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Changed <T> to <Type> #487

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Oct 20, 2023
Merged

Changed <T> to <Type> #487

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30 changes: 15 additions & 15 deletions inst/extdata/TMB_tests/distributions/test_dlnorm_distribution.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -15,18 +15,18 @@
/**
* @brief Model class defines and returns negative log-likelihood (nll)
*
* @tparam T
* @tparam Type
*/
template <typename T>
template <typename Type>
class Model {
using DataVector = typename ModelTraits<T>::DataVector;
using DataVector = typename ModelTraits<Type>::DataVector;
public:
DataVector logy; /*!< observation */
T meanlog; /*!< mean of the distribution of log(x) */
T sdlog; /*!< standard deviation of the distribution of log(x) */
Type meanlog; /*!< mean of the distribution of log(x) */
Type sdlog; /*!< standard deviation of the distribution of log(x) */

// Initiate pointer to link .cpp to .hpp
static Model<T>* instance;
static Model<Type>* instance;

/** @brief Constructor.
*/
Expand All @@ -35,23 +35,23 @@
/**
* @brief Create new singleton class
*
* @return Model<T>*
* @return Model<Type>*
*/
static Model<T>* getInstance(){
return Model<T>::instance;
static Model<Type>* getInstance(){
return Model<Type>::instance;
}

/**
* @brief Function that calculates the negative log-likelihood given the data and parameters
*
* @return negative log-likelihood (nll)
*/
T evaluate(){
Type evaluate(){

T nll = 0;
Type nll = 0;
int i;
int n = logy.size();
fims::Dlnorm<T> nll_dlnorm;
fims::Dlnorm<Type> nll_dlnorm;
nll_dlnorm.meanlog = meanlog;
nll_dlnorm.sdlog = sdlog;
for(i =0; i < n; i++){
Expand All @@ -65,10 +65,10 @@
/**
* @brief Create new instance of Model
*
* @tparam T
* @tparam Type
*/
template<class T>
Model<T>* Model<T>::instance = new Model<T>();
template<class Type>
Model<Type>* Model<Type>::instance = new Model<Type>();
}


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26 changes: 13 additions & 13 deletions inst/extdata/TMB_tests/distributions/test_dmultinom_distribution.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -15,17 +15,17 @@
/**
* @brief Model class defines and returns negative log-likelihood (nll)
*
* @tparam T
* @tparam Type
*/
template <typename T>
template <typename Type>
class Model {
using Vector = typename ModelTraits<T>::EigenVector;
using Vector = typename ModelTraits<Type>::EigenVector;
public:
Vector x; /*!< Vector of length K of integers */
Vector p; /*!< Vector of length K, specifying the probability for the K classes (note, unlike in R these must sum to 1). */

// Initiate pointer to link .cpp to .hpp
static Model<T>* instance;
static Model<Type>* instance;

/** @brief Constructor.
*/
Expand All @@ -34,21 +34,21 @@
/**
* @brief Create new singleton class
*
* @return Model<T>*
* @return Model<Type>*
*/
static Model<T>* getInstance(){
return Model<T>::instance;
static Model<Type>* getInstance(){
return Model<Type>::instance;
}

/**
* @brief Function that calculates the negative log-likelihood given the data and parameters
*
* @return negative log-likelihood (nll)
*/
T evaluate(){
Type evaluate(){

T nll = 0;
fims::Dmultinom<T> nll_dmultinom;
Type nll = 0;
fims::Dmultinom<Type> nll_dmultinom;
nll_dmultinom.x = x;
nll_dmultinom.p = p;
nll -= nll_dmultinom.evaluate(true);
Expand All @@ -59,10 +59,10 @@
/**
* @brief Create new instance of Model
*
* @tparam T
* @tparam Type
*/
template<class T>
Model<T>* Model<T>::instance = new Model<T>();
template<class Type>
Model<Type>* Model<Type>::instance = new Model<Type>();
}


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30 changes: 15 additions & 15 deletions inst/extdata/TMB_tests/distributions/test_dnorm_distribution.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -15,18 +15,18 @@
/**
* @brief Model class defines and returns negative log-likelihood (nll)
*
* @tparam T
* @tparam Type
*/
template <typename T>
template <typename Type>
class Model {
using DataVector = typename ModelTraits<T>::DataVector;
using DataVector = typename ModelTraits<Type>::DataVector;
public:
DataVector y; /*!< observation */
T mean; /*!< mean of the normal distribution */
T sd; /*!< standard deviation of the normal distribution, must be strictly positive.*/
Type mean; /*!< mean of the normal distribution */
Type sd; /*!< standard deviation of the normal distribution, must be strictly positive.*/

// Initiate pointer to link .cpp to .hpp
static Model<T>* instance;
static Model<Type>* instance;

/** @brief Constructor.
*/
Expand All @@ -35,23 +35,23 @@
/**
* @brief Create new singleton class
*
* @return Model<T>*
* @return Model<Type>*
*/
static Model<T>* getInstance(){
return Model<T>::instance;
static Model<Type>* getInstance(){
return Model<Type>::instance;
}

/**
* @brief Function that calculates the negative log-likelihood given the data and parameters
*
* @return negative log-likelihood (nll)
*/
T evaluate(){
Type evaluate(){

T nll = 0;
Type nll = 0;
int i;
int n = y.size();
fims::Dnorm<T> nll_dnorm;
fims::Dnorm<Type> nll_dnorm;
nll_dnorm.mean = mean;
nll_dnorm.sd = sd;
for(i =0; i < n; i++){
Expand All @@ -65,10 +65,10 @@
/**
* @brief Create new instance of Model
*
* @tparam T
* @tparam Type
*/
template<class T>
Model<T>* Model<T>::instance = new Model<T>();
template<class Type>
Model<Type>* Model<Type>::instance = new Model<Type>();
}


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32 changes: 16 additions & 16 deletions inst/extdata/TMB_tests/distributions/test_fleet_acomp_nll.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -15,21 +15,21 @@
/**
* @brief Model class defines and returns negative log-likelihood (nll)
*
* @tparam T
* @tparam Type
*/
template <typename T>
template <typename Type>
class Model {

using Vector = typename ModelTraits<T>::EigenVector;
using Vector = typename ModelTraits<Type>::EigenVector;
public:
Vector x; /*!< Vector of length K of integers */
Vector p; /*!< Vector of length K, specifying the probability for the K classes (note, unlike in R these must sum to 1). */

// Initiate pointer to link .cpp to .hpp
static Model<T>* instance;
static Model<Type>* instance;


::objective_function<T> *of;
::objective_function<Type> *of;

/** @brief Constructor.
*/
Expand All @@ -38,28 +38,28 @@
/**
* @brief Create new singleton class
*
* @return Model<T>*
* @return Model<Type>*
*/
static Model<T>* getInstance(){
return Model<T>::instance;
static Model<Type>* getInstance(){
return Model<Type>::instance;
}

/**
* @brief Function that calculates the negative log-likelihood given the data and parameters
*
* @return negative log-likelihood (nll)
*/
T evaluate(){
Type evaluate(){

T nll = 0.0;
Type nll = 0.0;
int n = x.size();

fims::FleetAgeCompNLL<T> nll_fac;
fims::FleetAgeCompNLL<Type> nll_fac;
nll_fac.catch_numbers_at_age.resize(n);
nll_fac.nyears = 1;
nll_fac.nages = 10;
std::shared_ptr<fims::DataObject<T>> age_comp_data =
std::make_shared<fims::DataObject<T>>(10, 1);
std::shared_ptr<fims::DataObject<Type>> age_comp_data =
std::make_shared<fims::DataObject<Type>>(10, 1);

nll_fac.observed_agecomp_data = age_comp_data;
Vector obs;
Expand All @@ -86,10 +86,10 @@
/**
* @brief Create new instance of Model
*
* @tparam T
* @tparam Type
*/
template<class T>
Model<T>* Model<T>::instance = new Model<T>();
template<class Type>
Model<Type>* Model<Type>::instance = new Model<Type>();
}


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36 changes: 18 additions & 18 deletions inst/extdata/TMB_tests/distributions/test_fleet_index_nll.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -15,21 +15,21 @@
/**
* @brief Model class defines and returns negative log-likelihood (nll)
*
* @tparam T
* @tparam Type
*/
template <typename T>
template <typename Type>
class Model {
using DataVector = typename ModelTraits<T>::DataVector;
using Vector = typename ModelTraits<T>::EigenVector;
using DataVector = typename ModelTraits<Type>::DataVector;
using Vector = typename ModelTraits<Type>::EigenVector;
public:
DataVector y; /*!< observation */
Vector mean; /*!< expected fleet index */
T logsd; /*!< standard deviation of the fleet observation error, must be strictly positive.*/
Type logsd; /*!< standard deviation of the fleet observation error, must be strictly positive.*/

// Initiate pointer to link .cpp to .hpp
static Model<T>* instance;
static Model<Type>* instance;

::objective_function<T> *of;
::objective_function<Type> *of;

/** @brief Constructor.
*/
Expand All @@ -38,27 +38,27 @@
/**
* @brief Create new singleton class
*
* @return Model<T>*
* @return Model<Type>*
*/
static Model<T>* getInstance(){
return Model<T>::instance;
static Model<Type>* getInstance(){
return Model<Type>::instance;
}

/**
* @brief Function that calculates the negative log-likelihood given the data and parameters
*
* @return negative log-likelihood (nll)
*/
T evaluate(){
Type evaluate(){

T nll = 0;
Type nll = 0;
int n = y.size();

fims::FleetIndexNLL<T> nll_fleet_index;
fims::FleetIndexNLL<Type> nll_fleet_index;
nll_fleet_index.log_obs_error = logsd;

std::shared_ptr<fims::DataObject<T>> index_data =
std::make_shared<fims::DataObject<T>>(n);
std::shared_ptr<fims::DataObject<Type>> index_data =
std::make_shared<fims::DataObject<Type>>(n);
nll_fleet_index.observed_index_data = index_data;

nll_fleet_index.expected_index.resize(n);
Expand All @@ -80,10 +80,10 @@
/**
* @brief Create new instance of Model
*
* @tparam T
* @tparam Type
*/
template<class T>
Model<T>* Model<T>::instance = new Model<T>();
template<class Type>
Model<Type>* Model<Type>::instance = new Model<Type>();
}


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6 changes: 3 additions & 3 deletions inst/include/common/fims_math.hpp
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Expand Up @@ -210,9 +210,9 @@ inline const T ad_min(const T &a, const T &b, T C = 1e-5) {
* @param C default = 1e-5
* @return
*/
template <typename T>
inline const T ad_max(const T &a, const T &b, T C = 1e-5) {
return (a + b + fims::ad_fabs(a - b, C)) * static_cast<T>(.5);
template <typename Type>
inline const Type ad_max(const Type &a, const Type &b, Type C = 1e-5) {
return (a + b + fims::ad_fabs(a - b, C)) * static_cast<Type>(.5);
}

} // namespace fims
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