*************************************
Learning Vector Quantization (newlvq)
*************************************

Use  :py:func:`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:
	.. image:: _static/newlvq.png