net.py 文件源码

def newhop(target, transf=None, max_init=10, delta=0):
    """
    Create a Hopfield recurrent network

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

    """

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

    ci = len(target[0])
    if transf is None:
        transf = trans.HardLims()
    l = layer.Reccurent(ci, ci, transf, max_init, delta)
    w = l.np['w']
    b = l.np['b']

    # init weight
    for i in range(ci):
        for j in range(ci):
            if i == j:
                w[i, j] = 0.0
            else:
                w[i, j] = np.sum(target[:, i] * target[:, j]) / ci
        b[i] = 0.0
    l.initf = None

    minmax = transf.out_minmax if hasattr(transf, 'out_minmax') else [-1, 1]

    net = Net([minmax] * ci, ci, [l], [[-1], [0]], None, None)
    return net