Class HierarchicalOptimizationWrapper

• Defined in File hierarchicalOptimization.h

Inheritance Relationships

Base Type

• public parpe::GradientFunction (Class GradientFunction)

Class Documentation

class parpe::HierarchicalOptimizationWrapper : public parpe::GradientFunction

The HierarchicalOptimizationWrapper class is a wrapper for hierarchical optimization of scaling parameters.

Parameters with the given indices are hidden by the wrapper and computed analytically internally.

Computes the negative log likelihood for normally distributed measurement (others to be added).

Public Functions

HierarchicalOptimizationWrapper(AmiciSummedGradientFunction *wrapped_function, int numConditions = 0, int numObservables = 0)

For testing.

Parameters

• fun:

• numConditions:

• numObservables:

• numTimepoints:

HierarchicalOptimizationWrapper(AmiciSummedGradientFunction *wrapped_function, const H5::H5File &file, const std::string &hdf5RootPath, int numConditions, int numObservables, ErrorModel errorModel)

Get information on analytically computed parameters from HDF5 file.

Parameters

• fun:

• file:

• hdf5RootPath:

• numConditions:

• numObservables:

• errorModel:

HierarchicalOptimizationWrapper(AmiciSummedGradientFunction *wrapped_function, std::unique_ptr<AnalyticalParameterProvider> scalingReader, std::unique_ptr<AnalyticalParameterProvider> offsetReader, std::unique_ptr<AnalyticalParameterProvider> sigmaReader, int numConditions, int numObservables, ErrorModel errorModel)

Get information on analytically computed parameters from the provided objects.

Parameters

• fun:

• scalingReader:

• offsetReader:

• numConditions:

• numObservables:

• errorModel:

virtual FunctionEvaluationStatus evaluate(gsl::span<double const> parameters, double &fval, gsl::span<double> gradient, Logger *logger, double *cpuTime) const override

See base class.

Return

Parameters

• parameters:

• fval:

• gradient:

FunctionEvaluationStatus evaluate(gsl::span<double const> reducedParameters, double &fval, gsl::span<double> gradient, std::vector<double> &fullParameters, std::vector<double> &fullGradient, Logger *logger, double *cpuTime) const

std::vector<double> getDefaultScalingFactors() const

Get parameters for initial function evaluation.

Return

std::vector<double> getDefaultOffsetParameters() const

Get parameters for initial function evaluation.

Return

std::vector<double> getDefaultSigmaParameters() const

std::tuple<std::vector<std::vector<double>>, std::vector<std::vector<double>>> getUnscaledModelOutputsAndSigmas(const gsl::span<double const> reducedParameters, Logger *logger, double *cpuTime) const

Run simulations with scaling parameters set to 1.0 and collect model outputs.

Return

Vector of double vectors containing AMICI ReturnData::y (nt x ny, column-major)

Parameters

• reducedParameters: parameter vector for fun without scaling parameters

std::vector<double> computeAnalyticalScalings(std::vector<std::vector<double>> const &measurements, std::vector<std::vector<double>> const &modelOutputsUnscaled) const

Compute proportionality factors.

Return

the computed scaling factors

Parameters

• modelOutputs: Model outputs as provided by getModelOutputs

void applyOptimalScalings(std::vector<double> const &proportionalityFactors, std::vector<std::vector<double>> &modelOutputs) const

std::vector<double> computeAnalyticalOffsets(const std::vector<std::vector<double>> &measurements, std::vector<std::vector<double>> &modelOutputsUnscaled) const

Compute offset parameters.

Return

the computed offset parameters

Parameters

• modelOutputs: Model outputs as provided by getModelOutputs

std::vector<double> computeAnalyticalSigmas(std::vector<std::vector<double>> const &measurements, const std::vector<std::vector<double>> &modelOutputsScaled) const

void applyOptimalOffsets(std::vector<double> const &offsetParameters, std::vector<std::vector<double>> &modelOutputs) const

void fillInAnalyticalSigmas(std::vector<std::vector<double>> &allSigmas, const std::vector<double> &analyticalSigmas) const

Create vector with sigma matrix for each condition and timepoints from the given analytically computed sigmas.

Return

Parameters

• sigmas:

virtual FunctionEvaluationStatus evaluateWithOptimalParameters(std::vector<double> const &fullParameters, std::vector<double> const &sigmas, std::vector<std::vector<double>> const &measurements, std::vector<std::vector<double>> const &modelOutputsScaled, std::vector<std::vector<double>> &fullSigmaMatrices, double &fval, const gsl::span<double> gradient, std::vector<double> &fullGradient, Logger *logger, double *cpuTime) const

Evaluate fun using the computed optimal scaling and offset parameters.

Return

Parameters

• reducedParameters: Parameter vector without scaling and offset parameters

• scalings: Optimal scaling parameters

• offsets: Optimal offset parameters

• modelOutputsUnscaled: Model outputs before applying optimal offset and scaling parameters

• fval: out: computed function value

• gradient: out: computed function gradient

virtual int numParameters() const override

Get number of parameters the function expects.

Return

That

int numProportionalityFactors() const

std::vector<int> const &getProportionalityFactorIndices() const

int numOffsetParameters() const

int numSigmaParameters() const

std::vector<int> const &getOffsetParameterIndices() const

std::vector<int> const &getSigmaParameterIndices() const

std::vector<int> getAnalyticalParameterIndices() const

AmiciSummedGradientFunction *getWrappedFunction() const

virtual std::vector<std::string> getParameterIds() const override