virtual const Covariance& covariance() const
{
Mean mu = mean();
cov_ = Covariance::Zero(dimension(), dimension());
for(int i = 0; i < locations_.size(); i++)
{
Mean delta = (locations_[i].template cast<Real>()-mu);
cov_ += prob_mass(i) * delta * delta.transpose();
}
return cov_;
}
int main(void)
{
int array[] = { 10, 2, 3, 34, 3, 4, 45, 99 };
int array_size = sizeof( array ) / sizeof( array[0] );
Mean< int > mean;
for ( unsigned int i = 0; i < array_size; ++i )
{
mean( array[i] );
}
std::cout << "mean: " << mean.value() << std::endl;
return 0;
}
NormalDistribution::NormalDistribution(const Mean& m,
const StdDeviation& sigma)
: DistributionBase(), mean_(m.value()), stdDev_(sigma.value())
{
if(stdDev_ < 0.0)
throw runtime_error("NormalDistribution : in ctor, sigma must be positive.");
lower_ = mean_ - stdDev_*2;
upper_ = mean_ + stdDev_*2;
pLower_ = getUntruncatedCDF(lower_);
pUpper_ = getUntruncatedCDF(upper_);
}
NormalDistribution::NormalDistribution(const Mean& m,
const StdDeviation& sigma,
double numDeviations)
: DistributionBase(), mean_(m.value()), stdDev_(sigma.value())
{
if(stdDev_ < 0.0)
throw std::runtime_error("NormalDistribution : in ctor, sigma must be positive.");
if(numDeviations < 0.0)
throw runtime_error("NormalDistribution : in ctor, numDevations must be positive.");
lower_ = mean_ - numDeviations*stdDev_;
upper_ = mean_ + numDeviations*stdDev_;
pLower_ = getUntruncatedCDF(lower_);
pUpper_ = getUntruncatedCDF(upper_);
}
/**
* Record performance measures
*/
void record(uint time, const Model& model, double control = 1){
//uint year = IOSKJ::year(time);
uint quarter = IOSKJ::quarter(time);
times.append();
// Catch magnitude
auto catch_total = model.catches_taken(sum);
catches_total.append(catch_total);
auto catches_by_method = model.catches_taken(sum,by(methods));
catches_ps.append(catches_by_method(PS));
catches_pl.append(catches_by_method(PL));
catches_gn.append(catches_by_method(GN));
// Years catch goes below baseline
catches_lower.append(catch_total < 425000/4.0);
// Catch variability
if(quarter == 0 and catch_total>0){
catches_var.append(catch_total);
catches_mapc.append(catch_total);
}
// Shutdown defined as quarterly catches <10% of recent average
// catches of about 400000t
catches_shut.append(catch_total<1000);
// Changes in MP control e.g. catch or effort limit
if (quarter == 0) {
if (std::isfinite(control_last_)) {
control_ups.append(control>control_last_);
control_downs.append(control<control_last_);
}
control_last_ = control;
}
// Stock status relative to unfished
auto status = model.biomass_status();
status_mean.append(status);
status_b10.append(status<0.1);
status_b20.append(status<0.2);
// Biomass relative to B40
auto b = model.biomass_spawners(sum)/model.biomass_spawners_40;
b_ratio.append(b);
// F relative to F40
auto f = model.fishing_mortality_get()/model.f_40;
f_ratio.append(f);
// Kobe plot
// Determine quadrant
char quadrant;
if(b>=1){
if(f<=1) quadrant = 'a';
else quadrant = 'b';
} else {
if(f<=1) quadrant = 'c';
else quadrant = 'd';
}
// Update performance measures for proportion of time spent in each quadrant
kobe_a.append(quadrant=='a');
kobe_b.append(quadrant=='b');
kobe_c.append(quadrant=='c');
kobe_d.append(quadrant=='d');
// Update performance measure for time taken to get back into quadrant A
if(quadrant=='a'){
// If previously outside of A then append the time that
// have been outside to the mean and reset time counter to zero.
if(kobe_out_a>0){
kobe_to_a.append(kobe_out_a);
kobe_out_a = 0;
}
} else {
// Outside of A so increment time counter.
kobe_out_a++;
}
// Catch rates
// Use vulnerable (i.e. selected) biomass for the three main regions/gears
// relative to the start year as a measure of catch rates (CPUE)
if(times==1){
// Record baselines
for(auto region : regions){
for(auto method : methods){
cpue_baseline(region,method) = model.biomass_vulnerable(region,method);
}
}
} else {
// Record relative to baseline
for(auto region : regions){
for(auto method : methods){
cpue_mean(region,method).append(
model.biomass_vulnerable(region,method)/cpue_baseline(region,method)
);
}
}
}
}