.. currentmodule:: playdoh

.. _example-maximize_groups:

Example: maximize_groups
========================

Example of :func:`maximize` with several groups. Groups allow to optimize
a fitness function with different parameters in parallel but by
vectorizing the fitness evaluation for all groups.

::

    from playdoh import *
    import numpy
    
    
    # The fitness function is a Gaussian with different centers for
    # different groups ``shared_data`` contains the different centers.
    def fun(x, y, nodesize, shared_data, groups):
        # Expand ``x0`` and ``y0`` to match the total population size
        x0 = numpy.kron(shared_data['x0'], numpy.ones(nodesize / groups))
        y0 = numpy.kron(shared_data['y0'], numpy.ones(nodesize / groups))
        # Compute the Gaussian for all centers in a vectorized fashion
        result = numpy.exp(-(x - x0) ** 2 - (y - y0) ** 2)
        return result
    
    
    if __name__ == '__main__':
        # Maximize the fitness function in parallel
        results = maximize(fun,
                           popsize=50,  # size of the population for each group
                           maxiter=10,  # maximum number of iterations
                           cpu=1,  # number of CPUs to use on the local machine
                           groups=3,  # number of groups
                           algorithm=CMAES,  # optimization algorithm, can be PSO,
                                             # GA or CMAES
                           shared_data={'x0': [0, 1, 2],  # centers of the Gaussian
                                                          # for each group
                                        'y0': [3, 4, 5]},
                           x_initrange=[-10, 10],  # initial interval for the
                                                     # ``x`` parameter
                           y_initrange=[-10, 10])  # initial interval for the
                                                     # ``y`` parameter
    
        # Display the final result in a table
        print_table(results)