static void Main()
{
Debug.Assert(CCopasiRootContainer.getRoot() != null);
// create a new datamodel
CCopasiDataModel dataModel = CCopasiRootContainer.addDatamodel();
Debug.Assert(CCopasiRootContainer.getDatamodelList().size() == 1);
// get the model from the datamodel
CModel model = dataModel.getModel();
Debug.Assert(model != null);
model.setVolumeUnit(CUnit.fl);
model.setTimeUnit(CUnit.s);
model.setQuantityUnit(CUnit.fMol);
CModelValue fixedModelValue = model.createModelValue("F");
Debug.Assert(fixedModelValue != null);
fixedModelValue.setStatus(CModelEntity.FIXED);
fixedModelValue.setInitialValue(3.0);
CModelValue variableModelValue = model.createModelValue("V");
Debug.Assert(variableModelValue != null);
variableModelValue.setStatus(CModelEntity.ASSIGNMENT);
// we create a very simple assignment that is easy on the optimization
// a parabole with the minimum at x=6 should do just fine
string s = fixedModelValue.getValueReference().getCN().getString();
s = "(<" + s + "> - 6.0)^2";
variableModelValue.setExpression(s);
// now we compile the model and tell COPASI which values have changed so
// that COPASI can update the values that depend on those
model.compileIfNecessary();
ObjectStdVector changedObjects = new ObjectStdVector();
changedObjects.Add(fixedModelValue.getInitialValueReference());
changedObjects.Add(variableModelValue.getInitialValueReference());
model.updateInitialValues(changedObjects);
// now we set up the optimization
// we want to do an optimization for the time course
// so we have to set up the time course task first
CTrajectoryTask timeCourseTask = (CTrajectoryTask)dataModel.getTask("Time-Course");
Debug.Assert(timeCourseTask != null);
// since for this example it really doesn't matter how long we run the time course
// we run for 1 second and calculate 10 steps
// run a deterministic time course
timeCourseTask.setMethodType(CTaskEnum.deterministic);
// pass a pointer of the model to the problem
timeCourseTask.getProblem().setModel(dataModel.getModel());
// get the problem for the task to set some parameters
CTrajectoryProblem problem = (CTrajectoryProblem)timeCourseTask.getProblem();
Debug.Assert(problem != null);
// simulate 10 steps
problem.setStepNumber(10);
// start at time 0
dataModel.getModel().setInitialTime(0.0);
// simulate a duration of 1 time units
problem.setDuration(1);
// tell the problem to actually generate time series data
problem.setTimeSeriesRequested(true);
// get the optimization task
COptTask optTask = (COptTask)dataModel.getTask("Optimization");
Debug.Assert(optTask != null);
// we want to use Levenberg-Marquardt as the optimization method
optTask.setMethodType(CTaskEnum.LevenbergMarquardt);
// next we need to set subtask type on the problem
COptProblem optProblem = (COptProblem)optTask.getProblem();
Debug.Assert(optProblem != null);
optProblem.setSubtaskType(CTaskEnum.timeCourse);
// we create the objective function
// we want to minimize the value of the variable model value at the end of
// the simulation
// the objective function is normally minimized
string objectiveFunction = variableModelValue.getObject(new CCopasiObjectName("Reference=Value")).getCN().getString();
// we need to put the angled brackets around the common name of the object
objectiveFunction = "<" + objectiveFunction + ">";
// now we set the objective function in the problem
optProblem.setObjectiveFunction(objectiveFunction);
// now we create the optimization items
// i.e. the model elements that have to be changed during the optimization
// in order to get to the optimal solution
COptItem optItem = optProblem.addOptItem(new CCopasiObjectName(fixedModelValue.getObject(new CCopasiObjectName("Reference=InitialValue")).getCN()));
// we want to change the fixed model value from -100 to +100 with a start
// value of 50
optItem.setStartValue(50.0);
optItem.setLowerBound(new CCopasiObjectName("-100"));
optItem.setUpperBound(new CCopasiObjectName("100"));
// now we set some parameters on the method
// these parameters are specific to the method type we set above
// (in this case Levenberg-Marquardt)
COptMethod optMethod = (COptMethod)optTask.getMethod();
Debug.Assert(optMethod != null);
// now we set some method parameters for the optimization method
// iteration limit
CCopasiParameter parameter = optMethod.getParameter("Iteration Limit");
Debug.Assert(parameter != null);
parameter.setIntValue(2000);
// tolerance
parameter = optMethod.getParameter("Tolerance");
Debug.Assert(parameter != null);
parameter.setDblValue(1.0e-5);
// create a report with the correct filename and all the species against
// time.
CReportDefinitionVector reports = dataModel.getReportDefinitionList();
// create a new report definition object
CReportDefinition report = reports.createReportDefinition("Report", "Output for optimization");
// set the task type for the report definition to timecourse
report.setTaskType(CTaskEnum.optimization);
// we don't want a table
report.setIsTable(false);
// the entries in the output should be seperated by a ", "
report.setSeparator(new CCopasiReportSeparator(", "));
// we need a handle to the header and the body
// the header will display the ids of the metabolites and "time" for
// the first column
// the body will contain the actual timecourse data
ReportItemVector header = report.getHeaderAddr();
ReportItemVector body = report.getBodyAddr();
// in the report header we write two strings and a separator
header.Add(new CRegisteredObjectName(new CCopasiStaticString("best value of objective function").getCN().getString()));
header.Add(new CRegisteredObjectName(report.getSeparator().getCN().getString()));
header.Add(new CRegisteredObjectName(new CCopasiStaticString("initial value of F").getCN().getString()));
// in the report body we write the best value of the objective function and
// the initial value of the fixed parameter separated by a komma
body.Add(new CRegisteredObjectName(optProblem.getObject(new CCopasiObjectName("Reference=Best Value")).getCN().getString()));
body.Add(new CRegisteredObjectName(report.getSeparator().getCN().getString()));
body.Add(new CRegisteredObjectName(fixedModelValue.getObject(new CCopasiObjectName("Reference=InitialValue")).getCN().getString()));
// set the report for the task
optTask.getReport().setReportDefinition(report);
// set the output filename
optTask.getReport().setTarget("example5.txt");
// don't append output if the file exists, but overwrite the file
optTask.getReport().setAppend(false);
bool result = false;
try
{
result = optTask.processWithOutputFlags(true, (int)CCopasiTask.ONLY_TIME_SERIES);
}
catch (System.ApplicationException e)
{
System.Console.Error.WriteLine("ERROR: " + e.Message);
String lastErrors = optTask.getProcessError();
// check if there are additional error messages
if (!string.IsNullOrEmpty(lastErrors))
{
// print the messages in chronological order
System.Console.Error.WriteLine(lastErrors);
}
System.Environment.Exit(1);
}
if (!result)
{
System.Console.Error.WriteLine("Running the optimization failed.");
String lastErrors = optTask.getProcessError();
// check if there are additional error messages
if (!string.IsNullOrEmpty(lastErrors))
{
// print the messages in chronological order
System.Console.Error.WriteLine(lastErrors);
}
System.Environment.Exit(1);
}
// now we check if the optimization actually got the correct result
// the best value it should have is 0 and the best parameter value for
// that result should be 6 for the initial value of the fixed parameter
double bestValue = optProblem.getSolutionValue();
Debug.Assert(System.Math.Abs(bestValue) < 1e-3);
// we should only have one solution variable since we only have one
// optimization item
Debug.Assert(optProblem.getSolutionVariables().size() == 1);
double solution = optProblem.getSolutionVariables().get(0);
Debug.Assert(System.Math.Abs((solution - 6.0) / 6.0) < 1e-3);
}
