org.eclipse.january.dataset

Class Stats

java.lang.Object

org.eclipse.january.dataset.Stats

public class Stats
extends Object

Statistics class TODO Where is mode? http://en.wikipedia.org/wiki/Mode_(statistics)

Constructor Summary

Constructors

Constructor and Description
Stats()

Method Summary

Methods

Modifier and Type	Method and Description
static Object	averageDeviation(Dataset a)
static Dataset	covariance(Dataset a) See covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof) with b = null, rowvar = true, bias = false and ddof = null.
static Dataset	covariance(Dataset a, boolean rowvar, boolean bias, Integer ddof) See covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof) with b = null.
static Dataset	covariance(Dataset a, Dataset b) See covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof) with b = null, rowvar = true, bias = false and ddof = null.
static Dataset	covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof) Calculate the covariance matrix (array) of a concatenated with b.
static Dataset	cumulativeProduct(Dataset a, boolean... ignoreInvalids)
static Dataset	cumulativeProduct(Dataset a, int axis, boolean... ignoreInvalids)
static Dataset	cumulativeSum(Dataset a, boolean... ignoreInvalids)
static Dataset	cumulativeSum(Dataset a, int axis, boolean... ignoreInvalids)
static Object	iqr(Dataset a) Interquartile range: Q3 - Q1
static Dataset	iqr(Dataset a, int axis) Interquartile range: Q3 - Q1
static Object	kurtosis(Dataset a, boolean... ignoreInvalids)
static Dataset	kurtosis(Dataset a, int axis, boolean... ignoreInvalids)
static Object	median(Dataset a)
static Dataset	median(Dataset a, int axis)
static double[]	outlierValues(Dataset a, double lo, double hi, int length) Calculate approximate outlier values.
static Object	product(Dataset a, boolean... ignoreInvalids)
static Dataset	product(Dataset a, int axis, boolean... ignoreInvalids)
static double[]	quantile(Dataset a, double... values) Calculate quantiles of dataset which is defined as the inverse of the cumulative distribution function (CDF)
static double	quantile(Dataset a, double q) Calculate quantile of dataset which is defined as the inverse of the cumulative distribution function (CDF)
static Dataset[]	quantile(Dataset a, int axis, double... values) Calculate quantiles of dataset which is defined as the inverse of the cumulative distribution function (CDF)
static double	residual(Dataset a, Dataset b) The residual is the sum of squared differences
static Object	skewness(Dataset a, boolean... ignoreInvalids)
static Dataset	skewness(Dataset a, int axis, boolean... ignoreInvalids)
static Object	sum(Dataset a, boolean... ignoreInvalids)
static Object	typedProduct(Dataset a, int dtype, boolean... ignoreInvalids)
static Dataset	typedProduct(Dataset a, int dtype, int axis, boolean... ignoreInvalids)
static Object	typedSum(Dataset a, int dtype, boolean... ignoreInvalids)
static Dataset	typedSum(Dataset a, int dtype, int axis, boolean... ignoreInvalids)
static double	weightedResidual(Dataset a, Dataset b, Dataset w) The residual is the sum of squared differences

Methods inherited from class java.lang.Object

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

Constructor Detail

Stats

public Stats()

Method Detail

quantile

public static double quantile(Dataset a,
              double q)

Calculate quantile of dataset which is defined as the inverse of the cumulative distribution function (CDF)

Parameters:

a -

q -

Returns:

point at which CDF has value q

quantile

public static double[] quantile(Dataset a,
                double... values)

Calculate quantiles of dataset which is defined as the inverse of the cumulative distribution function (CDF)

Parameters:

a -

values -

Returns:

points at which CDF has given values

quantile

public static Dataset[] quantile(Dataset a,
                 int axis,
                 double... values)

Calculate quantiles of dataset which is defined as the inverse of the cumulative distribution function (CDF)

Parameters:

a -

axis -

values -

Returns:

points at which CDF has given values

median

public static Dataset median(Dataset a,
             int axis)

Parameters:

a - dataset

axis -

Returns:

median

median

public static Object median(Dataset a)

Parameters:

a - dataset

Returns:

median

iqr

public static Object iqr(Dataset a)

Interquartile range: Q3 - Q1

Parameters:

a -

Returns:

range

iqr

public static Dataset iqr(Dataset a,
          int axis)

Interquartile range: Q3 - Q1

Parameters:

a -

axis -

Returns:

range

skewness

public static Object skewness(Dataset a,
              boolean... ignoreInvalids)

Parameters:

a - dataset

ignoreInvalids - see IDataset.max(boolean...)

Returns:

skewness

Since:

2.0

kurtosis

public static Object kurtosis(Dataset a,
              boolean... ignoreInvalids)

Parameters:

a - dataset

ignoreInvalids - see IDataset.max(boolean...)

Returns:

kurtosis

Since:

2.0

skewness

public static Dataset skewness(Dataset a,
               int axis,
               boolean... ignoreInvalids)

Parameters:

a - dataset

axis -

ignoreInvalids - see Dataset.max(int, boolean...)

Returns:

skewness along axis in dataset

Since:

2.0

kurtosis

public static Dataset kurtosis(Dataset a,
               int axis,
               boolean... ignoreInvalids)

Parameters:

a - dataset

axis -

ignoreInvalids - see Dataset.max(int, boolean...)

Returns:

kurtosis along axis in dataset

Since:

2.0

sum

public static Object sum(Dataset a,
         boolean... ignoreInvalids)

Parameters:

a - dataset

ignoreInvalids - see IDataset.max(boolean...)

Returns:

sum of dataset

Since:

2.0

typedSum

public static Object typedSum(Dataset a,
              int dtype,
              boolean... ignoreInvalids)

Parameters:

a - dataset

dtype -

ignoreInvalids - see IDataset.max(boolean...)

Returns:

typed sum of dataset

Since:

2.0

typedSum

public static Dataset typedSum(Dataset a,
               int dtype,
               int axis,
               boolean... ignoreInvalids)

Parameters:

a - dataset

dtype -

axis -

ignoreInvalids - see Dataset.max(int, boolean...)

Returns:

typed sum of items along axis in dataset

Since:

2.0

product

public static Object product(Dataset a,
             boolean... ignoreInvalids)

Parameters:

a - dataset

ignoreInvalids - see IDataset.max(boolean...)

Returns:

product of all items in dataset

Since:

2.0

product

public static Dataset product(Dataset a,
              int axis,
              boolean... ignoreInvalids)

Parameters:

a - dataset

axis -

ignoreInvalids - see Dataset.max(int, boolean...)

Returns:

product of items along axis in dataset

Since:

2.0

typedProduct

public static Object typedProduct(Dataset a,
                  int dtype,
                  boolean... ignoreInvalids)

Parameters:

a - dataset

dtype -

ignoreInvalids - see IDataset.max(boolean...)

Returns:

typed product of all items in dataset

Since:

2.0

typedProduct

public static Dataset typedProduct(Dataset a,
                   int dtype,
                   int axis,
                   boolean... ignoreInvalids)

Parameters:

a - dataset

dtype -

axis -

ignoreInvalids - see IDataset.max(boolean...)

Returns:

typed product of items along axis in dataset

Since:

2.0

cumulativeProduct

public static Dataset cumulativeProduct(Dataset a,
                        boolean... ignoreInvalids)

Parameters:

a - dataset

ignoreInvalids - see IDataset.max(boolean...)

Returns:

cumulative product of items in flattened dataset

Since:

2.0

cumulativeProduct

public static Dataset cumulativeProduct(Dataset a,
                        int axis,
                        boolean... ignoreInvalids)

Parameters:

a - dataset

axis -

ignoreInvalids - see Dataset.max(int, boolean...)

Returns:

cumulative product of items along axis in dataset

Since:

2.0

cumulativeSum

public static Dataset cumulativeSum(Dataset a,
                    boolean... ignoreInvalids)

Parameters:

a - dataset

ignoreInvalids - see IDataset.max(boolean...)

Returns:

cumulative sum of items in flattened dataset

Since:

2.0

cumulativeSum

public static Dataset cumulativeSum(Dataset a,
                    int axis,
                    boolean... ignoreInvalids)

Parameters:

a - dataset

axis -

ignoreInvalids - see Dataset.max(int, boolean...)

Returns:

cumulative sum of items along axis in dataset

Since:

2.0

averageDeviation

public static Object averageDeviation(Dataset a)

Parameters:

a -

Returns:

average deviation value of all items the dataset

residual

public static double residual(Dataset a,
              Dataset b)

The residual is the sum of squared differences

Parameters:

a -

b -

Returns:

residual value

weightedResidual

public static double weightedResidual(Dataset a,
                      Dataset b,
                      Dataset w)

The residual is the sum of squared differences

Parameters:

a -

b -

w -

Returns:

residual value

outlierValues

public static double[] outlierValues(Dataset a,
                     double lo,
                     double hi,
                     int length)

Calculate approximate outlier values. These are defined as the values in the dataset that are approximately below and above the given thresholds - in terms of percentages of dataset size.

It approximates by limiting the number of items (given by length) used internally by data structures - the larger this is, the more accurate will those outlier values become. The actual thresholds used are returned in the array.

Also, the low and high values will be made distinct if possible by adjusting the thresholds

Parameters:

a -

lo - percentage threshold for lower limit

hi - percentage threshold for higher limit

length - maximum number of items used internally, if negative, then unlimited

Returns:

double array with low and high values, and low and high percentage thresholds

covariance

public static Dataset covariance(Dataset a)

See covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof) with b = null, rowvar = true, bias = false and ddof = null.

Parameters:

a -

Returns:

covariance array of a

covariance

public static Dataset covariance(Dataset a,
                 boolean rowvar,
                 boolean bias,
                 Integer ddof)

See covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof) with b = null.

Parameters:

a -

Returns:

covariance array of a

Since:

2.0

covariance

public static Dataset covariance(Dataset a,
                 Dataset b)

See covariance(Dataset a, Dataset b, boolean rowvar, boolean bias, Integer ddof) with b = null, rowvar = true, bias = false and ddof = null.

Parameters:

a -

Returns:

covariance array of a concatenated with b

covariance

public static Dataset covariance(Dataset a,
                 Dataset b,
                 boolean rowvar,
                 boolean bias,
                 Integer ddof)

Calculate the covariance matrix (array) of a concatenated with b. This method is directly based on the implementation in numpy (cov).

Parameters:

a - Array containing multiple variable and observations. Each row represents a variable, each column an observation.

b - An extra set of variables and observations. Must be of same type as a and have a compatible shape.

rowvar - When true (default), each row is a variable; when false each column is a variable.

bias - Default normalisation is (N - 1) - N is number of observations. If set true, normalisation is (N).

ddof - Default normalisation is (N - 1). If ddof is set, then normalisation is (N - ddof).

Returns:

covariance array of a concatenated with b

Since:

2.0