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Diffstat (limited to 'v_windows/v/old/vlib/math/stats/stats.v')
| -rw-r--r-- | v_windows/v/old/vlib/math/stats/stats.v | 249 |
1 files changed, 249 insertions, 0 deletions
diff --git a/v_windows/v/old/vlib/math/stats/stats.v b/v_windows/v/old/vlib/math/stats/stats.v new file mode 100644 index 0000000..d7317bf --- /dev/null +++ b/v_windows/v/old/vlib/math/stats/stats.v @@ -0,0 +1,249 @@ +module stats + +import math + +// TODO: Implement all of them with generics + +// This module defines the following statistical operations on f64 array +// --------------------------- +// | Summary of Functions | +// --------------------------- +// ----------------------------------------------------------------------- +// freq - Frequency +// mean - Mean +// geometric_mean - Geometric Mean +// harmonic_mean - Harmonic Mean +// median - Median +// mode - Mode +// rms - Root Mean Square +// population_variance - Population Variance +// sample_variance - Sample Variance +// population_stddev - Population Standard Deviation +// sample_stddev - Sample Standard Deviation +// mean_absdev - Mean Absolute Deviation +// min - Minimum of the Array +// max - Maximum of the Array +// range - Range of the Array ( max - min ) +// ----------------------------------------------------------------------- + +// Measure of Occurance +// Frequency of a given number +// Based on +// https://www.mathsisfun.com/data/frequency-distribution.html +pub fn freq(arr []f64, val f64) int { + if arr.len == 0 { + return 0 + } + mut count := 0 + for v in arr { + if v == val { + count++ + } + } + return count +} + +// Measure of Central Tendancy +// Mean of the given input array +// Based on +// https://www.mathsisfun.com/data/central-measures.html +pub fn mean(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + mut sum := f64(0) + for v in arr { + sum += v + } + return sum / f64(arr.len) +} + +// Measure of Central Tendancy +// Geometric Mean of the given input array +// Based on +// https://www.mathsisfun.com/numbers/geometric-mean.html +pub fn geometric_mean(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + mut sum := f64(1) + for v in arr { + sum *= v + } + return math.pow(sum, f64(1) / arr.len) +} + +// Measure of Central Tendancy +// Harmonic Mean of the given input array +// Based on +// https://www.mathsisfun.com/numbers/harmonic-mean.html +pub fn harmonic_mean(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + mut sum := f64(0) + for v in arr { + sum += f64(1) / v + } + return f64(arr.len) / sum +} + +// Measure of Central Tendancy +// Median 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(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + if arr.len % 2 == 0 { + mid := (arr.len / 2) - 1 + return (arr[mid] + arr[mid + 1]) / f64(2) + } else { + return arr[((arr.len - 1) / 2)] + } +} + +// Measure of Central Tendancy +// Mode of the given input array +// Based on +// https://www.mathsisfun.com/data/central-measures.html +pub fn mode(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + mut freqs := []int{} + for v in arr { + freqs << freq(arr, v) + } + mut max := 0 + for i in 0 .. freqs.len { + if freqs[i] > freqs[max] { + max = i + } + } + return arr[max] +} + +// Root Mean Square of the given input array +// Based on +// https://en.wikipedia.org/wiki/Root_mean_square +pub fn rms(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + mut sum := f64(0) + for v in arr { + sum += math.pow(v, 2) + } + return math.sqrt(sum / f64(arr.len)) +} + +// Measure of Dispersion / Spread +// Population Variance of the given input array +// Based on +// https://www.mathsisfun.com/data/standard-deviation.html +pub fn population_variance(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + m := mean(arr) + mut sum := f64(0) + for v in arr { + sum += math.pow(v - m, 2) + } + return sum / f64(arr.len) +} + +// Measure of Dispersion / Spread +// Sample Variance of the given input array +// Based on +// https://www.mathsisfun.com/data/standard-deviation.html +pub fn sample_variance(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + m := mean(arr) + mut sum := f64(0) + for v in arr { + sum += math.pow(v - m, 2) + } + return sum / f64(arr.len - 1) +} + +// Measure of Dispersion / Spread +// Population Standard Deviation of the given input array +// Based on +// https://www.mathsisfun.com/data/standard-deviation.html +pub fn population_stddev(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + return math.sqrt(population_variance(arr)) +} + +// Measure of Dispersion / Spread +// Sample Standard Deviation of the given input array +// Based on +// https://www.mathsisfun.com/data/standard-deviation.html +pub fn sample_stddev(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + return math.sqrt(sample_variance(arr)) +} + +// Measure of Dispersion / Spread +// Mean Absolute Deviation of the given input array +// Based on +// https://en.wikipedia.org/wiki/Average_absolute_deviation +pub fn mean_absdev(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + amean := mean(arr) + mut sum := f64(0) + for v in arr { + sum += math.abs(v - amean) + } + return sum / f64(arr.len) +} + +// Minimum of the given input array +pub fn min(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + mut min := arr[0] + for v in arr { + if v < min { + min = v + } + } + return min +} + +// Maximum of the given input array +pub fn max(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + mut max := arr[0] + for v in arr { + if v > max { + max = v + } + } + return max +} + +// Measure of Dispersion / Spread +// Range ( Maximum - Minimum ) of the given input array +// Based on +// https://www.mathsisfun.com/data/range.html +pub fn range(arr []f64) f64 { + if arr.len == 0 { + return f64(0) + } + return max(arr) - min(arr) +} |