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< 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) } mean := mean(arr) mut sum := f64(0) for v in arr { sum += math.abs(v-mean) } 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) }