net.py 文件源码

def newlvq(minmax, cn0, pc):
    """
    Create a learning vector quantization (LVQ) network

    :Parameters:
        minmax: list of list, the outer list is the number of input neurons, 
            inner lists must contain 2 elements: min and max
            Range of input value
        cn0: int
            Number of neurons in input layer
        pc: list
            List of percent, sum(pc) == 1
    :Returns:
        net: Net
    :Example:
        >>> # create network with 2 inputs,
        >>> # 2 layers and 10 neurons in each layer
        >>> net = newlvq([[-1, 1], [-1, 1]], 10, [0.6, 0.4])

    """
    pc = np.asfarray(pc)
    assert sum(pc) == 1
    ci = len(minmax)
    cn1 = len(pc)
    assert cn0 > cn1

    layer_inp = layer.Competitive(ci, cn0)
    layer_out = layer.Perceptron(cn0, cn1, trans.PureLin())
    layer_out.initf = None
    layer_out.np['b'].fill(0.0)
    layer_out.np['w'].fill(0.0)
    inx = np.floor(cn0 * pc.cumsum())
    for n, i in enumerate(inx):
        st = 0 if n == 0 else inx[n - 1]
        layer_out.np['w'][n][st:i].fill(1.0)
    net = Net(minmax, cn1, [layer_inp, layer_out],
                            [[-1], [0], [1]], train.train_lvq, error.MSE())

    return net