# In Kohonen Feature Map, neurons are organizing themselves according to certain input values.
# wse 4x4 neutrons and plot the results as line. 
# (c) Chekanov

from jhplot  import  *
from jhpro.nnet import *
from java.util import Random
from java.awt import Color
import math


# make empty canvas in some range
c1 =HPlot("Canvas")
c1.visible()
c1.setLegend(0)
c1.setRange(0,100,0,150)
c1.setMarginLeft(70)
c1.setNameX("X")
c1.setNameY("Y")

p1= P1D("X-Y data")
rand = Random(10)

inputSize=100   # number of random points in 2D 
for i in range(100):
      x=i+10*rand.nextGaussian()
      y=50+30*math.cos(0.2*x) + 10*rand.nextGaussian()
      p1.add(x,y)
c1.draw(p1)


kfm=KohonenFeatureMap()
mapSizeX=4   # map size is 4x4 neurons
maxCycle=100000000
im = InputMatrix(inputSize, 2);
kfm.setMaxLearningCycles(maxCycle);
kfm.createMapLayer(mapSizeX,mapSizeX ) # create a map in X-X
kfm.setStopArea(0.01)                  # stop learning here
kfm.setInitActivationArea(1)
kfm.setInitLearningRate(0.6)

im.setInputXY(p1)
kfm.connectLayers(im);
  
i=0
# to represent outputs
p2=P1D("weights")
p2.setColor(Color.red)
p2.setStyle("l")
    
while kfm.finishedLearning() == False:
    kfm.learn()
    i=i+1
    if (i%50 ==0):
       weights=kfm.getWeightValues()
       p2.clear(); c1.clearData()
       print "print  rate=",kfm.getLearningRate(), "Activation area=",kfm.getActivationArea(), "Elapsed time=",kfm.getElapsedTime()
       for j in range(mapSizeX*mapSizeX):  
                       p2.add(weights[0][j], weights[1][j])
       p2.sort(0) # to draw a line, we should sort the result in X 
       c1.draw(p1)
       c1.draw(p2)
 
 
print "Final activation area=",kfm.getStopArea()
p3=P1D("weights")
p3.setColor(Color.red)
p3.setStyle("l")

print p2