// Copyright ©2017 The Gonum Authors. All rights reserved. // Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. package mat_test import ( "fmt" "log" "math" "gonum.org/v1/gonum/mat" ) func ExampleGSVD() { // Perform a GSVD factorization on food production/consumption data for the // three years 1990, 2000 and 2014, for Africa and Latin America/Caribbean. // // See Lee et al. doi:10.1371/journal.pone.0030098 and // Alter at al. doi:10.1073/pnas.0530258100 for more details. var gsvd mat.GSVD ok := gsvd.Factorize(FAO.Africa, FAO.LatinAmericaCaribbean, mat.GSVDU|mat.GSVDV|mat.GSVDQ) if !ok { log.Fatal("GSVD factorization failed") } var u, v mat.Dense gsvd.UTo(&u) gsvd.VTo(&v) s1 := gsvd.ValuesA(nil) s2 := gsvd.ValuesB(nil) fmt.Printf("Africa\n\ts1 = %.4f\n\n\tU = %.4f\n\n", s1, mat.Formatted(&u, mat.Prefix("\t "), mat.Excerpt(2))) fmt.Printf("Latin America/Caribbean\n\ts2 = %.4f\n\n\tV = %.4f\n", s2, mat.Formatted(&v, mat.Prefix("\t "), mat.Excerpt(2))) var q, zR mat.Dense gsvd.QTo(&q) gsvd.ZeroRTo(&zR) q.Mul(&zR, &q) fmt.Printf("\nCommon basis vectors\n\n\tQᵀ = %.4f\n", mat.Formatted(q.T(), mat.Prefix("\t "))) // Calculate the antisymmetric angular distances for each eigenvariable. fmt.Println("\nSignificance:") for i := 0; i < 3; i++ { fmt.Printf("\teigenvar_%d: %+.4f\n", i, math.Atan(s1[i]/s2[i])-math.Pi/4) } // Output: // // Africa // s1 = [1.0000 0.9344 0.5118] // // U = Dims(21, 21) // ⎡-0.0005 0.0142 ... ... -0.0060 -0.0055⎤ // ⎢-0.0010 0.0019 0.0071 0.0075⎥ // . // . // . // ⎢-0.0007 -0.0024 0.9999 -0.0001⎥ // ⎣-0.0010 -0.0016 ... ... -0.0001 0.9999⎦ // // Latin America/Caribbean // s2 = [0.0047 0.3563 0.8591] // // V = Dims(14, 14) // ⎡ 0.1362 0.0008 ... ... 0.0700 0.2636⎤ // ⎢ 0.1830 -0.0040 0.2908 0.7834⎥ // . // . // . // ⎢-0.2598 -0.0324 0.9339 -0.2170⎥ // ⎣-0.8386 0.1494 ... ... -0.1639 0.4121⎦ // // Common basis vectors // // Qᵀ = ⎡ 14508.5881 4524.2933 -4813.9616⎤ // ⎢ 15562.9323 12397.1070 -16364.8933⎥ // ⎣-14262.7217 -10902.1488 15762.8719⎦ // // Significance: // eigenvar_0: +0.7807 // eigenvar_1: +0.4211 // eigenvar_2: -0.2482 }