esraMath
Class Statistics

java.lang.Object
  esraMath.Statistics

public class Statistics

extends java.lang.Object

Author:

Vincent Kraeutler, Moritz Winger

Nested Class Summary

class

Statistics.Cluster litttle helper class for the clustering algorithm

Constructor Summary

Statistics()

Method Summary

static double	average(double[] values)
static double[]	bin(double[] values, double lower, double upper, int nBins)
static java.util.ArrayList	cluster(double[][] matrix, double clusterCutoff, int clusterSizeCutoff) cluster analysis
static double[][][]	gridDensities3D(double[] gridOrigin, double[] gridDimensions, int[] nGridPoints, double[][] points, double[] weights)
static double[]	leastsquare(double[] x, double[] y) Linear Regression to set of {xi, yi} tuples.
static double	max(double[] values)
static double	min(double[] values)
static double[][]	reweight(double[][] pos, double[] weights)
static double[][]	rmsdMatrix(double[][][] trajectory) computes the rmsd matrix for a trajectory.
static double[][]	smoothen(double[][] matrix, int size) reduce the size of a matrix by averaging over blocks.
static double	stddev(double[] values, double average) A naive implementation for calculating a naive measure for the standard deviation.

Methods inherited from class java.lang.Object

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

Constructor Detail

Statistics

public Statistics()

Method Detail

min

public static double min(double[] values)

Parameters:

values -

Returns:

the minimum value

max

public static double max(double[] values)

Parameters:

values -

Returns:

the maximum value

bin

public static double[] bin(double[] values,
                           double lower,
                           double upper,
                           int nBins)

Parameters:

values -

lower -

upper -

nBins -

Returns:

a distribution (with resolution nBins) of the values

average

public static double average(double[] values)

Parameters:

values -

Returns:

the average of values

stddev

public static double stddev(double[] values,
                            double average)

A naive implementation for calculating a naive measure for the standard deviation.

σ_x = (&Sigma_i=1,..,N (x_i - x_a)² / N)^0.5

Parameters:

values -

average -

Returns:

the standard deviation

leastsquare

public static double[] leastsquare(double[] x,
                                   double[] y)

Linear Regression to set of {xi, yi} tuples.

Parameters:

x - array of x-values

y - array of y-values

Returns:

ab array[2] = {a, b} of slope (a) and intersect (b)

reweight

public static double[][] reweight(double[][] pos,
                                  double[] weights)

Parameters:

pos -

weights -

Returns:

the reweighted positions

smoothen

public static final double[][] smoothen(double[][] matrix,
                                        int size)

reduce the size of a matrix by averaging over blocks.

Parameters:

matrix - the input matrix

size - the size of the output matrix

Returns:

the smoothened matrix

rmsdMatrix

public static double[][] rmsdMatrix(double[][][] trajectory)

computes the rmsd matrix for a trajectory. assumes the centers of mass of all frames have already been shifted to the origin (i.e. no translational fit needed anymore).

Parameters:

trajectory -

Returns:

the rmsd matrix

cluster

public static java.util.ArrayList cluster(double[][] matrix,
                                          double clusterCutoff,
                                          int clusterSizeCutoff)

cluster analysis

Parameters:

matrix - the cluster matrix

clusterCutoff - if abs(matrix[ii][jj]) < clusterCutoff then ii and jj are neighbors.

clusterSizeCutoff - stop clustering once cluster sizes reach this number

Returns:

an ArrayList of clusters

gridDensities3D

public static double[][][] gridDensities3D(double[] gridOrigin,
                                           double[] gridDimensions,
                                           int[] nGridPoints,
                                           double[][] points,
                                           double[] weights)