pyclustering/core/som_wrapper.py
def som_load(som_pointer, weights, award, capture_objects):
    """!
    @brief Load dump of the network to SOM.
    @details Initialize SOM using existed weights, amount of captured objects by each neuron, captured
              objects by each neuron. Initialization is not performed if weights are empty.

    @param[in] som_pointer (POINTER): pointer to object of self-organized map.
    @param[in] weights (list): weights that should assigned to neurons.
    @param[in] awards (list): amount of captured objects by each neuron.
    @param[in] capture_objects (list): captured objects by each neuron.

    """

    if len(weights) == 0:
        return

    ccore = ccore_library.get()

    package_weights = package_builder(weights, c_double).create()
    package_award = package_builder(award, c_size_t).create()
    package_capture_objects = package_builder(capture_objects, c_size_t).create()

    ccore.som_load(som_pointer, package_weights, package_award, package_capture_objects)
def som_load(som_pointer, weights, award, capture_objects):
    """!
    @brief Load dump of the network to SOM.
    @details Initialize SOM using existed weights, amount of captured objects by each neuron, captured
              objects by each neuron. Initialization is not performed if weights are empty.

    @param[in] som_pointer (POINTER): pointer to object of self-organized map.
    @param[in] weights (list): weights that should assigned to neurons.
    @param[in] awards (list): amount of captured objects by each neuron.
    @param[in] capture_objects (list): captured objects by each neuron.

    """

    if len(weights) == 0:
        return

    ccore = ccore_library.get()

    package_weights = package_builder(weights, c_double).create()
    package_award = package_builder(award, c_size_t).create()
    package_capture_objects = package_builder(capture_objects, c_size_t).create()

    ccore.som_load(som_pointer, package_weights, package_award, package_capture_objects)
def som_load ( som_pointer , weights , award , capture_objects ) : if len ( weights ) == 0 : return ccore = ccore_library . get ( ) package_weights = package_builder ( weights , c_double ) . create ( ) package_award = package_builder ( award , c_size_t ) . create ( ) package_capture_objects = package_builder ( capture_objects , c_size_t ) . create ( ) ccore . som_load ( som_pointer , package_weights , package_award , package_capture_objects )
!
    @brief Load dump of the network to SOM.
    @details Initialize SOM using existed weights, amount of captured objects by each neuron, captured
              objects by each neuron. Initialization is not performed if weights are empty.

    @param[in] som_pointer (POINTER): pointer to object of self-organized map.
    @param[in] weights (list): weights that should assigned to neurons.
    @param[in] awards (list): amount of captured objects by each neuron.
    @param[in] capture_objects (list): captured objects by each neuron.
!