def __init__(self):
self.instances = self.initializeInstances()
self.inputLayer = 7
self.hiddenLayer = 5
self.outputLayer = 1
self.trainingIterations = 1000
self.factory = BackPropagationNetworkFactory()
self.measure = SumOfSquaresError()
self.set = DataSet(self.instances)
self.networks = [None] * 3
self.nnop = [None] * 3
self.oa = [None] * 3
self.oaNames = ["RHC", "SA", "GA"]
self.results = ""
class AbaloneTest:
def __init__(self):
self.instances = self.initializeInstances()
self.inputLayer = 7
self.hiddenLayer = 5
self.outputLayer = 1
self.trainingIterations = 1000
self.factory = BackPropagationNetworkFactory()
self.measure = SumOfSquaresError()
self.set = DataSet(self.instances)
self.networks = [None] * 3
self.nnop = [None] * 3
self.oa = [None] * 3
self.oaNames = ["RHC", "SA", "GA"]
self.results = ""
def run(self):
for i in range(len(self.oa)):
self.networks[i] = self.factory.createClassificationNetwork(
[self.inputLayer, self.hiddenLayer, self.outputLayer]
)
self.nnop[i] = NeuralNetworkOptimizationProblem(self.set, self.networks[i], self.measure)
self.oa[0] = RandomizedHillClimbing(self.nnop[0])
self.oa[1] = SimulatedAnnealing(1e11, 0.95, self.nnop[1])
self.oa[2] = StandardGeneticAlgorithm(200, 100, 10, self.nnop[2])
for i in range(len(self.oa)):
start = time.time()
correct = 0
incorrect = 0
self.train(self.oa[i], self.networks[i], self.oaNames[i]) # trainer.train()
end = time.time()
trainingTime = end - start
optimalInstance = self.oa[i].getOptimal()
self.networks[i].setWeights(optimalInstance.getData())
start = time.time()
for j in range(len(self.instances)):
self.networks[i].setInputValues(self.instances[j].getData())
self.networks[i].run()
predicted = self.instances[j].getLabel().toString()
actual = self.networks[i].getOutputValues().toString()
if abs(float(predicted) - float(actual)) < 0.5:
correct = correct + 1
else:
incorrect = incorrect + 1
end = time.time()
testingTime = end - start
results = [
"Results for " + self.oaNames[i] + ": ",
"Correctly classified " + str(correct) + " instances.",
"Incorrectly classified " + str(incorrect) + " instances.",
"Percent correctly classified: ",
str(float(correct) / (correct + incorrect) * 100) + "%",
"Training time: " + str(trainingTime) + " seconds",
"Testing time: " + str(testingTime) + " seconds",
]
print "\n".join(results)
# System.out.println(results)
def train(self, oa, network, oaName):
print ("\nError results for " + oaName + "\n---------------------------")
res = []
for i in range(self.trainingIterations):
oa.train()
error = 0
for j in range(len(self.instances)):
# print "self.instances[j].getData()" + str(self.instances[j].getData())
network.setInputValues(self.instances[j].getData())
# print network.__class__
network.run()
output = self.instances[j].getLabel()
# print "abalonetest.train.self.instances[j].getLabel(): " + str(self.instances[j].getLabel().toString())
print "abalonetest.train.network.getOutputValues(): " + str(network.getOutputValues().toString())
example = Instance(data=network.getOutputValues())
example.setLabel(Instance(data=network.getOutputValues()))
error = error + self.measure.value(output, example)
res.append(error)
# for a,b,c in zip(res[::3],res[1::3],res[2::3]):
# print '{:<10}{:<10}{:<}'.format(a,b,c)
print res
# 0.455,0.365,0.095,0.514,0.2245,0.101,0.15,15
# 0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,7
# 0.53,0.42,0.135,0.677,0.2565,0.1415,0.21,9
# 0.44,0.365,0.125,0.516,0.2155,0.114,0.155,10
# 0.33,0.255,0.08,0.205,0.0895,0.0395,0.055,7
# 0.425,0.3,0.095,0.3515,0.141,0.0775,0.12,8
# 0.53,0.415,0.15,0.7775,0.237,0.1415,0.33,20
# 0.545,0.425,0.125,0.768,0.294,0.1495,0.26,16
# 0.475,0.37,0.125,0.5095,0.2165,0.1125,0.165,9
# 0.55,0.44,0.15,0.8945,0.3145,0.151,0.32,19
def initializeInstances(self):
# attributes = double[4177][][]
# Basically read the CSV.
attributes = []
with open("./src/opt/test/abalone.txt", "rb") as csvfile:
spamreader = csv.reader(csvfile, delimiter=",", quotechar="|")
for row in spamreader:
if int(row[-1]) < 15:
attributes.append(row[:-1] + [row[-1]] + [0])
else:
attributes.append(row[:-1] + [row[-1]] + [1])
instances = [None] * 100 # len(attributes)
for i in range(len(instances)):
instances[i] = Instance(ds=attributes[i][:-2])
# print instances[i].toString()
instances[i].setLabel(Instance(val=attributes[i][-1]))
# print attributes[i][-1]
# print "instances[i].getLabel().toString()" + str(instances[i].getLabel().toString())
return instances
