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Numerics

Namespace: UTILSLIB  ·  Library: Utilities Library

#include <utils/numerics.h>

class UTILSLIB::Numerics

Static numerical helpers: integer GCD and log2, nchoose2, sparsity test, fixed-width histogram binning and time-series baseline rescaling shared across MATHLIB, DISP and the evoked / source-estimate pipelines.

Static scalar/per-vector numerical helpers (GCD, log2, histograms, baseline rescaling).

Public Methods​

~Numerics()​

Destroys the Numerics object.

Static Methods​

gcd(iA, iB)​

Finds the Greatest Common Divisor (GCD) of two integer values.

Parameters:

  • iA : int First input integer.

  • iB : int Second input integer.

Returns:

  • int — The Greatest Common Divisor (GCD) of iA and iB.

issparse(v)​

Determines if a given data (stored as vector v) are representing a sparse matrix.

Parameters:

  • v : Eigen::VectorXd & Data to be tested.

Returns:

  • bool — true if sparse, false otherwise.

nchoose2(n)​

Calculates the combination of n over 2 (nchoosek(n,2)).

Parameters:

  • n : int The number of elements which should be combined with each other (n over 2).

Returns:

  • int — The number of combinations.

rescale(data, times, baseline, mode)​

Rescale (baseline correct) data.

Parameters:

  • data : const Eigen::MatrixXd & Data Matrix (m x n_time).

  • times : const Eigen::RowVectorXf & Time instants in seconds.

  • baseline : const QPair< float, float > & If baseline is (a, b) the interval is between "a (s)" and "b (s)". If a and b are equal, use interval between the beginning of the data and time 0.

  • mode : QString Baseline correction mode: "logratio" | "ratio" | "zscore" | "mean" | "percent".

Returns:

  • Eigen::MatrixXd — Rescaled data matrix.

rescale(data, times, baseline, mode)​

Rescale (baseline correct) data.

Parameters:

  • data : const Eigen::MatrixXd & Data Matrix (m x n_time).

  • times : const Eigen::RowVectorXf & Time instants in seconds.

  • baseline : const std::pair< float, float > & If baseline is (a, b) the interval is between "a (s)" and "b (s)". If a and b are equal, use interval between the beginning of the data and time 0.

  • mode : const std::string & Baseline correction mode: "logratio" | "ratio" | "zscore" | "mean" | "percent".

Returns:

  • Eigen::MatrixXd — Rescaled data matrix.

log2(d)​

Compute log base 2 of a given number.

Parameters:

  • d : const T Input value.

Returns:

  • double — log2 of d.

histcounts(matRawData, bMakeSymmetrical, iClassAmount, vecResultClassLimits, vecResultFrequency, dGlobalMin, dGlobalMax)​

Creates a class and frequency distribution from data matrix.

Parameters:

  • matRawData : const Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > & Raw data matrix that needs to be analyzed.

  • bMakeSymmetrical : bool User input to turn the x-axis symmetric.

  • iClassAmount : int User input to determine the amount of classes in the histogram.

  • vecResultClassLimits : Eigen::VectorXd & The upper limit of each individual class.

  • vecResultFrequency : Eigen::VectorXi & The amount of data that fits in the appropriate class ranges.

  • dGlobalMin : double User input to determine the minimum value allowed in the histogram.

  • dGlobalMax : double User input to determine the maximum value allowed in the histogram.

histcounts(matRawData, bMakeSymmetrical, iClassAmount, vecResultClassLimits, vecResultFrequency, dGlobalMin, dGlobalMax)​

histcounts(matRawData, bMakeSymmetrical, iClassAmount, vecResultClassLimits, vecResultFrequency, dGlobalMin, dGlobalMax)​

Authors of this file​