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()");
