import numpy as np
from DStreamII import DStreamII
# 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()
import numpy as np
from DStreamII import DStreamII
# 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()
## Import your algorithms here.
from DStreamII import DStreamII
## For different tests, these values will vary.
inputFilePath = "DStreamIITestInput.csv"
outputFilePath = "DStreamIITestOutput.csv"
numTrainingSamples = 30;
numExecuteSamples = 100;
inputFile = open(inputFilePath);
outputFile = open(outputFilePath);
inputReader = csv.reader(inputFile);
outputReader = csv.reader(outputFile);
## Change the name of the algorithm to test it out.
dStreamIITest = DStreamII(0, 2, 0, [0, 0],{'gridSize': [1,1], 'gridUpperRange':[10,10], 'gridLowerRange':[0,0]});
dStreamIITimestamps = {};
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;
## Import your algorithms here.
from DStreamII import DStreamII
## For different tests, these values will vary.
inputFilePath = "DStreamIITestInput.csv"
outputFilePath = "DStreamIITestOutput.csv"
numTrainingSamples = 30;
numExecuteSamples = 100;
inputFile = open(inputFilePath);
outputFile = open(outputFilePath);
inputReader = csv.reader(inputFile);
outputReader = csv.reader(outputFile);
## Change the name of the algorithm to test it out.
dStreamIITest = DStreamII(0, 2, 0, [0, 0],{'gridSize': [1,1], 'gridUpperRange':[10,10], 'gridLowerRange':[0,0], 'key':'AESKEY'});
dStreamIITimestamps = {};
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;
## For different tests, these values will vary.
inputFilePath = "DStreamIITestInput.csv"
outputFilePath = "DStreamIITestOutput.csv"
numTrainingSamples = 30
numExecuteSamples = 100
inputFile = open(inputFilePath)
outputFile = open(outputFilePath)
inputReader = csv.reader(inputFile)
outputReader = csv.reader(outputFile)
## Change the name of the algorithm to test it out.
dStreamIITest = DStreamII(0, 2, 0, [0, 0], {
'gridSize': [1, 1],
'gridUpperRange': [10, 10],
'gridLowerRange': [0, 0]
})
dStreamIITimestamps = {}
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()
