v/vlib/math/stats_test.v

import math.stats as stats

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 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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('5.159932')
	data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
	o = stats.geometric_mean(data)
	println(o)
	// Some issue with precision comparison in f64 using == operator hence serializing to string
	assert o.str().eq('nan') || o.str().eq('-nan') || o.str().eq('-1.#IND00') || o == f64(0) // 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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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 o.str().eq('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)
}