sliceutil

type DescStats[V typeutil.Number] struct {
	// Count is the total number of items in the data set.
	Count int `json:"count"`

	// Entropy is the entropy of the value distribution, expressed in nats
	// (natural logarithm, base e); divide by ln(2) to convert to bits.
	// It is meaningful only for non-negative data with a positive sum, where the
	// values can be interpreted as an unnormalized probability distribution.
	// For data whose sum is not strictly positive (e.g. signed data, all zeros,
	// or a zero/negative total) the entropy is reported as 0 to avoid NaN/Inf;
	// individual non-positive values are skipped for the same reason.
	Entropy float64 `json:"entropy"`

	// ExKurtosis is the sample excess kurtosis (G2) of the data set: the
	// bias-corrected fourth standardized moment with 3.0 subtracted, so that the
	// excess kurtosis of a normal distribution is zero.
	// It is defined only for n >= 4 with a non-zero standard deviation; otherwise
	// it is reported as 0.
	ExKurtosis float64 `json:"exkurtosis"`

	// Max is the maximum value of the data.
	Max V `json:"max"`

	// MaxID is the index (key) of the Max malue in a data set.
	MaxID int `json:"maxid"`

	// Mean or Average is a central tendency of the data.
	Mean float64 `json:"mean"`

	// MeanDev is the Mean Absolute Deviation: the average of the absolute
	// differences between each value and Mean, normalized by n (unlike the
	// sample Variance, which divides by n-1).
	MeanDev float64 `json:"meandev"`

	// Median is the value that divides the data into 2 equal parts.
	// When the data is sorted, the number of terms on the left and right side of median is the same.
	Median float64 `json:"median"`

	// Min is the minimal value of the data.
	Min V `json:"min"`

	// MinID is the index (key) of the Min malue in a data set.
	MinID int `json:"minid"`

	// Mode is the value with the highest frequency in the data set. Ties are
	// broken deterministically by choosing the smallest tied value.
	// When ModeFreq == 1 no value repeats and there is no true mode; Mode is
	// then the smallest value in the data set.
	Mode V `json:"mode"`

	// ModeFreq is the frequency of the Mode value. A value of 1 means no value
	// repeats in the data set (see Mode).
	ModeFreq int `json:"modefreq"`

	// Range is the difference between the highest (Max) and lowest (Min) value,
	// using the element type V. As with Sum, this can overflow for narrow signed
	// integer element types with extreme values.
	Range V `json:"range"`

	// Skewness measures the asymmetry of the distribution about its mean, as the
	// adjusted Fisher-Pearson standardized moment coefficient (sample G1).
	// It is defined only for n >= 3 with a non-zero standard deviation; otherwise
	// it is reported as 0.
	Skewness float64 `json:"skewness"`

	// StdDev is the sample standard deviation: the square root of the sample
	// Variance (which uses the n-1 divisor).
	StdDev float64 `json:"stddev"`

	// Sum is the total of all values, using the element type V. For narrow
	// integer element types or very large data sets it can overflow; use a wide
	// element type (int64/float64) when exact large totals are required.
	Sum V `json:"sum"`

	// Variance is the sample variance: the sum of squared deviations from Mean
	// divided by n-1 (Bessel's correction). It is 0 for a single element, where
	// the sample variance is undefined.
	Variance float64 `json:"variance"`
}