Example 6 with ProfileDataElement
use of org.vcell.optimization.ProfileDataElement in project vcell by virtualcell.
the class MicroscopyXmlReader method getProfileDataElement.
private ProfileDataElement getProfileDataElement(Element profileDataElementElement) {
ProfileDataElement profileDataElement = null;
if (profileDataElementElement != null) {
String paramName = unMangle(profileDataElementElement.getAttributeValue(MicroscopyXMLTags.profileDataElementParameterNameAttrTag));
double paramVal = new Double(unMangle(profileDataElementElement.getAttributeValue(MicroscopyXMLTags.profileDataElementParameterValueAttrTag)));
double likelihood = new Double(unMangle(profileDataElementElement.getAttributeValue(MicroscopyXMLTags.profileDataElementLikelihoodAttrTag)));
@SuppressWarnings("unchecked") List<Element> parameterElementList = profileDataElementElement.getChildren(OptXmlTags.Parameter_Tag);
Parameter[] parameters = new Parameter[parameterElementList.size()];
int paramCounter = 0;
for (Element paramElement : parameterElementList) {
parameters[paramCounter] = getParameter(paramElement);
paramCounter++;
}
profileDataElement = new ProfileDataElement(paramName, paramVal, likelihood, parameters);
}
return profileDataElement;
}
Also used :
ProfileDataElement(org.vcell.optimization.ProfileDataElement)
ProfileDataElement(org.vcell.optimization.ProfileDataElement)
Element(org.jdom.Element)
Parameter(cbit.vcell.opt.Parameter)
Example 7 with ProfileDataElement
use of org.vcell.optimization.ProfileDataElement in project vcell by virtualcell.
the class FRAPOptData method evaluateParameters.
public ProfileData[] evaluateParameters(Parameter[] currentParams, ClientTaskStatusSupport clientTaskStatusSupport) throws Exception {
// long startTime =System.currentTimeMillis();
int totalParamLen = currentParams.length;
ProfileData[] resultData = new ProfileData[totalParamLen];
FRAPStudy frapStudy = getExpFrapStudy();
for (int j = 0; j < totalParamLen; j++) {
ProfileData profileData = new ProfileData();
// add the fixed parameter to profileData, output exp data and opt results
setNumEstimatedParams(totalParamLen);
Parameter[] newBestParameters = getBestParamters(currentParams, frapStudy.getSelectedROIsForErrorCalculation(), null, true);
double iniError = getLeastError();
// fixed parameter
Parameter fixedParam = newBestParameters[j];
if (// log function cannot take 0 as parameter
fixedParam.getInitialGuess() == 0) {
fixedParam = new Parameter(fixedParam.getName(), fixedParam.getLowerBound(), fixedParam.getUpperBound(), fixedParam.getScale(), FRAPOptimizationUtils.epsilon);
}
if (clientTaskStatusSupport != null) {
if (totalParamLen == FRAPModel.NUM_MODEL_PARAMETERS_ONE_DIFF) {
clientTaskStatusSupport.setMessage("<html>Evaluating confidence intervals of \'" + fixedParam.getName() + "\' <br> of diffusion with one diffusing component model.</html>");
} else if (totalParamLen == FRAPModel.NUM_MODEL_PARAMETERS_TWO_DIFF) {
clientTaskStatusSupport.setMessage("<html>Evaluating confidence intervals of \'" + fixedParam.getName() + "\' <br> of diffusion with two diffusing components model.</html>");
}
// start evaluation of a parameter.
clientTaskStatusSupport.setProgress(0);
}
ProfileDataElement pde = new ProfileDataElement(fixedParam.getName(), Math.log10(fixedParam.getInitialGuess()), iniError, newBestParameters);
profileData.addElement(pde);
Parameter[] unFixedParams = new Parameter[totalParamLen - 1];
int indexCounter = 0;
for (int i = 0; i < totalParamLen; i++) {
if (!newBestParameters[i].getName().equals(fixedParam.getName())) {
unFixedParams[indexCounter] = newBestParameters[i];
indexCounter++;
} else
continue;
}
// increase
int iterationCount = 1;
double paramLogVal = Math.log10(fixedParam.getInitialGuess());
double lastError = iniError;
boolean isBoundReached = false;
double incrementStep = DEFAULT_CI_STEPS[j];
int stepIncreaseCount = 0;
while (true) {
if (// if exceeds the maximum iterations, break;
iterationCount > MAX_ITERATION) {
break;
}
if (isBoundReached) {
break;
}
paramLogVal = paramLogVal + incrementStep;
double paramVal = Math.pow(10, paramLogVal);
if (paramVal > (fixedParam.getUpperBound() - FRAPOptimizationUtils.epsilon)) {
paramVal = fixedParam.getUpperBound();
paramLogVal = Math.log10(fixedParam.getUpperBound());
isBoundReached = true;
}
Parameter increasedParam = new Parameter(fixedParam.getName(), fixedParam.getLowerBound(), fixedParam.getUpperBound(), fixedParam.getScale(), paramVal);
// getBestParameters returns the whole set of parameters including the fixed parameters
setNumEstimatedParams(totalParamLen - 1);
Parameter[] newParameters = getBestParamters(unFixedParams, frapStudy.getSelectedROIsForErrorCalculation(), increasedParam, true);
for (// use last step unfixed parameter values to optimize
int i = 0; // use last step unfixed parameter values to optimize
i < newParameters.length; // use last step unfixed parameter values to optimize
i++) {
for (int k = 0; k < unFixedParams.length; k++) {
if (newParameters[i].getName().equals(unFixedParams[k].getName())) {
Parameter tempParameter = new Parameter(unFixedParams[k].getName(), unFixedParams[k].getLowerBound(), unFixedParams[k].getUpperBound(), unFixedParams[k].getScale(), newParameters[i].getInitialGuess());
unFixedParams[k] = tempParameter;
}
}
}
double error = getLeastError();
pde = new ProfileDataElement(increasedParam.getName(), paramLogVal, error, newParameters);
profileData.addElement(pde);
// check if the we run enough to get confidence intervals(99% @6.635, we plus 10 over the min error)
if (error > (iniError + 10)) {
break;
}
if (Math.abs((error - lastError) / lastError) < MIN_LIKELIHOOD_CHANGE) {
stepIncreaseCount++;
incrementStep = DEFAULT_CI_STEPS[j] * Math.pow(2, stepIncreaseCount);
} else {
if (stepIncreaseCount > 1) {
incrementStep = DEFAULT_CI_STEPS[j] / Math.pow(2, stepIncreaseCount);
stepIncreaseCount--;
}
}
if (clientTaskStatusSupport.isInterrupted()) {
throw UserCancelException.CANCEL_GENERIC;
}
lastError = error;
iterationCount++;
clientTaskStatusSupport.setProgress((int) ((iterationCount * 1.0 / MAX_ITERATION) * 0.5 * 100));
}
// half way through evaluation of a parameter.
clientTaskStatusSupport.setProgress(50);
// decrease
iterationCount = 1;
paramLogVal = Math.log10(fixedParam.getInitialGuess());
;
;
lastError = iniError;
isBoundReached = false;
double decrementStep = DEFAULT_CI_STEPS[j];
stepIncreaseCount = 0;
while (true) {
if (// if exceeds the maximum iterations, break;
iterationCount > MAX_ITERATION) {
break;
}
if (isBoundReached) {
break;
}
paramLogVal = paramLogVal - decrementStep;
double paramVal = Math.pow(10, paramLogVal);
if (paramVal < (fixedParam.getLowerBound() + FRAPOptimizationUtils.epsilon)) {
paramVal = FRAPOptimizationUtils.epsilon;
paramLogVal = Math.log10(FRAPOptimizationUtils.epsilon);
isBoundReached = true;
}
Parameter decreasedParam = new Parameter(fixedParam.getName(), fixedParam.getLowerBound(), fixedParam.getUpperBound(), fixedParam.getScale(), paramVal);
// getBestParameters returns the whole set of parameters including the fixed parameters
setNumEstimatedParams(totalParamLen - 1);
Parameter[] newParameters = getBestParamters(unFixedParams, frapStudy.getSelectedROIsForErrorCalculation(), decreasedParam, true);
for (// use last step unfixed parameter values to optimize
int i = 0; // use last step unfixed parameter values to optimize
i < newParameters.length; // use last step unfixed parameter values to optimize
i++) {
for (int k = 0; k < unFixedParams.length; k++) {
if (newParameters[i].getName().equals(unFixedParams[k].getName())) {
Parameter tempParameter = new Parameter(unFixedParams[k].getName(), unFixedParams[k].getLowerBound(), unFixedParams[k].getUpperBound(), unFixedParams[k].getScale(), newParameters[i].getInitialGuess());
unFixedParams[k] = tempParameter;
}
}
}
double error = getLeastError();
pde = new ProfileDataElement(decreasedParam.getName(), paramLogVal, error, newParameters);
profileData.addElement(0, pde);
if (error > (iniError + 10)) {
break;
}
if (Math.abs((error - lastError) / lastError) < MIN_LIKELIHOOD_CHANGE) {
stepIncreaseCount++;
decrementStep = DEFAULT_CI_STEPS[j] * Math.pow(2, stepIncreaseCount);
} else {
if (stepIncreaseCount > 1) {
incrementStep = DEFAULT_CI_STEPS[j] / Math.pow(2, stepIncreaseCount);
stepIncreaseCount--;
}
}
if (clientTaskStatusSupport.isInterrupted()) {
throw UserCancelException.CANCEL_GENERIC;
}
lastError = error;
iterationCount++;
clientTaskStatusSupport.setProgress((int) (((iterationCount + MAX_ITERATION) * 1.0 / MAX_ITERATION) * 0.5 * 100));
}
resultData[j] = profileData;
// finish evaluation of a parameter
clientTaskStatusSupport.setProgress(100);
}
// this message is specifically set for batchrun, the message will stay in the status panel. It doesn't affect single run,which disappears quickly that user won't notice.
clientTaskStatusSupport.setMessage("Evaluating confidence intervals ...");
// System.out.println("total time used:" + (endTime - startTime));
return resultData;
}
Also used :
ProfileDataElement(org.vcell.optimization.ProfileDataElement)
Parameter(cbit.vcell.opt.Parameter)
ProfileData(org.vcell.optimization.ProfileData)
Example 8 with ProfileDataElement
use of org.vcell.optimization.ProfileDataElement in project vcell by virtualcell.
the class FRAPOptimizationUtils method getSummaryFromProfileData.
// getting a profileSummary for each parameter that has acquired a profile likelihood distribution
public static ProfileSummaryData getSummaryFromProfileData(ProfileData profileData) {
ArrayList<ProfileDataElement> profileElements = profileData.getProfileDataElements();
int dataSize = profileElements.size();
double[] paramValArray = new double[dataSize];
double[] errorArray = new double[dataSize];
if (dataSize > 0) {
// profile likelihood curve
String paramName = profileElements.get(0).getParamName();
// find the parameter to locate the upper and lower bounds
Parameter parameter = null;
Parameter[] bestParameters = profileElements.get(0).getBestParameters();
for (int i = 0; i < bestParameters.length; i++) {
if (bestParameters[i] != null && bestParameters[i].getName().equals(paramName)) {
parameter = bestParameters[i];
}
}
// double logLowerBound = (lowerBound == 0)? 0: Math.log10(lowerBound);
for (int i = 0; i < dataSize; i++) {
paramValArray[i] = profileElements.get(i).getParameterValue();
errorArray[i] = profileElements.get(i).getLikelihood();
}
PlotData dataPlot = new PlotData(paramValArray, errorArray);
// get confidence interval line
// make array copy in order to not change the data orders afte the sorting
double[] paramValArrayCopy = new double[paramValArray.length];
System.arraycopy(paramValArray, 0, paramValArrayCopy, 0, paramValArray.length);
double[] errorArrayCopy = new double[errorArray.length];
System.arraycopy(errorArray, 0, errorArrayCopy, 0, errorArray.length);
DescriptiveStatistics paramValStat = DescriptiveStatistics.CreateBasicStatistics(paramValArrayCopy);
DescriptiveStatistics errorStat = DescriptiveStatistics.CreateBasicStatistics(errorArrayCopy);
double[] xArray = new double[2];
double[][] yArray = new double[ConfidenceInterval.NUM_CONFIDENCE_LEVELS][2];
// get confidence level plot lines
xArray[0] = paramValStat.getMin() - (Math.abs(paramValStat.getMin()) * 0.2);
xArray[1] = paramValStat.getMax() + (Math.abs(paramValStat.getMax()) * 0.2);
for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
yArray[i][0] = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
yArray[i][1] = yArray[i][0];
}
PlotData confidence80Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_80]);
PlotData confidence90Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_90]);
PlotData confidence95Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_95]);
PlotData confidence99Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_99]);
// generate plot2D data
Plot2D plots = new Plot2D(null, null, new String[] { "profile Likelihood Data", "80% confidence", "90% confidence", "95% confidence", "99% confidence" }, new PlotData[] { dataPlot, confidence80Plot, confidence90Plot, confidence95Plot, confidence99Plot }, new String[] { "Profile likelihood of " + paramName, "Log base 10 of " + paramName, "Profile Likelihood" }, new boolean[] { true, true, true, true, true });
// get the best parameter for the minimal error
int minErrIndex = -1;
for (int i = 0; i < errorArray.length; i++) {
if (errorArray[i] == errorStat.getMin()) {
minErrIndex = i;
break;
}
}
double bestParamVal = Math.pow(10, paramValArray[minErrIndex]);
// find confidence interval points
ConfidenceInterval[] intervals = new ConfidenceInterval[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
// half loop through the errors(left side curve)
int[] smallLeftIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
int[] bigLeftIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
smallLeftIdx[i] = -1;
bigLeftIdx[i] = -1;
for (// loop from bigger error to smaller error
int j = 1; // loop from bigger error to smaller error
j < minErrIndex + 1; // loop from bigger error to smaller error
j++) {
if ((errorArray[j] < (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i])) && (errorArray[j - 1] > (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]))) {
smallLeftIdx[i] = j - 1;
bigLeftIdx[i] = j;
break;
}
}
}
// another half loop through the errors(right side curve)
int[] smallRightIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
int[] bigRightIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
smallRightIdx[i] = -1;
bigRightIdx[i] = -1;
for (// loop from bigger error to smaller error
int j = (minErrIndex + 1); // loop from bigger error to smaller error
j < errorArray.length; // loop from bigger error to smaller error
j++) {
if ((errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]) < errorArray[j] && (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]) > errorArray[j - 1]) {
smallRightIdx[i] = j - 1;
bigRightIdx[i] = j;
break;
}
}
}
// calculate intervals
for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
double lowerBound = Double.NEGATIVE_INFINITY;
boolean bLowerBoundOpen = true;
double upperBound = Double.POSITIVE_INFINITY;
boolean bUpperBoundOpen = true;
if (// no lower bound
smallLeftIdx[i] == -1 && bigLeftIdx[i] == -1) {
lowerBound = parameter.getLowerBound();
bLowerBoundOpen = false;
} else if (// there is a lower bound
smallLeftIdx[i] != -1 && bigLeftIdx[i] != -1) {
// x=x1+(x2-x1)*(y-y1)/(y2-y1);
double x1 = paramValArray[smallLeftIdx[i]];
double x2 = paramValArray[bigLeftIdx[i]];
double y = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
double y1 = errorArray[smallLeftIdx[i]];
double y2 = errorArray[bigLeftIdx[i]];
lowerBound = x1 + (x2 - x1) * (y - y1) / (y2 - y1);
lowerBound = Math.pow(10, lowerBound);
bLowerBoundOpen = false;
}
if (// no upper bound
smallRightIdx[i] == -1 && bigRightIdx[i] == -1) {
upperBound = parameter.getUpperBound();
bUpperBoundOpen = false;
} else if (// there is a upper bound
smallRightIdx[i] != -1 && bigRightIdx[i] != -1) {
// x=x1+(x2-x1)*(y-y1)/(y2-y1);
double x1 = paramValArray[smallRightIdx[i]];
double x2 = paramValArray[bigRightIdx[i]];
double y = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
double y1 = errorArray[smallRightIdx[i]];
double y2 = errorArray[bigRightIdx[i]];
upperBound = x1 + (x2 - x1) * (y - y1) / (y2 - y1);
upperBound = Math.pow(10, upperBound);
bUpperBoundOpen = false;
}
intervals[i] = new ConfidenceInterval(lowerBound, bLowerBoundOpen, upperBound, bUpperBoundOpen);
}
return new ProfileSummaryData(plots, bestParamVal, intervals, paramName);
}
return null;
}
Also used :
PlotData(cbit.plot.PlotData)
DescriptiveStatistics(org.vcell.util.DescriptiveStatistics)
ProfileSummaryData(org.vcell.optimization.ProfileSummaryData)
ProfileDataElement(org.vcell.optimization.ProfileDataElement)
Parameter(cbit.vcell.opt.Parameter)
Plot2D(cbit.plot.Plot2D)
ConfidenceInterval(org.vcell.optimization.ConfidenceInterval)
Example 9 with ProfileDataElement
use of org.vcell.optimization.ProfileDataElement in project vcell by virtualcell.
the class MicroscopyXmlproducer method getXML.
private static Element getXML(ProfileDataElement profileDataElement) {
Element profileDataElementNode = new Element(MicroscopyXMLTags.ProfieDataElementTag);
profileDataElementNode.setAttribute(MicroscopyXMLTags.profileDataElementParameterNameAttrTag, profileDataElement.getParamName());
profileDataElementNode.setAttribute(MicroscopyXMLTags.profileDataElementParameterValueAttrTag, profileDataElement.getParameterValue() + "");
profileDataElementNode.setAttribute(MicroscopyXMLTags.profileDataElementLikelihoodAttrTag, profileDataElement.getLikelihood() + "");
Parameter[] parameters = profileDataElement.getBestParameters();
for (int i = 0; i < parameters.length; i++) {
if (// some of parameters in reaction off rate model are null.
parameters[i] != null) {
profileDataElementNode.addContent(getXML(parameters[i]));
}
}
return profileDataElementNode;
}
Also used :
ProfileDataElement(org.vcell.optimization.ProfileDataElement)
Element(org.jdom.Element)
Parameter(cbit.vcell.opt.Parameter)
Example 10 with ProfileDataElement
use of org.vcell.optimization.ProfileDataElement in project vcell by virtualcell.
the class DisplayProfileLikelihoodPlotsOp method getSummaryFromProfileData.
// getting a profileSummary for each parameter that has acquired a profile likelihood distribution
ProfileSummaryData getSummaryFromProfileData(ProfileData profileData) {
ArrayList<ProfileDataElement> profileElements = profileData.getProfileDataElements();
int dataSize = profileElements.size();
double[] paramValArray = new double[dataSize];
double[] errorArray = new double[dataSize];
if (dataSize > 0) {
// profile likelihood curve
String paramName = profileElements.get(0).getParamName();
// find the parameter to locate the upper and lower bounds
Parameter parameter = null;
Parameter[] bestParameters = profileElements.get(0).getBestParameters();
for (int i = 0; i < bestParameters.length; i++) {
if (bestParameters[i] != null && bestParameters[i].getName().equals(paramName)) {
parameter = bestParameters[i];
}
}
// double logLowerBound = (lowerBound == 0)? 0: Math.log10(lowerBound);
for (int i = 0; i < dataSize; i++) {
paramValArray[i] = profileElements.get(i).getParameterValue();
errorArray[i] = profileElements.get(i).getLikelihood();
}
PlotData dataPlot = new PlotData(paramValArray, errorArray);
// get confidence interval line
// make array copy in order to not change the data orders afte the sorting
double[] paramValArrayCopy = new double[paramValArray.length];
System.arraycopy(paramValArray, 0, paramValArrayCopy, 0, paramValArray.length);
double[] errorArrayCopy = new double[errorArray.length];
System.arraycopy(errorArray, 0, errorArrayCopy, 0, errorArray.length);
DescriptiveStatistics paramValStat = DescriptiveStatistics.CreateBasicStatistics(paramValArrayCopy);
DescriptiveStatistics errorStat = DescriptiveStatistics.CreateBasicStatistics(errorArrayCopy);
double[] xArray = new double[2];
double[][] yArray = new double[ConfidenceInterval.NUM_CONFIDENCE_LEVELS][2];
// get confidence level plot lines
xArray[0] = paramValStat.getMin() - (Math.abs(paramValStat.getMin()) * 0.2);
xArray[1] = paramValStat.getMax() + (Math.abs(paramValStat.getMax()) * 0.2);
for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
yArray[i][0] = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
yArray[i][1] = yArray[i][0];
}
PlotData confidence80Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_80]);
PlotData confidence90Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_90]);
PlotData confidence95Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_95]);
PlotData confidence99Plot = new PlotData(xArray, yArray[ConfidenceInterval.IDX_DELTA_ALPHA_99]);
// generate plot2D data
Plot2D plots = new Plot2D(null, null, new String[] { "profile Likelihood Data", "80% confidence", "90% confidence", "95% confidence", "99% confidence" }, new PlotData[] { dataPlot, confidence80Plot, confidence90Plot, confidence95Plot, confidence99Plot }, new String[] { "Profile likelihood of " + paramName, "Log base 10 of " + paramName, "Profile Likelihood" }, new boolean[] { true, true, true, true, true });
// get the best parameter for the minimal error
int minErrIndex = -1;
for (int i = 0; i < errorArray.length; i++) {
if (errorArray[i] == errorStat.getMin()) {
minErrIndex = i;
break;
}
}
double bestParamVal = Math.pow(10, paramValArray[minErrIndex]);
// find confidence interval points
ConfidenceInterval[] intervals = new ConfidenceInterval[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
// half loop through the errors(left side curve)
int[] smallLeftIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
int[] bigLeftIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
smallLeftIdx[i] = -1;
bigLeftIdx[i] = -1;
for (// loop from bigger error to smaller error
int j = 1; // loop from bigger error to smaller error
j < minErrIndex + 1; // loop from bigger error to smaller error
j++) {
if ((errorArray[j] < (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i])) && (errorArray[j - 1] > (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]))) {
smallLeftIdx[i] = j - 1;
bigLeftIdx[i] = j;
break;
}
}
}
// another half loop through the errors(right side curve)
int[] smallRightIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
int[] bigRightIdx = new int[ConfidenceInterval.NUM_CONFIDENCE_LEVELS];
for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
smallRightIdx[i] = -1;
bigRightIdx[i] = -1;
for (// loop from bigger error to smaller error
int j = (minErrIndex + 1); // loop from bigger error to smaller error
j < errorArray.length; // loop from bigger error to smaller error
j++) {
if ((errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]) < errorArray[j] && (errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i]) > errorArray[j - 1]) {
smallRightIdx[i] = j - 1;
bigRightIdx[i] = j;
break;
}
}
}
// calculate intervals
for (int i = 0; i < ConfidenceInterval.NUM_CONFIDENCE_LEVELS; i++) {
double lowerBound = Double.NEGATIVE_INFINITY;
boolean bLowerBoundOpen = true;
double upperBound = Double.POSITIVE_INFINITY;
boolean bUpperBoundOpen = true;
if (// no lower bound
smallLeftIdx[i] == -1 && bigLeftIdx[i] == -1) {
lowerBound = parameter.getLowerBound();
bLowerBoundOpen = false;
} else if (// there is a lower bound
smallLeftIdx[i] != -1 && bigLeftIdx[i] != -1) {
// x=x1+(x2-x1)*(y-y1)/(y2-y1);
double x1 = paramValArray[smallLeftIdx[i]];
double x2 = paramValArray[bigLeftIdx[i]];
double y = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
double y1 = errorArray[smallLeftIdx[i]];
double y2 = errorArray[bigLeftIdx[i]];
lowerBound = x1 + (x2 - x1) * (y - y1) / (y2 - y1);
lowerBound = Math.pow(10, lowerBound);
bLowerBoundOpen = false;
}
if (// no upper bound
smallRightIdx[i] == -1 && bigRightIdx[i] == -1) {
upperBound = parameter.getUpperBound();
bUpperBoundOpen = false;
} else if (// there is a upper bound
smallRightIdx[i] != -1 && bigRightIdx[i] != -1) {
// x=x1+(x2-x1)*(y-y1)/(y2-y1);
double x1 = paramValArray[smallRightIdx[i]];
double x2 = paramValArray[bigRightIdx[i]];
double y = errorStat.getMin() + ConfidenceInterval.DELTA_ALPHA_VALUE[i];
double y1 = errorArray[smallRightIdx[i]];
double y2 = errorArray[bigRightIdx[i]];
upperBound = x1 + (x2 - x1) * (y - y1) / (y2 - y1);
upperBound = Math.pow(10, upperBound);
bUpperBoundOpen = false;
}
intervals[i] = new ConfidenceInterval(lowerBound, bLowerBoundOpen, upperBound, bUpperBoundOpen);
}
return new ProfileSummaryData(plots, bestParamVal, intervals, paramName);
}
return null;
}
Also used :
PlotData(cbit.plot.PlotData)
DescriptiveStatistics(org.vcell.util.DescriptiveStatistics)
ProfileSummaryData(org.vcell.optimization.ProfileSummaryData)
ProfileDataElement(org.vcell.optimization.ProfileDataElement)
Parameter(cbit.vcell.opt.Parameter)
Plot2D(cbit.plot.Plot2D)
ConfidenceInterval(org.vcell.optimization.ConfidenceInterval)
Aggregations
ProfileDataElement (org.vcell.optimization.ProfileDataElement)10
Parameter (cbit.vcell.opt.Parameter)8
ProfileData (org.vcell.optimization.ProfileData)5
Element (org.jdom.Element)4
Plot2D (cbit.plot.Plot2D)2
PlotData (cbit.plot.PlotData)2
ConfidenceInterval (org.vcell.optimization.ConfidenceInterval)2
ProfileSummaryData (org.vcell.optimization.ProfileSummaryData)2
DescriptiveStatistics (org.vcell.util.DescriptiveStatistics)2
Point (java.awt.Point)1
File (java.io.File)1
IOException (java.io.IOException)1
ClientTaskStatusSupport (org.vcell.util.ClientTaskStatusSupport)1
ProgressDialogListener (org.vcell.util.ProgressDialogListener)1
