mirror/v
1
0
Fork 0
mirror of https://github.com/vlang/v.git synced 2023-08-10 21:13:21 +03:00
Code Issues Projects Releases Wiki Activity

math: update documentation (#14457)

Browse Source
This commit is contained in:
David 'Epper' Marshall
2022-05-20 01:45:54 -04:00
committed by GitHub
parent 23568f19da
commit 120f31b4d9
12 changed files with 108 additions and 51 deletions
Download Patch File Download Diff File Expand all files Collapse all files

80
vlib/math/stats/stats.v
View File

@ -2,7 +2,7 @@ module stats
import math
// Measure of Occurance
// freq calculates the Measure of Occurance
// Frequency of a given number
// Based on
// https://www.mathsisfun.com/data/frequency-distribution.html
@ -19,8 +19,8 @@ pub fn freq<T>(data []T, val T) int {
return count
}
// Measure of Central Tendancy
// Mean of the given input array
// mean calculates the average
// of the given input array, sum(data)/data.len
// Based on
// https://www.mathsisfun.com/data/central-measures.html
pub fn mean<T>(data []T) T {
@ -34,8 +34,8 @@ pub fn mean<T>(data []T) T {
return sum / T(data.len)
}
// Measure of Central Tendancy
// Geometric Mean of the given input array
// geometric_mean calculates the central tendency
// of the given input array, product(data)**1/data.len
// Based on
// https://www.mathsisfun.com/numbers/geometric-mean.html
pub fn geometric_mean<T>(data []T) T {
@ -49,8 +49,8 @@ pub fn geometric_mean<T>(data []T) T {
return math.pow(sum, 1.0 / T(data.len))
}
// Measure of Central Tendancy
// Harmonic Mean of the given input array
// harmonic_mean calculates the reciprocal of the average of reciprocals
// of the given input array
// Based on
// https://www.mathsisfun.com/numbers/harmonic-mean.html
pub fn harmonic_mean<T>(data []T) T {
@ -64,8 +64,7 @@ pub fn harmonic_mean<T>(data []T) T {
return T(data.len) / sum
}
// Measure of Central Tendancy
// Median of the given input array ( input array is assumed to be sorted )
// median returns the middlemost value of the given input array ( input array is assumed to be sorted )
// Based on
// https://www.mathsisfun.com/data/central-measures.html
pub fn median<T>(sorted_data []T) T {
@ -80,8 +79,7 @@ pub fn median<T>(sorted_data []T) T {
}
}
// Measure of Central Tendancy
// Mode of the given input array
// mode calculates the highest occuring value of the given input array
// Based on
// https://www.mathsisfun.com/data/central-measures.html
pub fn mode<T>(data []T) T {
@ -101,7 +99,7 @@ pub fn mode<T>(data []T) T {
return data[max]
}
// Root Mean Square of the given input array
// rms, Root Mean Square, calculates the sqrt of the mean of the squares of the given input array
// Based on
// https://en.wikipedia.org/wiki/Root_mean_square
pub fn rms<T>(data []T) T {
@ -115,8 +113,8 @@ pub fn rms<T>(data []T) T {
return math.sqrt(sum / T(data.len))
}
// Measure of Dispersion / Spread
// Population Variance of the given input array
// population_variance is the Measure of Dispersion / Spread
// of the given input array
// Based on
// https://www.mathsisfun.com/data/standard-deviation.html
[inline]
@ -128,8 +126,8 @@ pub fn population_variance<T>(data []T) T {
return population_variance_mean<T>(data, data_mean)
}
// Measure of Dispersion / Spread
// Population Variance of the given input array
// population_variance_mean is the Measure of Dispersion / Spread
// of the given input array, with the provided mean
// Based on
// https://www.mathsisfun.com/data/standard-deviation.html
pub fn population_variance_mean<T>(data []T, mean T) T {
@ -143,8 +141,7 @@ pub fn population_variance_mean<T>(data []T, mean T) T {
return sum / T(data.len)
}
// Measure of Dispersion / Spread
// Sample Variance of the given input array
// sample_variance calculates the spread of dataset around the mean
// Based on
// https://www.mathsisfun.com/data/standard-deviation.html
[inline]
@ -156,8 +153,7 @@ pub fn sample_variance<T>(data []T) T {
return sample_variance_mean<T>(data, data_mean)
}
// Measure of Dispersion / Spread
// Sample Variance of the given input array
// sample_variance calculates the spread of dataset around the provided mean
// Based on
// https://www.mathsisfun.com/data/standard-deviation.html
pub fn sample_variance_mean<T>(data []T, mean T) T {
@ -171,8 +167,7 @@ pub fn sample_variance_mean<T>(data []T, mean T) T {
return sum / T(data.len - 1)
}
// Measure of Dispersion / Spread
// Population Standard Deviation of the given input array
// population_stddev calculates how spread out the dataset is
// Based on
// https://www.mathsisfun.com/data/standard-deviation.html
[inline]
@ -183,8 +178,7 @@ pub fn population_stddev<T>(data []T) T {
return math.sqrt(population_variance<T>(data))
}
// Measure of Dispersion / Spread
// Population Standard Deviation of the given input array
// population_stddev_mean calculates how spread out the dataset is, with the provide mean
// Based on
// https://www.mathsisfun.com/data/standard-deviation.html
[inline]
@ -219,8 +213,7 @@ pub fn sample_stddev_mean<T>(data []T, mean T) T {
return T(math.sqrt(f64(sample_variance_mean<T>(data, mean))))
}
// Measure of Dispersion / Spread
// Mean Absolute Deviation of the given input array
// absdev calculates the average distance between each data point and the mean
// Based on
// https://en.wikipedia.org/wiki/Average_absolute_deviation
[inline]
@ -232,8 +225,7 @@ pub fn absdev<T>(data []T) T {
return absdev_mean<T>(data, data_mean)
}
// Measure of Dispersion / Spread
// Mean Absolute Deviation of the given input array
// absdev_mean calculates the average distance between each data point and the provided mean
// Based on
// https://en.wikipedia.org/wiki/Average_absolute_deviation
pub fn absdev_mean<T>(data []T, mean T) T {
@ -247,7 +239,7 @@ pub fn absdev_mean<T>(data []T, mean T) T {
return sum / T(data.len)
}
// Sum of squares
// tts, Sum of squares, calculates the sum over all squared differences between values and overall mean
[inline]
pub fn tss<T>(data []T) T {
if data.len == 0 {
@ -257,7 +249,7 @@ pub fn tss<T>(data []T) T {
return tss_mean<T>(data, data_mean)
}
// Sum of squares about the mean
// tts_mean, Sum of squares, calculates the sum over all squared differences between values and the provided mean
pub fn tss_mean<T>(data []T, mean T) T {
if data.len == 0 {
return T(0)
@ -269,7 +261,7 @@ pub fn tss_mean<T>(data []T, mean T) T {
return tss
}
// Minimum of the given input array
// min finds the minimum value from the dataset
pub fn min<T>(data []T) T {
if data.len == 0 {
return T(0)
@ -283,7 +275,7 @@ pub fn min<T>(data []T) T {
return min
}
// Maximum of the given input array
// max finds the maximum value from the dataset
pub fn max<T>(data []T) T {
if data.len == 0 {
return T(0)
@ -297,7 +289,7 @@ pub fn max<T>(data []T) T {
return max
}
// Minimum and maximum of the given input array
// minmax finds the minimum and maximum value from the dataset
pub fn minmax<T>(data []T) (T, T) {
if data.len == 0 {
return T(0), T(0)
@ -315,7 +307,7 @@ pub fn minmax<T>(data []T) (T, T) {
return min, max
}
// Minimum of the given input array
// min_index finds the first index of the minimum value
pub fn min_index<T>(data []T) int {
if data.len == 0 {
return 0
@ -331,7 +323,7 @@ pub fn min_index<T>(data []T) int {
return min_index
}
// Maximum of the given input array
// max_index finds the first index of the maximum value
pub fn max_index<T>(data []T) int {
if data.len == 0 {
return 0
@ -347,7 +339,7 @@ pub fn max_index<T>(data []T) int {
return max_index
}
// Minimum and maximum of the given input array
// minmax_index finds the first index of the minimum and maximum value
pub fn minmax_index<T>(data []T) (int, int) {
if data.len == 0 {
return 0, 0
@ -369,7 +361,7 @@ pub fn minmax_index<T>(data []T) (int, int) {
return min_index, max_index
}
// Measure of Dispersion / Spread
// range calculates the difference between the min and max
// Range ( Maximum - Minimum ) of the given input array
// Based on
// https://www.mathsisfun.com/data/range.html
@ -381,6 +373,8 @@ pub fn range<T>(data []T) T {
return max - min
}
// covariance calculates directional association between datasets
// positive value denotes variables move in same direction and negative denotes variables move in opposite directions
[inline]
pub fn covariance<T>(data1 []T, data2 []T) T {
mean1 := mean<T>(data1)
@ -388,7 +382,7 @@ pub fn covariance<T>(data1 []T, data2 []T) T {
return covariance_mean<T>(data1, data2, mean1, mean2)
}
// Compute the covariance of a dataset using
// covariance_mean computes the covariance of a dataset with means provided
// the recurrence relation
pub fn covariance_mean<T>(data1 []T, data2 []T, mean1 T, mean2 T) T {
n := int(math.min(data1.len, data2.len))
@ -404,13 +398,16 @@ pub fn covariance_mean<T>(data1 []T, data2 []T, mean1 T, mean2 T) T {
return covariance
}
// lag1_autocorrelation_mean calculates the correlation between values that are one time period apart
// of a dataset, based on the mean
[inline]
pub fn lag1_autocorrelation<T>(data []T) T {
data_mean := mean<T>(data)
return lag1_autocorrelation_mean<T>(data, data_mean)
}
// Compute the lag-1 autocorrelation of a dataset using
// lag1_autocorrelation_mean calculates the correlation between values that are one time period apart
// of a dataset, using
// the recurrence relation
pub fn lag1_autocorrelation_mean<T>(data []T, mean T) T {
if data.len == 0 {
@ -427,6 +424,7 @@ pub fn lag1_autocorrelation_mean<T>(data []T, mean T) T {
return q / v
}
// kurtosis calculates the measure of the 'tailedness' of the data by finding mean and standard of deviation
[inline]
pub fn kurtosis<T>(data []T) T {
data_mean := mean<T>(data)
@ -434,7 +432,7 @@ pub fn kurtosis<T>(data []T) T {
return kurtosis_mean_stddev<T>(data, data_mean, sd)
}
// Takes a dataset and finds the kurtosis
// kurtosis_mean_stddev calculates the measure of the 'tailedness' of the data
// using the fourth moment the deviations, normalized by the sd
pub fn kurtosis_mean_stddev<T>(data []T, mean T, sd T) T {
mut avg := T(0) // find the fourth moment the deviations, normalized by the sd
@ -449,6 +447,7 @@ pub fn kurtosis_mean_stddev<T>(data []T, mean T, sd T) T {
return avg - T(3.0)
}
// skew calculates the mean and standard of deviation to find the skew from the data
[inline]
pub fn skew<T>(data []T) T {
data_mean := mean<T>(data)
@ -456,6 +455,7 @@ pub fn skew<T>(data []T) T {
return skew_mean_stddev<T>(data, data_mean, sd)
}
// skew_mean_stddev calculates the skewness of data
pub fn skew_mean_stddev<T>(data []T, mean T, sd T) T {
mut skew := T(0) // find the sum of the cubed deviations, normalized by the sd.
/*