# D-StreamII training objects
trainer = DStreamII(complexity=0, numInputs=1, discreteOutputs=0, discreteInputs=0, appFieldsDict= {'gridSize': [0.25], 'gridUpperRange':[25], 'gridLowerRange':[0]});
trainInputData = []
trainOutputData = []
# Train to determine the grid size
with open("trace.txt", mode='r') as fp:
for line in fp:
dataInfo = line.split()
trainInputData.append(dataInfo)
with open("trace_obs.txt", mode='r') as fp:
for line in fp:
dataInfo = line.split()
trainOutputData.append(dataInfo)
trainer.addBatchObservations(trainInputData, trainOutputData);
trainer.train();
inputData = []
#Execute D-Stream II clustering algorithm
with open("power_use.txt") as fp:
for line in fp:
dataInfo = line.split()
trainer.execute(dataInfo)
trainer.printClusters()
for trainingSample in range(numTrainingSamples):
inputRow = next(inputReader);
outputRow = next(outputReader);
if (len(inputRow) > 0):
input1 = float(inputRow[0]);
input2 = float(inputRow[1]);
output = float(outputRow[0]);
firstTS = time.time();
dStreamIITest.addSingleObservation([input1, input2], output);
secondTS = time.time();
dStreamIITimestamps["load" + str(trainingSample)] = secondTS - firstTS;
firstTS = time.time();
dStreamIITest.train();
secondTS = time.time();
dStreamIITimestamps["train"] = secondTS - firstTS;
runningTotal = 0;
for executeSample in range(numExecuteSamples):
inputRow = next(inputReader);
outputRow = next(outputReader);
if (len(inputRow) > 0):
input1 = float(inputRow[0]);
output = float(outputRow[0]);
firstTS = time.time();
dStreamIITest.execute([input1, input2]);
theor = output;
secondTS = time.time();
'gridLowerRange': [0]
})
trainInputData = []
trainOutputData = []
# Train to determine the grid size
with open("trace.txt", mode='r') as fp:
for line in fp:
dataInfo = line.split()
trainInputData.append(dataInfo)
with open("trace_obs.txt", mode='r') as fp:
for line in fp:
dataInfo = line.split()
trainOutputData.append(dataInfo)
trainer.addBatchObservations(trainInputData, trainOutputData)
trainer.train()
inputData = []
#Execute D-Stream II clustering algorithm
with open("power_use.txt") as fp:
for line in fp:
dataInfo = line.split()
trainer.execute(dataInfo)
trainer.printClusters()
for trainingSample in range(numTrainingSamples):
inputRow = next(inputReader)
outputRow = next(outputReader)
if (len(inputRow) > 0):
input1 = float(inputRow[0])
input2 = float(inputRow[1])
output = float(outputRow[0])
firstTS = time.time()
dStreamIITest.addSingleObservation([input1, input2], output)
secondTS = time.time()
dStreamIITimestamps["load" + str(trainingSample)] = secondTS - firstTS
firstTS = time.time()
dStreamIITest.train()
secondTS = time.time()
dStreamIITimestamps["train"] = secondTS - firstTS
runningTotal = 0
for executeSample in range(numExecuteSamples):
inputRow = next(inputReader)
outputRow = next(outputReader)
if (len(inputRow) > 0):
input1 = float(inputRow[0])
output = float(outputRow[0])
firstTS = time.time()
dStreamIITest.execute([input1, input2])
theor = output
secondTS = time.time()
