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192 lines
7.3 KiB
Go
192 lines
7.3 KiB
Go
// Copyright ©2016 The Gonum Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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package stat_test
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import (
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"testing"
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"gonum.org/v1/gonum/floats"
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"gonum.org/v1/gonum/mat"
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"gonum.org/v1/gonum/stat"
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)
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func TestCanonicalCorrelations(t *testing.T) {
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tests:
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for i, test := range []struct {
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xdata mat.Matrix
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ydata mat.Matrix
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weights []float64
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wantCorrs []float64
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wantpVecs *mat.Dense
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wantqVecs *mat.Dense
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wantphiVs *mat.Dense
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wantpsiVs *mat.Dense
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epsilon float64
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}{
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// Test results verified using R.
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{ // Truncated iris data, Sepal vs Petal measurements.
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xdata: mat.NewDense(10, 2, []float64{
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5.1, 3.5,
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4.9, 3.0,
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4.7, 3.2,
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4.6, 3.1,
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5.0, 3.6,
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5.4, 3.9,
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4.6, 3.4,
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5.0, 3.4,
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4.4, 2.9,
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4.9, 3.1,
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}),
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ydata: mat.NewDense(10, 2, []float64{
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1.4, 0.2,
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1.4, 0.2,
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1.3, 0.2,
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1.5, 0.2,
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1.4, 0.2,
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1.7, 0.4,
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1.4, 0.3,
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1.5, 0.2,
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1.4, 0.2,
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1.5, 0.1,
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}),
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wantCorrs: []float64{0.7250624174504773, 0.5547679185730191},
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wantpVecs: mat.NewDense(2, 2, []float64{
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0.0765914610875867, 0.9970625597666721,
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0.9970625597666721, -0.0765914610875868,
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}),
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wantqVecs: mat.NewDense(2, 2, []float64{
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0.3075184850910837, 0.9515421069649439,
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0.9515421069649439, -0.3075184850910837,
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}),
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wantphiVs: mat.NewDense(2, 2, []float64{
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-1.9794877596804641, 5.2016325219025124,
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4.5211829944066553, -2.7263663170835697,
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}),
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wantpsiVs: mat.NewDense(2, 2, []float64{
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-0.0613084818030103, 10.8514169865438941,
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12.7209032660734298, -7.6793888180353775,
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}),
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epsilon: 1e-12,
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},
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// Test results compared to those results presented in examples by
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// Koch, Inge. Analysis of multivariate and high-dimensional data.
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// Vol. 32. Cambridge University Press, 2013. ISBN: 9780521887939
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{ // ASA Car Exposition Data of Ramos and Donoho (1983)
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// Displacement, Horsepower, Weight
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xdata: carData.Slice(0, 392, 0, 3),
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// Acceleration, MPG
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ydata: carData.Slice(0, 392, 3, 5),
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wantCorrs: []float64{0.8782187384352336, 0.6328187219216761},
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wantpVecs: mat.NewDense(3, 2, []float64{
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0.3218296374829181, 0.3947540257657075,
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0.4162807660635797, 0.7573719053303306,
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0.8503740401982725, -0.5201509936144236,
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}),
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wantqVecs: mat.NewDense(2, 2, []float64{
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-0.5161984172278830, -0.8564690269072364,
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-0.8564690269072364, 0.5161984172278830,
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}),
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wantphiVs: mat.NewDense(3, 2, []float64{
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0.0025033152994308, 0.0047795464118615,
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0.0201923608080173, 0.0409150208725958,
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-0.0000247374128745, -0.0026766435161875,
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}),
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wantpsiVs: mat.NewDense(2, 2, []float64{
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-0.1666196759760772, -0.3637393866139658,
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-0.0915512109649727, 0.1077863777929168,
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}),
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epsilon: 1e-12,
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},
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// Test results compared to those results presented in examples by
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// Koch, Inge. Analysis of multivariate and high-dimensional data.
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// Vol. 32. Cambridge University Press, 2013. ISBN: 9780521887939
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{ // Boston Housing Data of Harrison and Rubinfeld (1978)
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// Per capita crime rate by town,
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// Proportion of non-retail business acres per town,
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// Nitric oxide concentration (parts per 10 million),
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// Weighted distances to Boston employment centres,
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// Index of accessibility to radial highways,
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// Pupil-teacher ratio by town, Proportion of blacks by town
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xdata: bostonData.Slice(0, 506, 0, 7),
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// Average number of rooms per dwelling,
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// Proportion of owner-occupied units built prior to 1940,
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// Full-value property-tax rate per $10000,
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// Median value of owner-occupied homes in $1000s
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ydata: bostonData.Slice(0, 506, 7, 11),
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wantCorrs: []float64{0.9451239443886021, 0.6786622733370654, 0.5714338361583764, 0.2009739704710440},
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wantpVecs: mat.NewDense(7, 4, []float64{
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-0.2574391924541896, -0.015847751662118038, -0.21221699346310258, -0.09457338038947205,
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-0.48365944300184865, -0.3837101908138455, -0.14744483174159395, 0.6597324886718278,
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-0.08007763658732961, -0.34935567428092285, -0.3287336458109394, -0.2862040444334662,
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0.127758636038638, 0.7337427663667616, -0.4851134819036985, 0.22479648659701942,
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-0.6969432006136685, 0.43417487760028844, 0.360287288763638, 0.029066160862628414,
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-0.0990903250057202, -0.05034112154538474, -0.6384330631742202, 0.10223671362182897,
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0.42604599637650303, -0.032333435130815824, 0.22895275160308087, 0.6419232947608798,
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}),
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wantqVecs: mat.NewDense(4, 4, []float64{
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0.018166050236326788, 0.1583489460479047, 0.006672357764289544, -0.9871935400650647,
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-0.23476990459861324, -0.9483314614936598, 0.14624205056313114, -0.1554470767919039,
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-0.9700704038477144, 0.24060717410000537, 0.025183898422704167, 0.020913407435834964,
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0.05930006823184807, 0.13304600030976868, 0.9889057151969495, 0.029116149472076858,
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}),
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wantphiVs: mat.NewDense(7, 4, []float64{
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-0.002746223410819314, -0.009344451350088911, -0.04896439327142919, -0.015496718980582016,
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-0.042856445527953785, 0.024170870211944927, -0.036072347209397136, 0.18389832305881182,
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-1.2248435648802678, -5.603092136472504, -5.809414458379886, -4.792681219042103,
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-0.00436848250946508, 0.34241011649776265, -0.4469961215717922, 0.11501618143536857,
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-0.07415340695219563, 0.11931357949236807, 0.1115518305471455, 0.002163875832307984,
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-0.023327032310162924, -0.1046330818178401, -0.38530459750774165, -0.016092787010290065,
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0.00012930513878583387, -0.0004540746921447011, 0.0030296315865439264, 0.008189547797465318,
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}),
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wantpsiVs: mat.NewDense(4, 4, []float64{
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0.030159336201738367, 0.3002219289647159, -0.08782173775936601, -1.9583226531517122,
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-0.00654831040738931, -0.03922120867162458, 0.011757077620998818, -0.006111306448187141,
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-0.0052075523350125505, 0.004577020045295936, 0.0022762313289591976, 0.0008441873006823151,
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0.0020111735096325924, -0.0037352799829939247, 0.12925780716217938, 0.10377090563297825,
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}),
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epsilon: 1e-12,
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},
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} {
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var cc stat.CC
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var corrs []float64
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var pVecs, qVecs mat.Dense
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var phiVs, psiVs mat.Dense
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for j := 0; j < 2; j++ {
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err := cc.CanonicalCorrelations(test.xdata, test.ydata, test.weights)
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if err != nil {
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t.Errorf("%d use %d: unexpected error: %v", i, j, err)
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continue tests
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}
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corrs = cc.CorrsTo(corrs)
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cc.LeftTo(&pVecs, true)
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cc.RightTo(&qVecs, true)
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cc.LeftTo(&phiVs, false)
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cc.RightTo(&psiVs, false)
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if !floats.EqualApprox(corrs, test.wantCorrs, test.epsilon) {
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t.Errorf("%d use %d: unexpected variance result got:%v, want:%v",
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i, j, corrs, test.wantCorrs)
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}
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if !mat.EqualApprox(&pVecs, test.wantpVecs, test.epsilon) {
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t.Errorf("%d use %d: unexpected CCA result got:\n%v\nwant:\n%v",
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i, j, mat.Formatted(&pVecs), mat.Formatted(test.wantpVecs))
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}
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if !mat.EqualApprox(&qVecs, test.wantqVecs, test.epsilon) {
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t.Errorf("%d use %d: unexpected CCA result got:\n%v\nwant:\n%v",
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i, j, mat.Formatted(&qVecs), mat.Formatted(test.wantqVecs))
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}
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if !mat.EqualApprox(&phiVs, test.wantphiVs, test.epsilon) {
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t.Errorf("%d use %d: unexpected CCA result got:\n%v\nwant:\n%v",
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i, j, mat.Formatted(&phiVs), mat.Formatted(test.wantphiVs))
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}
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if !mat.EqualApprox(&psiVs, test.wantpsiVs, test.epsilon) {
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t.Errorf("%d use %d: unexpected CCA result got:\n%v\nwant:\n%v",
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i, j, mat.Formatted(&psiVs), mat.Formatted(test.wantpsiVs))
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}
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}
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}
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}
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