math/stats: added basic stats operations

vlib/math/stats/stats.v

@@ -0,0 +1,251 @@
+module stats
+
+import math
+
+// 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 i := 0
+	mut max := 0
+	for i < freqs.len {
+		if freqs[i] > freqs[max] {
+			max = i
+		}
+		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)
+}