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()
Beispiel #2
0
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()