#include <FMTyieldmodelnn.hpp>
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static const std::vector< std::string > | GetNextLineAndSplitIntoTokens (std::istream &str) |
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static const std::vector< float > | Standardize (std::vector< float > &input, const std::vector< float > &means, const std::vector< float > &vars) |
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static std::chrono::time_point< std::chrono::high_resolution_clock > | getclock () |
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template<class chrono > |
static double | getduration (const std::chrono::time_point< std::chrono::high_resolution_clock > &startclock) |
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static std::string | getdurationinseconds (const std::chrono::time_point< std::chrono::high_resolution_clock > &startclock) |
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FMTyieldmodelnn is an abstract class to be implemented as a machine learning model.
◆ FMTyieldmodelnn() [1/3]
Construct a FMTyieldmodelnn based on a JSON file and an input yield name list.
◆ ~FMTyieldmodelnn()
virtual Core::FMTyieldmodelnn::~FMTyieldmodelnn |
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◆ FMTyieldmodelnn() [2/3]
Core::FMTyieldmodelnn::FMTyieldmodelnn |
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default |
◆ FMTyieldmodelnn() [3/3]
◆ GetInputValues()
virtual const std::vector< double > Core::FMTyieldmodelnn::GetInputValues |
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const Graph::FMTpredictor & |
predictor | ) |
const |
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protectedpure virtual |
◆ GetModelOutputNames()
const std::vector< std::string > & Core::FMTyieldmodelnn::GetModelOutputNames |
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const |
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Return model outputs' names.
◆ GetModelType()
const std::string & Core::FMTyieldmodelnn::GetModelType |
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const |
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◆ GetNextLineAndSplitIntoTokens()
static const std::vector< std::string > Core::FMTyieldmodelnn::GetNextLineAndSplitIntoTokens |
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std::istream & |
str | ) |
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staticprotected |
Utility function used to read a csv file line by line.
◆ GetStandardParamMeans()
const std::vector< float > & Core::FMTyieldmodelnn::GetStandardParamMeans |
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const |
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Returns input variables' means used in the standartization process when predicting.
◆ GetStandardParamVars()
const std::vector< float > & Core::FMTyieldmodelnn::GetStandardParamVars |
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const |
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Returns input variables' variances used in the standartization process when predicting.
◆ operator std::string()
virtual Core::FMTyieldmodelnn::operator std::string |
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const |
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◆ Predict()
const std::vector< double > Core::FMTyieldmodelnn::Predict |
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const Core::FMTyieldrequest & |
request | ) |
const |
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◆ RemoveNans()
const void Core::FMTyieldmodelnn::RemoveNans |
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std::vector< float > & |
input | ) |
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Replaces nan values with default values.
◆ Standardize()
static const std::vector< float > Core::FMTyieldmodelnn::Standardize |
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std::vector< float > & |
input, |
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const std::vector< float > & |
means, |
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const std::vector< float > & |
vars |
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staticprotected |
Utility function that applies the standardization formula, a feature scaling technique, on the inputs of a machine learning model.
◆ ValidateInputYields()
void Core::FMTyieldmodelnn::ValidateInputYields |
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std::vector< std::string > & |
expectedYields, |
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std::vector< std::string > & |
inputYields |
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Validates that there is the expected number of inputs in the model.
◆ boost::serialization::access
friend class boost::serialization::access |
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friend |
◆ envPtr
std::unique_ptr<Ort::Env> Core::FMTyieldmodelnn::envPtr |
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staticprotected |
◆ JSON_PROP_MODEL_OUTPUTS
const std::string Core::FMTyieldmodelnn::JSON_PROP_MODEL_OUTPUTS = "outputNames" |
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◆ JSON_PROP_MODEL_TYPE
const std::string Core::FMTyieldmodelnn::JSON_PROP_MODEL_TYPE = "modelType" |
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◆ JSON_PROP_MODEL_YIELDS
const std::string Core::FMTyieldmodelnn::JSON_PROP_MODEL_YIELDS = "modelYields" |
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◆ JSON_PROP_STAND_FILE_PATH
const std::string Core::FMTyieldmodelnn::JSON_PROP_STAND_FILE_PATH = "csvStandardisationFile" |
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◆ modelOutputs
std::vector<std::string> Core::FMTyieldmodelnn::modelOutputs = {} |
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◆ modelType
std::string Core::FMTyieldmodelnn::modelType |
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◆ sessionPtr
std::unique_ptr<Ort::Session> Core::FMTyieldmodelnn::sessionPtr |
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◆ standardParamMeans
std::vector<float> Core::FMTyieldmodelnn::standardParamMeans = {} |
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◆ standardParamVars
std::vector<float> Core::FMTyieldmodelnn::standardParamVars = {} |
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◆ UNKNOWN_DISTURBANCE_CODE
const float Core::FMTyieldmodelnn::UNKNOWN_DISTURBANCE_CODE |
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staticprotected |
The documentation for this class was generated from the following file: