Constant Model¶

class pints.toy.ConstantModel(n, force_multi_output=False)[source]¶

Toy model that’s constant over time, linear over the parameters, mostly useful for unit testing.

For an n-dimensional model, evaluated with parameters p = [p_1, p_2, ..., p_n], the simulated values are time-invariant, so that for any time t

\[f(t) = (p_1, 2 p_2, 3 p_3, ..., n p_n)\]

The derivatives with respect to the parameters are time-invariant, and simply equal

\[\begin{split}\frac{\partial{f_i(t)}}{dp_j} = \begin{cases} i, i = j\\0, i \neq j \end{cases}\end{split}\]

Parameters:

n (int) – The number of parameters (and outputs) the model should have.
force_multi_output (boolean) – Set to True to always return output of the shape (n_times, n_outputs), even if n_outputs == 1.

Example

times = np.linspace(0, 1, 100)
m = pints.ConstantModel(2)
m.simulate([1, 2], times)

In this example, the returned output is [1, 4] at every point in time.

simulate(parameters, times)[source]¶: See pints.ForwardModel.simulate().

simulateS1(parameters, times)[source]¶: See pints.ForwardModel.simulateS1().

suggested_parameters()¶

Returns an NumPy array of the parameter values that are representative of the model.

For example, these parameters might reproduce a particular result that the model is famous for.

suggested_times()¶: Returns an NumPy array of time points that is representative of the model

Pints