singleOdorTest.py

from lib import epl 
from lib import OSN
from lib import readout
from lib import plots
import numpy as np
import pickle 

#Load Data
rf = open('./data/singleOdorData.pi', 'r');
trainingOdors = np.array(pickle.load(rf));
testOdors = np.array(pickle.load(rf));
rf.close();
nOdors = len(trainingOdors); 
nTestPerOdor = len(testOdors)/nOdors;  
print("Number of odors to train = " + str(len(trainingOdors))); 
print("Number of odors to test = " + str(len(testOdors))); 

#Network initialization
nMCs = len(trainingOdors[0]); 
GCsPerNeurogenesis = 5; 
nGCs = nMCs*GCsPerNeurogenesis*nOdors;     #every MC has 5 GCs per odor  
epl = epl.EPL(nMCs, nGCs, GCsPerNeurogenesis); 

#Sniff
def sniff(odor, learn_flag=0, nGammaPerOdor=5, gPeriod=40):
    sensorInput = OSN.OSN_encoding(odor); 
    for j in range(0, nGammaPerOdor):
        for k in range(0, gPeriod): 
            epl.update(sensorInput, learn_flag=learn_flag);
            pass; 
    epl.reset(); 

#Training
for i in range(0, len(trainingOdors)):
    print("Training odor " + str(i+1)) 
    sniff(trainingOdors[i], learn_flag=1);
    epl.GClayer.invokeNeurogenesis();
    sniff(trainingOdors[i], learn_flag=0);

#Testing
for i in range(0, len(testOdors)):
    sniff(testOdors[i], learn_flag=0); 
    if(i==len(testOdors)-1):
        print(str(i+1) + " odors tested"); 

#Readout
sMatrix = readout.readout(epl.gammaCode, nOdors, nTestPerOdor)  

#Plots
plots.plotFigure3b(epl.gammaCode); 
plots.plotFigure3d(sMatrix);