public float[] fitdata(
double[] params,
int[] fixes1,
double[][] constraints,
float[] data,
float[] weights1,
double[] stats) {
// this function fits the array data to an arbitrary function denoted by
// the NLLSfitinterface
// the returned array is the fit
// stats returns the chisquared
// the weights are set to 1 if weights1 is null
// if fixes[i] is zero, the ith parameter is fit, if not, the ith
// parameter is fixed
// constraints is a 3 by nparams array containing the upper and lower
// bounds for each parameter
// and the parameter shift for the grid search
int nparams = params.length;
int npts = data.length;
int fitparams = nparams;
int[] fixes = new int[nparams];
if (fixes1 != null) {
for (int i = 0; i < nparams; i++) {
fitparams -= fixes1[i];
fixes[i] = fixes1[i];
}
}
for (int i = 0; i < nparams; i++) {
if (fixes[i] == 0) {
if (Math.abs(constraints[1][i] - constraints[0][i]) < constraints[2][i]) {
fixes[i] = 1;
params[i] = 0.5 * (constraints[0][i] + constraints[1][i]);
fitparams--;
}
}
}
double[] weights = new double[npts];
if (weights1 == null) {
for (int i = 0; i < npts; i++) {
weights[i] = 1.0;
}
} else {
for (int i = 0; i < npts; i++) {
weights[i] = weights1[i];
}
}
stats[1] = search_param(0, fitparams, params, fixes, constraints, data, weights);
StringBuffer resstring = new StringBuffer();
resstring.append("final minc2 =" + (float) stats[1]);
for (int i = 0; i < params.length; i++) {
resstring.append(" , " + (float) params[i]);
}
if (output) {
fitclass.showresults(resstring.toString());
}
double[] fit = fitclass.fitfunc(params);
float[] ffit = new float[fit.length];
for (int i = 0; i < fit.length; i++) {
ffit[i] = (float) fit[i];
}
return ffit;
}
public double search_param(
int paramid,
int numfit,
double[] params,
int[] fixes,
double[][] constraints,
float[] data,
double[] weights) {
// this method recursively searches over parameter space and returns the
// minimum parameter and chi squared
if (paramid == (params.length - 1)) {
if (fixes[paramid] == 1) {
return calculate_c2_params(params, numfit, data, weights);
} else {
params[paramid] = constraints[1][paramid];
double minc2 = calculate_c2_params(params, numfit, data, weights);
double minparam = params[paramid];
for (double x = constraints[1][paramid] - constraints[2][paramid];
x >= constraints[0][paramid];
x -= constraints[2][paramid]) {
params[paramid] = x;
double tempc2 = calculate_c2_params(params, numfit, data, weights);
if (tempc2 > 0.0) {
if (tempc2 < minc2 || minc2 == 0.0f) {
minparam = params[paramid];
minc2 = tempc2;
}
}
}
params[paramid] = minparam;
return minc2;
}
} else {
int counter = 1;
if (fixes[paramid] == 1) {
return search_param(paramid + 1, numfit, params, fixes, constraints, data, weights);
} else {
params[paramid] = constraints[1][paramid];
double minc2 = search_param(paramid + 1, numfit, params, fixes, constraints, data, weights);
double[] minparams = params.clone();
if (paramid < 2) {
StringBuffer resstring = new StringBuffer();
resstring.append("" + paramid + " , " + counter + " , " + (float) minc2);
for (int i = 0; i < params.length; i++) {
resstring.append(" , " + (float) params[i]);
}
if (output) {
fitclass.showresults(resstring.toString());
}
}
for (double x = constraints[1][paramid] - constraints[2][paramid];
x >= constraints[0][paramid];
x -= constraints[2][paramid]) {
params[paramid] = x;
double tempc2 =
search_param(paramid + 1, numfit, params, fixes, constraints, data, weights);
if (tempc2 > 0.0) {
if (tempc2 < minc2 || minc2 == 0.0f) {
minc2 = tempc2;
minparams = params.clone();
}
}
counter++;
if (paramid < 2) {
StringBuffer resstring = new StringBuffer();
resstring.append("" + paramid + " , " + counter + " , " + (float) minc2);
for (int i = 0; i < params.length; i++) {
resstring.append(" , " + (float) params[i]);
}
if (output) {
fitclass.showresults(resstring.toString());
}
}
}
System.arraycopy(minparams, 0, params, 0, params.length);
return minc2;
}
}
}
public double calculate_c2_params(
double[] params, int numfit, float[] data, double[] weights, boolean[] fitmask) {
// this function calculates chisquared using the parameter array
double[] tempfit = fitclass.fitfunc(params);
return calculate_c2_fit(tempfit, numfit, data, weights, fitmask);
}
