Apache Commons Math of Curve Fitting how to constraint Parameters

[qiqi tang]

Dear Sir/Madam,
       I recently used the CurveFitter tool inside the commons package and
I found a problem that I couldn't constrain the relevant parameters.
The specific relevant codes are as follows:

public class MyFuncFitter extends AbstractCurveFitter {

    @Override
    protected LeastSquaresProblem
getProblem(Collection<WeightedObservedPoint> points) {
        final int len = points.size();
        final double[] target = new double[len];
        final double[] weights = new double[len];
        final double[] initialGuess = {50, 1.0, 1.0};

        int i = 0;
        for (WeightedObservedPoint point : points) {
            target[i] = point.getY();
            weights[i] = point.getWeight();
            i += 1;
        }

        final AbstractCurveFitter.TheoreticalValuesFunction model =
new AbstractCurveFitter.TheoreticalValuesFunction(new MyFunc(),
points);

        return new LeastSquaresBuilder().
                maxEvaluations(Integer.MAX_VALUE).
                maxIterations(Integer.MAX_VALUE).
                start(initialGuess).
                target(target).
                weight(new DiagonalMatrix(weights)).
                model(model.getModelFunction(),
model.getModelFunctionJacobian()).build();
    }}

public class MyFunc implements ParametricUnivariateFunction {
    @Override
    public double value(double x, double... parameters) {
        double m = parameters[0], k = parameters[1], b = parameters[2];
        return m * k * b * Math.exp(-k * x) * Math.pow(1 - Math.exp(-k
* x), b - 1);
    }

    @Override
    public double[] gradient(double x, double... parameters) {
        final double m = parameters[0];
        final double k = parameters[1];
        final double b = parameters[2];
        return new double[]{
                b * k * Math.exp(-k * x) * Math.pow(1 - Math.exp(-k *
x), b - 1),
                (b - 1) * b * k * m * x * Math.exp(-2 * k * x) *
Math.pow(1 - Math.exp(-k * x), b - 2) + b * m * Math.exp(-k * x) *
Math.pow(1 - Math.exp(-k * x), b - 1) - b * k * m * x * Math.exp(-k *
x) * Math.pow(1 - Math.exp(-k * x), b - 1),
                k * m * Math.exp(-k * x) * Math.pow(1 - Math.exp(-k *
x), b - 1) + b * k * m * Math.exp(-k * x) * Math.pow(1 - Math.exp(-k *
x), b - 1) * Math.log(1 - Math.exp(-k * x))
        };
    }

    public static void main(String[] args) {
        MyFuncFitter fitter = new MyFuncFitter();
        ArrayList<WeightedObservedPoint> points = new ArrayList<>();
        points.add(new WeightedObservedPoint(1.0, 0.25, 3.801713179));
        points.add(new WeightedObservedPoint(1.0, 4, 10.46561902));
        final double coeffs[] = fitter.fit(points);
        System.out.println(Arrays.toString(coeffs));
    }}

Then, What should I do to limit the parameters 'm' to 1 to 100 and 'k'
to 100 to 10,000，It would be nice if the code dome was available.
Please help me thank you very much!
Best Regards