de.jungblut.math

Class MathUtils

java.lang.Object

de.jungblut.math.MathUtils

public final class MathUtils
extends Object

Math utils that features normalizations and other fancy stuff.

Author:

thomas.jungblut

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	MathUtils.PredictionOutcomePair

Field Summary

Fields

Modifier and Type	Field and Description
static double	EPS

Method Summary

Modifier and Type	Method and Description
static double	computeAUC(List<MathUtils.PredictionOutcomePair> outcomePredictedPairs) This is actually taken from Kaggle's C# implementation: ://www.kaggle.com/c/SemiSupervisedFeatureLearning /forums/t/919/auc-implementation/6136#post6136.
static de.jungblut.math.dense.DenseDoubleMatrix	createPolynomials(de.jungblut.math.dense.DenseDoubleMatrix seed, int num) Creates a new matrix consisting out of polynomials of the input matrix. Considering you want to do a 2 polynomial out of 3 columns you get: (SEED: x^1 | y^1 | z^1 )| x^2 | y^2 | z^2 for the columns of the returned matrix.
static double	guardedLogarithm(double input)
static de.jungblut.math.DoubleMatrix	logMatrix(de.jungblut.math.DoubleMatrix input)
static de.jungblut.math.DoubleVector	logVector(de.jungblut.math.DoubleVector input)
static de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleVector,de.jungblut.math.DoubleVector>	meanNormalizeColumns(Dataset dataset) Normalizes the given dataset (inplace), by subtracting the mean and dividing by the stddev.
static de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleVector,de.jungblut.math.DoubleVector>	meanNormalizeColumns(Dataset dataset, java.util.function.Predicate<FeatureOutcomePair> filterPredicate) Normalizes the given dataset (inplace), by subtracting the mean and dividing by the stddev.
static de.jungblut.math.tuple.Tuple3<de.jungblut.math.DoubleMatrix,de.jungblut.math.DoubleVector,de.jungblut.math.DoubleVector>	meanNormalizeColumns(de.jungblut.math.DoubleMatrix x)
static de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleMatrix,de.jungblut.math.DoubleVector>	meanNormalizeRows(de.jungblut.math.DoubleMatrix pMatrix)
static double	minMaxScale(double x, double fromMin, double fromMax, double toMin, double toMax) Scales a single input into the interval given by min and max.
static de.jungblut.math.DoubleMatrix	minMaxScale(de.jungblut.math.DoubleMatrix input, double fromMin, double fromMax, double toMin, double toMax) Scales a matrix into the interval given by min and max.
static de.jungblut.math.DoubleVector	minMaxScale(de.jungblut.math.DoubleVector input, double fromMin, double fromMax, double toMin, double toMax) Scales a vector into the interval given by min and max.
static de.jungblut.math.DoubleVector	numericalGradient(de.jungblut.math.DoubleVector vector, CostFunction f) Calculates the numerical gradient from a cost function using the central difference theorem.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

EPS

public static final double EPS

Method Detail

meanNormalizeRows

public static de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleMatrix,de.jungblut.math.DoubleVector> meanNormalizeRows(de.jungblut.math.DoubleMatrix pMatrix)

Returns:

mean normalized matrix (0 mean and stddev of 1) as well as the mean.

meanNormalizeColumns

public static de.jungblut.math.tuple.Tuple3<de.jungblut.math.DoubleMatrix,de.jungblut.math.DoubleVector,de.jungblut.math.DoubleVector> meanNormalizeColumns(de.jungblut.math.DoubleMatrix x)

Returns:

the normalized matrix (0 mean and stddev of 1) as well as the mean and the stddev.

meanNormalizeColumns

public static de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleVector,de.jungblut.math.DoubleVector> meanNormalizeColumns(Dataset dataset)

Normalizes the given dataset (inplace), by subtracting the mean and dividing by the stddev. Dataset will have 0 mean and stddev of 1.

Returns:

a tuple of the mean and the stddev.

meanNormalizeColumns

public static de.jungblut.math.tuple.Tuple<de.jungblut.math.DoubleVector,de.jungblut.math.DoubleVector> meanNormalizeColumns(Dataset dataset,
                                                                                                                             java.util.function.Predicate<FeatureOutcomePair> filterPredicate)

Normalizes the given dataset (inplace), by subtracting the mean and dividing by the stddev. Dataset will have 0 mean and stddev of 1. Additionally you can supply a predicate, if you want to only execute this on a specific sub part of the dataset.

Returns:

a tuple of the mean and the stddev.

createPolynomials

public static de.jungblut.math.dense.DenseDoubleMatrix createPolynomials(de.jungblut.math.dense.DenseDoubleMatrix seed,
                                                                         int num)

Creates a new matrix consisting out of polynomials of the input matrix.
Considering you want to do a 2 polynomial out of 3 columns you get:
(SEED: x^1 | y^1 | z^1 )| x^2 | y^2 | z^2 for the columns of the returned matrix.

Parameters:

seed - matrix to add polynoms of it.

num - how many polynoms, 2 for quadratic, 3 for cubic and so forth.

Returns:

the new matrix.

numericalGradient

public static de.jungblut.math.DoubleVector numericalGradient(de.jungblut.math.DoubleVector vector,
                                                              CostFunction f)

Calculates the numerical gradient from a cost function using the central difference theorem. f'(x) = (f(x + h) - f(x - h)) / 2.

Parameters:

vector - the parameters to derive.

f - the costfunction to return the cost at a given parameterset.

Returns:

a numerical gradient.

logMatrix

public static de.jungblut.math.DoubleMatrix logMatrix(de.jungblut.math.DoubleMatrix input)

Returns:

a log'd matrix that was guarded against edge cases of the logarithm.

logVector

public static de.jungblut.math.DoubleVector logVector(de.jungblut.math.DoubleVector input)

Returns:

a log'd matrix that was guarded against edge cases of the logarithm.

minMaxScale

public static de.jungblut.math.DoubleMatrix minMaxScale(de.jungblut.math.DoubleMatrix input,
                                                        double fromMin,
                                                        double fromMax,
                                                        double toMin,
                                                        double toMax)

Scales a matrix into the interval given by min and max.

Parameters:

input - the input value.

fromMin - the lower bound of the input interval.

fromMax - the upper bound of the input interval.

toMin - the lower bound of the target interval.

toMax - the upper bound of the target interval.

Returns:

the new matrix with scaled values.

minMaxScale

public static de.jungblut.math.DoubleVector minMaxScale(de.jungblut.math.DoubleVector input,
                                                        double fromMin,
                                                        double fromMax,
                                                        double toMin,
                                                        double toMax)

Scales a vector into the interval given by min and max.

Parameters:

input - the input vector.

fromMin - the lower bound of the input interval.

fromMax - the upper bound of the input interval.

toMin - the lower bound of the target interval.

toMax - the upper bound of the target interval.

Returns:

the new vector with scaled values.

minMaxScale

public static double minMaxScale(double x,
                                 double fromMin,
                                 double fromMax,
                                 double toMin,
                                 double toMax)

Scales a single input into the interval given by min and max.

Parameters:

x - the input value.

fromMin - the lower bound of the input interval.

fromMax - the upper bound of the input interval.

toMin - the lower bound of the target interval.

toMax - the upper bound of the target interval.

Returns:

the bounded value.

guardedLogarithm

public static double guardedLogarithm(double input)

Returns:

a log'd value of the input that is guarded.

computeAUC

public static double computeAUC(List<MathUtils.PredictionOutcomePair> outcomePredictedPairs)

This is actually taken from Kaggle's C# implementation: ://www.kaggle.com/c/SemiSupervisedFeatureLearning /forums/t/919/auc-implementation/6136#post6136.

Parameters:

outcomePredictedPairs - the list of PredictionOutcomePair: class (0 or 1) -> predicted value

Returns:

the AUC value.