Examples with OptimizationSpec cbit.vcell.opt.OptimizationSpec used on opensource projects

Example 1 with OptimizationSpec

use of cbit.vcell.opt.OptimizationSpec in project vcell by virtualcell.

the class FRAPOptimizationUtils method estimate.

// estimate best parameters and return the least error
public static double estimate(FRAPOptData argOptData, Parameter[] inParams, String[] outParaNames, double[] outParaVals, boolean[] eoi) throws Exception {
    /*PowellSolver  solver = new PowellSolver();
		LookupTableObjectiveFunction func = new LookupTableObjectiveFunction(argOptData);
		
		//best point found. we have only one dimension here.
		double[] p = new double[1];
		p[0] = iniDiffGuess;
		//current direction set
		double[][] xi = new double[1][1];
		for (int i=0;i<1;i++)
		{
		   for (int j=0;j<1;j++)
		   {
		       xi[i][j]=(i == j ? 1.0 : 0.0);
		   }
		}
		//run powell with initial guess, initial direction set and objective function
		double minError = solver.powell(1, p, xi, FTOL, func);
		parameters[FRAPOptData.idxOptDiffRate] = p[0];
		parameters[FRAPOptData.idxMinError] = minError;*/
    // long startTime =System.currentTimeMillis();
    // create optimization solver
    PowellOptimizationSolver optSolver = new PowellOptimizationSolver();
    // create optimization spec
    OptimizationSpec optSpec = new OptimizationSpec();
    // add opt function
    DefaultScalarFunction scalarFunc = new LookupTableObjectiveFunction(argOptData, eoi);
    optSpec.setObjectiveFunction(new ImplicitObjectiveFunction(scalarFunc));
    // create solver spec
    OptimizationSolverSpec optSolverSpec = new OptimizationSolverSpec(OptimizationSolverSpec.SOLVERTYPE_POWELL, FRAPOptimizationUtils.FTOL);
    // create solver call back
    OptSolverCallbacks optSolverCallbacks = new DefaultOptSolverCallbacks();
    // create optimization result set
    OptimizationResultSet optResultSet = null;
    for (int i = 0; i < inParams.length; i++) {
        // add parameters
        optSpec.addParameter(inParams[i]);
    }
    optResultSet = optSolver.solve(optSpec, optSolverSpec, optSolverCallbacks);
    OptSolverResultSet optSolverResultSet = optResultSet.getOptSolverResultSet();
    // if the parameters are 5, we have to go over again to see if we get the best answer.
    if (// 5 parameters
    inParams.length == 5) {
        OptimizationSpec optSpec2 = new OptimizationSpec();
        optSpec2.setObjectiveFunction(new ImplicitObjectiveFunction(scalarFunc));
        Parameter[] inParamsFromResult = generateInParamSet(inParams, optSolverResultSet.getBestEstimates());
        for (int i = 0; i < inParamsFromResult.length; i++) {
            // add parameters
            optSpec2.addParameter(inParamsFromResult[i]);
        }
        OptimizationResultSet tempOptResultSet = optSolver.solve(optSpec2, optSolverSpec, optSolverCallbacks);
        OptSolverResultSet tempOptSolverResultSet = tempOptResultSet.getOptSolverResultSet();
        if (optSolverResultSet.getLeastObjectiveFunctionValue() > tempOptSolverResultSet.getLeastObjectiveFunctionValue()) {
            optSolverResultSet = tempOptSolverResultSet;
        }
    }
    // System.out.println("obj function value:"+optResultSet.getObjectiveFunctionValue());
    // System.out.println("");
    // copy results to output parameters
    String[] names = optSolverResultSet.getParameterNames();
    double[] values = optSolverResultSet.getBestEstimates();
    for (int i = 0; i < names.length; i++) {
        outParaNames[i] = names[i];
        outParaVals[i] = values[i];
    }
    // System.out.println("total: " + ( endTime - startTime) );
    return optSolverResultSet.getLeastObjectiveFunctionValue();
}

Example 2 with OptimizationSpec

use of cbit.vcell.opt.OptimizationSpec in project vcell by virtualcell.

the class CurveFitting method solveSpatial.

public static OptimizationResultSet solveSpatial(Simulation sim, Parameter[] parameters, SpatialReferenceData refData, File dataDir, FieldDataIdentifierSpec[] fieldDataIDSs) throws ExpressionException, OptimizationException, IOException {
    // choose optimization solver, currently we have Powell
    PowellOptimizationSolver optService = new PowellOptimizationSolver();
    OptimizationSpec optSpec = new OptimizationSpec();
    // send to optimization service
    optSpec.setObjectiveFunction(new PdeObjectiveFunction(sim.getMathDescription(), refData, dataDir, fieldDataIDSs));
    double[] parameterValues = new double[parameters.length];
    for (int i = 0; i < parameters.length; i++) {
        parameterValues[i] = parameters[i].getInitialGuess();
        System.out.println("initial " + parameters[i].getName() + " = " + parameterValues[i] + ";\tLB = " + parameters[i].getLowerBound() + ";\tUB = " + parameters[i].getUpperBound());
    }
    // get the initial guess to send it to the f() function. ....
    for (int i = 0; i < parameters.length; i++) {
        optSpec.addParameter(parameters[i]);
    }
    // Parameters in OptimizationSolverSpec are solver type and objective function change tolerance.
    OptimizationSolverSpec optSolverSpec = new OptimizationSolverSpec(OptimizationSolverSpec.SOLVERTYPE_POWELL, 0.000001);
    OptSolverCallbacks optSolverCallbacks = new DefaultOptSolverCallbacks();
    OptimizationResultSet optResultSet = null;
    optResultSet = optService.solve(optSpec, optSolverSpec, optSolverCallbacks);
    OptSolverResultSet optSolverResultSet = optResultSet.getOptSolverResultSet();
    String[] paramNames = optSolverResultSet.getParameterNames();
    double[] paramValues = optSolverResultSet.getBestEstimates();
    for (int i = 0; i < paramNames.length; i++) {
        System.out.println("finally:   " + paramNames[i] + " = " + paramValues[i]);
    }
    return optResultSet;
}

Example 3 with OptimizationSpec

use of cbit.vcell.opt.OptimizationSpec in project vcell by virtualcell.

the class ReactionDominantTest method solve.

private OptimizationResultSet solve() throws ExpressionException, IOException {
    Expression Fbleached_bleachFastExp = new Expression(Fbleached_bleachFast);
    Expression OmegaExp = new Expression(strOmega);
    Expression Omega_cExp = new Expression(strOmega_c);
    Expression WExp = new Expression(strW);
    Fbleached_bleachFastExp.substituteInPlace(OmegaExp, new Expression(Omega));
    Fbleached_bleachFastExp.substituteInPlace(Omega_cExp, new Expression(Omega_c));
    Fbleached_bleachFastExp.substituteInPlace(WExp, new Expression(W));
    Parameter[] parameters = new Parameter[] { para_alpha, para_Kon_star, para_Koff };
    // Expression Fbleached_bleachFast = Fbleached_bleachFastExp.flatten();
    // choose optimization solver, currently we have Powell and CFSQP
    PowellOptimizationSolver optService = new PowellOptimizationSolver();
    OptimizationSpec optSpec = new OptimizationSpec();
    // create simple reference data
    double[][] realData = new double[2][t.length];
    for (int i = 0; i < t.length; i++) {
        realData[0][i] = t[i];
        realData[1][i] = data_bleached[i];
    }
    String[] colNames = new String[] { "t", "intensity" };
    double[] weights = new double[] { 1.0, 1.0 };
    SimpleReferenceData refData = new SimpleReferenceData(colNames, weights, realData);
    // send to optimization service
    ExplicitFitObjectiveFunction.ExpressionDataPair oneExpDataPair = new ExplicitFitObjectiveFunction.ExpressionDataPair(Fbleached_bleachFastExp.flatten(), 1);
    ExplicitFitObjectiveFunction.ExpressionDataPair[] expDataPairs = new ExplicitFitObjectiveFunction.ExpressionDataPair[] { oneExpDataPair };
    optSpec.setObjectiveFunction(new ExplicitFitObjectiveFunction(expDataPairs, refData));
    optSpec.setComputeProfileDistributions(true);
    // get the initial guess to send it to the f() function. ....
    for (int i = 0; i < parameters.length; i++) {
        optSpec.addParameter(parameters[i]);
    }
    // Parameters in OptimizationSolverSpec are solver type and objective function change tolerance.
    OptimizationSolverSpec optSolverSpec = new OptimizationSolverSpec(OptimizationSolverSpec.SOLVERTYPE_POWELL, 0.000001);
    OptSolverCallbacks optSolverCallbacks = new DefaultOptSolverCallbacks();
    OptimizationResultSet optResultSet = null;
    optResultSet = optService.solve(optSpec, optSolverSpec, optSolverCallbacks);
    String[] paramNames = optResultSet.getOptSolverResultSet().getParameterNames();
    ArrayList<ProfileDistribution> profileDistributionList = optResultSet.getOptSolverResultSet().getProfileDistributionList();
    for (int i = 0; i < profileDistributionList.size(); i++) {
        outputProfileLikelihood(profileDistributionList.get(i), i, paramNames[i], new File(fileDir));
    }
    return optResultSet;
}

Example 4 with OptimizationSpec

use of cbit.vcell.opt.OptimizationSpec in project vcell by virtualcell.

the class OptContextSolver method estimate.

public static double estimate(OptContext optContext, Parameter[] inParams, String[] outParaNames, double[] outParaVals) throws Exception {
    // long startTime =System.currentTimeMillis();
    // create optimization solver
    PowellOptimizationSolver optSolver = new PowellOptimizationSolver();
    // create optimization spec
    OptimizationSpec optSpec = new OptimizationSpec();
    DefaultScalarFunction scalarFunc = new OptContextObjectiveFunction(optContext);
    optSpec.setObjectiveFunction(new ImplicitObjectiveFunction(scalarFunc));
    // create solver spec
    OptimizationSolverSpec optSolverSpec = new OptimizationSolverSpec(OptimizationSolverSpec.SOLVERTYPE_POWELL, FTOL);
    // create solver call back
    OptSolverCallbacks optSolverCallbacks = new DefaultOptSolverCallbacks();
    // create optimization result set
    OptimizationResultSet optResultSet = null;
    for (int i = 0; i < inParams.length; i++) {
        // add parameters
        optSpec.addParameter(inParams[i]);
    }
    optResultSet = optSolver.solve(optSpec, optSolverSpec, optSolverCallbacks);
    OptSolverResultSet optSolverResultSet = optResultSet.getOptSolverResultSet();
    // System.out.println("obj function value:"+optResultSet.getObjectiveFunctionValue());
    // System.out.println("");
    // copy results to output parameters
    String[] names = optSolverResultSet.getParameterNames();
    double[] values = optSolverResultSet.getBestEstimates();
    for (int i = 0; i < names.length; i++) {
        outParaNames[i] = names[i];
        outParaVals[i] = values[i];
    }
    // System.out.println("total: " + ( endTime - startTime) );
    return optSolverResultSet.getLeastObjectiveFunctionValue();
}

Example 5 with OptimizationSpec

use of cbit.vcell.opt.OptimizationSpec in project vcell by virtualcell.

the class ModelOptimizationMapping method computeOptimizationSpec.

/**
 * Insert the method's description here.
 * Creation date: (8/22/2005 9:26:52 AM)
 * @return cbit.vcell.opt.OptimizationSpec
 * @param modelOptimizationSpec cbit.vcell.modelopt.ModelOptimizationSpec
 */
MathSymbolMapping computeOptimizationSpec() throws MathException, MappingException {
    if (getModelOptimizationSpec().getReferenceData() == null) {
        System.out.println("no referenced data defined");
        return null;
    }
    OptimizationSpec optSpec = new OptimizationSpec();
    optSpec.setComputeProfileDistributions(modelOptimizationSpec.isComputeProfileDistributions());
    parameterMappings = null;
    // 
    // get original MathDescription (later to be substituted for local/global parameters).
    // 
    SimulationContext simContext = modelOptimizationSpec.getSimulationContext();
    MathMapping mathMapping = simContext.createNewMathMapping();
    MathDescription origMathDesc = null;
    mathSymbolMapping = null;
    try {
        origMathDesc = mathMapping.getMathDescription();
        mathSymbolMapping = mathMapping.getMathSymbolMapping();
    } catch (MatrixException e) {
        e.printStackTrace(System.out);
        throw new MappingException(e.getMessage());
    } catch (ModelException e) {
        e.printStackTrace(System.out);
        throw new MappingException(e.getMessage());
    } catch (ExpressionException e) {
        e.printStackTrace(System.out);
        throw new MathException(e.getMessage());
    }
    // 
    // create objective function (mathDesc and data)
    // 
    ReferenceData referenceData = getRemappedReferenceData(mathMapping);
    if (referenceData == null) {
        throw new RuntimeException("no referenced data defined");
    }
    // 
    // get parameter mappings
    // 
    ParameterMappingSpec[] parameterMappingSpecs = modelOptimizationSpec.getParameterMappingSpecs();
    Vector<ParameterMapping> parameterMappingList = new Vector<ParameterMapping>();
    Variable[] allVars = (Variable[]) BeanUtils.getArray(origMathDesc.getVariables(), Variable.class);
    for (int i = 0; i < parameterMappingSpecs.length; i++) {
        cbit.vcell.model.Parameter modelParameter = parameterMappingSpecs[i].getModelParameter();
        String mathSymbol = null;
        Variable mathVariable = null;
        if (mathSymbolMapping != null) {
            Variable variable = mathSymbolMapping.getVariable(modelParameter);
            if (variable != null) {
                mathSymbol = variable.getName();
            }
            if (mathSymbol != null) {
                mathVariable = origMathDesc.getVariable(mathSymbol);
            }
        }
        if (mathVariable != null) {
            if (parameterMappingSpecs[i].isSelected()) {
                if (parameterMappingSpecs[i].getHigh() < parameterMappingSpecs[i].getLow()) {
                    throw new MathException("The lower bound for Parameter '" + parameterMappingSpecs[i].getModelParameter().getName() + "' is greater than its upper bound.");
                }
                if (parameterMappingSpecs[i].getCurrent() < parameterMappingSpecs[i].getLow()) {
                    throw new MathException("The initial guess of '" + parameterMappingSpecs[i].getModelParameter().getName() + "' is smaller than its lower bound.");
                }
                if (parameterMappingSpecs[i].getCurrent() > parameterMappingSpecs[i].getHigh()) {
                    throw new MathException("The initial guess of '" + parameterMappingSpecs[i].getModelParameter().getName() + "' is greater than its upper bound.");
                }
                if (parameterMappingSpecs[i].getLow() < 0) {
                    throw new MathException("The lower bound for Parameter '" + parameterMappingSpecs[i].getModelParameter().getName() + "' is negative. All lower bounds must not be negative.");
                }
                if (Double.isInfinite(parameterMappingSpecs[i].getLow())) {
                    throw new MathException("The lower bound for Parameter '" + parameterMappingSpecs[i].getModelParameter().getName() + "' is infinity. Lower bounds must not be infinity.");
                }
                if (parameterMappingSpecs[i].getHigh() <= 0) {
                    throw new MathException("The upper bound for Parameter '" + parameterMappingSpecs[i].getModelParameter().getName() + "' is negative. All upper bounds must be positive.");
                }
                if (Double.isInfinite(parameterMappingSpecs[i].getHigh())) {
                    throw new MathException("The upper bound for Parameter '" + parameterMappingSpecs[i].getModelParameter().getName() + "' is infinity. Upper bounds must not be infinity.");
                }
            }
            double low = parameterMappingSpecs[i].isSelected() && parameterMappingSpecs[i].getLow() == 0 ? 1e-8 : parameterMappingSpecs[i].getLow();
            double high = parameterMappingSpecs[i].getHigh();
            double scale = Math.abs(parameterMappingSpecs[i].getCurrent()) < 1.0E-10 ? 1.0 : Math.abs(parameterMappingSpecs[i].getCurrent());
            double current = parameterMappingSpecs[i].getCurrent();
            low = Math.min(low, current);
            high = Math.max(high, current);
            Parameter optParameter = new Parameter(mathSymbol, low, high, scale, current);
            ParameterMapping parameterMapping = new ParameterMapping(modelParameter, optParameter, mathVariable);
            // 
            if (mathVariable instanceof Constant) {
                Constant origConstant = (Constant) mathVariable;
                for (int j = 0; j < allVars.length; j++) {
                    if (allVars[j].equals(origConstant)) {
                        if (parameterMappingSpecs[i].isSelected()) {
                            allVars[j] = new ParameterVariable(origConstant.getName());
                        } else {
                            allVars[j] = new Constant(origConstant.getName(), new Expression(optParameter.getInitialGuess()));
                        }
                        break;
                    }
                }
            }
            // 
            if (parameterMappingSpecs[i].isSelected()) {
                parameterMappingList.add(parameterMapping);
            }
        }
    }
    parameterMappings = (ParameterMapping[]) BeanUtils.getArray(parameterMappingList, ParameterMapping.class);
    try {
        origMathDesc.setAllVariables(allVars);
    } catch (ExpressionBindingException e) {
        e.printStackTrace(System.out);
        throw new MathException(e.getMessage());
    }
    // 
    for (int i = 0; i < parameterMappings.length; i++) {
        optSpec.addParameter(parameterMappings[i].getOptParameter());
    }
    Vector<Issue> issueList = new Vector<Issue>();
    IssueContext issueContext = new IssueContext();
    optSpec.gatherIssues(issueContext, issueList);
    for (int i = 0; i < issueList.size(); i++) {
        Issue issue = issueList.elementAt(i);
        if (issue.getSeverity() == Issue.SEVERITY_ERROR) {
            throw new RuntimeException(issue.getMessage());
        }
    }
    // 
    // 
    // 
    optimizationSpec = optSpec;
    return mathSymbolMapping;
}