mirror of
https://github.com/vlang/v.git
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261 lines
9.7 KiB
Go
261 lines
9.7 KiB
Go
import math.stats as stats
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fn test_freq() {
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// Tests were also verified on Wolfram Alpha
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data := [f64(10.0),f64(10.0),f64(5.9),f64(2.7)]
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mut o := stats.freq(data,10.0)
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assert o == 2
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o = stats.freq(data,2.7)
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assert o == 1
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o = stats.freq(data,15)
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assert o == 0
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}
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fn test_mean() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.mean(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('5.762500')
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.mean(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('17.650000')
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.mean(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('37.708000')
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}
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fn test_geometric_mean() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.geometric_mean(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('5.159932')
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.geometric_mean(data)
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println(o)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('nan') || o.str().eq('-nan') || o.str().eq('-1.#IND00') || o == f64(0) || o.str().eq('-nan(ind)') // Because in math it yields a complex number
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.geometric_mean(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('25.064496')
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}
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fn test_harmonic_mean() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.harmonic_mean(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('4.626519')
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.harmonic_mean(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('9.134577')
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.harmonic_mean(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('16.555477')
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}
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fn test_median() {
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// Tests were also verified on Wolfram Alpha
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// Assumes sorted array
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// Even
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mut data := [f64(2.7),f64(4.45),f64(5.9),f64(10.0)]
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mut o := stats.median(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('5.175000')
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data = [f64(-3.0),f64(1.89),f64(4.4),f64(67.31)]
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o = stats.median(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('3.145000')
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data = [f64(7.88),f64(12.0),f64(54.83),f64(76.122)]
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o = stats.median(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('33.415000')
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// Odd
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data = [f64(2.7),f64(4.45),f64(5.9),f64(10.0),f64(22)]
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o = stats.median(data)
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assert o == f64(5.9)
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data = [f64(-3.0),f64(1.89),f64(4.4),f64(9),f64(67.31)]
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o = stats.median(data)
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assert o == f64(4.4)
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data = [f64(7.88),f64(3.3),f64(12.0),f64(54.83),f64(76.122)]
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o = stats.median(data)
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assert o == f64(12.0)
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}
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fn test_mode() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(2.7),f64(2.7),f64(4.45),f64(5.9),f64(10.0)]
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mut o := stats.mode(data)
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assert o == f64(2.7)
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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)]
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o = stats.mode(data)
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assert o == f64(1.89)
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// Testing greedy nature
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data = [f64(2.0),f64(4.0),f64(2.0),f64(4.0)]
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o = stats.mode(data)
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assert o == f64(2.0)
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}
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fn test_rms() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.rms(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('6.362046')
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.rms(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('33.773393')
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.rms(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('47.452561')
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}
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fn test_population_variance() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.population_variance(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('7.269219')
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.population_variance(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('829.119550')
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.population_variance(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('829.852282')
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}
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fn test_sample_variance() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.sample_variance(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('9.692292')
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.sample_variance(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('1105.492733')
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.sample_variance(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('1106.469709')
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}
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fn test_population_stddev() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.population_stddev(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('2.696149')
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.population_stddev(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('28.794436')
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.population_stddev(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('28.807157')
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}
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fn test_sample_stddev() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.sample_stddev(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('3.113245')
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.sample_stddev(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('33.248951')
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.sample_stddev(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('33.263639')
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}
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fn test_mean_absdev() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.mean_absdev(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('2.187500')
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.mean_absdev(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('24.830000')
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.mean_absdev(data)
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// Some issue with precision comparison in f64 using == operator hence serializing to string
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assert o.str().eq('27.768000')
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}
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fn test_min() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.min(data)
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assert o == f64(2.7)
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.min(data)
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assert o == f64(-3.0)
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.min(data)
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assert o == f64(7.88)
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}
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fn test_max() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.max(data)
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assert o == f64(10.0)
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.max(data)
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assert o == f64(67.31)
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.max(data)
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assert o == f64(76.122)
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}
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fn test_range() {
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// Tests were also verified on Wolfram Alpha
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mut data := [f64(10.0),f64(4.45),f64(5.9),f64(2.7)]
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mut o := stats.range(data)
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assert o == f64(7.3)
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data = [f64(-3.0),f64(67.31),f64(4.4),f64(1.89)]
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o = stats.range(data)
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assert o == f64(70.31)
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data = [f64(12.0),f64(7.88),f64(76.122),f64(54.83)]
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o = stats.range(data)
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assert o == f64(68.242)
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}
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fn test_passing_empty() {
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data := []f64
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assert stats.freq(data,0) == 0
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assert stats.mean(data) == f64(0)
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assert stats.geometric_mean(data) == f64(0)
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assert stats.harmonic_mean(data) == f64(0)
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assert stats.median(data) == f64(0)
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assert stats.mode(data) == f64(0)
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assert stats.rms(data) == f64(0)
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assert stats.population_variance(data) == f64(0)
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assert stats.sample_variance(data) == f64(0)
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assert stats.population_stddev(data) == f64(0)
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assert stats.sample_stddev(data) == f64(0)
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assert stats.mean_absdev(data) == f64(0)
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assert stats.min(data) == f64(0)
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assert stats.max(data) == f64(0)
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assert stats.range(data) == f64(0)
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}
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