java.lang.Object
- edu.umbc.cs.maple.utils.MathUtils

```
public class MathUtils
extends Object
```
A collection of mathematical utility functions.
Copyright (c) 2008 Eric Eaton
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Version:

0.1

Author:

Eric Eaton ([email protected])
University of Maryland Baltimore County

Nested Class Summary

Nested Classes
Modifier and Type Class Description

static class MathUtils.SimilarityMetric
Defines a similarity metric measure

Constructor Summary

Constructors
Constructor Description

MathUtils()

Method Summary

All Methods Static Methods Concrete Methods
Modifier and Type	Method	Description
`static double[]`	`append(double[] v1, double d)`	Appends an element to a vector.
`static double[]`	`append(double[] v1, double[] v2)`	Appends two vectors.
`static int[]`	`append(int[] v1, int d)`	Appends an element to a vector.
`static int[]`	`append(int[] v1, int[] v2)`	Appends two vectors.
`static double[]`	`arrayAdd(double[] d1, double[] d2)`	Adds corresponding elements of two arrays.
`static double[]`	`arrayDivide(double[] d1, double denominator)`	Divides each element of an array by a number.
`static double`	`capValue(double value, double minValue, double maxValue)`	Caps a value to a given range.
`static int`	`capValue(int value, int minValue, int maxValue)`	Caps a value to a given range.
`static double`	`computeSimilarity(int[] targetV, int[] v, MathUtils.SimilarityMetric similarityMetric)`	Computes the similarity of the two vectors.
`static double`	`correlation(double[] p, double[] q)`	Computes the correlation between two arrays of the same length, p and q.
`static double`	`correlation(int[] p, int[] q)`	Computes the correlation between two arrays of the same length, p and q.
`static double[][]`	`getConfusionMatrix(int[] p, int[] q)`	Computes the normalized confusion matrix for two vectors.
`static double`	`getMachinePrecision()`	Gets the computed machine precision.
`static Random`	`getRandomGenerator()`	Retrieves the initialized random number generator.
`static void`	`initializeRandomGenerator(long seed)`	Initializes a random number generator with the given seed.
`static boolean`	`isApproxEqual(double value1, double value2)`	Determines whether two numbers are approximately equal according to the machine precision.
`static boolean`	`isApproxEqual(double value1, double value2, double precision)`	Determines whether two numbers are approximately equal according to the precision.
`static double`	`log2(double d)`	Computes the log-base-2 of a number.
`static void`	`main(String[] args)`
`static int`	`maxIndex(double[] v)`	Gets the index of the maximum element in the array.
`static int`	`maxIndex(int[] v)`	Gets the index of the maximum element in the array.
`static int`	`maxIndexRand(double[] v)`	Gets the index of the maximum element in the array.
`static int`	`maxIndexRand(int[] v)`	Gets the index of the maximum element in the array.
`static double`	`maxValue(double[] values)`	Computes the maximum value in the given array.
`static int`	`maxValue(int[] values)`	Computes the maximum value in the given array.
`static double`	`mean(double[] values)`	Computes the mean of the values in the given array.
`static double`	`mean(int[] values)`	Computes the mean of the values in the given array.
`static int`	`minIndex(double[] v)`	Gets the index of the minimum element in the array.
`static int`	`minIndex(int[] v)`	Gets the index of the minimum element in the array.
`static int`	`minIndexRand(double[] v)`	Gets the index of the minimum element in the array.
`static int`	`minIndexRand(int[] v)`	Gets the index of the minimum element in the array.
`static double`	`minValue(double[] values)`	Computes the minimum value in the given array.
`static int`	`minValue(int[] values)`	Computes the minimum value in the given array.
`static double`	`mutualInformation(int[] p, int[] q)`	Computes the mutual information between two vectors.
`static double`	`nextRandomGaussian(double mean, double stdev)`	Gets a random number from a one dimensional Gaussian distribution with the given mean and variance.
`static double`	`nextRandomGaussian(Random randGenerator, double mean, double stdev)`	Gets a random number from a one dimensional Gaussian distribution with the given mean and variance.
`static double`	`pairwiseAgreement(int[] p, int[] q)`	Computes the pairwise agreement between two pairwise arrays of labelings.
`static int[]`	`permutation(int n)`	Generates a random permutation of the numbers {0, ..., n-1}
`static int[]`	`permutation(int n, Random rand)`	Generates a random permutation of the numbers {0, ..., n-1}
`static void`	`rangeCheck(double value, double min, double max)`	Throws an exception whenever a value falls outside the given range.
`static void`	`rangeCheck(int value, int min, int max)`	Throws an exception whenever a value falls outside the given range.
`static double[]`	`reverse(double[] array)`	Reverses the given array.
`static int[]`	`reverse(int[] array)`	Reverses the given array.
`static int[]`	`reverseCuthillMcKee(int[][] adjacencyLists, boolean runTwiceForOptimalResult)`	Runs the reverse Cuthill-McKee algorithm on the adjacency lists to provide an efficient ordering of the vertices
`static double`	`rmse(double[] a, double[] b)`	Computes the root mean squared error between two vectors.
`static double`	`round(double d, int decimalPlacesRequired)`	Reduces the precision of a double to a specified number of decimal places.
`static int`	`roundToMultiple(int value, int multipleOf)`	Rounds a value to the nearest number that is a specific multiple.
`static <T> Collection<T>`	`sampleWithoutReplacement(T[] objs, double[] probabilityDistribution, int numSamples)`	Samples from a set of weighted objects without replacement.
`static <T> Collection<T>`	`sampleWithReplacement(T[] objs, double[] probabilityDistribution, int numSamples)`	Samples from a set of weighted objects with replacement.
`static int[]`	`sortOrder(double[] values)`	Sorts the given values and returns the order of the original indices.
`static int[]`	`sortOrder(int[] values)`	Sorts the given values and returns the order of the original indices.
`static double`	`sum(double[] values)`	Sums the values in the given array.
`static int`	`sum(int[] values)`	Sums the values in the given array.
`static double[]`	`uniqueValues(double[] v)`	Determines the unique values of v.
`static int[]`	`uniqueValues(int[] v)`	Determines the unique values of v.
`static double[]`	`vectorAdd(double[] v1, double[] v2)`	Performs vector addition.
`static double`	`vectorL2Norm(double[] v)`	Computes the L2 norm of the given vector.
`static double[]`	`vectorMultiply(double s, double[] v)`	Performs scalar multiplication on a vector.
`static double[]`	`vectorNormalize(double[] v)`	Normalizes the given vector.
`static double[]`	`vectorSubtract(double[] v1, double[] v2)`	Performs vector subtraction.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - MathUtils
```
public MathUtils()
```
- Method Detail
  - reverseCuthillMcKee
```
public static int[] reverseCuthillMcKee(int[][] adjacencyLists,
                                        boolean runTwiceForOptimalResult)
```
    Runs the reverse Cuthill-McKee algorithm on the adjacency lists to provide an efficient ordering of the vertices
    
    Parameters:
    
    adjacencyLists - a list of the adjacency lists of the graph
    
    runTwiceForOptimalResult - whether to run the algorithm twice to get the optimal result
    
    Returns:
    
    a permutation of the vertices that is efficient for a sparse matrix
  - permutation
```
public static int[] permutation(int n)
```
    Generates a random permutation of the numbers {0, ..., n-1}
    
    Parameters:
    
    n - the length of the set of numbers to permute
    
    Returns:
    
    a random permutation of the numbers {0, ..., n-1}
  - permutation
```
public static int[] permutation(int n,
                                Random rand)
```
    Generates a random permutation of the numbers {0, ..., n-1}
    
    Parameters:
    
    n - the length of the set of numbers to permute
    
    rand - the random number generator
    
    Returns:
    
    a random permutation of the numbers {0, ..., n-1}
  - append
```
public static int[] append(int[] v1,
                           int d)
```
    Appends an element to a vector.
    
    Parameters:
    
    v1 - the vector.
    
    d - the element to append.
    
    Returns:
    
    A vector containing all the elements of v1 followed by d.
  - append
```
public static double[] append(double[] v1,
                              double d)
```
    Appends an element to a vector.
    
    Parameters:
    
    v1 - the vector.
    
    d - the element to append.
    
    Returns:
    
    A vector containing all the elements of v1 followed by d.
  - append
```
public static double[] append(double[] v1,
                              double[] v2)
```
    Appends two vectors.
    
    Parameters:
    
    v1 - the first vector.
    
    v2 - the second vector.
    
    Returns:
    
    A vector containing all the elements of v1 followed by all the elements of v2.
  - append
```
public static int[] append(int[] v1,
                           int[] v2)
```
    Appends two vectors.
    
    Parameters:
    
    v1 - the first vector.
    
    v2 - the second vector.
    
    Returns:
    
    A vector containing all the elements of v1 followed by all the elements of v2.
  - vectorAdd
```
public static double[] vectorAdd(double[] v1,
                                 double[] v2)
```
    Performs vector addition.
    
    Parameters:
    
    v1 - the first vector.
    
    v2 - the second vector.
    
    Returns:
    
    v1 + v2
  - vectorSubtract
```
public static double[] vectorSubtract(double[] v1,
                                      double[] v2)
```
    Performs vector subtraction.
    
    Parameters:
    
    v1 - the first vector.
    
    v2 - the second vector.
    
    Returns:
    
    v1 - v2
  - vectorMultiply
```
public static double[] vectorMultiply(double s,
                                      double[] v)
```
    Performs scalar multiplication on a vector.
    
    Parameters:
    
    s - a scalar value.
    
    v - the vector.
    
    Returns:
    
    the scalar product of s and v.
  - vectorL2Norm
```
public static double vectorL2Norm(double[] v)
```
    Computes the L2 norm of the given vector.
    
    Parameters:
    
    v - the vector.
    
    Returns:
    
    the L2 norm of v.
  - vectorNormalize
```
public static double[] vectorNormalize(double[] v)
```
    Normalizes the given vector.
    
    Parameters:
    
    v - the vector.
    
    Returns:
    
    v with the values normalized.
  - getRandomGenerator
```
public static Random getRandomGenerator()
```
    Retrieves the initialized random number generator. Use of this rand generator allows an entire program to use a single rand generator.
    
    Returns:
    
    the initialized random number generator.
  - initializeRandomGenerator
```
public static void initializeRandomGenerator(long seed)
```
    Initializes a random number generator with the given seed. Repeated calls to this initialize function create a new random generator.
    
    Parameters:
    
    seed - the seed for the random generator.
  - nextRandomGaussian
```
public static double nextRandomGaussian(double mean,
                                        double stdev)
```
    Gets a random number from a one dimensional Gaussian distribution with the given mean and variance.
    
    Parameters:
    
    mean - the mean of the Gaussian
    
    stdev - the standard deviation of the Gaussian
    
    Returns:
    
    a random number from the specified distribution
  - nextRandomGaussian
```
public static double nextRandomGaussian(Random randGenerator,
                                        double mean,
                                        double stdev)
```
    Gets a random number from a one dimensional Gaussian distribution with the given mean and variance.
    
    Parameters:
    
    randGenerator - the random generator.
    
    mean - the mean of the Gaussian
    
    stdev - the standard deviation of the Gaussian
    
    Returns:
    
    a random number from the specified distribution
  - rmse
```
public static double rmse(double[] a,
                          double[] b)
```
    Computes the root mean squared error between two vectors.
    
    Parameters:
    
    a -
    
    b -
    
    Returns:
    
    the RMSE between a and b
  - main
```
public static void main(String[] args)
```
  - sampleWithoutReplacement
```
public static <T> Collection<T> sampleWithoutReplacement(T[] objs,
                                                         double[] probabilityDistribution,
                                                         int numSamples)
```
    Samples from a set of weighted objects without replacement.
    
    Parameters:
    
    objs - the array of objects
    
    probabilityDistribution - the probability distribution over the set of objects (i.e. the weight assigned to each object)
    
    numSamples - the desired number of samples
  - sampleWithReplacement
```
public static <T> Collection<T> sampleWithReplacement(T[] objs,
                                                      double[] probabilityDistribution,
                                                      int numSamples)
```
    Samples from a set of weighted objects with replacement.
    
    Parameters:
    
    objs - the array of objects
    
    probabilityDistribution - the probability distribution over the set of objects (i.e. the weight assigned to each object)
    
    numSamples - the desired number of samples
  - capValue
```
public static double capValue(double value,
                              double minValue,
                              double maxValue)
```
    Caps a value to a given range.
    
    Parameters:
    
    value - the value to constrain.
    
    minValue - the min value.
    
    maxValue - the max value.
    
    Returns:
    
    the value altered to lie in [minValue, maxValue].
  - capValue
```
public static int capValue(int value,
                           int minValue,
                           int maxValue)
```
    Caps a value to a given range.
    
    Parameters:
    
    value - the value to constrain.
    
    minValue - the min value.
    
    maxValue - the max value.
    
    Returns:
    
    the value altered to lie in [minValue, maxValue].
  - rangeCheck
```
public static void rangeCheck(int value,
                              int min,
                              int max)
```
    Throws an exception whenever a value falls outside the given range.
    
    Parameters:
    
    value -
    
    min - the minimal value
    
    max - the maximal value
    
    Throws:
    
    IllegalArgumentException - if the value is outside of the given range.
  - rangeCheck
```
public static void rangeCheck(double value,
                              double min,
                              double max)
```
    Throws an exception whenever a value falls outside the given range.
    
    Parameters:
    
    value -
    
    min - the minimal value
    
    max - the maximal value
    
    Throws:
    
    IllegalArgumentException - if the value is outside of the given range.
  - round
```
public static final double round(double d,
                                 int decimalPlacesRequired)
```
    Reduces the precision of a double to a specified number of decimal places.
    
    Parameters:
    
    d - number to reduce precision
    
    decimalPlacesRequired - the number of required decimal places
    
    Returns:
    
    d reduced to the specified precision
  - roundToMultiple
```
public static final int roundToMultiple(int value,
                                        int multipleOf)
```
    Rounds a value to the nearest number that is a specific multiple.
    
    Parameters:
    
    value - the value to round
    
    multipleOf - the value that the final number should be a multiple of.
    
    Returns:
    
    value rounded to the nearest number that is a multiple of multipleOf.
  - minIndex
```
public static int minIndex(double[] v)
```
    Gets the index of the minimum element in the array. Returns the first min index if there are multiple matching elements.
    
    Parameters:
    
    v -
    
    Returns:
    
    the index of the minimum element of v.
  - minIndex
```
public static int minIndex(int[] v)
```
    Gets the index of the minimum element in the array. Returns the first min index if there are multiple matching elements.
    
    Parameters:
    
    v -
    
    Returns:
    
    the index of the minimum element of v.
  - minIndexRand
```
public static int minIndexRand(double[] v)
```
    Gets the index of the minimum element in the array. Randomly chooses the min index if there are multiple matching elements.
    
    Parameters:
    
    v -
    
    Returns:
    
    the index of the minimum element of v.
  - minIndexRand
```
public static int minIndexRand(int[] v)
```
    Gets the index of the minimum element in the array. Randomly chooses the min index if there are multiple matching elements.
    
    Parameters:
    
    v -
    
    Returns:
    
    the index of the minimum element of v.
  - maxIndex
```
public static int maxIndex(double[] v)
```
    Gets the index of the maximum element in the array. Returns the first max index if there are multiple matching elements.
    
    Parameters:
    
    v -
    
    Returns:
    
    the index of the maximum element of v.
  - maxIndex
```
public static int maxIndex(int[] v)
```
    Gets the index of the maximum element in the array. Returns the first max index if there are multiple matching elements.
    
    Parameters:
    
    v -
    
    Returns:
    
    the index of the maximum element of v.
  - maxIndexRand
```
public static int maxIndexRand(double[] v)
```
    Gets the index of the maximum element in the array. Randomly chooses the max index if there are multiple matching elements.
    
    Parameters:
    
    v -
    
    Returns:
    
    the index of the maximum element of v.
  - maxIndexRand
```
public static int maxIndexRand(int[] v)
```
    Gets the index of the maximum element in the array. Randomly chooses the max index if there are multiple matching elements.
    
    Parameters:
    
    v -
    
    Returns:
    
    the index of the maximum element of v.
  - maxValue
```
public static int maxValue(int[] values)
```
    Computes the maximum value in the given array.
    
    Parameters:
    
    values -
    
    Returns:
    
    the maximum value in the given array.
  - maxValue
```
public static double maxValue(double[] values)
```
    Computes the maximum value in the given array.
    
    Parameters:
    
    values -
    
    Returns:
    
    the maximum value in the given array.
  - minValue
```
public static double minValue(double[] values)
```
    Computes the minimum value in the given array.
    
    Parameters:
    
    values -
    
    Returns:
    
    the minimum value in the given array.
  - minValue
```
public static int minValue(int[] values)
```
    Computes the minimum value in the given array.
    
    Parameters:
    
    values -
    
    Returns:
    
    the minimum value in the given array.
  - mean
```
public static double mean(double[] values)
```
    Computes the mean of the values in the given array.
    
    Parameters:
    
    values -
    
    Returns:
    
    the mean of the values in the given array.
  - mean
```
public static double mean(int[] values)
```
    Computes the mean of the values in the given array.
    
    Parameters:
    
    values -
    
    Returns:
    
    the mean of the values in the given array.
  - sum
```
public static double sum(double[] values)
```
    Sums the values in the given array.
    
    Parameters:
    
    values -
    
    Returns:
    
    the sum of the values in the given array.
  - sum
```
public static int sum(int[] values)
```
    Sums the values in the given array.
    
    Parameters:
    
    values -
    
    Returns:
    
    the sum of the values in the given array.
  - arrayAdd
```
public static double[] arrayAdd(double[] d1,
                                double[] d2)
```
    Adds corresponding elements of two arrays.
    
    Parameters:
    
    d1 - the first array
    
    d2 - the second array
    
    Returns:
    
    the element-wise sum of d1 + d2
  - arrayDivide
```
public static double[] arrayDivide(double[] d1,
                                   double denominator)
```
    Divides each element of an array by a number.
    
    Parameters:
    
    d1 - the first array
    
    denominator - the denominator for the devision
    
    Returns:
    
    d1 / denominator
  - sortOrder
```
public static int[] sortOrder(int[] values)
```
    Sorts the given values and returns the order of the original indices.
    
    Parameters:
    
    values - the values to sort
    
    Returns:
    
    an array of the original indices of values reordered according to the sort.
  - sortOrder
```
public static int[] sortOrder(double[] values)
```
    Sorts the given values and returns the order of the original indices.
    
    Parameters:
    
    values - the values to sort
    
    Returns:
    
    an array of the original indices of values reordered according to the sort.
  - reverse
```
public static int[] reverse(int[] array)
```
    Reverses the given array.
    
    Parameters:
    
    array -
    
    Returns:
    
    the array with the elements in reverse order
  - reverse
```
public static double[] reverse(double[] array)
```
    Reverses the given array.
    
    Parameters:
    
    array -
    
    Returns:
    
    the array with the elements in reverse order
  - getMachinePrecision
```
public static double getMachinePrecision()
```
    Gets the computed machine precision.
    
    Returns:
    
    the computed machine precision.
  - isApproxEqual
```
public static boolean isApproxEqual(double value1,
                                    double value2)
```
    Determines whether two numbers are approximately equal according to the machine precision.
    
    Parameters:
    
    value1 -
    
    value2 -
    
    Returns:
    
    whether the two numbers are approximately equal
  - isApproxEqual
```
public static boolean isApproxEqual(double value1,
                                    double value2,
                                    double precision)
```
    Determines whether two numbers are approximately equal according to the precision.
    
    Parameters:
    
    value1 -
    
    value2 -
    
    precision -
    
    Returns:
    
    whether the two numbers are approximately equal
  - correlation
```
public static double correlation(int[] p,
                                 int[] q)
```
    Computes the correlation between two arrays of the same length, p and q. Computes the correlation between p and q as r = (|p| * \sum_i(p[i]*q[i]) - \sum_i(p[i]) * \sum_i(q[i]))/ sqrt((|p| * \sum_i((p[i])^2) - (\sum_i(p[i]))^2) * (|p| * \sum_i((p[i])^2) - (\sum_i(p[i]))^2)) This correlation can be tested for statistical significance via t-tests. See e.g.: http://www.socialresearchmethods.net/kb/statcorr.htm
    
    Returns:
    
    The correlation between the elements of the two arrays.
  - correlation
```
public static double correlation(double[] p,
                                 double[] q)
```
    Computes the correlation between two arrays of the same length, p and q. Computes the correlation between p and q as r = (|p| * \sum_i(p[i]*q[i]) - \sum_i(p[i]) * \sum_i(q[i]))/ sqrt((|p| * \sum_i((p[i])^2) - (\sum_i(p[i]))^2) * (|p| * \sum_i((p[i])^2) - (\sum_i(p[i]))^2)) This correlation can be tested for statistical significance via t-tests. See e.g.: http://www.socialresearchmethods.net/kb/statcorr.htm
    
    Returns:
    
    The correlation between the elements of the two arrays.
  - pairwiseAgreement
```
public static double pairwiseAgreement(int[] p,
                                       int[] q)
```
    Computes the pairwise agreement between two pairwise arrays of labelings. The pairwise agreement is the number-of-pairs-in-agreement / the-total-number-of-pairs. The two arrays must be the same length.
    
    Parameters:
    
    p - An array of labels.
    
    q - An array of labels.
    
    Returns:
    
    The pairwise agreement between the labelings in p and q.
  - uniqueValues
```
public static int[] uniqueValues(int[] v)
```
    Determines the unique values of v. The values are returned in no particular order.
    
    Parameters:
    
    v -
    
    Returns:
    
    the unique values of v in no particular order.
  - uniqueValues
```
public static double[] uniqueValues(double[] v)
```
    Determines the unique values of v. The values are returned in no particular order.
    
    Parameters:
    
    v -
    
    Returns:
    
    the unique values of v in no particular order.
  - getConfusionMatrix
```
public static double[][] getConfusionMatrix(int[] p,
                                            int[] q)
```
    Computes the normalized confusion matrix for two vectors.
    
    Parameters:
    
    p - the first vector
    
    q - the second vector
    
    Returns:
    
    the normalized confusion matrix for p and q
  - mutualInformation
```
public static double mutualInformation(int[] p,
                                       int[] q)
```
    Computes the mutual information between two vectors.
    
    Parameters:
    
    p - the first vector.
    
    q - the second vector.
    
    Returns:
    
    the mutual information between p and q.
  - computeSimilarity
```
public static double computeSimilarity(int[] targetV,
                                       int[] v,
                                       MathUtils.SimilarityMetric similarityMetric)
```
    Computes the similarity of the two vectors.
    
    Parameters:
    
    targetV - the target vector that forms the basis for comparison.
    
    v - the vector for comparison
    
    similarityMetric - the metric to use for computing the similarity
    
    Returns:
    
    The similarity of the two models.
  - log2
```
public static double log2(double d)
```
    Computes the log-base-2 of a number.
    
    Parameters:
    
    d -
    
    Returns:
    
    the log-base-2 of d

Class MathUtils

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

MathUtils

Method Detail

reverseCuthillMcKee

permutation

permutation

append

append

append

append

vectorAdd

vectorSubtract

vectorMultiply

vectorL2Norm

vectorNormalize

getRandomGenerator

initializeRandomGenerator

nextRandomGaussian

nextRandomGaussian

rmse

main

sampleWithoutReplacement

sampleWithReplacement

capValue

capValue

rangeCheck

rangeCheck

round

roundToMultiple

minIndex

minIndex

minIndexRand

minIndexRand

maxIndex

maxIndex

maxIndexRand

maxIndexRand

maxValue

maxValue

minValue

minValue

mean

mean

sum

sum

arrayAdd

arrayDivide

sortOrder

sortOrder

reverse

reverse

getMachinePrecision

isApproxEqual

isApproxEqual

correlation

correlation

pairwiseAgreement

uniqueValues

uniqueValues

getConfusionMatrix

mutualInformation

computeSimilarity

log2