maths

Documentation

Index

• Variables
• func Max[T EssentialNumTypes](v1 ...T) T
• func Min[T EssentialNumTypes](v1 ...T) T
• func RoundToN[T floats](value T, places int) T
• func TDistribConfInterval(mean, stdev float64, sampleSize int, conf SignificanceLevel) (float64, float64, error)
• func TValueTwoTail(df int, conf SignificanceLevel) (float64, error)
• func WilsonCI(succ float64, sampleSize int, signif SignificanceLevel) (float64, float64, error)
• type EssentialNumTypes
• type FreqInfo
• type OnlineMean
  • func (m OnlineMean) Add(incoming float64) OnlineMean
  • func (m OnlineMean) Mean() float64
  • func (m OnlineMean) Stdev() float64
• type Ordered
• type Quartiles
  • func GetQuartiles[T FreqInfo](data Ordered[T]) (Quartiles, error)
  • func (q Quartiles) IQR() int
• type SignificanceLevel

Variables

var (
	ErrValueNotAvailable = errors.New("t-value not available for provided degrees of freedom")

	ErrUnsupportedSignifLevel = errors.New("unsupported significance level")

	ErrTooSmallDataset = errors.New("too small dataset")
)

Functions

func Max

func Max[T EssentialNumTypes](v1 ...T) T

Max finds maximum number for int, int64, float32, float64

func Min

func Min[T EssentialNumTypes](v1 ...T) T

Min finds manimum number for int, int64, float32, float64

func RoundToN

func RoundToN[T floats](value T, places int) T

func TDistribConfInterval

func TDistribConfInterval(mean, stdev float64, sampleSize int, conf SignificanceLevel) (float64, float64, error)

TDistribConfInterval calculates a confidence interval for a sample mean and standard deviation in case population std. deviation is unknown and the values are "roughly normal". Please note that the function calculates respective t-values using a simple lookup table and is reliable up to 100 degrees of freedom. Higher values will be likely approximated by 1000 df which may or may not serve well. The provided confidence level is always applied in "two tails" mode.

func TValueTwoTail

func TValueTwoTail(df int, conf SignificanceLevel) (float64, error)

TValueTwoTail gets t-value with two-tailed confidence level

func WilsonCI

func WilsonCI(succ float64, sampleSize int, signif SignificanceLevel) (float64, float64, error)

WilsonCI calculates Wilson confidence interval for a random variable with binomial distribution. The input arguments are represented as: `succ` successful trials out of `sampleSize`

Types

type EssentialNumTypes

type EssentialNumTypes interface {
	int | int64 | float32 | float64
}

type FreqInfo

type FreqInfo interface {
	Freq() int
}

type OnlineMean

type OnlineMean struct {
	// contains filtered or unexported fields
}

func (OnlineMean) Add

func (m OnlineMean) Add(incoming float64) OnlineMean

func (OnlineMean) Mean

func (m OnlineMean) Mean() float64

func (OnlineMean) Stdev

func (m OnlineMean) Stdev() float64

type Ordered

type Ordered[T any] interface {
	Get(idx int) T
	Len() int
}

type Quartiles

type Quartiles struct {
	Q1Idx int
	Q1    int
	Q2Idx int
	Q2    int
	Q3Idx int
	Q3    int
}

func GetQuartiles

func GetQuartiles[T FreqInfo](data Ordered[T]) (Quartiles, error)

GetQuartiles calculates quartiles on an ordered and (sorted) list. The function requires at least 7 items in the data. Otherwise, it returns ErrTooSmallDataset

func (Quartiles) IQR

func (q Quartiles) IQR() int

type SignificanceLevel

type SignificanceLevel string

const (
	Significance_1_00  SignificanceLevel = "1.00"
	Significance_0_50  SignificanceLevel = "0.50"
	Significance_0_40  SignificanceLevel = "0.40"
	Significance_0_30  SignificanceLevel = "0.30"
	Significance_0_20  SignificanceLevel = "0.20"
	Significance_0_10  SignificanceLevel = "0.10"
	Significance_0_05  SignificanceLevel = "0.05"
	Significance_0_02  SignificanceLevel = "0.02"
	Significance_0_01  SignificanceLevel = "0.01"
	Significance_0_002 SignificanceLevel = "0.002"
	Significance_0_001 SignificanceLevel = "0.001"
)