Re: [math] Struggling with least squares
I figured it out. The issue was with my derivative calculation. I was using
(a - b) / DELTA * 2. Changing this to (b - a) / DELTA * 2 fixes this
problem.
a = morton3pTime(cp - DELTA, wPrime, pmax, p);
b = morton3pTime(cp + DELTA, wPrime, pmax, p);
jacobian.setEntry(i, 0, (b - a) / (DELTA * 2));
Thanks
On Fri, Dec 28, 2018 at 8:33 PM Gilles wrote:
> Hello.
>
> On Thu, 27 Dec 2018 07:32:30 -0600 (CST), David Tinker wrote:
> > Hi Guys. I am struggling to use the least squares optimizer to fit a
> > 2
> > variable non-linear function to a curve of observed data points. I am
> > pretty
> > sure I am doing something stupid because I don't know the maths. My
> > MultivariateJacobianFunction moves away from my starting values on
> > the first
> > iteration but then converges back to the starting values. My
> > variables are
> > CP and W' and this is what happens:
> >
> > cp 250.0 W' 24000.0 (starting values)
> > cp 197.17292724155843 W' 5627.212534968825
> > cp 233.7927945744999 W' 18662.916420529345
> > cp 245.72618039512386 W' 22592.92114117481
> > cp 248.91747806252718 W' 23643.613738664597
> > cp 249.9974080222 W' 23999.146684
> > ...
> > cp 249.9993520052 W' 23999.7866709
> > optimum {250; 24,000}
> >
> > Any ideas?
>
> What happens when different starting points?
>
> Gilles
>
> > Here is the code:
> >
> > public class LeastSquaresExample {
> >
> > private static final double DELTA = 0.01; // for calculating
> > derivatives
> >
> > public static void main(String[] args) {
> > Vector2D[] observedPoints = new Vector2D[3];
> > observedPoints[0] = new Vector2D(388, 250); // maps power in
> > watts
> > to time in seconds
> > observedPoints[1] = new Vector2D(368, 450);
> > observedPoints[2] = new Vector2D(321, 780);
> >
> > int pmax = 961;
> >
> > MultivariateJacobianFunction fn = point -> {
> > double cp = point.getEntry(0);
> > double wPrime = point.getEntry(1);
> > System.out.println("cp " + cp + " W' " + wPrime);
> >
> > RealVector value = new
> > ArrayRealVector(observedPoints.length);
> > RealMatrix jacobian = new
> > Array2DRowRealMatrix(observedPoints.length, 2);
> > for (int i = 0; i < observedPoints.length; i++) {
> > double p = observedPoints[i].getX();
> > value.setEntry(i, morton3pTime(cp, wPrime, pmax, p));
> >
> > // each row in the jacobian is a measurement and cols
> > are
> > partial derivatives wrt cp(0) and wPrime(1)
> > double a, b;
> > a = morton3pTime(cp - DELTA, wPrime, pmax, p);
> > b = morton3pTime(cp + DELTA, wPrime, pmax, p);
> > jacobian.setEntry(i, 0, (a - b) / (DELTA * 2));
> >
> > a = morton3pTime(cp, wPrime - DELTA, pmax, p);
> > b = morton3pTime(cp, wPrime + DELTA, pmax, p);
> > jacobian.setEntry(i, 1, (a - b) / (DELTA * 2));
> > }
> > return new Pair<>(value, jacobian);
> > };
> >
> > double[] target = new double[observedPoints.length];
> > for (int i = 0; i < observedPoints.length; i++) target[i] =
> > observedPoints[i].getY();
> >
> > LeastSquaresProblem problem = new LeastSquaresBuilder()
> > .start(new double[]{250.0, 24000.0})
> > .model(fn)
> > .target(target)
> > .maxEvaluations(1000)
> > .maxIterations(1000)
> > .build();
> > LeastSquaresOptimizer.Optimum optimum = new
> > LevenbergMarquardtOptimizer().optimize(problem);
> > RealVector pt = optimum.getPoint();
> > System.out.println("optimum " + pt);
> > }
> >
> > private static double morton3pTime(double cp, double wPrime,
> > double
> > pmax, double p) {
> > return wPrime / (p - cp) + wPrime / (cp - pmax);
> > }
> > }
> >
>
>
> -
> To unsubscribe, e-mail: user-unsubscr...@commons.apache.org
> For additional commands, e-mail: user-h...@commons.apache.org
>
>
Re: [math] Struggling with least squares
The same. Example:
cp 300.0 W' 12000.0
cp 333.74051281161115 W' -12839.244450996564
cp 298.7156407940922 W' 7487.731100602964
cp 298.9550329672171 W' 11242.142876024549
cp 299.7463658640514 W' 11824.469512284873
cp 299.9341647360811 W' 11954.82219199176
...
cp 299.9974568135 W' 11999.999825967237
cp 299.9993642035 W' 11999.56491805
optimum {300; 12,000}
On Fri, Dec 28, 2018 at 8:33 PM Gilles wrote:
> Hello.
>
> On Thu, 27 Dec 2018 07:32:30 -0600 (CST), David Tinker wrote:
> > Hi Guys. I am struggling to use the least squares optimizer to fit a
> > 2
> > variable non-linear function to a curve of observed data points. I am
> > pretty
> > sure I am doing something stupid because I don't know the maths. My
> > MultivariateJacobianFunction moves away from my starting values on
> > the first
> > iteration but then converges back to the starting values. My
> > variables are
> > CP and W' and this is what happens:
> >
> > cp 250.0 W' 24000.0 (starting values)
> > cp 197.17292724155843 W' 5627.212534968825
> > cp 233.7927945744999 W' 18662.916420529345
> > cp 245.72618039512386 W' 22592.92114117481
> > cp 248.91747806252718 W' 23643.613738664597
> > cp 249.9974080222 W' 23999.146684
> > ...
> > cp 249.9993520052 W' 23999.7866709
> > optimum {250; 24,000}
> >
> > Any ideas?
>
> What happens when different starting points?
>
> Gilles
>
> > Here is the code:
> >
> > public class LeastSquaresExample {
> >
> > private static final double DELTA = 0.01; // for calculating
> > derivatives
> >
> > public static void main(String[] args) {
> > Vector2D[] observedPoints = new Vector2D[3];
> > observedPoints[0] = new Vector2D(388, 250); // maps power in
> > watts
> > to time in seconds
> > observedPoints[1] = new Vector2D(368, 450);
> > observedPoints[2] = new Vector2D(321, 780);
> >
> > int pmax = 961;
> >
> > MultivariateJacobianFunction fn = point -> {
> > double cp = point.getEntry(0);
> > double wPrime = point.getEntry(1);
> > System.out.println("cp " + cp + " W' " + wPrime);
> >
> > RealVector value = new
> > ArrayRealVector(observedPoints.length);
> > RealMatrix jacobian = new
> > Array2DRowRealMatrix(observedPoints.length, 2);
> > for (int i = 0; i < observedPoints.length; i++) {
> > double p = observedPoints[i].getX();
> > value.setEntry(i, morton3pTime(cp, wPrime, pmax, p));
> >
> > // each row in the jacobian is a measurement and cols
> > are
> > partial derivatives wrt cp(0) and wPrime(1)
> > double a, b;
> > a = morton3pTime(cp - DELTA, wPrime, pmax, p);
> > b = morton3pTime(cp + DELTA, wPrime, pmax, p);
> > jacobian.setEntry(i, 0, (a - b) / (DELTA * 2));
> >
> > a = morton3pTime(cp, wPrime - DELTA, pmax, p);
> > b = morton3pTime(cp, wPrime + DELTA, pmax, p);
> > jacobian.setEntry(i, 1, (a - b) / (DELTA * 2));
> > }
> > return new Pair<>(value, jacobian);
> > };
> >
> > double[] target = new double[observedPoints.length];
> > for (int i = 0; i < observedPoints.length; i++) target[i] =
> > observedPoints[i].getY();
> >
> > LeastSquaresProblem problem = new LeastSquaresBuilder()
> > .start(new double[]{250.0, 24000.0})
> > .model(fn)
> > .target(target)
> > .maxEvaluations(1000)
> > .maxIterations(1000)
> > .build();
> > LeastSquaresOptimizer.Optimum optimum = new
> > LevenbergMarquardtOptimizer().optimize(problem);
> > RealVector pt = optimum.getPoint();
> > System.out.println("optimum " + pt);
> > }
> >
> > private static double morton3pTime(double cp, double wPrime,
> > double
> > pmax, double p) {
> > return wPrime / (p - cp) + wPrime / (cp - pmax);
> > }
> > }
> >
>
>
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> To unsubscribe, e-mail: user-unsubscr...@commons.apache.org
> For additional commands, e-mail: user-h...@commons.apache.org
>
>
Re: [math] Struggling with least squares
Hello.
On Thu, 27 Dec 2018 07:32:30 -0600 (CST), David Tinker wrote:
Hi Guys. I am struggling to use the least squares optimizer to fit a
2
variable non-linear function to a curve of observed data points. I am
pretty
sure I am doing something stupid because I don't know the maths. My
MultivariateJacobianFunction moves away from my starting values on
the first
iteration but then converges back to the starting values. My
variables are
CP and W' and this is what happens:
cp 250.0 W' 24000.0 (starting values)
cp 197.17292724155843 W' 5627.212534968825
cp 233.7927945744999 W' 18662.916420529345
cp 245.72618039512386 W' 22592.92114117481
cp 248.91747806252718 W' 23643.613738664597
cp 249.9974080222 W' 23999.146684
...
cp 249.9993520052 W' 23999.7866709
optimum {250; 24,000}
Any ideas?
What happens when different starting points?
Gilles
Here is the code:
public class LeastSquaresExample {
private static final double DELTA = 0.01; // for calculating
derivatives
public static void main(String[] args) {
Vector2D[] observedPoints = new Vector2D[3];
observedPoints[0] = new Vector2D(388, 250); // maps power in
watts
to time in seconds
observedPoints[1] = new Vector2D(368, 450);
observedPoints[2] = new Vector2D(321, 780);
int pmax = 961;
MultivariateJacobianFunction fn = point -> {
double cp = point.getEntry(0);
double wPrime = point.getEntry(1);
System.out.println("cp " + cp + " W' " + wPrime);
RealVector value = new
ArrayRealVector(observedPoints.length);
RealMatrix jacobian = new
Array2DRowRealMatrix(observedPoints.length, 2);
for (int i = 0; i < observedPoints.length; i++) {
double p = observedPoints[i].getX();
value.setEntry(i, morton3pTime(cp, wPrime, pmax, p));
// each row in the jacobian is a measurement and cols
are
partial derivatives wrt cp(0) and wPrime(1)
double a, b;
a = morton3pTime(cp - DELTA, wPrime, pmax, p);
b = morton3pTime(cp + DELTA, wPrime, pmax, p);
jacobian.setEntry(i, 0, (a - b) / (DELTA * 2));
a = morton3pTime(cp, wPrime - DELTA, pmax, p);
b = morton3pTime(cp, wPrime + DELTA, pmax, p);
jacobian.setEntry(i, 1, (a - b) / (DELTA * 2));
}
return new Pair<>(value, jacobian);
};
double[] target = new double[observedPoints.length];
for (int i = 0; i < observedPoints.length; i++) target[i] =
observedPoints[i].getY();
LeastSquaresProblem problem = new LeastSquaresBuilder()
.start(new double[]{250.0, 24000.0})
.model(fn)
.target(target)
.maxEvaluations(1000)
.maxIterations(1000)
.build();
LeastSquaresOptimizer.Optimum optimum = new
LevenbergMarquardtOptimizer().optimize(problem);
RealVector pt = optimum.getPoint();
System.out.println("optimum " + pt);
}
private static double morton3pTime(double cp, double wPrime,
double
pmax, double p) {
return wPrime / (p - cp) + wPrime / (cp - pmax);
}
}
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[math] Struggling with least squares
Hi Guys. I am struggling to use the least squares optimizer to fit a 2
variable non-linear function to a curve of observed data points. I am pretty
sure I am doing something stupid because I don't know the maths. My
MultivariateJacobianFunction moves away from my starting values on the first
iteration but then converges back to the starting values. My variables are
CP and W' and this is what happens:
cp 250.0 W' 24000.0 (starting values)
cp 197.17292724155843 W' 5627.212534968825
cp 233.7927945744999 W' 18662.916420529345
cp 245.72618039512386 W' 22592.92114117481
cp 248.91747806252718 W' 23643.613738664597
cp 249.9974080222 W' 23999.146684
...
cp 249.9993520052 W' 23999.7866709
optimum {250; 24,000}
Any ideas? Here is the code:
public class LeastSquaresExample {
private static final double DELTA = 0.01; // for calculating
derivatives
public static void main(String[] args) {
Vector2D[] observedPoints = new Vector2D[3];
observedPoints[0] = new Vector2D(388, 250); // maps power in watts
to time in seconds
observedPoints[1] = new Vector2D(368, 450);
observedPoints[2] = new Vector2D(321, 780);
int pmax = 961;
MultivariateJacobianFunction fn = point -> {
double cp = point.getEntry(0);
double wPrime = point.getEntry(1);
System.out.println("cp " + cp + " W' " + wPrime);
RealVector value = new ArrayRealVector(observedPoints.length);
RealMatrix jacobian = new
Array2DRowRealMatrix(observedPoints.length, 2);
for (int i = 0; i < observedPoints.length; i++) {
double p = observedPoints[i].getX();
value.setEntry(i, morton3pTime(cp, wPrime, pmax, p));
// each row in the jacobian is a measurement and cols are
partial derivatives wrt cp(0) and wPrime(1)
double a, b;
a = morton3pTime(cp - DELTA, wPrime, pmax, p);
b = morton3pTime(cp + DELTA, wPrime, pmax, p);
jacobian.setEntry(i, 0, (a - b) / (DELTA * 2));
a = morton3pTime(cp, wPrime - DELTA, pmax, p);
b = morton3pTime(cp, wPrime + DELTA, pmax, p);
jacobian.setEntry(i, 1, (a - b) / (DELTA * 2));
}
return new Pair<>(value, jacobian);
};
double[] target = new double[observedPoints.length];
for (int i = 0; i < observedPoints.length; i++) target[i] =
observedPoints[i].getY();
LeastSquaresProblem problem = new LeastSquaresBuilder()
.start(new double[]{250.0, 24000.0})
.model(fn)
.target(target)
.maxEvaluations(1000)
.maxIterations(1000)
.build();
LeastSquaresOptimizer.Optimum optimum = new
LevenbergMarquardtOptimizer().optimize(problem);
RealVector pt = optimum.getPoint();
System.out.println("optimum " + pt);
}
private static double morton3pTime(double cp, double wPrime, double
pmax, double p) {
return wPrime / (p - cp) + wPrime / (cp - pmax);
}
}
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