(calibration_minimisation_constructing)=
# Constructing the `TMinimisation` object

As stated above, the only available constructor is the one whose prototype includes an instance of a
**TRun**-inheriting object. This approach provides a simple way to change the evaluator (e.g., from 
a C++ function to a Python function or an external code) and to use either a sequential approach (for a 
code) or a threaded one (to distribute locally the estimates).

In this case, the constructor should look like this:

````{only} cpp
```cpp
// Constructor with a runner
TMinimisation(TDataServer *tds, TRun *runner, Int_t ns=1, Option_t *option="");
```
````

````{only} py
```python
# Constructor with a runner
TMinimisation(tds, runner, ns=1, option="")
```
````

This method takes up to four arguments, two of which are optional:

1. **tds**: a {{tds}} object containing one attribute for each parameter to be calibrated. This is the  
{{tds}} object called `tdsPar`, defined in 
[](#calibration_classes_functions_observations_data_model).

2. **runner**: a `TRun`-inheriting instance that contains all the model information and whose type determines how 
the estimates are distributed: it can either be a `TSequentialRun` instance or `TThreadedRun` for 
distributed computations.
              
3. **ns** (optional): the number of samples to be produced. This parameter is not used in this context.

4. **option** (optional): the options that can be applied to the method. Options common to all calibration 
classes (those defined in the `TCalibration` class) are discussed in 
[](#calibration_classes_functions_observations_calibration_classes_running).

A crucial step in this constructor is the creation of the instance that inherits from `TRun`. As mentioned 
earlier, its type determines how the estimates are distributed. To construct such an object, one needs 
to provide an evaluator (the available evaluators are described in detail in [](#relauncher_teval)).

The final step is to construct the `TDistanceLikelihoodFunction`, a mandatory step that 
must always follow the constructor, using the `setDistance` method (following the prototype presented in [](#calibration_classes_functions_observations_data_and_distance_recommended_distance)).
Advanced users also have the option to define a custom distance by following the prototype:

````{only} cpp
```cpp
void setDistance(TDistanceLikelihoodFunction *distFunc, TDataServer *tdsRef, const char *input, const char *reference, const char *weight="");
```
````

````{only} py
```python
setDistance(distFunc, tdsRef, input, reference, weight="")
```
````