# Read data from the file
data = np.loadtxt('MackeyGlass_t17.txt')
# Normalize the raw data
minMax = pp.MinMaxScaler((-1, 1))
data = minMax.fit_transform(data)
#Get only 6000 points
data = data[:5000].reshape((5000, 1))
# Number of points - 5000
trainingData, testingData = util.splitData2(data, 0.4)
nTesting = testingData.shape[0]
# Form feature vectors
inputTrainingData, outputTrainingData = util.formFeatureVectors(trainingData)
# Tune the network
size = 256
initialTransient = 50
# Input-to-reservoir fully connected
inputWeight = topology.ClassicInputTopology(
inputSize=inputTrainingData.shape[1],
reservoirSize=size).generateWeightMatrix()
# Reservoir-to-reservoir fully connected
reservoirWeight = topology.ClassicReservoirTopology(
size=size).generateWeightMatrix()
res = ESN.Reservoir(size=size,
data = np.loadtxt("MackeyGlass_t17.txt")
# Normalize the raw data
minMax = pp.MinMaxScaler((-1, 1))
data = minMax.fit_transform(data)
# Get only 6000 points
data = data[:6000].reshape((6000, 1))
# Split the data into training, validation and testing
trainingData, validationData, testingData = util.splitData(data, 0.6, 0.3, 0.1)
nValidation = validationData.shape[0]
nTesting = testingData.shape[0]
# Form feature vectors for training data
trainingInputData, trainingOutputData = util.formFeatureVectors(trainingData)
actualOutputData = minMax.inverse_transform(testingData)[:, 0]
# Initial seed
initialSeedForValidation = trainingData[-1]
networkSize = 500
populationSize = 10
noOfBest = int(populationSize / 2)
noOfGenerations = 10
predictedOutputData, bestPopulation = utilityGA.tuneTrainPredictConnectivityGA(
trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
validationOutputData=validationData,
initialInputSeedForValidation=initialSeedForValidation,
horizon=nTesting,
noOfBest=noOfBest,
data = np.loadtxt('MackeyGlass_t17.txt')
# Normalize the raw data
minMax = pp.MinMaxScaler((-1,1))
data = minMax.fit_transform(data)
#Get only 4000 points
data = data[:5000].reshape((5000, 1))
# Split the data into training, validation and testing
trainingData, validationData, testingData = util.splitData(data, 0.4, 0.4, 0.2)
nValidation = validationData.shape[0]
nTesting = testingData.shape[0]
# Form feature vectors for training data
trainingInputData, trainingOutputData = util.formFeatureVectors(trainingData)
validationInputData, validationOutputData = util.formFeatureVectors(validationData)
spectralRadiusBound = (0.0, 1.00)
inputScalingBound = (0.0, 1.0)
reservoirScalingBound = (0.0, 1.0)
leakingRateBound = (0.0, 1.0)
size = 256
initialTransient = 50
resTuner = tuner.ReservoirTuner(size=size,
initialTransient=initialTransient,
trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
validationInputData=validationInputData,
validationOutputData=validationOutputData,
spectralRadiusBound=spectralRadiusBound,
data = np.loadtxt('MackeyGlass_t17.txt')
# Normalize the raw data
minMax = pp.MinMaxScaler((-1, 1))
data = minMax.fit_transform(data)
#Get only 5000 points
data = data[:5000].reshape((5000, 1))
# Split the data into training, validation and testing
trainingData, validationData, testingData = util.splitData(data, 0.4, 0.4, 0.2)
nValidation = validationData.shape[0]
nTesting = testingData.shape[0]
# Form feature vectors for training data
trainingInputData, trainingOutputData = util.formFeatureVectors(trainingData)
actualOutputData = minMax.inverse_transform(testingData)[:, 0]
# Initial seed
initialSeedForValidation = trainingData[-1]
predictedOutputData = util.tuneTrainPredictConnectivityNonBrute(
trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
validationOutputData=validationData,
initialInputSeedForValidation=initialSeedForValidation,
horizon=nTesting,
resTopology=util.Topology.SmallWorldGraphs)
predictedOutputData = minMax.inverse_transform(predictedOutputData)
# Read data from the file
data = np.loadtxt('MackeyGlass_t17.txt')
# Normalize the raw data
minMax = pp.MinMaxScaler((-1,1))
data = minMax.fit_transform(data)
#Get only 6000 points
data = data[:5000].reshape((5000, 1))
# Number of points - 5000
trainingData, testingData = util.splitData2(data, 0.4)
nTesting = testingData.shape[0]
# Form feature vectors
inputTrainingData, outputTrainingData = util.formFeatureVectors(trainingData)
# Tune the network
size = 256
initialTransient = 50
# Input-to-reservoir fully connected
inputWeight = topology.ClassicInputTopology(inputSize=inputTrainingData.shape[1], reservoirSize=size).generateWeightMatrix()
# Reservoir-to-reservoir fully connected
reservoirWeight = topology.ClassicReservoirTopology(size=size).generateWeightMatrix()
res = ESN.Reservoir(size=size,
inputData=inputTrainingData,
outputData=outputTrainingData,
spectralRadius=0.79,
data = np.loadtxt('MackeyGlass_t17.txt')
# Normalize the raw data
minMax = pp.MinMaxScaler((-1, 1))
data = minMax.fit_transform(data)
#Get only 4000 points
data = data[:5000].reshape((5000, 1))
# Split the data into training, validation and testing
trainingData, validationData, testingData = util.splitData(data, 0.4, 0.4, 0.2)
nValidation = validationData.shape[0]
nTesting = testingData.shape[0]
# Form feature vectors for training data
trainingInputData, trainingOutputData = util.formFeatureVectors(trainingData)
validationInputData, validationOutputData = util.formFeatureVectors(
validationData)
spectralRadiusBound = (0.0, 1.00)
inputScalingBound = (0.0, 1.0)
reservoirScalingBound = (0.0, 1.0)
leakingRateBound = (0.0, 1.0)
size = 256
initialTransient = 50
resTuner = tuner.ReservoirTuner(size=size,
initialTransient=initialTransient,
trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
validationInputData=validationInputData,
validationOutputData=validationOutputData,
