def main(RUNS = 10, numH = 2): """ Cancer """ # try: print ">>STARTING..."; proben = proben1(); D = proben.breast_cancer(); DCrossVal = kfold.kfold(D = D['train'], numFolds = RUNS); netConfig = {'numI': D['train']['INFO']['num_inputs'], 'numO': D['train']['INFO']['num_outputs'], 'numH': numH }; for ri in xrange(RUNS): print ">>>> RUN {0} of {1}".format(ri, RUNS); print "ON :", D['name']; coevo = ndmCoevoOptim.ndmCoevoOptim(dataset_name = D['name'], train_set = DCrossVal[ri][0], valid_set = DCrossVal[ri][1], test_set = D['test'], netConfig = netConfig); coevo.init_populations(); coevo.coevolve();
def main(RUNS = 10, numH = 2): """ Card """ try: print ">>STARTING..."; proben = proben1(); D = proben.australian_cc(); DCrossVal = kfold.kfold(D = D['train'], numFolds = RUNS); netConfig = {'numI': D['train']['INFO']['num_inputs'], 'numO': D['train']['INFO']['num_outputs'], 'numH': numH }; for ri in xrange(RUNS): print ">>>> RUN {0} of {1}".format(ri, RUNS); print "ON :", D['name']; coevo = ndmCoevoOptim.ndmCoevoOptim(dataset_name = D['name'], train_set = DCrossVal[ri][0], valid_set = DCrossVal[ri][1], test_set = D['test'], netConfig = netConfig); coevo.init_populations(); coevo.coevolve(); #send notification #notify.noticeEMail(D['name']+' DONE'); except: """ """ print "ERROR";
def test_glass(RUNS = 10): """ GLASS """ proben = proben1(); D = proben.glass(); DCrossVal = kfold.kfold(D = D['train'], numFolds = RUNS); netConfig = {'numI': D['test']['INFO']['num_inputs'], 'numO': D['test']['INFO']['num_outputs'], 'numH': 2 }; for ri in xrange(RUNS): coevo = ndmCoevoOptim.ndmCoevoOptim(dataset_name = D['name'], train_set = DCrossVal[ri][0], valid_set = DCrossVal[ri][1], test_set = D['test'], netConfig = netConfig); #disable random inject coevo.params['randomNodesInject'] = False; coevo.init_populations(); m = coevo.coevolve(); del proben;
def main(RUNS=10, numH=2): """ Horse """ # try: print ">>STARTING..." proben = proben1() D = proben.horse() DCrossVal = kfold.kfold(D=D["train"], numFolds=RUNS) netConfig = {"numI": D["train"]["INFO"]["num_inputs"], "numO": D["train"]["INFO"]["num_outputs"], "numH": numH} for ri in xrange(RUNS): print ">>>> RUN {0} of {1}".format(ri, RUNS) print "ON :", D["name"] coevo = ndmCoevoOptim.ndmCoevoOptim( dataset_name=D["name"], train_set=DCrossVal[ri][0], valid_set=DCrossVal[ri][1], test_set=D["test"], netConfig=netConfig, ) coevo.init_populations() coevo.coevolve()
import ndmModel; import numpy as np; import datasets; from benchmark import proben1_bechmark as proben1; from benchmark import lab_bencmark; import kfold; import profile; import visualisation.visualiseOutputs2D as vis2d; from PyQt4 import QtCore, QtGui from visualiseNDMNet import *; coevo = ndmCoevoOptim.ndmCoevoOptim(); errors_train =[]; errors_test = []; benchmark = proben1(); lab_bencmark = lab_bencmark(); K = 10; # D = kfold.kfold(D = benchmark.mushroom()['train'],numFolds = K); D2 = kfold.kfold(D = lab_bencmark.iris()['train'],numFolds = K); for i in xrange(1): print ">>>", i; coevo.init_populations(); # coevo.train_set = D2[i][0]; # coevo.validation_set = D2[i][1]; profile.run("coevo.coevolve()");