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);
}
}
}
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);
}
}
}
/**
* Save the comparison that was done during an observation to the list of comparisons. Each comparison contributes part to a score
* and we will need to know what those parts are when reporting.
*
* @param category The name of the comparison
* @param age The age of the population being compared
* @param expected The value generated by the model
* @param observed The value passed in from the configuration file
* @param error_value The error value for this comparison
* @param score The amount of score for this comparison
*/
void Observation::SaveComparison(string category, Double expected, Double observed,
Double process_error, Double error_value, Double adjusted_error, Double delta, Double score) {
SaveComparison(category, 0, 0, expected, observed, process_error, error_value,adjusted_error, delta, score);
}
