func TestQuality(t *testing.T) {
// On a certain function we know that the prediction is good
// confirm that it is
nDim := 2
nTrain := 160
xTrain, yTrain := generateRandomSamples(nTrain, nDim)
nTest := 1000
xTest, yTest := generateRandomSamples(nTest, nDim)
nFeatures := 300
sigmaSq := 0.01
kernel := &IsoSqExp{LogScale: math.Log(sigmaSq)}
sink := NewTrainer(nDim, nDim, nFeatures, kernel)
parameters := train.LinearSolve(sink, nil, xTrain, yTrain, nil, regularize.None{})
sink.SetParameters(parameters)
/*
batchGrad := train.NewBatchGradBased(sink, true, xTrain, yTrain, nil, loss.SquaredDistance{}, regularize.None{})
derivative := make([]float64, sink.NumParameters())
batchGrad.ObjGrad(parameters, derivative)
fmt.Println("Quality derivative")
//fmt.Println(derivative)
fmt.Println("sum deriv = ", floats.Sum(derivative))
sink.RandomizeParameters()
sink.Parameters(parameters)
batchGrad.ObjGrad(parameters, derivative)
fmt.Println("sum deriv 2 = ", floats.Sum(derivative))
*/
// Predict on new values
pred, err := sink.PredictBatch(xTest, nil)
if err != nil {
t.Errorf(err.Error())
}
for i := 0; i < nTest; i++ {
for j := 0; j < nDim; j++ {
diff := pred.At(i, j) - yTest.At(i, j)
if math.Abs(diff) > 1e-9 {
t.Errorf("Mismatch sample %v, output %v. Expected %v, Found %v", i, j, yTest.At(i, j), pred.At(i, j))
}
}
}
}
// TestLinearsolveAndDeriv compares the optimal weights found from gradient-based optimization with those found
// from computing a linear solve
func TestLinearsolveAndDeriv(t *testing.T, linear train.LinearTrainable, inputs, trueOutputs common.RowMatrix, name string) {
// Compare with no weights
rows, cols := trueOutputs.Dims()
predOutLinear := mat64.NewDense(rows, cols, nil)
parametersLinearSolve := train.LinearSolve(linear, nil, inputs, trueOutputs, nil, nil)
linear.SetParameters(parametersLinearSolve)
linear.Predictor().PredictBatch(inputs, predOutLinear)
//fmt.Println("Pred out linear", predOutLinear)
linear.RandomizeParameters()
parameters := linear.Parameters(nil)
batch := train.NewBatchGradBased(linear, true, inputs, trueOutputs, nil, loss.SquaredDistance{}, regularize.None{})
problem := batch
settings := multivariate.DefaultSettings()
settings.GradAbsTol = 1e-11
//settings. = 0
result, err := multivariate.OptimizeGrad(problem, parameters, settings, nil)
if err != nil {
t.Errorf("Error training: %v", err)
}
parametersDeriv := result.Loc
deriv := make([]float64, linear.NumParameters())
loss1 := batch.ObjGrad(parametersDeriv, deriv)
linear.SetParameters(parametersDeriv)
predOutDeriv := mat64.NewDense(rows, cols, nil)
linear.Predictor().PredictBatch(inputs, predOutDeriv)
linear.RandomizeParameters()
init2 := linear.Parameters(nil)
batch2 := train.NewBatchGradBased(linear, true, inputs, trueOutputs, nil, loss.SquaredDistance{}, regularize.None{})
problem2 := batch2
result2, err := multivariate.OptimizeGrad(problem2, init2, settings, nil)
parametersDeriv2 := result2.Loc
//fmt.Println("starting deriv2 loss")
deriv2 := make([]float64, linear.NumParameters())
loss2 := batch2.ObjGrad(parametersDeriv2, deriv2)
//fmt.Println("starting derivlin loss")
derivlinear := make([]float64, linear.NumParameters())
lossLin := batch2.ObjGrad(parametersLinearSolve, derivlinear)
_ = loss1
_ = loss2
_ = lossLin
/*
fmt.Println("param deriv 1 =", parametersDeriv)
fmt.Println("param deriv2 =", parametersDeriv2)
fmt.Println("linear params =", parametersLinearSolve)
fmt.Println("deriv1 loss =", loss1)
fmt.Println("deriv2 loss =", loss2)
fmt.Println("lin loss =", lossLin)
fmt.Println("deriv =", deriv)
fmt.Println("deriv2 =", deriv2)
fmt.Println("linderiv =", derivlinear)
//fmt.Println("Pred out deriv", predOutDeriv)
*/
/*
for i := 0; i < rows; i++ {
fmt.Println(predOutLinear.RowView(i), predOutBatch.RowView(i))
}
*/
if !floats.EqualApprox(parametersLinearSolve, parametersDeriv, 1e-8) {
t.Errorf("Parameters don't match for gradient based and linear solve.")
//for i := range parametersDeriv {
// fmt.Printf("index %v: Deriv = %v, linsolve = %v, diff = %v\n", i, parametersDeriv[i], parametersLinearSolve[i], parametersDeriv[i]-parametersLinearSolve[i])
//}
}
}