forked from kscience/kmath
add assertEquals to middle and difficult test
This commit is contained in:
Margarita Lashina
parent
346e2e97f2
commit
f91b018d4f
@ -22,63 +22,63 @@ class TestLmAlgorithm {
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companion object {
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fun funcEasyForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
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val m = t.shape.component1()
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var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
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var yHat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, 1)))
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if (exampleNumber == 1) {
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y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0]))).times(p[0, 0]) + t.times(p[2, 0]).times(
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yHat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0]))).times(p[0, 0]) + t.times(p[2, 0]).times(
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DoubleTensorAlgebra.exp((t.times(-1.0 / p[3, 0])))
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)
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}
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else if (exampleNumber == 2) {
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val mt = t.max()
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y_hat = (t.times(1.0 / mt)).times(p[0, 0]) +
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yHat = (t.times(1.0 / mt)).times(p[0, 0]) +
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(t.times(1.0 / mt)).pow(2).times(p[1, 0]) +
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(t.times(1.0 / mt)).pow(3).times(p[2, 0]) +
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(t.times(1.0 / mt)).pow(4).times(p[3, 0])
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}
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else if (exampleNumber == 3) {
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y_hat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0])))
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yHat = DoubleTensorAlgebra.exp((t.times(-1.0 / p[1, 0])))
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.times(p[0, 0]) + DoubleTensorAlgebra.sin((t.times(1.0 / p[3, 0]))).times(p[2, 0])
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}
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return y_hat.as2D()
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return yHat.as2D()
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}
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fun funcMiddleForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
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val m = t.shape.component1()
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var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
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var yHat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
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val mt = t.max()
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for(i in 0 until p.shape.component1()){
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y_hat += (t.times(1.0 / mt)).times(p[i, 0])
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yHat += (t.times(1.0 / mt)).times(p[i, 0])
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}
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for(i in 0 until 5){
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y_hat = funcEasyForLm(y_hat.as2D(), p, exampleNumber).asDoubleTensor()
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yHat = funcEasyForLm(yHat.as2D(), p, exampleNumber).asDoubleTensor()
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}
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return y_hat.as2D()
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return yHat.as2D()
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}
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fun funcDifficultForLm(t: MutableStructure2D<Double>, p: MutableStructure2D<Double>, exampleNumber: Int): MutableStructure2D<Double> {
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val m = t.shape.component1()
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var y_hat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
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var yHat = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf (m, 1)))
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val mt = t.max()
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for(i in 0 until p.shape.component1()){
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y_hat = y_hat.plus( (t.times(1.0 / mt)).times(p[i, 0]) )
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yHat = yHat.plus( (t.times(1.0 / mt)).times(p[i, 0]) )
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}
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for(i in 0 until 4){
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y_hat = funcEasyForLm((y_hat.as2D() + t).as2D(), p, exampleNumber).asDoubleTensor()
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yHat = funcEasyForLm((yHat.as2D() + t).as2D(), p, exampleNumber).asDoubleTensor()
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}
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return y_hat.as2D()
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return yHat.as2D()
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}
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}
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@Test
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fun testLMEasy() = DoubleTensorAlgebra {
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val lm_matx_y_dat = doubleArrayOf(
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val lmMatxYDat = doubleArrayOf(
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19.6594, 18.6096, 17.6792, 17.2747, 16.3065, 17.1458, 16.0467, 16.7023, 15.7809, 15.9807,
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14.7620, 15.1128, 16.0973, 15.1934, 15.8636, 15.4763, 15.6860, 15.1895, 15.3495, 16.6054,
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16.2247, 15.9854, 16.1421, 17.0960, 16.7769, 17.1997, 17.2767, 17.5882, 17.5378, 16.7894,
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@ -91,7 +91,7 @@ class TestLmAlgorithm {
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14.7665, 13.3718, 15.0587, 13.8320, 14.7873, 13.6824, 14.2579, 14.2154, 13.5818, 13.8157
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)
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var example_number = 1
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var exampleNumber = 1
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val p_init = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(4, 1)), doubleArrayOf(5.0, 2.0, 0.2, 10.0)
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).as2D()
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@ -101,8 +101,8 @@ class TestLmAlgorithm {
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t[i, 0] = t[i, 0] * (i + 1)
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}
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val y_dat = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(100, 1)), lm_matx_y_dat
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val yDat = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(100, 1)), lmMatxYDat
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).as2D()
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val weight = 4.0
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@ -111,20 +111,16 @@ class TestLmAlgorithm {
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
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).as2D()
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val p_min = BroadcastDoubleTensorAlgebra.fromArray(
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val pMin = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(4, 1)), doubleArrayOf(-50.0, -20.0, -2.0, -100.0)
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).as2D()
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val p_max = BroadcastDoubleTensorAlgebra.fromArray(
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val pMax = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(4, 1)), doubleArrayOf(50.0, 20.0, 2.0, 100.0)
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).as2D()
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val opts = doubleArrayOf(3.0, 100.0, 1e-3, 1e-3, 1e-1, 1e-1, 1e-2, 11.0, 9.0, 1.0)
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val inputData = LMInput(::funcEasyForLm, p_init, t, y_dat, weight, dp, p_min, p_max, opts[1].toInt(),
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doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
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doubleArrayOf(opts[6], opts[7], opts[8]),
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opts[9].toInt(), 10, example_number)
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val inputData = LMInput(::funcEasyForLm, p_init, t, yDat, weight, dp, pMin, pMax, 100,
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doubleArrayOf(1e-3, 1e-3, 1e-1, 1e-1), doubleArrayOf(1e-2, 11.0, 9.0), 1, 10, exampleNumber)
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val result = levenbergMarquardt(inputData)
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assertEquals(13, result.iterations)
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@ -149,46 +145,46 @@ class TestLmAlgorithm {
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@Test
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fun TestLMMiddle() = DoubleTensorAlgebra {
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val NData = 100
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var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
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val tExample = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
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for (i in 0 until NData) {
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t_example[i, 0] = t_example[i, 0] * (i + 1)
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tExample[i, 0] = tExample[i, 0] * (i + 1)
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}
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val Nparams = 20
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var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
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val pExample = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
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for (i in 0 until Nparams) {
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p_example[i, 0] = p_example[i, 0] + i - 25
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pExample[i, 0] = pExample[i, 0] + i - 25
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}
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val exampleNumber = 1
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var y_hat = funcMiddleForLm(t_example, p_example, exampleNumber)
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val yHat = funcMiddleForLm(tExample, pExample, exampleNumber)
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var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
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val pInit = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
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for (i in 0 until Nparams) {
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p_init[i, 0] = (p_example[i, 0] + 0.9)
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pInit[i, 0] = (pExample[i, 0] + 0.9)
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}
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var t = t_example
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val y_dat = y_hat
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val t = tExample
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val yDat = yHat
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val weight = 1.0
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val dp = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
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).as2D()
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var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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p_min = p_min.div(1.0 / -50.0)
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val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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p_min = p_min.div(1.0 / 50.0)
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var pMin = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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pMin = pMin.div(1.0 / -50.0)
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val pMax = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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pMin = pMin.div(1.0 / 50.0)
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val opts = doubleArrayOf(3.0, 7000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 11.0, 9.0, 1.0)
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val inputData = LMInput(::funcMiddleForLm,
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p_init.as2D(),
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pInit.as2D(),
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t,
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y_dat,
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yDat,
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weight,
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dp,
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p_min.as2D(),
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p_max.as2D(),
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pMin.as2D(),
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pMax.as2D(),
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opts[1].toInt(),
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doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
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doubleArrayOf(opts[6], opts[7], opts[8]),
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@ -197,51 +193,67 @@ class TestLmAlgorithm {
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1)
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val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
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assertEquals(46, result.iterations)
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assertEquals(113, result.funcCalls)
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assertEquals(0.000005977, (result.resultChiSq * 1e9).roundToLong() / 1e9)
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assertEquals(1.0 * 1e-7, (result.resultLambda * 1e13).roundToLong() / 1e13)
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assertEquals(result.typeOfConvergence, TypeOfConvergence.InReducedChiSquare)
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val expectedParameters = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(Nparams, 1)), doubleArrayOf( -23.9717, -18.6686, -21.7971,
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-20.9681, -22.086, -20.5859, -19.0384, -17.4957, -15.9991, -14.576, -13.2441, -
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12.0201, -10.9256, -9.9878, -9.2309, -8.6589, -8.2365, -7.8783, -7.4598, -6.8511)).as2D()
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result.resultParameters = result.resultParameters.map { x -> (x * 1e4).roundToLong() / 1e4}.as2D()
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val receivedParameters = zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
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for (i in 0 until Nparams) {
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receivedParameters[i, 0] = result.resultParameters[i, 0]
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assertEquals(expectedParameters[i, 0], result.resultParameters[i, 0])
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}
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}
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@Test
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fun TestLMDifficult() = DoubleTensorAlgebra {
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val NData = 200
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var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
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var tExample = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
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for (i in 0 until NData) {
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t_example[i, 0] = t_example[i, 0] * (i + 1) - 104
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tExample[i, 0] = tExample[i, 0] * (i + 1) - 104
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}
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val Nparams = 15
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var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
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var pExample = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
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for (i in 0 until Nparams) {
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p_example[i, 0] = p_example[i, 0] + i - 25
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pExample[i, 0] = pExample[i, 0] + i - 25
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}
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val exampleNumber = 1
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var y_hat = funcDifficultForLm(t_example, p_example, exampleNumber)
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var yHat = funcDifficultForLm(tExample, pExample, exampleNumber)
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var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
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var pInit = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
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for (i in 0 until Nparams) {
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p_init[i, 0] = (p_example[i, 0] + 0.9)
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pInit[i, 0] = (pExample[i, 0] + 0.9)
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}
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var t = t_example
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val y_dat = y_hat
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var t = tExample
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val yDat = yHat
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val weight = 1.0 / Nparams * 1.0 - 0.085
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val dp = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
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).as2D()
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var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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p_min = p_min.div(1.0 / -50.0)
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val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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p_min = p_min.div(1.0 / 50.0)
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var pMin = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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pMin = pMin.div(1.0 / -50.0)
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val pMax = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
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pMin = pMin.div(1.0 / 50.0)
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val opts = doubleArrayOf(3.0, 7000.0, 1e-2, 1e-3, 1e-2, 1e-2, 1e-2, 11.0, 9.0, 1.0)
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val inputData = LMInput(::funcDifficultForLm,
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p_init.as2D(),
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pInit.as2D(),
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t,
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y_dat,
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yDat,
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weight,
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dp,
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p_min.as2D(),
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p_max.as2D(),
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pMin.as2D(),
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pMax.as2D(),
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opts[1].toInt(),
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doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
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doubleArrayOf(opts[6], opts[7], opts[8]),
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@ -250,5 +262,19 @@ class TestLmAlgorithm {
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1)
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val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
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assertEquals(2375, result.iterations)
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assertEquals(4858, result.funcCalls)
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assertEquals(5.14347, (result.resultLambda * 1e5).roundToLong() / 1e5)
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assertEquals(result.typeOfConvergence, TypeOfConvergence.InParameters)
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val expectedParameters = BroadcastDoubleTensorAlgebra.fromArray(
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ShapeND(intArrayOf(Nparams, 1)), doubleArrayOf(-23.6412, -16.7402, -21.5705, -21.0464,
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-17.2852, -17.2959, -17.298, 0.9999, -17.2885, -17.3008, -17.2941, -17.2923, -17.2976, -17.3028, -17.2891)).as2D()
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result.resultParameters = result.resultParameters.map { x -> (x * 1e4).roundToLong() / 1e4}.as2D()
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val receivedParameters = zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
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for (i in 0 until Nparams) {
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receivedParameters[i, 0] = result.resultParameters[i, 0]
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assertEquals(expectedParameters[i, 0], result.resultParameters[i, 0])
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}
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}
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}
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