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# Learning Vector Quantization (newlvq)

Use `neurolab.net.newlvq()`

    import numpy as np
    import neurolab as nl

    # Create train samples
    input = np.array([[-3, 0], [-2, 1], [-2, -1], [0, 2], [0, 1], [0, -1], [0, -2],
                                                                                                                    [2, 1], [2, -1], [3, 0]])
    target = np.array([[1, 0], [1, 0], [1, 0], [0, 1], [0, 1], [0, 1], [0, 1],
                                                                                                                    [1, 0], [1, 0], [1, 0]])

    # Create network with 2 layers:4 neurons in input layer(Competitive)
    # and 2 neurons in output layer(liner)
    net = nl.net.newlvq(nl.tool.minmax(input), 4, [.6, .4])
    # Train network
    error = net.train(input, target, epochs=1000, goal=-1)

    # Plot result
    import pylab as pl
    xx, yy = np.meshgrid(np.arange(-3, 3.4, 0.2), np.arange(-3, 3.4, 0.2))
    xx.shape = xx.size, 1
    yy.shape = yy.size, 1
    i = np.concatenate((xx, yy), axis=1)
    o = net.sim(i)
    grid1 = i[o[:, 0]>0]
    grid2 = i[o[:, 1]>0]

    class1 = input[target[:, 0]>0]
    class2 = input[target[:, 1]>0]

    pl.plot(class1[:,0], class1[:,1], 'bo', class2[:,0], class2[:,1], 'go')
    pl.plot(grid1[:,0], grid1[:,1], 'b.', grid2[:,0], grid2[:,1], 'gx')
    pl.axis([-3.2, 3.2, -3, 3])
    pl.xlabel('Input[:, 0]')
    pl.ylabel('Input[:, 1]')
    pl.legend(['class 1', 'class 2', 'detected class 1', 'detected class 2'])
    pl.show()

Result: |

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