Adds most of the toolsHEADmaster

2 files changed, 518 insertions, 0 deletions

diff --git a/v_windows/v/vlib/math/stats/stats.v b/v_windows/v/vlib/math/stats/stats.v
new file mode 100644
index 0000000..d7317bf
--- /dev/null
+++ b/v_windows/v/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)
+}
diff --git a/v_windows/v/vlib/math/stats/stats_test.v b/v_windows/v/vlib/math/stats/stats_test.v
new file mode 100644
index 0000000..c18daff
--- /dev/null
+++ b/v_windows/v/vlib/math/stats/stats_test.v
@@ -0,0 +1,269 @@
+import math.stats
+import math
+
+fn test_freq() {
+	// Tests were also verified on Wolfram Alpha
+	data := [f64(10.0), f64(10.0), f64(5.9), f64(2.7)]
+	mut o := stats.freq(data, 10.0)
+	assert o == 2
+	o = stats.freq(data, 2.7)
+	assert o == 1
+	o = stats.freq(data, 15)
+	assert o == 0
+}
+
+fn tst_res(str1 string, str2 string) bool {
+	if (math.abs(str1.f64() - str2.f64())) < 1e-5 {
+		return true
+	}
+	return false
+}
+
+fn test_mean() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.mean(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '5.762500')
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.mean(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '17.650000')
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.mean(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '37.708000')
+}
+
+fn test_geometric_mean() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.geometric_mean(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '5.15993')
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.geometric_mean(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert o.str() == 'nan' || o.str() == '-nan' || o.str() == '-1.#IND00' || o == f64(0)
+		|| o.str() == '-nan(ind)' // Because in math it yields a complex number
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.geometric_mean(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '25.064496')
+}
+
+fn test_harmonic_mean() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.harmonic_mean(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '4.626519')
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.harmonic_mean(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '9.134577')
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.harmonic_mean(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '16.555477')
+}
+
+fn test_median() {
+	// Tests were also verified on Wolfram Alpha
+	// Assumes sorted array
+
+	// Even
+	mut data := [f64(2.7), f64(4.45), f64(5.9), f64(10.0)]
+	mut o := stats.median(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '5.175000')
+	data = [f64(-3.0), f64(1.89), f64(4.4), f64(67.31)]
+	o = stats.median(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '3.145000')
+	data = [f64(7.88), f64(12.0), f64(54.83), f64(76.122)]
+	o = stats.median(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '33.415000')
+
+	// Odd
+	data = [f64(2.7), f64(4.45), f64(5.9), f64(10.0), f64(22)]
+	o = stats.median(data)
+	assert o == f64(5.9)
+	data = [f64(-3.0), f64(1.89), f64(4.4), f64(9), f64(67.31)]
+	o = stats.median(data)
+	assert o == f64(4.4)
+	data = [f64(7.88), f64(3.3), f64(12.0), f64(54.83), f64(76.122)]
+	o = stats.median(data)
+	assert o == f64(12.0)
+}
+
+fn test_mode() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(2.7), f64(2.7), f64(4.45), f64(5.9), f64(10.0)]
+	mut o := stats.mode(data)
+	assert o == f64(2.7)
+	data = [f64(-3.0), f64(1.89), f64(1.89), f64(1.89), f64(9), f64(4.4), f64(4.4), f64(9),
+		f64(67.31),
+	]
+	o = stats.mode(data)
+	assert o == f64(1.89)
+	// Testing greedy nature
+	data = [f64(2.0), f64(4.0), f64(2.0), f64(4.0)]
+	o = stats.mode(data)
+	assert o == f64(2.0)
+}
+
+fn test_rms() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.rms(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '6.362046')
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.rms(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '33.773393')
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.rms(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '47.452561')
+}
+
+fn test_population_variance() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.population_variance(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '7.269219')
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.population_variance(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '829.119550')
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.population_variance(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '829.852282')
+}
+
+fn test_sample_variance() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.sample_variance(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '9.692292')
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.sample_variance(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '1105.492733')
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.sample_variance(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '1106.469709')
+}
+
+fn test_population_stddev() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.population_stddev(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '2.696149')
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.population_stddev(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '28.794436')
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.population_stddev(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '28.807157')
+}
+
+fn test_sample_stddev() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.sample_stddev(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '3.113245')
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.sample_stddev(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '33.248951')
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.sample_stddev(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '33.263639')
+}
+
+fn test_mean_absdev() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.mean_absdev(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '2.187500')
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.mean_absdev(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '24.830000')
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.mean_absdev(data)
+	// Some issue with precision comparison in f64 using == operator hence serializing to string
+	assert tst_res(o.str(), '27.768000')
+}
+
+fn test_min() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.min(data)
+	assert o == f64(2.7)
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.min(data)
+	assert o == f64(-3.0)
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.min(data)
+	assert o == f64(7.88)
+}
+
+fn test_max() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.max(data)
+	assert o == f64(10.0)
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.max(data)
+	assert o == f64(67.31)
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.max(data)
+	assert o == f64(76.122)
+}
+
+fn test_range() {
+	// Tests were also verified on Wolfram Alpha
+	mut data := [f64(10.0), f64(4.45), f64(5.9), f64(2.7)]
+	mut o := stats.range(data)
+	assert o == f64(7.3)
+	data = [f64(-3.0), f64(67.31), f64(4.4), f64(1.89)]
+	o = stats.range(data)
+	assert o == f64(70.31)
+	data = [f64(12.0), f64(7.88), f64(76.122), f64(54.83)]
+	o = stats.range(data)
+	assert o == f64(68.242)
+}
+
+fn test_passing_empty() {
+	data := []f64{}
+	assert stats.freq(data, 0) == 0
+	assert stats.mean(data) == f64(0)
+	assert stats.geometric_mean(data) == f64(0)
+	assert stats.harmonic_mean(data) == f64(0)
+	assert stats.median(data) == f64(0)
+	assert stats.mode(data) == f64(0)
+	assert stats.rms(data) == f64(0)
+	assert stats.population_variance(data) == f64(0)
+	assert stats.sample_variance(data) == f64(0)
+	assert stats.population_stddev(data) == f64(0)
+	assert stats.sample_stddev(data) == f64(0)
+	assert stats.mean_absdev(data) == f64(0)
+	assert stats.min(data) == f64(0)
+	assert stats.max(data) == f64(0)
+	assert stats.range(data) == f64(0)
+}