slow_sine.py

###########################################################
# slow sine genration task with standard and BP ESN
#
# see Jaeger H. (2002), "Tutorial on training recurrent
# neural networks, covering BPPT, RTRL, EKF and the
# echo state network approach"
#
# 2007, Georg Holzmann
###########################################################

import numpy as N
import pylab as P
from aureservoir import *
import sys
sys.path.append("../")
from aureservoir import *
import errorcalc, filteresn


###########################################################
# FUNCTIONS

def setup_STD_ESN():
        """ configuration of a standard ESN """
        net = DoubleESN()
        net.setSize(20)
        net.setInputs(1)
        net.setOutputs(1)
        net.setInitParam( CONNECTIVITY, 0.2 )
        net.setInitParam( ALPHA, 0.45 )
        net.setInitParam( IN_CONNECTIVITY, 0. )
        net.setInitParam( IN_SCALE, 0. )
        net.setInitParam( FB_CONNECTIVITY, 1. )
        net.setInitParam( FB_SCALE, 1. )
        net.setReservoirAct( ACT_TANH )
        net.setOutputAct( ACT_LINEAR )
        net.setSimAlgorithm( SIM_STD )
        net.setTrainAlgorithm( TRAIN_PI )
        trainnoise = 1e-6
        testnoise = 0.
        return net, trainnoise, testnoise

def setup_ESN_BP():
        """ configuration of a bandpass ESN with cutoff frequency
        at the sine frequency """
        net = filteresn.BPESN()
        net.setSize(20)
        net.setInputs(1)
        net.setOutputs(1)
        net.setInitParam( CONNECTIVITY, 0.2 )
        net.setInitParam( ALPHA, 0.3 )
        net.setInitParam( IN_CONNECTIVITY, 0. )
        net.setInitParam( IN_SCALE, 0. )
        net.setInitParam( FB_CONNECTIVITY, 1. )
        net.setInitParam( FB_SCALE, 1. )
        net.setReservoirAct( ACT_TANH )
        net.setOutputAct( ACT_LINEAR )
        net.setSimAlgorithm( SIM_BP )
        net.setTrainAlgorithm( TRAIN_PI )
        net.setConstCutoffs(0.01, 0.01) # sine frequency
        trainnoise = 0.
        testnoise = 0.
        return net, trainnoise, testnoise

def generate_slow_sine(size,ampl=1):
        """ generates a slow sinewave:
            y[n] = ampl * sin(n/100)
            omega = 2*pi*f -> f = 0.0015915494309189536
            -> Periode = 628.318 """
        x = N.arange( float(size) )
        y = ampl*N.sin( x/100 )
        return y

def get_esn_data(signal,trainsize,testsize):
        """ returns trainin, trainout, testin, testout """
        
        trainout = signal[0:trainsize]
        trainout.shape = 1,-1
        trainin = N.zeros(trainout.shape)
        
        testout = signal[trainsize:trainsize+testsize]
        testout.shape = 1,-1
        testin = N.zeros(testout.shape)
        
        return trainin, trainout, testin, testout

def plot(esnout,testout):
        """ plotting """
        P.title('Original=blue, ESNout=red')
        P.plot(testout,'b',esnout,'r')
        P.show()


###########################################################
# MAIN

trainsize = 4000
washout = 2000
testsize = 8000

# choose ESN: compare STD-ESN with BP-ESN
net, trainnoise, testnoise = setup_STD_ESN()
#net, trainnoise, testnoise = setup_ESN_BP()
net.init()

# generate signals
slsine = generate_slow_sine(trainsize+testsize)
trainin, trainout, testin, testout = get_esn_data(slsine,trainsize,testsize)

# ESN training
net.setNoise(trainnoise)
net.train(trainin,trainout,washout)
print "output weights:"
print "\tmean: ", net.getWout().mean(), "\tmax: ", abs(net.getWout()).max()

# ESN simulation
esnout = N.empty(testout.shape)
net.setNoise(testnoise)
net.simulate(testin,esnout)
print "\nNRMSE: ", errorcalc.nrmse( esnout, testout, 50 )
plot(esnout,testout)

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