def main(RUNS = 10, numH = 5): """ Parkinsons """ # try: print ">>STARTING..."; lb_bench = lab_bencmark(); D = lb_bench.parkinsons(); D['name'] = 'Parkinsons'; DCrossVal = kfold.kfold(D = D['train'], numFolds = RUNS); netConfig = {'numI': 22, 'numO': 1, '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): """ Lung Cancer """ FOLDS = 2 # try: print ">>STARTING..." for i in xrange(RUNS): lb_bench = lab_bencmark() D = lb_bench.lung_cancer() D["name"] = "Lung_cancer" DCrossVal = kfold.kfold(D=D["train"], numFolds=FOLDS) netConfig = {"numI": 56, "numO": 1, "numH": numH} for ri in xrange(FOLDS): 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 test_iris(RUNS = 10): """ test for the iris dataset """ lab_data = lab_bencmark(); print ">>>IRIS"; D = lab_data.iris(); DCrossVal = kfold.kfold(D = D['train'], numFolds = RUNS); netConfig = {'numI': 4, 'numO': 1, 'numH': 2 }; for ri in xrange(RUNS): coevo = ndmCoevoOptim.ndmCoevoOptim(dataset_name = 'IRIS', 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(); coevo.coevolve(); del lab_data;
def test_sonar(RUNS = 10): """ SONAR """ lab_data = lab_bencmark(); D = lab_data.sonar(); coevo = ndmCoevoOptim.ndmCoevoOptim(dataset_name = 'SONAR', train_set = D['train'], test_set = D['test']); for ri in xrange(RUNS): coevo.init_populations(); #disable random inject coevo.params['randomNodesInject'] = False; m = coevo.coevolve(); del lab_data;
def test_xor(RUNS = 5,N = 10): """ test for XOR dataset """ lab_data = lab_bencmark(); #dataset divided into train and test as ratio of 70:30 D = lab_data.xor(N); netConfig = {'numI': D['test']['INFO']['num_inputs'], 'numO': D['test']['INFO']['num_outputs'], 'numH': 2 }; coevo = ndmCoevoOptim.ndmCoevoOptim(dataset_name = 'XOR', train_set = D['train'], valid_set = D['test'], test_set = D['test']); for ri in xrange(RUNS): #disable random inject coevo.params['randomNodesInject'] = False; coevo.init_populations(); # coevo.params['numI'] = m = coevo.coevolve();
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()");