private CovarianceProduct(CovarianceProduct original, Cloner cloner)
: base(original, cloner)
{
this.factors = cloner.Clone(original.factors);
this.numberOfVariables = original.numberOfVariables;
}
private ICovarianceFunction TreeToCovarianceFunction(ISymbolicExpressionTreeNode node)
{
switch (node.Symbol.Name)
{
case "Sum": {
var sum = new CovarianceSum();
sum.Terms.Add(TreeToCovarianceFunction(node.GetSubtree(0)));
sum.Terms.Add(TreeToCovarianceFunction(node.GetSubtree(1)));
return(sum);
}
case "Product": {
var prod = new CovarianceProduct();
prod.Factors.Add(TreeToCovarianceFunction(node.GetSubtree(0)));
prod.Factors.Add(TreeToCovarianceFunction(node.GetSubtree(1)));
return(prod);
}
// covFunction is cloned by the model so we can reuse instances of terminal covariance functions
case "Linear": return(linear);
case "LinearArd": return(linearArd);
case "MaternIso1": return(maternIso1);
case "MaternIso3": return(maternIso3);
case "MaternIso5": return(maternIso5);
case "NeuralNetwork": return(neuralNetwork);
case "Periodic": return(periodic);
case "PiecewisePolynomial0": return(piecewisePoly0);
case "PiecewisePolynomial1": return(piecewisePoly1);
case "PiecewisePolynomial2": return(piecewisePoly2);
case "PiecewisePolynomial3": return(piecewisePoly3);
case "Polynomial2": return(poly2);
case "Polynomial3": return(poly3);
case "RationalQuadraticArd": return(ratQuadraticArd);
case "RationalQuadraticIso": return(ratQuadraticIso);
case "SpectralMixture1": return(spectralMixture1);
case "SpectralMixture3": return(spectralMixture3);
case "SpectralMixture5": return(spectralMixture5);
case "SquaredExponentialArd": return(sqrExpArd);
case "SquaredExponentialIso": return(sqrExpIso);
default: throw new InvalidProgramException(string.Format("Found invalid symbol {0}", node.Symbol.Name));
}
}
public void CovProdTest() {
var cov = new CovarianceProduct();
cov.Factors.Add(new CovarianceSquaredExponentialIso());
cov.Factors.Add(new CovarianceLinear());
TestCovarianceFunction(cov, 0,
new double[,]
{
{ 0.5245, 0.5389, 1.6120, 0.4836, 1.5106, 0.7554, 0.8386, 0.8239, 0.6239, 1.1336},
{ 0.2889, 0.5225, 0.8337, 0.2496, 1.0595, 0.5710, 0.5052, 0.5547, 0.3724, 0.6822},
{ 1.0623, 1.1239, 1.7263, 0.8459, 1.0618, 1.6839, 1.3988, 0.7607, 0.9521, 1.3106},
{ 0.6585, 0.3625, 1.1117, 0.5349, 0.5137, 0.7756, 1.0648, 0.7942, 0.7510, 0.8516},
{ 0.7040, 0.3275, 0.9490, 0.7125, 0.3548, 0.4404, 0.7037, 0.7073, 0.6680, 0.7798},
{ 0.4641, 0.3741, 0.6234, 0.3844, 0.3673, 0.5306, 0.6413, 0.5193, 0.5036, 0.5608},
{ 0.5385, 0.9383, 1.1840, 0.4565, 1.7927, 0.8766, 0.7518, 0.5198, 0.5765, 1.0608},
{ 0.8694, 1.0060, 1.8506, 0.7108, 1.7052, 1.3654, 1.2234, 0.6698, 0.8427, 1.4060},
{ 0.6989, 0.9246, 1.7118, 0.6277, 1.6927, 1.0667, 0.9339, 0.7510, 0.7057, 1.2768},
{ 0.1972, 0.5055, 0.6262, 0.1263, 0.7928, 0.7002, 0.4950, 0.4913, 0.2894, 0.4889},
},
new double[][,]
{
new double[,]
{
{ 0, 0.4127, 1.0287, 0.6741, 0.5630, 0.4325, 0.5722, 1.0336, 0.4752, 0.5617},
{ 0.4127, 0, 0.6880, 0.3738, 0.2907, 0.1730, 0.4679, 0.8675, 0.4878, 0.1286},
{ 1.0287, 0.6880, 0, 0.7937, 0.6606, 0.5323, 0.9538, 0.9634, 0.8138, 0.6381},
{ 0.6741, 0.3738, 0.7937, 0, 0.3455, 0.1881, 0.6345, 0.9714, 0.7339, 0.3214},
{ 0.5630, 0.2907, 0.6606, 0.3455, 0, 0.1313, 0.5056, 0.8485, 0.6038, 0.2630},
{ 0.4325, 0.1730, 0.5323, 0.1881, 0.1313, 0, 0.3748, 0.6687, 0.4734, 0.1491},
{ 0.5722, 0.4679, 0.9538, 0.6345, 0.5056, 0.3748, 0, 0.6955, 0.5871, 0.5059},
{ 1.0336, 0.8675, 0.9634, 0.9714, 0.8485, 0.6687, 0.6955, 0, 0.9475, 0.6932},
{ 0.4752, 0.4878, 0.8138, 0.7339, 0.6038, 0.4734, 0.5871, 0.9475, 0, 0.6046},
{ 0.5617, 0.1286, 0.6381, 0.3214, 0.2630, 0.1491, 0.5059, 0.6932, 0.6046, 0},
},
new double[,]
{
{ 4.0023, 2.1261, 1.8626, 1.6054, 1.4771, 0.9102, 2.7697, 2.9858, 3.3730, 1.2556},
{ 2.1261, 1.9372, 1.0981, 0.7172, 0.9121, 0.6569, 1.6947, 1.2703, 2.1250, 1.5231},
{ 1.8626, 1.0981, 4.6784, 1.6055, 1.6490, 1.1138, 1.9438, 3.6140, 3.0330, 0.9581},
{ 1.6054, 0.7172, 1.6055, 2.5635, 1.2584, 1.1688, 1.1565, 1.9117, 1.1689, 0.5622},
{ 1.4771, 0.9121, 1.6490, 1.2584, 1.8469, 0.9049, 0.8350, 1.2221, 1.5977, 0.6577},
{ 0.9102, 0.6569, 1.1138, 1.1688, 0.9049, 0.8258, 0.7611, 0.9541, 0.8954, 0.6253},
{ 2.7697, 1.6947, 1.9438, 1.1565, 0.8350, 0.7611, 3.6346, 3.5720, 2.9305, 0.9789},
{ 2.9858, 1.2703, 3.6140, 1.9117, 1.2221, 0.9541, 3.5720, 5.8240, 3.4327, 0.7368},
{ 3.3730, 2.1250, 3.0330, 1.1689, 1.5977, 0.8954, 2.9305, 3.4327, 4.3277, 1.4057},
{ 1.2556, 1.5231, 0.9581, 0.5622, 0.6577, 0.6253, 0.9789, 0.7368, 1.4057, 1.7151},
},
}
);
cov = new CovarianceProduct();
cov.Factors.Add(new CovarianceSquaredExponentialIso());
cov.Factors.Add(new CovarianceLinear());
TestCovarianceFunction(cov, 1,
new double[,]
{
{ 6.2342, 6.7795, 13.6134, 5.8887, 14.2405, 10.1987, 8.8183, 7.8217, 6.6344, 9.9609},
{ 3.2242, 5.2384, 8.2928, 2.8624, 10.2826, 7.2613, 5.2743, 4.8651, 3.6758, 6.0948},
{ 9.7926, 10.6338, 14.7280, 8.6537, 12.8640, 15.3218, 12.0130, 8.0240, 8.9653, 11.3359},
{ 6.0497, 4.4825, 10.1916, 5.2725, 7.5223, 9.0848, 8.6907, 6.5563, 6.2868, 7.4352},
{ 5.7877, 3.8005, 8.8010, 5.7766, 5.6360, 6.2037, 6.3872, 5.6164, 5.3671, 6.5321},
{ 3.8501, 3.6110, 6.2543, 3.3286, 5.0963, 6.1672, 5.3421, 4.0085, 3.8812, 4.6925},
{ 6.0719, 8.8301, 11.5124, 5.4433, 14.9080, 10.6099, 8.0304, 5.7589, 6.0753, 9.3107},
{ 9.8306, 11.1183, 16.3892, 8.8231, 16.9213, 15.2084, 12.3844, 8.3980, 9.3789, 12.8390},
{ 7.6019, 9.3580, 14.3105, 7.1044, 15.2800, 12.2846, 9.6031, 7.6887, 7.3699, 10.8667},
{ 2.3372, 4.9884, 6.9233, 1.6100, 8.7070, 7.9328, 5.0646, 4.3597, 2.9643, 4.8145},
},
new double[][,]
{
new double[,]
{
{ 0, 0.4881, 1.6583, 0.9692, 0.7827, 0.6522, 0.6841, 1.3942, 0.5367, 0.8270},
{ 0.4881, 0, 1.1827, 0.5867, 0.3830, 0.2172, 0.5941, 1.5658, 0.5949, 0.1383},
{ 1.6583, 1.1827, 0, 1.2171, 0.9340, 0.8047, 1.4579, 1.2131, 1.0263, 1.1349},
{ 0.9692, 0.5867, 1.2171, 0, 0.4382, 0.2161, 1.0197, 1.5073, 1.2629, 0.5270},
{ 0.7827, 0.3830, 0.9340, 0.4382, 0, 0.1488, 0.8534, 1.5465, 0.8372, 0.3717},
{ 0.6522, 0.2172, 0.8047, 0.2161, 0.1488, 0, 0.5737, 1.2259, 0.7478, 0.1832},
{ 0.6841, 0.5941, 1.4579, 1.0197, 0.8534, 0.5737, 0, 0.8230, 0.6981, 0.7909},
{ 1.3942, 1.5658, 1.2131, 1.5073, 1.5465, 1.2259, 0.8230, 0, 1.2028, 1.5638},
{ 0.5367, 0.5949, 1.0263, 1.2629, 0.8372, 0.7478, 0.6981, 1.2028, 0, 0.8770},
{ 0.8270, 0.1383, 1.1349, 0.5270, 0.3717, 0.1832, 0.7909, 1.5638, 0.8770, 0},
},
new double[,]
{
{ 29.5729, 18.5805, 22.1876, 17.0550, 15.1746, 10.1428, 24.4678, 29.7604, 28.1516, 13.6594},
{ 18.5805, 14.3139, 13.9483, 8.3170, 8.8785, 6.0953, 15.8990, 16.9410, 19.1490, 12.1067},
{ 22.1876, 13.9483, 34.5691, 18.1905, 17.2281, 12.4415, 21.9535, 33.6266, 28.2630, 12.5918},
{ 17.0550, 8.3170, 18.1905, 18.9418, 11.7900, 9.9253, 13.7325, 21.9184, 14.8634, 6.8107},
{ 15.1746, 8.8785, 17.2281, 11.7900, 13.6471, 7.5799, 10.4142, 16.4592, 16.3685, 6.8676},
{ 10.1428, 6.0953, 12.4415, 9.9253, 7.5799, 6.1018, 8.6086, 12.9238, 10.4507, 5.6783},
{ 24.4678, 15.8990, 21.9535, 13.7325, 10.4142, 8.6086, 26.8561, 31.2335, 25.7489, 11.3079},
{ 29.7604, 16.9410, 33.6266, 21.9184, 16.4592, 12.9238, 31.2335, 43.0336, 32.2012, 12.2815},
{ 28.1516, 19.1490, 28.2630, 14.8634, 16.3685, 10.4507, 25.7489, 32.2012, 31.9778, 15.0663},
{ 13.6594, 12.1067, 12.5918, 6.8107, 6.8676, 5.6783, 11.3079, 12.2815, 15.0663, 12.6728},
},
}
, 5e-3);
}
private CovarianceProduct(CovarianceProduct original, Cloner cloner)
: base(original, cloner) {
this.factors = cloner.Clone(original.factors);
this.numberOfVariables = original.numberOfVariables;
}
