net.py 文件源码

def newhem(target, transf=None, max_iter=10, delta=0):
    """
    Create a Hemming recurrent network with 2 layers

    :Parameters:
        target: array like (l x net.co)
            train target patterns
        transf: func (default SatLinPrm(0.1, 0, 10))
            Activation function of input layer
        max_init: int (default 10)
            Maximum of recurrent iterations
        delta: float (default 0)
            Minimum dereference between 2 outputs for stop recurrent cycle
    :Returns:
        net: Net
    :Example:
        >>> net = newhop([[-1, -1, -1], [1, -1, 1]])
        >>> output = net.sim([[-1, 1, -1], [1, -1, 1]])

    """

    target = np.asfarray(target)
    assert target.ndim == 2

    cn = target.shape[0]
    ci = target.shape[1]

    if transf is None:
        transf = trans.SatLinPrm(0.1, 0, 10)
    layer_inp = layer.Perceptron(ci, cn, transf)

    # init input layer
    layer_inp.initf = None
    layer_inp.np['b'][:] = float(ci) / 2
    for i, tar in enumerate(target):
        layer_inp.np['w'][i][:] = tar / 2

    layer_out = layer.Reccurent(cn, cn, trans.SatLinPrm(1, 0, 1e6), max_iter, delta)
    # init output layer
    layer_out.initf = None
    layer_out.np['b'][:] = 0
    eps = - 1.0 / cn
    for i in range(cn):
        layer_out.np['w'][i][:] = [eps] * cn
        layer_out.np['w'][i][i] = 1
    # create network
    minmax = [[-1, 1]] * ci
    layers = [layer_inp, layer_out]
    connect = [[-1], [0], [1]]
    net = Net(minmax, cn, layers, connect, None, None)
    return net