stat

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 Go to latest Published: Jun 23, 2013 License: GPL-3.0 Imports: 1 Imported by: 18

README

Statistics Build Status

Pure Go implementation of the GSL Statistics library.

For the API overview see Godoc.

Why create this repository when there is also "github.com/grd/statistics" ? Three reasons:

  • Updated API. The interfaces are simpler and the types are more conformal to Go.
  • License changed from GPL v2 to v3 (GNU GSL 1.15 uses GPL v3).
  • Package name "stat" is shorter than "statistics", while retaining logic.
API Interfaces:
  • It uses interfaces for the data access. So different data types can be used.
  • Two datatypes are pre-defined: Float64Slice and IntSlice.
  • For sampling purposes there is a Strider type provided.

Testing 100% pass. Testing covers the complete functionality.
Tested on Debian 6 and Windows 7, both 32- and 64-bit.

Documentation

Overview

GNU GSL Statistics library (v1.15, GPLv3) implemented in Go

Index

Examples

Constants

This section is empty.

Variables

This section is empty.

Functions

func Absdev 

func Absdev(data Interface) float64

func AbsdevMean 

func AbsdevMean(data Interface, mean float64) float64

AbsdevMean finds the absolute deviation of the data interface

func Correlation 

func Correlation(data1, data2 Interface) (res float64)

Correlation() 

Calculate Pearson correlation = cov(X, Y) / (sigma_X * sigma_Y)

This routine efficiently computes the correlation in one pass of the data and makes use of the algorithm described in:

B. P. Welford, "Note on a Method for Calculating Corrected Sums of Squares and Products", Technometrics, Vol 4, No 3, 1962.

This paper derives a numerically stable recurrence to compute a sum of products

S = sum_{i=1..N} [ (x_i - mu_x) * (y_i - mu_y) ]

with the relation

S_n = S_{n-1} + ((n-1)/n) * (x_n - mu_x_{n-1}) * (y_n - mu_y_{n-1})

func Covariance 

func Covariance(data1, data2 Interface) float64

func CovarianceMean 

func CovarianceMean(data1, data2 Interface, mean1, mean2 float64) float64

func Kurtosis 

func Kurtosis(data Interface) float64

func KurtosisMainSd 

func KurtosisMainSd(data Interface, mean, sd float64) float64

func Lag1Autocorrelation 

func Lag1Autocorrelation(data Interface) float64

func Lag1AutocorrelationMean 

func Lag1AutocorrelationMean(data Interface, mean float64) float64

func Max 

func Max(data Interface) (max float64, max_index int)

Max finds the first largest member and the members position within the data

Example 
package main

import (
	"fmt"
	"github.com/grd/stat"
)

func main() {
	data := stat.Float64Slice{17.2, 18.1, 16.5, 18.3, 12.6}
	largest, index := stat.Max(data)
	fmt.Printf("The largest value is %g and the index is %d", largest, index)
}

Output:

The largest value is 18.3 and the index is 3

func Mean 

func Mean(data Interface) (mean float64)

Mean calculates the arithmetic mean with the recurrence relation

Example 
package main

import (
	"fmt"
	"github.com/grd/stat"
)

func main() {
	data := stat.Float64Slice{17.2, 18.1, 16.5, 18.3, 12.6}
	mean := stat.Mean(data)
	fmt.Printf("The sample mean is %g", mean)
}

Output:

The sample mean is 16.54

func MedianFromSortedData 

func MedianFromSortedData(sortedData Interface) (median float64)

MedianFromSortedData calculates the median of the sorted data. Note that the function doesn't check wheather the data is actually sorted.

Example 
package main

import (
	"fmt"
	"github.com/grd/stat"
	"sort"
)

func main() {
	data := stat.Float64Slice{17.2, 18.1, 16.5, 18.3, 12.6}
	sort.Sort(data)
	median := stat.MedianFromSortedData(data)
	fmt.Printf("Sorted dataset: %v\n", data)
	fmt.Printf("The median is %g\n", median)
}

Output:

Sorted dataset: [12.6 16.5 17.2 18.1 18.3]
The median is 17.2

func Min 

func Min(data Interface) (min float64, min_index int)

Min finds the first smallest member and the members position within the data

Example 
package main

import (
	"fmt"
	"github.com/grd/stat"
)

func main() {
	data := stat.Float64Slice{17.2, 18.1, 16.5, 18.3, 12.6}
	smallest, index := stat.Min(data)
	fmt.Printf("The smallest value is %g and the index is %d", smallest, index)
}

Output:

The smallest value is 12.6 and the index is 4

func Minmax 

func Minmax(data Interface) (min float64, min_index int, max float64, max_index int)

Minmax finds the first smallest and largest members and the members positions within the data

func PVariance 

func PVariance(data1, data2 Interface) float64

PVariance finds the pooled variance of two datasets

func QuantileFromSortedData 

func QuantileFromSortedData(sortedData Interface, f float64) (result float64)

QuantileFromSortedData performs the quantile function, also called percent point function or inverse cumulative distribution function, on the sorted data. Note that the function doesn't check wheather the data is actually sorted.

Example 
package main

import (
	"fmt"
	"github.com/grd/stat"
	"sort"
)

func main() {
	data := stat.Float64Slice{17.2, 18.1, 16.5, 18.3, 12.6}
	sort.Sort(data)
	upperq := stat.QuantileFromSortedData(data, 0.75)
	lowerq := stat.QuantileFromSortedData(data, 0.25)

	fmt.Printf("Sorted dataset: %v\n", data)
	fmt.Printf("The upper quartile is %g\n", upperq)
	fmt.Printf("The lower quartile is %g\n", lowerq)
}

Output:

Sorted dataset: [12.6 16.5 17.2 18.1 18.3]
The upper quartile is 18.1
The lower quartile is 16.5

func Sd 

func Sd(data Interface) float64

func SdMean 

func SdMean(data Interface, mean float64) float64

func SdWithFixedMean 

func SdWithFixedMean(data Interface, mean float64) float64

func Skew 

func Skew(data Interface) float64

func SkewMeanSd 

func SkewMeanSd(data Interface, mean, sd float64) (skew float64)

SkewMeanSd calculates the skewness of a dataset

func TTest 

func TTest(data1, data2 Interface) float64

runs a t-test between two datasets representing independent samples. Tests to see if the difference between means of the samples is different from zero.

func Tss 

func Tss(data Interface) float64

func TssMean 

func TssMean(data Interface, mean float64) (res float64)

TssMean takes a dataset and finds the sum of squares about the mean

func Variance 

func Variance(data Interface) float64
Example 
package main

import (
	"fmt"
	"github.com/grd/stat"
)

func main() {
	data := stat.Float64Slice{17.2, 18.1, 16.5, 18.3, 12.6}
	variance := stat.Variance(data)
	fmt.Printf("The estimated variance is %.4f", variance)
}

Output:

The estimated variance is 5.3730

func VarianceMean 

func VarianceMean(data Interface, mean float64) float64

func VarianceWithFixedMean 

func VarianceWithFixedMean(data Interface, mean float64) float64

func WAbsdev 

func WAbsdev(w, data Interface) float64

func WAbsdevMean 

func WAbsdevMean(w, data Interface, wmean float64) (wabsdev float64)

WAbsdevMean calculates the weighted absolute deviation of a dataset

func WKurtosis 

func WKurtosis(w, data Interface) float64

func WKurtosisMeanSd 

func WKurtosisMeanSd(w, data Interface, wmean, wsd float64) float64

WKurtosisMean calculates the kurtosis of a dataset

func WMean 

func WMean(w, data Interface) (wmean float64)

WMean calculates the weighted arithmetic mean of a dataset

func WSd 

func WSd(w, data Interface) float64

func WSdMean 

func WSdMean(w, data Interface, wmean float64) float64

func WSkew 

func WSkew(w, data Interface) float64

func WSkewMeanSd 

func WSkewMeanSd(w, data Interface, wmean, wsd float64) (wskew float64)

Compute the weighted skewness of a dataset

func WTss 

func WTss(w, data Interface) float64

func WTssMean 

func WTssMean(w, data Interface, wmean float64) (res float64)

WTssMean takes a dataset and finds the weighted sum of squares about wmean

func WVariance 

func WVariance(w, data Interface) float64

func WVarianceMean 

func WVarianceMean(w, data Interface, wmean float64) float64

func WVarianceWithFixedMean 

func WVarianceWithFixedMean(w, data Interface, wmean float64) float64

func WsdWithFixedMean 

func WsdWithFixedMean(w, data Interface, wmean float64) float64

Types

type Float64Slice 

type Float64Slice []float64

Float64Slice is a predifined float64 slice type which implements the Interface and Sort interfaces.

func (Float64Slice) Get 

func (f Float64Slice) Get(i int) float64

func (Float64Slice) Len 

func (f Float64Slice) Len() int

func (Float64Slice) Less 

func (f Float64Slice) Less(i, j int) bool

func (Float64Slice) Swap 

func (f Float64Slice) Swap(i, j int)

type IntSlice 

type IntSlice []int64

IntSlice is a predifined int64 slice type which implements the Interface and Sort interfaces.

func (IntSlice) Get 

func (f IntSlice) Get(i int) float64

func (IntSlice) Len 

func (f IntSlice) Len() int

func (IntSlice) Less 

func (f IntSlice) Less(i, j int) bool

func (IntSlice) Swap 

func (f IntSlice) Swap(i, j int)

type Interface 

type Interface interface {
	Get(int) float64
	Len() int
}

Interface is used throughout the package.

type Sort 

type Sort interface {
	Interface
	Less(int, int) bool
	Swap(int, int)
}

Sort is Interface with sorting functionality.

type SortStrider 

type SortStrider struct {
	Sort
	// contains filtered or unexported fields
}

SortStrider strides over the data, for sampling purposes. It also has sorting functionality.

func NewSortStrider 

func NewSortStrider(data Sort, stride int) SortStrider

func (SortStrider) Get 

func (p SortStrider) Get(i int) float64

func (SortStrider) Len 

func (p SortStrider) Len() int

func (SortStrider) Less 

func (p SortStrider) Less(i, j int) bool

func (SortStrider) Swap 

func (p SortStrider) Swap(i, j int)

type Strider 

type Strider struct {
	Interface
	// contains filtered or unexported fields
}

Strider strides over the data, for sampling purposes.

Example 
package main

import (
	"fmt"
	"github.com/grd/stat"
)

func main() {
	data := stat.Float64Slice{
		.0421, .0941, .1064, .0242,
		.1331, .0773, .0243, .0815,
		.1186, .0356, .0728, .0999,
		.0614, .0479}
	strider := stat.NewStrider(data, 4)
	for i := 0; i < strider.Len(); i++ {
		fmt.Println(strider.Get(i))
	}
}

Output:

0.0421
0.1331
0.1186

func NewStrider 

func NewStrider(data Interface, stride int) Strider
Example 
package main

import (
	"fmt"
	"github.com/grd/stat"
)

func main() {
	data := stat.Float64Slice{
		.0421, .0941, .1064, .0242,
		.1331, .0773, .0243, .0815,
		.1186, .0356, .0728, .0999,
		.0614, .0479}
	strider := stat.NewStrider(data, 4)
	fmt.Printf("mean data is %.4f\n", stat.Mean(data))
	fmt.Printf("mean strider is %.4f\n", stat.Mean(strider))
}

Output:

mean data is 0.0728
mean strider is 0.0979

func (Strider) Get 

func (p Strider) Get(i int) float64

func (Strider) Len 

func (p Strider) Len() int

Source Files

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