forked from kscience/kmath
add minor fixes
This commit is contained in:
Margarita Lashina
parent
0655642933
commit
346e2e97f2
@ -131,14 +131,14 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
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var p = inputData.startParameters
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val t = inputData.independentVariables
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val Npar = length(p) // number of parameters
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val Npnt = length(inputData.realValues) // number of data points
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var pOld = zeros(ShapeND(intArrayOf(Npar, 1))).as2D() // previous set of parameters
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var yOld = zeros(ShapeND(intArrayOf(Npnt, 1))).as2D() // previous model, y_old = y_hat(t;p_old)
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var X2 = 1e-3 / eps // a really big initial Chi-sq value
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var X2Old = 1e-3 / eps // a really big initial Chi-sq value
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var J = zeros(ShapeND(intArrayOf(Npnt, Npar))).as2D() // Jacobian matrix
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val DoF = Npnt - Npar // statistical degrees of freedom
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val Npar = length(p) // number of parameters
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val Npnt = length(inputData.realValues) // number of data points
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var pOld = zeros(ShapeND(intArrayOf(Npar, 1))).as2D() // previous set of parameters
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var yOld = zeros(ShapeND(intArrayOf(Npnt, 1))).as2D() // previous model, y_old = y_hat(t;p_old)
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var X2 = 1e-3 / eps // a really big initial Chi-sq value
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var X2Old = 1e-3 / eps // a really big initial Chi-sq value
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var J = zeros(ShapeND(intArrayOf(Npnt, Npar))).as2D() // Jacobian matrix
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val DoF = Npnt - Npar // statistical degrees of freedom
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var weight = fromArray(ShapeND(intArrayOf(1, 1)), doubleArrayOf(inputData.weight)).as2D()
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if (inputData.nargin < 5) {
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@ -165,16 +165,15 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
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}
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var maxIterations = inputData.maxIterations
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var epsilon1 = inputData.epsilons[0] // convergence tolerance for gradient
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var epsilon2 = inputData.epsilons[1] // convergence tolerance for parameters
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var epsilon3 = inputData.epsilons[2] // convergence tolerance for Chi-square
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var epsilon4 = inputData.epsilons[3] // determines acceptance of a L-M step
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var lambda0 = inputData.lambdas[0] // initial value of damping paramter, lambda
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var lambdaUpFac = inputData.lambdas[1] // factor for increasing lambda
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var lambdaDnFac = inputData.lambdas[2] // factor for decreasing lambda
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var updateType = inputData.updateType // 1: Levenberg-Marquardt lambda update
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// 2: Quadratic update
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// 3: Nielsen's lambda update equations
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var epsilon1 = inputData.epsilons[0]
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var epsilon2 = inputData.epsilons[1]
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var epsilon3 = inputData.epsilons[2]
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var epsilon4 = inputData.epsilons[3]
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var lambda0 = inputData.lambdas[0]
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var lambdaUpFac = inputData.lambdas[1]
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var lambdaDnFac = inputData.lambdas[2]
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var updateType = inputData.updateType
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if (inputData.nargin < 9) {
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maxIterations = 10 * Npar
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epsilon1 = 1e-3
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@ -194,7 +193,7 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
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dp = ones(ShapeND(intArrayOf(Npar, 1))).div(1 / dp[0, 0]).as2D()
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}
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var stop = false // termination flag
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var stop = false // termination flag
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if (weight.shape.component1() == 1 || variance(weight) == 0.0) { // identical weights vector
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weight = ones(ShapeND(intArrayOf(Npnt, 1))).div(1 / kotlin.math.abs(weight[0, 0])).as2D()
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@ -218,39 +217,35 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
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var lambda = 1.0
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var nu = 1
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when (updateType) {
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1 -> lambda = lambda0 // Marquardt: init'l lambda
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else -> { // Quadratic and Nielsen
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lambda = lambda0 * (makeColumnFromDiagonal(JtWJ)).max()!!
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nu = 2
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}
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if (updateType == 1) {
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lambda = lambda0 // Marquardt: init'l lambda
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}
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else {
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lambda = lambda0 * (makeColumnFromDiagonal(JtWJ)).max()
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nu = 2
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}
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X2Old = X2 // previous value of X2
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var h: DoubleTensor
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while (!stop && settings.iteration <= maxIterations) { //--- Start Main Loop
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while (!stop && settings.iteration <= maxIterations) {
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settings.iteration += 1
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// incremental change in parameters
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h = when (updateType) {
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1 -> { // Marquardt
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val solve =
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solve(JtWJ.plus(makeMatrixWithDiagonal(makeColumnFromDiagonal(JtWJ)).div(1 / lambda)).as2D(), JtWdy)
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solve.asDoubleTensor()
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}
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else -> { // Quadratic and Nielsen
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val solve = solve(JtWJ.plus(lmEye(Npar).div(1 / lambda)).as2D(), JtWdy)
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solve.asDoubleTensor()
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}
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h = if (updateType == 1) { // Marquardt
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val solve = solve(JtWJ.plus(makeMatrixWithDiagonal(makeColumnFromDiagonal(JtWJ)).div(1 / lambda)).as2D(), JtWdy)
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solve.asDoubleTensor()
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} else { // Quadratic and Nielsen
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val solve = solve(JtWJ.plus(lmEye(Npar).div(1 / lambda)).as2D(), JtWdy)
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solve.asDoubleTensor()
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}
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var pTry = (p + h).as2D() // update the [idx] elements
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pTry = smallestElementComparison(largestElementComparison(minParameters, pTry.as2D()), maxParameters) // apply constraints
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pTry = smallestElementComparison(largestElementComparison(minParameters, pTry.as2D()), maxParameters) // apply constraints
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var deltaY = inputData.realValues.minus(evaluateFunction(inputData.func, t, pTry, inputData.exampleNumber)) // residual error using p_try
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var deltaY = inputData.realValues.minus(evaluateFunction(inputData.func, t, pTry, inputData.exampleNumber)) // residual error using p_try
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for (i in 0 until deltaY.shape.component1()) { // floating point error; break
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for (j in 0 until deltaY.shape.component2()) {
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@ -264,21 +259,20 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
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settings.funcCalls += 1
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val tmp = deltaY.times(weight)
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var X2Try = deltaY.as2D().transpose().dot(tmp) // Chi-squared error criteria
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var X2Try = deltaY.as2D().transpose().dot(tmp) // Chi-squared error criteria
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val alpha = 1.0
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if (updateType == 2) { // Quadratic
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// One step of quadratic line update in the h direction for minimum X2
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val alpha = JtWdy.transpose().dot(h) / ((X2Try.minus(X2)).div(2.0).plus(2 * JtWdy.transpose().dot(h)))
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h = h.dot(alpha)
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pTry = p.plus(h).as2D() // update only [idx] elements
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pTry = smallestElementComparison(largestElementComparison(minParameters, pTry), maxParameters) // apply constraints
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deltaY = inputData.realValues.minus(evaluateFunction(inputData.func, t, pTry, inputData.exampleNumber)) // residual error using p_try
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deltaY = inputData.realValues.minus(evaluateFunction(inputData.func, t, pTry, inputData.exampleNumber)) // residual error using p_try
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settings.funcCalls += 1
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X2Try = deltaY.as2D().transpose().dot(deltaY.times(weight)) // Chi-squared error criteria
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X2Try = deltaY.as2D().transpose().dot(deltaY.times(weight)) // Chi-squared error criteria
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}
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val rho = when (updateType) { // Nielsen
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@ -287,7 +281,6 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
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.dot(makeMatrixWithDiagonal(makeColumnFromDiagonal(JtWJ)).div(1 / lambda).dot(h).plus(JtWdy))
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X2.minus(X2Try).as2D()[0, 0] / abs(tmp.as2D()).as2D()[0, 0]
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}
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else -> {
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val tmp = h.transposed().dot(h.div(1 / lambda).plus(JtWdy))
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X2.minus(X2Try).as2D()[0, 0] / abs(tmp.as2D()).as2D()[0, 0]
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@ -303,7 +296,6 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
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lmMatxAns = lmMatx(inputData.func, t, pOld, yOld, dX2.toInt(), J, p, inputData.realValues, weight, dp, settings)
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// decrease lambda ==> Gauss-Newton method
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JtWJ = lmMatxAns[0]
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JtWdy = lmMatxAns[1]
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X2 = lmMatxAns[2][0, 0]
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@ -519,7 +511,7 @@ private fun lmMatx(func: (MutableStructure2D<Double>, MutableStructure2D<Double>
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yDat: MutableStructure2D<Double>, weight: MutableStructure2D<Double>, dp:MutableStructure2D<Double>, settings:LMSettings) : Array<MutableStructure2D<Double>>
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{
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// default: dp = 0.001
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val Npar = length(p) // number of parameters
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val Npar = length(p) // number of parameters
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val yHat = evaluateFunction(func, t, p, settings.exampleNumber) // evaluate model using parameters 'p'
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settings.funcCalls += 1
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@ -558,8 +550,8 @@ private fun lmFdJ(func: (MutableStructure2D<Double>, MutableStructure2D<Double>,
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dp: MutableStructure2D<Double>, settings: LMSettings): MutableStructure2D<Double> {
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// default: dp = 0.001 * ones(1,n)
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val m = length(y) // number of data points
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val n = length(p) // number of parameters
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val m = length(y) // number of data points
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val n = length(p) // number of parameters
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val ps = p.copyToTensor().as2D()
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val J = BroadcastDoubleTensorAlgebra.zeros(ShapeND(intArrayOf(m, n))).as2D() // initialize Jacobian to Zero
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@ -568,7 +560,7 @@ private fun lmFdJ(func: (MutableStructure2D<Double>, MutableStructure2D<Double>,
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for (j in 0 until n) {
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del[j, 0] = dp[j, 0] * (1 + kotlin.math.abs(p[j, 0])) // parameter perturbation
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p[j, 0] = ps[j, 0] + del[j, 0] // perturb parameter p(j)
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p[j, 0] = ps[j, 0] + del[j, 0] // perturb parameter p(j)
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val epsilon = 0.0000001
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if (kotlin.math.abs(del[j, 0]) > epsilon) {
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