)->data_[offset] += amount;
if ((*from_iter)->data_[offset] < 0.0)
LOG_FATAL() << "Maturation rate caused a negative partition if ((*from_iter)->data_[offset] < 0.0) ";
}
}
}
/**
* This method is called at the end of a model iteration
* to calculate the score for the observation.
*/
void ProportionsMigrating::CalculateScore() {
/**
* Simulate or generate results
* During simulation mode we'll simulate results for this observation
*/
if (model_->run_mode() == RunMode::kSimulation) {
likelihood_->SimulateObserved(comparisons_);
} else {
/**
* The comparisons are already proportions so the can be sent straight to the likelihood
*/
for (unsigned year : years_) {
scores_[year] = likelihood_->GetInitialScore(comparisons_, year);
likelihood_->GetScores(comparisons_);
for (obs::Comparison comparison : comparisons_[year]) {
LOG_FINEST() << "[" << year << "]+ likelihood score: " << comparison.score_;
scores_[year] += comparison.score_;
}
}
}
}
/**
* This method collapses the Numbers at length by age matrix to numbers at age for a category
*/
void Category::CollapseAgeLengthDataToLength() {
LOG_TRACE();
if (age_length_matrix_.size() == 0)
LOG_CODE_ERROR() << "if (age_length_matrix_.size() == 0)";
LOG_FINE() << "age_length_matrix_.size(): " << age_length_matrix_.size();
LOG_FINE() << "age_length_matrix_[0].size(): " << age_length_matrix_[0].size();
length_data_.assign(model_->length_bins().size(), 0.0);
for (unsigned i = 0; i < age_length_matrix_.size(); ++i) {
for (unsigned j = 0; j < age_length_matrix_[i].size(); ++j) {
if (j >= length_data_.size())
LOG_CODE_ERROR() << "j >= length_data_.size()";
length_data_[j] += age_length_matrix_[i][j];
}
}
for (unsigned i = 0; i < length_data_.size(); ++i)
LOG_FINEST() << "length_data_[" << i << "]: " << length_data_[i];
}
/**
* Get the score for this penalty
*
* @return Penalty score
*/
Double ElementDifference::GetScore() {
LOG_TRACE();
vector<Double> values;
vector<Double> second_values;
// first parameter
if (addressable_vector_ != nullptr)
values.assign((*addressable_vector_).begin(), (*addressable_vector_).end());
else if (addressable_ptr_vector_ != nullptr) {
for (auto ptr : (*addressable_ptr_vector_))
values.push_back((*ptr));
} else if (addressable_map_ != nullptr) {
for (auto iter : (*addressable_map_))
values.push_back(iter.second);
} else if (addressable_ != nullptr) {
values.push_back((*addressable_));
} else
LOG_CODE_ERROR() << "(second_addressable_map_ != 0) && (second_addressable_vector_ != 0)";
// Second parameter
if (second_addressable_vector_ != nullptr)
second_values.assign((*second_addressable_vector_).begin(), (*second_addressable_vector_).end());
else if (second_addressable_ptr_vector_ != nullptr) {
for (auto ptr : (*second_addressable_ptr_vector_))
second_values.push_back((*ptr));
} else if (second_addressable_map_ != nullptr) {
for (auto iter : (*second_addressable_map_))
second_values.push_back(iter.second);
} else if (second_addressable_ != nullptr) {
second_values.push_back((*second_addressable_));
} else
LOG_CODE_ERROR() << "(second_addressable_map_ != 0) && (second_addressable_vector_ != 0)";
Double score = 0.0;
LOG_FINEST() << "size of first vector = " << values.size() << " size of second vector";
for(unsigned i = 0; i < values.size(); ++i)
score += pow(values[i] - second_values[i], 2);
return score * multiplier_;
}
/**
* Execute the time step during the initialisation phases
*/
void TimeStep::ExecuteForInitialisation(const string& phase_label) {
LOG_FINEST() << "Executing for initialisation phase: " << phase_label << " with " << initialisation_block_executors_.size() << " executors";
for (unsigned index = 0; index < initialisation_processes_[phase_label].size(); ++index) {
if (initialisation_mortality_blocks_[phase_label].first == index) {
for (auto executor : initialisation_block_executors_) {
executor->PreExecute();
}
}
initialisation_processes_[phase_label][index]->Execute(0u, "");
if (initialisation_mortality_blocks_[phase_label].second == index) {
for (auto executor : initialisation_block_executors_) {
executor->Execute();
}
}
}
if (initialisation_mortality_blocks_[phase_label].first == processes_.size()){
for (auto executor : initialisation_block_executors_) {
executor->PreExecute();
executor->Execute();
}
}
}
/**
* Execute the time step
*/
void TimeStep::Execute(unsigned year) {
LOG_TRACE();
for (auto executor : executors_[year])
executor->PreExecute();
for (unsigned index = 0; index < processes_.size(); ++index) {
if (index == mortality_block_.first) {
for (auto executor : block_executors_[year])
executor->PreExecute();
}
for(auto executor : process_executors_[year][index])
executor->PreExecute();
LOG_FINEST() << "Executing process: " << processes_[index]->label();
processes_[index]->Execute(year, label_);
for(auto executor : process_executors_[year][index])
executor->Execute();
if (index == mortality_block_.second) {
for (auto executor : block_executors_[year])
executor->Execute();
}
}
if (mortality_block_.first == processes_.size()){
for (auto executor : block_executors_[year]) {
executor->PreExecute();
executor->Execute();
}
}
for (auto executor : executors_[year])
executor->Execute();
}
void ProcessRemovalsByLength::Execute() {
LOG_TRACE();
/**
* Verify our cached partition and partition sizes are correct
*/
// auto categories = model_->categories();
unsigned year = model_->current_year();
unsigned year_index = year - model_->start_year();
unsigned time_step = model_->managers().time_step()->current_time_step();
auto cached_partition_iter = cached_partition_->Begin();
auto partition_iter = partition_->Begin(); // vector<vector<partition::Category> >
map<unsigned, map<string, map<string, vector<Double>>>> &Removals_at_age = mortality_instantaneous_->catch_at();
/**
* Loop through the provided categories. Each provided category (combination) will have a list of observations
* with it. We need to build a vector of proportions for each length using that combination and then
* compare it to the observations.
*/
for (unsigned category_offset = 0; category_offset < category_labels_.size(); ++category_offset, ++partition_iter, ++cached_partition_iter) {
LOG_FINEST() << "category: " << category_labels_[category_offset];
Double start_value = 0.0;
Double end_value = 0.0;
Double number_at_age = 0.0;
// LOG_WARNING() << "This is bad code because it allocates memory in the middle of an execute";
vector<Double> expected_values(number_bins_, 0.0);
vector<Double> numbers_at_length;
vector<vector<Double>> age_length_matrix;
/**
* Loop through the 2 combined categories building up the
* expected proportions values.
*/
auto category_iter = partition_iter->begin();
auto cached_category_iter = cached_partition_iter->begin();
for (; category_iter != partition_iter->end(); ++cached_category_iter, ++category_iter) {
// AgeLength* age_length = categories->age_length((*category_iter)->name_);
// LOG_WARNING() << "This is bad code because it allocates memory in the middle of an execute";
age_length_matrix.resize((*category_iter)->data_.size());
vector<Double> age_frequencies(length_bins_.size(), 0.0);
const auto& age_length_proportions = model_->partition().age_length_proportions((*category_iter)->name_)[year_index][time_step];
for (unsigned data_offset = 0; data_offset < (*category_iter)->data_.size(); ++data_offset) {
unsigned age = ((*category_iter)->min_age_ + data_offset);
// Calculate the age structure removed from the fishing process
number_at_age = Removals_at_age[year][method_][(*category_iter)->name_][data_offset];
LOG_FINEST() << "Numbers at age = " << age << " = " << number_at_age << " start value : " << start_value << " end value : " << end_value;
// Implement an algorithm similar to DoAgeLengthConversion() to convert numbers at age to numbers at length
// This is different to DoAgeLengthConversion as this number is now not related to the partition
// Double mu= (*category_iter)->mean_length_by_time_step_age_[time_step][age];
// LOG_FINEST() << "mean = " << mu << " cv = " << age_length->cv(year, time_step, age) << " distribution = " << age_length->distribution_label() << " and length plus group = " << length_plus_;
// age_length->CummulativeNormal(mu, age_length->cv(year, time_step, age), age_frequencies, length_bins_, length_plus_);
// LOG_WARNING() << "This is bad code because it allocates memory in the middle of an execute";
age_length_matrix[data_offset].resize(number_bins_);
// Loop through the length bins and multiple the partition of the current age to go from
// length frequencies to age length numbers
for (unsigned j = 0; j < number_bins_; ++j) {
age_length_matrix[data_offset][j] = number_at_age * age_length_proportions[data_offset][j];
LOG_FINEST() << "The proportion of fish in length bin : " << length_bins_[j] << " = " << age_frequencies[j];
}
}
if (age_length_matrix.size() == 0)
LOG_CODE_ERROR()<< "if (age_length_matrix_.size() == 0)";
numbers_at_length.assign(age_length_matrix[0].size(), 0.0);
for (unsigned i = 0; i < age_length_matrix.size(); ++i) {
for (unsigned j = 0; j < age_length_matrix[i].size(); ++j) {
numbers_at_length[j] += age_length_matrix[i][j];
}
}
for (unsigned length_offset = 0; length_offset < number_bins_; ++length_offset) {
LOG_FINEST() << " numbers for length bin : " << length_bins_[length_offset] << " = " << numbers_at_length[length_offset];
expected_values[length_offset] += numbers_at_length[length_offset];
LOG_FINE() << "----------";
LOG_FINE() << "Category: " << (*category_iter)->name_ << " at length " << length_bins_[length_offset];
LOG_FINE() << "start_value: " << start_value << "; end_value: " << end_value << "; final_value: " << numbers_at_length[length_offset];
LOG_FINE() << "expected_value becomes: " << expected_values[length_offset];
}
}
if (expected_values.size() != proportions_[model_->current_year()][category_labels_[category_offset]].size())
LOG_CODE_ERROR()<< "expected_values.size(" << expected_values.size() << ") != proportions_[category_offset].size("
<< proportions_[model_->current_year()][category_labels_[category_offset]].size() << ")";
/**
* save our comparisons so we can use them to generate the score from the likelihoods later
*/
for (unsigned i = 0; i < expected_values.size(); ++i) {
SaveComparison(category_labels_[category_offset], 0, length_bins_[i], expected_values[i], proportions_[model_->current_year()][category_labels_[category_offset]][i],
process_errors_by_year_[model_->current_year()], error_values_[model_->current_year()][category_labels_[category_offset]][i], 0.0, delta_, 0.0);
}
}
}
/**
* Execute our mortality event object.
*
*/
void MortalityInitialisationEventBiomass::DoExecute() {
LOG_TRACE();
unsigned time_step_index = model_->managers().time_step()->current_time_step();
// only apply if initialisation phase
if (model_->state() == State::kInitialise) {
/**
* Work our how much of the stock is available or vulnerable to exploit
*/
Double vulnerable = 0.0;
unsigned i = 0;
for (auto categories : partition_) {
unsigned j = 0;
//categories->UpdateMeanWeightData();
for (Double& data : categories->data_) {
Double temp = data * selectivities_[i]->GetAgeResult(categories->min_age_ + j, categories->age_length_);
vulnerable += temp * categories->mean_weight_by_time_step_age_[time_step_index][categories->min_age_ + j];
++j;
}
++i;
}
/**
* Work out the exploitation rate to remove (catch/vulnerable)
*/
Double exploitation = 0;
LOG_FINEST() << "vulnerable biomass = " << vulnerable << " catch = " << catch_;
exploitation = catch_ / utilities::doublecompare::ZeroFun(vulnerable);
if (exploitation > u_max_) {
exploitation = u_max_;
if (penalty_)
penalty_->Trigger(label_, catch_, vulnerable*u_max_);
} else if (exploitation < 0.0) {
exploitation = 0.0;
}
LOG_FINEST() << "; exploitation: " << AS_DOUBLE(exploitation);
/**
* Remove the stock now. The amount to remove is
* vulnerable * exploitation
*/
// Report catches and exploitation rates for each category for each iteration
/*
StoreForReport("initialisation_iteration: ", init_iteration_);
StoreForReport("Exploitation: ", AS_DOUBLE(exploitation));
StoreForReport("Catch: ", AS_DOUBLE(catch_));
*/
Double removals =0;
for (auto categories : partition_) {
unsigned offset = 0;
for (Double& data : categories->data_) {
// report
removals = vulnerable_[categories->name_][categories->min_age_ + offset] * exploitation;
//StoreForReport(categories->name_ + "_Removals: ", AS_DOUBLE(removals));
data -= removals;
offset++;
}
}
++init_iteration_;
}
}
/**
* Execute this process
*/
void MortalityPreySuitability::DoExecute() {
// Check if we are executing this process in current year
if (std::find(years_.begin(), years_.end(), model_->current_year()) != years_.end()) {
Double TotalPreyVulnerable = 0;
Double TotalPreyAvailability = 0;
Double TotalPredatorVulnerable = 0;
Double SumMortality = 0.0;
map<string, Double> Vulnerable_by_Prey;
map<string, Double> Exploitation_by_Prey;
auto partition_iter = prey_partition_->Begin(); // vector<vector<partition::Category> >
for (unsigned category_offset = 0; category_offset < prey_category_labels_.size(); ++category_offset, ++partition_iter) {
/**
* Loop through the combined categories building up the prey abundance for each prey category label
*/
auto category_iter = partition_iter->begin();
for (; category_iter != partition_iter->end(); ++category_iter) {
for (unsigned data_offset = 0; data_offset < (*category_iter)->data_.size(); ++data_offset) {
Double vulnerable = (*category_iter)->data_[data_offset] * prey_selectivities_[category_offset]->GetResult((*category_iter)->min_age_ + data_offset, (*category_iter)->age_length_);
if (vulnerable <= 0.0)
vulnerable = 0.0;
Vulnerable_by_Prey[prey_category_labels_[category_offset]] += vulnerable;
TotalPreyVulnerable += vulnerable * electivities_[category_offset];
TotalPreyAvailability += vulnerable;
}
}
LOG_FINEST() << ": Vulnerable abundance for prey category " << prey_category_labels_[category_offset] << " = " << Vulnerable_by_Prey[prey_category_labels_[category_offset]];
}
TotalPreyAvailability = dc::ZeroFun(TotalPreyAvailability, ZERO);
TotalPreyVulnerable = dc::ZeroFun(TotalPreyVulnerable / TotalPreyAvailability, ZERO);
/*
* Loop through the predators calculating vulnerable predator abyundance
*/
auto predator_partition_iter = predator_partition_->Begin();
for (unsigned category_offset = 0; category_offset < predator_category_labels_.size(); ++category_offset, ++predator_partition_iter) {
/*
* Loop through the combined categories building up the predator abundance for each prey category label
*/
auto category_iter = predator_partition_iter->begin();
for (; category_iter != predator_partition_iter->end(); ++category_iter) {
for (unsigned data_offset = 0; data_offset < (*category_iter)->data_.size(); ++data_offset) {
Double predator_vulnerable = (*category_iter)->data_[data_offset] * predator_selectivities_[category_offset]->GetResult((*category_iter)->min_age_ + data_offset, (*category_iter)->age_length_);
if (predator_vulnerable <= 0.0)
predator_vulnerable = 0.0;
TotalPredatorVulnerable += predator_vulnerable;
}
}
}
LOG_FINEST() << ": Total predator abundance = " << TotalPredatorVulnerable;
/*
* Work out exploitation rate for each prey category
*/
for (unsigned category_offset = 0; category_offset < prey_category_labels_.size(); ++category_offset) {
Double Exploitation = TotalPredatorVulnerable * consumption_rate_ * ((Vulnerable_by_Prey[prey_category_labels_[category_offset]] / TotalPreyAvailability) * electivities_[category_offset])
/ TotalPreyVulnerable;
if (Exploitation > u_max_) {
Exploitation = u_max_;
if (penalty_) // Throw Penalty
penalty_->Trigger(penalty_label_, Exploitation, (Vulnerable_by_Prey[prey_category_labels_[category_offset]] * u_max_));
} else if (Exploitation < 0.0)
Exploitation = 0.0;
Exploitation_by_Prey[prey_category_labels_[category_offset]] = Exploitation;
LOG_FINEST() << ": Exploitation rate for prey category " << prey_category_labels_[category_offset] << " = " << Exploitation_by_Prey[prey_category_labels_[category_offset]];
}
// removal of prey components using the exploitation rate.
partition_iter = prey_partition_->Begin();
for (unsigned category_offset = 0; category_offset < prey_category_labels_.size(); ++category_offset, ++partition_iter) {
auto category_iter = partition_iter->begin();
for (; category_iter != partition_iter->end(); ++category_iter) {
for (unsigned data_offset = 0; data_offset < (*category_iter)->data_.size(); ++data_offset) {
Double Current = (*category_iter)->data_.size() * prey_selectivities_[category_offset]->GetResult((*category_iter)->min_age_ + data_offset, (*category_iter)->age_length_)
* Exploitation_by_Prey[prey_category_labels_[category_offset]];
if (Current <= 0.0) {
LOG_WARNING() << ": Negative partition create";
continue;
}
// remove abundance
(*category_iter)->data_[data_offset] -= Current;
SumMortality += Current;
}
}
}
} // if (std::find(years_.begin(), years_.end(), model_->current_year()) != years_.end()) {
}
/**
* Execute Cinitial this code follows from the original CASAL algorithm
*/
void Cinitial::Execute() {
LOG_TRACE();
map<string, vector<Double>> category_by_age_total;
auto partition_iter = partition_->Begin();
for (unsigned category_offset = 0; category_offset < category_labels_.size(); ++category_offset, ++partition_iter) {
category_by_age_total[category_labels_[category_offset]].assign((max_age_ - min_age_ + 1), 0.0);
/**
* Loop through the combined categories building up the total abundance for each category label
*/
auto category_iter = partition_iter->begin();
for (; category_iter != partition_iter->end(); ++category_iter) {
for (unsigned data_offset = 0; data_offset < (max_age_ - min_age_ + 1); ++data_offset) {
unsigned age_offset = (*category_iter)->min_age_ - min_age_;
// if this category min_age occurs after model min age ignore current age
if (data_offset < age_offset)
continue;
category_by_age_total[category_labels_[category_offset]][data_offset] += (*category_iter)->data_[data_offset - age_offset];
}
}
}
LOG_TRACE();
// loop through the category_labels and calculate the cinitial factor, which is the n_ / col_sums
map<string, vector<Double>> category_by_age_factor;
for (unsigned category_offset = 0; category_offset < category_labels_.size(); ++category_offset) {
category_by_age_factor[category_labels_[category_offset]].assign((max_age_ - min_age_ + 1), 0.0);
for (unsigned data_offset = 0; data_offset < (max_age_ - min_age_ + 1); ++data_offset) {
if (category_by_age_total[category_labels_[category_offset]][data_offset] == 0.0)
category_by_age_factor[category_labels_[category_offset]][data_offset] = 1.0;
else {
category_by_age_factor[category_labels_[category_offset]][data_offset] = n_[utilities::ToLowercase(category_labels_[category_offset])][data_offset]
/ category_by_age_total[category_labels_[category_offset]][data_offset];
}
}
}
LOG_TRACE();
// Now loop through the combined categories multiplying each category by the factory
// from the combined group it belongs to
partition_iter = partition_->Begin();
for (unsigned category_offset = 0; category_offset < category_labels_.size(); ++category_offset, ++partition_iter) {
/**
* Loop through the combined categories building up the total abundance for each category label
*/
auto category_iter = partition_iter->begin();
for (; category_iter != partition_iter->end(); ++category_iter) {
for (unsigned data_offset = 0; data_offset < (max_age_ - min_age_ + 1); ++data_offset) {
unsigned age_offset = (*category_iter)->min_age_ - min_age_;
// if this category min_age occurs after model min age ignore this age
if (data_offset < age_offset)
continue;
(*category_iter)->data_[data_offset - age_offset] *= category_by_age_factor[category_labels_[category_offset]][data_offset];
}
}
}
// Build cache
LOG_FINEST() << "finished calculating Cinitial";
cached_partition_->BuildCache();
// Execute the annual cycle for one year to calculate Cinitial
timesteps::Manager* time_step_manager = model_->managers().time_step();
time_step_manager->ExecuteInitialisation(label_, 1);
// Store that SSB value ssb_offset times in the Cintiial phase GetPhaseIndex
LOG_FINE() << "derived_ptr_.size(): " << derived_ptr_.size();
for (auto derived_quantities : derived_ptr_) {
vector<vector<Double>>& initialisation_values = derived_quantities->initialisation_values();
unsigned cinit_phase_index = model_->managers().initialisation_phase()->GetPhaseIndex(label_);
LOG_FINE() << "initialisation_values size: " << initialisation_values.size();
LOG_FINE() << "ssb_offset: " << ssb_offset_;
LOG_FINE() << "cinit_phase_index: " << cinit_phase_index;
LOG_FINE() << "init_values[cinit_phase].size(): " << initialisation_values[cinit_phase_index].size();
for(unsigned i = 0; i < ssb_offset_; ++i)
initialisation_values[cinit_phase_index].push_back(*initialisation_values[cinit_phase_index].rbegin());
}
// set the partition back to Cinitial state
auto cached_partition_iter = cached_partition_->Begin();
partition_iter = partition_->Begin();
for (unsigned category_offset = 0; category_offset < category_labels_.size(); ++category_offset, ++partition_iter, ++cached_partition_iter) {
auto category_iter = partition_iter->begin();
auto cached_category_iter = cached_partition_iter->begin();
for (; category_iter != partition_iter->end(); ++cached_category_iter, ++category_iter) {
(*category_iter)->data_ = (*cached_category_iter).data_;
}
}
}
/**
* Parse the configuration file. Creating objects and loading
* the parameter objects
*/
void File::Parse() {
LOG_TRACE();
if (file_.fail() || !file_.is_open())
LOG_CODE_ERROR() << "Unable to parse the configuration file because a previous error has not been reported.\nFile: " << file_name_;
/**
* Iterate through our file parsing the contents
*/
string current_line = "";
while (getline(file_, current_line)) {
++line_number_;
if (current_line.length() == 0)
continue;
// Handle comments
HandleComments(current_line);
if (current_line.length() == 0)
continue;
/**
* Change tabs to spaces, remove any leading/trailing or multiple spaces
* so we can be sure the input is nicely formatted
*/
boost::replace_all(current_line, "\t", " ");
boost::trim_all(current_line);
LOG_FINEST() << "current_line == '" << current_line << "'";
/**
* Now we need to check if this line is an include line for a new
* file.
*/
if (current_line.length() > strlen(CONFIG_INCLUDE) + 2) {
string lower_line = util::ToLowercase(current_line);
if (current_line.substr(0, strlen(CONFIG_INCLUDE)) == CONFIG_INCLUDE) {
string include_name = current_line.substr(strlen(CONFIG_INCLUDE));
LOG_FINEST() << "Loading new configuration file via include " << include_name;
boost::replace_all(include_name, "\"", "");
boost::trim_all(include_name);
File include_file(loader_);
if (include_name.find('\\') == string::npos && file_name_.find('\\') != string::npos)
include_name = file_name_.substr(0, file_name_.find_last_of('\\') + 1) + include_name;
if (include_name.find('/') == string::npos && file_name_.find('/') != string::npos)
include_name = file_name_.substr(0, file_name_.find_last_of('/') + 1) + include_name;
if (!include_file.OpenFile(include_name))
LOG_FATAL() << "At line: " << line_number_ << " of " << file_name_
<< ": Include file '" << include_name << "' could not be opened. Does this file exist?";
include_file.Parse();
continue;
}
}
/**
* At this point everything is standard. We have a simple line of text that we now need to parse. All
* comments etc have been removed and we've gone through any include_file directives
*/
FileLine current_file_line;
current_file_line.file_name_ = file_name_;
current_file_line.line_number_ = line_number_;
current_file_line.line_ = current_line;
loader_.AddFileLine(current_file_line);
} // while(get_line())
}
/**
* Calculate the derived quantity value for the
* state of the model.
*
* This class will calculate a value that is the sum total
* of the population in the model filtered by category and
* multiplied by the selectivities.
*
*/
void Biomass::Execute() {
LOG_TRACE();
Double value = 0.0;
if (model_->state() == State::kInitialise) {
auto iterator = partition_.begin();
// iterate over each category
for (unsigned i = 0; i < partition_.size() && iterator != partition_.end(); ++i, ++iterator) {
(*iterator)->UpdateMeanWeightData();
for (unsigned j = 0; j < (*iterator)->data_.size(); ++j) {
unsigned age = (*iterator)->min_age_ + j;
value += (*iterator)->data_[j] * selectivities_[i]->GetResult(age, (*iterator)->age_length_) * (*iterator)->mean_weight_per_[age];
}
}
unsigned initialisation_phase = model_->managers().initialisation_phase()->current_initialisation_phase();
if (initialisation_values_.size() <= initialisation_phase)
initialisation_values_.resize(initialisation_phase + 1);
Double b0_value = 0;
if (time_step_proportion_ == 0.0) {
b0_value = cache_value_;
initialisation_values_[initialisation_phase].push_back(b0_value);
} else if (time_step_proportion_ == 1.0) {
b0_value = value;
initialisation_values_[initialisation_phase].push_back(b0_value);
} else if (mean_proportion_method_) {
b0_value = cache_value_ + ((value - cache_value_) * time_step_proportion_);
initialisation_values_[initialisation_phase].push_back(b0_value);
} else {
b0_value = pow(cache_value_, 1 - time_step_proportion_) * pow(value ,time_step_proportion_);
initialisation_values_[initialisation_phase].push_back(b0_value);
}
// Store b0 or binitial on the model depending on what initialisation phase we are using
vector<string> init_label = model_->initialisation_phases();
InitialisationPhase* Init_phase = model_->managers().initialisation_phase()->GetInitPhase(init_label[initialisation_phase]);
string type = Init_phase->type();
if (type == PARAM_DERIVED || type == PARAM_ITERATIVE)
model_->set_b0(label_, b0_value);
if (type == PARAM_CINITIAL)
model_->set_binitial(label_, b0_value);
} else {
auto iterator = partition_.begin();
// iterate over each category
LOG_FINEST() << "Partition size = " << partition_.size();
for (unsigned i = 0; i < partition_.size() && iterator != partition_.end(); ++i, ++iterator) {
(*iterator)->UpdateMeanWeightData();
for (unsigned j = 0; j < (*iterator)->data_.size(); ++j) {
unsigned age = (*iterator)->min_age_ + j;
value += (*iterator)->data_[j] * selectivities_[i]->GetResult(age, (*iterator)->age_length_) * (*iterator)->mean_weight_per_[age];
}
}
if (time_step_proportion_ == 0.0)
values_[model_->current_year()] = cache_value_;
else if (time_step_proportion_ == 1.0)
values_[model_->current_year()] = value;
if (mean_proportion_method_)
values_[model_->current_year()] = cache_value_ + ((value - cache_value_) * time_step_proportion_);
else
values_[model_->current_year()] = pow(cache_value_, 1 - time_step_proportion_) * pow(value ,time_step_proportion_);
}
LOG_FINEST() << " Pre Exploitation value " << cache_value_ << " Post exploitation " << value << " Final value " << values_[model_->current_year()];
}
/**
* Build our parameters
*/
void ElementDifference::DoBuild() {
LOG_TRACE();
string error = "";
if (!model_->objects().VerfiyAddressableForUse(second_parameter_, addressable::kLookup, error)) {
LOG_FATAL_P(PARAM_SECOND_PARAMETER) << "could not be verified for use in additional_prior.element_difference. Error was " << error;
}
error = "";
if (!model_->objects().VerfiyAddressableForUse(parameter_, addressable::kLookup, error)) {
LOG_FATAL_P(PARAM_PARAMETER) << "could not be verified for use in additional_prior.element_difference. Error was " << error;
}
// first parameter
addressable::Type addressable_type = model_->objects().GetAddressableType(parameter_);
LOG_FINEST() << "addressable type = " << addressable_type;
switch(addressable_type) {
case addressable::kInvalid:
LOG_CODE_ERROR() << "Invalid addressable type: " << parameter_;
break;
case addressable::kMultiple:
addressable_ptr_vector_ = model_->objects().GetAddressables(parameter_);
break;
case addressable::kVector:
addressable_vector_ = model_->objects().GetAddressableVector(parameter_);
break;
case addressable::kUnsignedMap:
addressable_map_ = model_->objects().GetAddressableUMap(parameter_);
break;
case addressable::kSingle:
addressable_ = model_->objects().GetAddressable(parameter_);
break;
default:
LOG_ERROR() << "The addressable you have provided for use in a additional priors: " << parameter_
<< " is not a type that is supported for vector smoothing additional priors";
break;
}
// Get second parameter estimates
addressable_type = model_->objects().GetAddressableType(second_parameter_);
LOG_FINEST() << "addressable type = " << addressable_type;
switch(addressable_type) {
case addressable::kInvalid:
LOG_CODE_ERROR() << "Invalid addressable type: " << second_parameter_;
break;
case addressable::kMultiple:
second_addressable_ptr_vector_ = model_->objects().GetAddressables(second_parameter_);
break;
case addressable::kVector:
second_addressable_vector_ = model_->objects().GetAddressableVector(second_parameter_);
break;
case addressable::kUnsignedMap:
second_addressable_map_ = model_->objects().GetAddressableUMap(second_parameter_);
break;
case addressable::kSingle:
second_addressable_ = model_->objects().GetAddressable(second_parameter_);
break;
default:
LOG_ERROR() << "The addressable you have provided for use in a additional priors: " << second_parameter_
<< " is not a type that is supported for difference element additional priors";
break;
}
// Check the two parameters are the same length
vector<Double> values;
vector<Double> second_values;
// Load first parameter
if (addressable_vector_ != nullptr)
values.assign((*addressable_vector_).begin(), (*addressable_vector_).end());
else if (addressable_ptr_vector_ != nullptr) {
for (auto ptr : (*addressable_ptr_vector_))
values.push_back((*ptr));
} else if (addressable_map_ != nullptr) {
for (auto iter : (*addressable_map_))
values.push_back(iter.second);
} else if (addressable_ != nullptr) {
values.push_back((*addressable_));
} else
LOG_CODE_ERROR() << "(addressable_map_ != 0) && (addressable_vector_ != 0)";
// Load second parameter
if (second_addressable_vector_ != nullptr)
second_values.assign((*second_addressable_vector_).begin(), (*second_addressable_vector_).end());
else if (second_addressable_ptr_vector_ != nullptr) {
for (auto ptr : (*second_addressable_ptr_vector_))
second_values.push_back((*ptr));
} else if (second_addressable_map_ != nullptr) {
for (auto iter : (*second_addressable_map_))
second_values.push_back(iter.second);
} else if (second_addressable_ != nullptr) {
second_values.push_back((*second_addressable_));
} else
LOG_CODE_ERROR() << "(second_addressable_map_ != 0) && (second_addressable_vector_ != 0) && (second_addressable_ != 0)";
if(second_values.size() != values.size())
LOG_ERROR_P(PARAM_SECOND_PARAMETER) << "The parameters are not the same size, which they need to be, the second parameter has " << second_values.size() << " elements where as, the first parameter has " << values.size() << " elements";
}
void ProportionsMigrating::Execute() {
LOG_TRACE();
/**
* Verify our cached partition and partition sizes are correct
*/
auto cached_partition_iter = cached_partition_->Begin();
auto partition_iter = partition_->Begin(); // vector<vector<partition::Category> >
/**
* Loop through the provided categories. Each provided category (combination) will have a list of observations
* with it. We need to build a vector of proportions for each age using that combination and then
* compare it to the observations.
*/
LOG_FINEST() << "Number of categories " << category_labels_.size();
for (unsigned category_offset = 0; category_offset < category_labels_.size(); ++category_offset, ++partition_iter, ++cached_partition_iter) {
Double start_value = 0.0;
Double end_value = 0.0;
vector<Double> expected_values(age_spread_, 0.0);
vector<Double> numbers_age_before((model_->age_spread() + 1), 0.0);
vector<Double> numbers_age_after((model_->age_spread() + 1), 0.0);
/**
* Loop through the 2 combined categories building up the
* expected proportions values.
*/
auto category_iter = partition_iter->begin();
auto cached_category_iter = cached_partition_iter->begin();
for (; category_iter != partition_iter->end(); ++cached_category_iter, ++category_iter) {
for (unsigned data_offset = 0; data_offset < (*category_iter)->data_.size(); ++data_offset) {
// We now need to loop through all ages to apply ageing misclassification matrix to account
// for ages older than max_age_ that could be classified as an individual within the observation range
unsigned age = ( (*category_iter)->min_age_ + data_offset);
start_value = (*cached_category_iter).data_[data_offset];
end_value = (*category_iter)->data_[data_offset];
numbers_age_before[data_offset] += start_value;
numbers_age_after[data_offset] += end_value;
LOG_FINE() << "----------";
LOG_FINE() << "Category: " << (*category_iter)->name_ << " at age " << age;
LOG_FINE() << "start_value: " << start_value << "; end_value: " << end_value;
}
}
/*
* Apply Ageing error on numbers at age before and after
*/
if (ageing_error_label_ != "") {
vector<vector<Double>>& mis_matrix = ageing_error_->mis_matrix();
vector<Double> temp_before(numbers_age_before.size(), 0.0);
vector<Double> temp_after(numbers_age_after.size(), 0.0);
for (unsigned i = 0; i < mis_matrix.size(); ++i) {
for (unsigned j = 0; j < mis_matrix[i].size(); ++j) {
temp_before[j] += numbers_age_before[i] * mis_matrix[i][j];
temp_after[j] += numbers_age_after[i] * mis_matrix[i][j];
}
}
numbers_age_before = temp_before;
numbers_age_after = temp_after;
}
/*
* Now collapse the number_age into out expected values
*/
Double plus_before = 0, plus_after = 0;
for (unsigned k = 0; k < numbers_age_before.size(); ++k) {
// this is the difference between the
unsigned age_offset = min_age_ - model_->min_age();
if (numbers_age_before[k] > 0) {
if (k >= age_offset && (k - age_offset + min_age_) <= max_age_) {
expected_values[k - age_offset] = (numbers_age_before[k] - numbers_age_after[k]) / numbers_age_before[k];
LOG_FINEST() << "Numbers before migration = " << numbers_age_before[k] << " numbers after migration = " << numbers_age_after[k]
<< " proportion migrated = " << expected_values[k - age_offset];
}
if (((k - age_offset + min_age_) > max_age_) && age_plus_) {
plus_before += numbers_age_before[k];
plus_after += numbers_age_after[k];
}
} else {
if (k >= age_offset && (k - age_offset + min_age_) <= max_age_)
expected_values[k] = 0;
if (((k - age_offset + min_age_) > max_age_) && age_plus_) {
plus_before += 0;
plus_after += 0;
}
}
}
LOG_FINEST() << "Plus group before migration = " << plus_before << " Plus group after migration = " << plus_after;
if (age_plus_)
expected_values[age_spread_ - 1] = (plus_before - plus_after) / plus_before;
if (expected_values.size() != proportions_[model_->current_year()][category_labels_[category_offset]].size())
LOG_CODE_ERROR() << "expected_values.size(" << expected_values.size() << ") != proportions_[category_offset].size("
<< proportions_[model_->current_year()][category_labels_[category_offset]].size() << ")";
/**
* save our comparisons so we can use them to generate the score from the likelihoods later
*/
for (unsigned i = 0; i < expected_values.size(); ++i) {
LOG_FINEST() << " Numbers at age " << min_age_ + i << " = " << expected_values[i];
SaveComparison(category_labels_[category_offset], min_age_ + i ,0.0 ,expected_values[i], proportions_[model_->current_year()][category_labels_[category_offset]][i],
process_errors_by_year_[model_->current_year()], error_values_[model_->current_year()][category_labels_[category_offset]][i], delta_, 0.0);
}
}
}