def __reservoirTrain__(self, x):
#Extract the parameters
spectralRadius = x[0]
inputScaling = x[1]
reservoirScaling = x[2]
leakingRate = x[3]
#Create the reservoir
res = classicESN.Reservoir(size=self.size,
spectralRadius=spectralRadius,
inputScaling=inputScaling,
reservoirScaling=reservoirScaling,
leakingRate=leakingRate,
initialTransient=self.initialTransient,
inputData=self.trainingInputData,
outputData=self.trainingOutputData,
inputWeightRandom=self.inputWeightRandom,
reservoirWeightRandom=self.reservoirWeightRandom)
#Train the reservoir
res.trainReservoir()
#Predict for the validation data
predictedOutputData = util.predictFuture(res, self.initialSeed, self.horizon)
#Calcuate the regression error
errorFunction = rmse.MeanSquareError()
regressionError = errorFunction.compute(self.validationOutputData, predictedOutputData)
#Return the error
print("\nThe Parameters: "+str(x)+" Regression error:"+str(regressionError))
return regressionError
def __reservoirTrain__(self, x):
#Extract the parameters
meanDegree, beta = x
meanDegree = int(meanDegree)
# To get rid off the randomness in assigning weights, run it 10 times and take the average error
times = 100
cumulativeError = 0
for i in range(times):
# Input and weight connectivity Matrix
inputWeightMatrix = topology.ClassicInputTopology(
self.inputD, self.size).generateWeightMatrix()
reservoirWeightMatrix = topology.SmallWorldGraphs(
size=self.size, meanDegree=meanDegree,
beta=beta).generateWeightMatrix()
#Create the reservoir
res = classicESN.Reservoir(
size=self.size,
spectralRadius=self.spectralRadius,
inputScaling=self.inputScaling,
reservoirScaling=self.reservoirScaling,
leakingRate=self.leakingRate,
initialTransient=self.initialTransient,
inputData=self.trainingInputData,
outputData=self.trainingOutputData,
inputWeightRandom=inputWeightMatrix,
reservoirWeightRandom=reservoirWeightMatrix)
#Train the reservoir
res.trainReservoir()
# Warm up
predictedTrainingOutputData = res.predict(
self.trainingInputData[-self.initialTransient:])
#Predict for the validation data
predictedOutputData = util.predictFuture(res, self.initialSeed,
self.horizon)
gc.collect()
#Calcuate the regression error
errorFunction = metrics.MeanSquareError()
error = errorFunction.compute(self.validationOutputData,
predictedOutputData)
cumulativeError += error
regressionError = cumulativeError / times
#Return the error
print("\nThe Parameters: " + str(x) + " Regression error:" +
str(regressionError))
return regressionError
def evaluate(self, x):
# Extract the parameters
attachment = int(x[0, 0])
# To get rid off the randomness in assigning weights, run it 10 times and take the average error
times = 1
cumulativeError = 0
for i in range(times):
# Input and weight connectivity Matrix
inputWeightMatrix = topology.ClassicInputTopology(
self.inputD, self.size).generateWeightMatrix()
network = topology.ScaleFreeNetworks(size=self.size,
attachmentCount=attachment)
reservoirWeightMatrix = network.generateWeightMatrix()
# Create the reservoir
res = esn.Reservoir(size=self.size,
spectralRadius=self.spectralRadius,
inputScaling=self.inputScaling,
reservoirScaling=self.reservoirScaling,
leakingRate=self.leakingRate,
initialTransient=self.initialTransient,
inputData=self.trainingInputData,
outputData=self.trainingOutputData,
inputWeightRandom=inputWeightMatrix,
reservoirWeightRandom=reservoirWeightMatrix)
# Train the reservoir
res.trainReservoir()
# Warm up
predictedTrainingOutputData = res.predict(
self.trainingInputData[-self.initialTransient:])
# Predict for the validation data
predictedOutputData = util.predictFuture(res, self.initialSeed,
self.horizon)
gc.collect()
# Calculate the regression error
errorFunction = metrics.MeanSquareError()
error = errorFunction.compute(self.validationOutputData,
predictedOutputData)
cumulativeError += error
regressionError = cumulativeError / times
# Return the error
#print("Attachment: "+str(attachment) + "Error: "+str(regressionError))
return regressionError
def evaluate(self, x):
# Extract the parameters
meanDegree = int(x[0,0])
beta = x[0,1]
# To get rid off the randomness in assigning weights, run it 10 times and take the average error
times = 1
cumulativeError = 0
for i in range(times):
# Input and weight connectivity Matrix
inputWeightMatrix = topology.ClassicInputTopology(self.inputD, self.size).generateWeightMatrix()
network = topology.SmallWorldGraphs(size=self.size, meanDegree=meanDegree, beta=beta)
reservoirWeightMatrix = network.generateWeightMatrix()
# Create the reservoir
res = esn.Reservoir(size=self.size,
spectralRadius=self.spectralRadius,
inputScaling=self.inputScaling,
reservoirScaling=self.reservoirScaling,
leakingRate=self.leakingRate,
initialTransient=self.initialTransient,
inputData=self.trainingInputData,
outputData=self.trainingOutputData,
inputWeightRandom=inputWeightMatrix,
reservoirWeightRandom=reservoirWeightMatrix)
# Train the reservoir
res.trainReservoir()
# Warm up
predictedTrainingOutputData = res.predict(self.trainingInputData[-self.initialTransient:])
# Predict for the validation data
predictedOutputData = util.predictFuture(res, self.initialSeed, self.horizon)
gc.collect()
# Calculate the regression error
errorFunction = metrics.MeanSquareError()
error = errorFunction.compute(self.validationOutputData, predictedOutputData)
cumulativeError += error
regressionError = cumulativeError/times
# Return the error
#print("SMG parameters: "+str(x) + "Error: "+str(regressionError))
return regressionError
def __reservoirTrain__(self, x):
#Extract the parameters
attachment = int(x)
# To get rid off the randomness in assigning weights, run it 10 times and take the average error
times = 100
cumulativeError = 0
for i in range(times):
# Input and weight connectivity Matrix
inputWeightMatrix = topology.ClassicInputTopology(self.inputD, self.size).generateWeightMatrix()
reservoirWeightMatrix = topology.ScaleFreeNetworks(size=self.size, attachmentCount=attachment).generateWeightMatrix()
#Create the reservoir
res = classicESN.Reservoir(size=self.size,
spectralRadius=self.spectralRadius,
inputScaling=self.inputScaling,
reservoirScaling=self.reservoirScaling,
leakingRate=self.leakingRate,
initialTransient=self.initialTransient,
inputData=self.trainingInputData,
outputData=self.trainingOutputData,
inputWeightRandom=inputWeightMatrix,
reservoirWeightRandom=reservoirWeightMatrix)
#Train the reservoir
res.trainReservoir()
# Warm up
predictedTrainingOutputData = res.predict(self.trainingInputData[-self.initialTransient:])
#Predict for the validation data
predictedOutputData = util.predictFuture(res, self.initialSeed, self.horizon)
gc.collect()
#Calcuate the regression error
errorFunction = metrics.MeanSquareError()
error = errorFunction.compute(self.validationOutputData, predictedOutputData)
cumulativeError += error
regressionError = cumulativeError/times
#Return the error
print("\nThe Parameters: "+str(x)+" Regression error:"+str(regressionError))
return regressionError
def __reservoirTrain__(self, x):
#Extract the parameters
spectralRadius = x[0]
inputScaling = x[1]
reservoirScaling = x[2]
leakingRate = x[3]
#Create the reservoir
res = classicESN.Reservoir(
size=self.size,
spectralRadius=spectralRadius,
inputScaling=inputScaling,
reservoirScaling=reservoirScaling,
leakingRate=leakingRate,
initialTransient=self.initialTransient,
inputData=self.trainingInputData,
outputData=self.trainingOutputData,
inputWeightRandom=self.inputWeightRandom,
reservoirWeightRandom=self.reservoirWeightRandom)
#Train the reservoir
res.trainReservoir()
# Warm up
predictedTrainingOutputData = res.predict(
self.trainingInputData[-self.initialTransient:])
#Predict for the validation data
predictedOutputData = util.predictFuture(res, self.initialSeed,
self.horizon)
gc.collect()
#Calcuate the regression error
errorFunction = metrics.MeanSquareError()
regressionError = errorFunction.compute(self.validationOutputData,
predictedOutputData)
#Return the error
print("\nThe Parameters: " + str(x) + " Regression error:" +
str(regressionError))
return regressionError
def evaluate(self, x):
#Extract the parameters
spectralRadius = x[0, 0]
inputScaling = x[0, 1]
reservoirScaling = x[0, 2]
leakingRate = x[0, 3]
#Create the reservoir
res = esn.Reservoir(size=self.size,
spectralRadius=spectralRadius,
inputScaling=inputScaling,
reservoirScaling=reservoirScaling,
leakingRate=leakingRate,
initialTransient=self.initialTransient,
inputData=self.trainingInputData,
outputData=self.trainingOutputData,
inputWeightRandom=self.inputWeightRandom,
reservoirWeightRandom=self.reservoirWeightRandom)
#Train the reservoir
res.trainReservoir()
# Warm up
predictedTrainingOutputData = res.predict(self.trainingInputData[-self.initialTransient:])
#Predict for the validation data
predictedOutputData = util.predictFuture(res, self.initialSeed, self.horizon)
gc.collect()
#Calcuate the regression error
errorFunction = metrics.MeanSquareError()
regressionError = errorFunction.compute(self.validationOutputData, predictedOutputData)
#Return the error
return regressionError
from timeit import default_timer as time
startTime = time()
#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 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,
from timeit import default_timer as time
startTime = time()
#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 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.tuneTrainPredictConnectivity(
trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
validationOutputData=validationData,
initialInputSeedForValidation=initialSeedForValidation,
from timeit import default_timer as time
startTime = time()
#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[:6000].reshape((6000, 1))
# Split the data into training, validation and testing
trainingData, validationData, testingData = util.splitData(
data, 0.5, 0.25, 0.25)
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 = utilGA.tuneTrainPredictGA(
trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
validationOutputData=validationData,
initialInputSeedForValidation=initialSeedForValidation,
from timeit import default_timer as time
startTime = time()
#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[:6000].reshape((6000, 1))
# Split the data into training, validation and testing
trainingData, validationData, testingData = util.splitData(data, 0.5, 0.25, 0.25)
nValidation = validationData.shape[0]
nTesting = testingData.shape[0]
# Form feature vectors for training data
trainingInputData, trainingOutputData = util.formFeatureVectors(trainingData)
#actualOutputData = minMax.inverse_transform(np.vstack((validationData[:nValidation],testingData[:nTesting])))[:,0]
actualOutputData = minMax.inverse_transform(testingData)[:,0]
# Initial seed
initialSeedForValidation = trainingData[-1]
predictedOutputData, error = util.tuneTrainPredict(trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
validationOutputData=validationData,
initialInputSeedForValidation=initialSeedForValidation,
from timeit import default_timer as time
startTime = time()
#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 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.tuneTrainPredictConnectivity(trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
validationOutputData=validationData,
initialInputSeedForValidation=initialSeedForValidation,
horizon = nTesting,
from sklearn import preprocessing as pp
from reservoir import Utility as util
from performance import ErrorMetrics as rmse
# 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()
os.mkdir(outputFolderName)
startTime = time()
#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[:6000].reshape((6000, 1))
# Split the data into training, validation and testing
trainingData, validationData, testingData = util.splitData(
data, 0.5, 0.25, 0.25)
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]
# Error function
errorFun = metrics.MeanSquareError()
# Number of iterations
iterations = 1
# Forecasting parameters
depth = 30
# Read data from the file
data = np.loadtxt("MackeyGlass_t17.txt")
# Normalize the raw data
# minMax = pp.MinMaxScaler((-1,1))
minMax = pp.StandardScaler()
data = minMax.fit_transform(data)
# Get only 6000 points
data = data[:5000].reshape((5000, 1))
# Number of points - 5000
trainingData, testingData = util.splitData2(data, 0.85)
availableData = trainingData
nTesting = testingData.shape[0]
# Divide the training data into training and validation
validationRatio = 0.4
trainingData, validationData = util.splitData2(trainingData, 1.0 - validationRatio)
# Form feature vectors
trainingFeatureVectors, trainingTargetVectors = formFeatureTargetVectors(trainingData, depth)
validationFeatureVectors, validationTargetVectors = formFeatureTargetVectors(validationData, depth)
testingFeatureVectors, testingTargetVectors = formFeatureTargetVectors(testingData, depth)
# Network parameters
in_out_neurons = 1
hidden_neurons = 200
os.mkdir(outputFolderName)
startTime = time()
#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[:6000].reshape((6000, 1))
# Split the data into training, validation and testing
trainingData, validationData, testingData = util.splitData(data, 0.5, 0.25, 0.25)
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]
# Error function
errorFun = metrics.MeanSquareError()
# Number of iterations
iterations = 1
results = open(outputFolderName + "/OptimalParameters.res", 'w')
startTime = time()
#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[:6000].reshape((6000, 1))
# Split the data into training, validation and testing
trainingData, validationData, testingData = util.splitData(
data, 0.5, 0.25, 0.25)
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]
# Error function
error = metrics.MeanSquareError()
# Number of iterations
iterations = 1
from timeit import default_timer as time
startTime = time()
#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 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,
def tuneTrainPredict(trainingInputData,
trainingOutputData,
validationOutputData,
initialInputSeedForValidation,
testingData,
size=256,
initialTransient=50,
spectralRadiusBound=(0.0, 1.0),
inputScalingBound=(0.0, 1.0),
reservoirScalingBound=(0.0, 1.0),
leakingRateBound=(0.0, 1.0),
reservoirTopology=None):
# Generate the input and reservoir weight matrices based on the reservoir topology
inputWeightMatrix = topology.ClassicInputTopology(
inputSize=trainingInputData.shape[1],
reservoirSize=size).generateWeightMatrix()
if reservoirTopology is None:
reservoirWeightMatrix = topology.ClassicReservoirTopology(
size=size).generateWeightMatrix()
else: #TODO - think about matrix multiplication
reservoirWeightMatrix = reservoirTopology.generateWeightMatrix()
resTuner = tuner.ReservoirParameterTuner(
size=size,
initialTransient=initialTransient,
trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
initialSeed=initialInputSeedForValidation,
validationOutputData=validationOutputData,
spectralRadiusBound=spectralRadiusBound,
inputScalingBound=inputScalingBound,
reservoirScalingBound=reservoirScalingBound,
leakingRateBound=leakingRateBound,
inputWeightMatrix=inputWeightMatrix,
reservoirWeightMatrix=reservoirWeightMatrix,
minimizer=tuner.Minimizer.DifferentialEvolution)
spectralRadiusOptimum, inputScalingOptimum, reservoirScalingOptimum, leakingRateOptimum = resTuner.getOptimalParameters(
)
#Train
network = ESN.Reservoir(size=size,
spectralRadius=spectralRadiusOptimum,
inputScaling=inputScalingOptimum,
reservoirScaling=reservoirScalingOptimum,
leakingRate=leakingRateOptimum,
initialTransient=initialTransient,
inputData=trainingInputData,
outputData=trainingOutputData,
inputWeightRandom=inputWeightMatrix,
reservoirWeightRandom=reservoirWeightMatrix)
network.trainReservoir()
warmupFeatureVectors, warmTargetVectors = formFeatureVectors(
validationOutputData)
predictedWarmup = network.predict(warmupFeatureVectors[-initialTransient:])
initialInputSeedForTesing = validationOutputData[-1]
predictedOutputData = predictFuture(network, initialInputSeedForTesing,
testingData.shape[0])[:, 0]
cumError = 0
times = 100
for i in range(times):
# Run for many time and get the average regression error
regressionError = util.trainAndGetError(
size=size,
spectralRadius=spectralRadiusOptimum,
inputScaling=inputScalingOptimum,
reservoirScaling=reservoirScalingOptimum,
leakingRate=leakingRateOptimum,
initialTransient=initialTransient,
trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
inputWeightMatrix=inputWeightMatrix,
reservoirWeightMatrix=reservoirWeightMatrix,
validationOutputData=validationOutputData,
horizon=testingData.shape[0],
testingActualOutputData=testingData)
cumError += regressionError
error = cumError / times
return predictedOutputData, error
def tuneTrainPredict(
trainingInputData,
trainingOutputData,
validationOutputData,
initialInputSeedForValidation,
testingData,
size=256,
initialTransient=50,
spectralRadiusBound=(0.0, 1.0),
inputScalingBound=(0.0, 1.0),
reservoirScalingBound=(0.0, 1.0),
leakingRateBound=(0.0, 1.0),
reservoirTopology=None,
):
# Generate the input and reservoir weight matrices based on the reservoir topology
inputWeightMatrix = topology.ClassicInputTopology(
inputSize=trainingInputData.shape[1], reservoirSize=size
).generateWeightMatrix()
if reservoirTopology is None:
reservoirWeightMatrix = topology.ClassicReservoirTopology(size=size).generateWeightMatrix()
else: # TODO - think about matrix multiplication
reservoirWeightMatrix = reservoirTopology.generateWeightMatrix()
resTuner = tuner.ReservoirParameterTuner(
size=size,
initialTransient=initialTransient,
trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
initialSeed=initialInputSeedForValidation,
validationOutputData=validationOutputData,
spectralRadiusBound=spectralRadiusBound,
inputScalingBound=inputScalingBound,
reservoirScalingBound=reservoirScalingBound,
leakingRateBound=leakingRateBound,
inputWeightMatrix=inputWeightMatrix,
reservoirWeightMatrix=reservoirWeightMatrix,
minimizer=tuner.Minimizer.DifferentialEvolution,
)
spectralRadiusOptimum, inputScalingOptimum, reservoirScalingOptimum, leakingRateOptimum = (
resTuner.getOptimalParameters()
)
# Train
network = ESN.Reservoir(
size=size,
spectralRadius=spectralRadiusOptimum,
inputScaling=inputScalingOptimum,
reservoirScaling=reservoirScalingOptimum,
leakingRate=leakingRateOptimum,
initialTransient=initialTransient,
inputData=trainingInputData,
outputData=trainingOutputData,
inputWeightRandom=inputWeightMatrix,
reservoirWeightRandom=reservoirWeightMatrix,
)
network.trainReservoir()
warmupFeatureVectors, warmTargetVectors = formFeatureVectors(validationOutputData)
predictedWarmup = network.predict(warmupFeatureVectors[-initialTransient:])
initialInputSeedForTesing = validationOutputData[-1]
predictedOutputData = predictFuture(network, initialInputSeedForTesing, testingData.shape[0])[:, 0]
cumError = 0
times = 100
for i in range(times):
# Run for many time and get the average regression error
regressionError = util.trainAndGetError(
size=size,
spectralRadius=spectralRadiusOptimum,
inputScaling=inputScalingOptimum,
reservoirScaling=reservoirScalingOptimum,
leakingRate=leakingRateOptimum,
initialTransient=initialTransient,
trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
inputWeightMatrix=inputWeightMatrix,
reservoirWeightMatrix=reservoirWeightMatrix,
validationOutputData=validationOutputData,
horizon=testingData.shape[0],
testingActualOutputData=testingData,
)
cumError += regressionError
error = cumError / times
return predictedOutputData, error
from sklearn import preprocessing as pp
import numpy as np
from reservoir import Utility as util
#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 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,
from sklearn import preprocessing as pp
import numpy as np
from reservoir import Utility as util
#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 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,
# Forecasting parameters
depth = 30
# Read data from the file
data = np.loadtxt('MackeyGlass_t17.txt')
# Normalize the raw data
#minMax = pp.MinMaxScaler((-1,1))
minMax = pp.StandardScaler()
data = minMax.fit_transform(data)
# Get only 6000 points
data = data[:5000].reshape((5000, 1))
# Number of points - 5000
trainingData, testingData = util.splitData2(data, 0.85)
availableData = trainingData
nTesting = testingData.shape[0]
# Divide the training data into training and validation
validationRatio = 0.4
trainingData, validationData = util.splitData2(trainingData,
1.0 - validationRatio)
# Form feature vectors
trainingFeatureVectors, trainingTargetVectors = formFeatureTargetVectors(
trainingData, depth)
validationFeatureVectors, validationTargetVectors = formFeatureTargetVectors(
validationData, depth)
testingFeatureVectors, testingTargetVectors = formFeatureTargetVectors(
testingData, depth)
from timeit import default_timer as time
startTime = time()
# 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[: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,
from timeit import default_timer as time
startTime = time()
#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[:6000].reshape((6000, 1))
# Split the data into training, validation and testing
trainingData, validationData, testingData = util.splitData(data, 0.5, 0.25, 0.25)
nValidation = validationData.shape[0]
nTesting = testingData.shape[0]
# Form feature vectors for training data
trainingInputData, trainingOutputData = util.formFeatureVectors(trainingData)
#actualOutputData = minMax.inverse_transform(np.vstack((validationData[:nValidation],testingData[:nTesting])))[:,0]
actualOutputData = minMax.inverse_transform(testingData)[:,0]
# Initial seed
initialSeedForValidation = trainingData[-1]
predictedOutputData = util.tuneTrainPredict(trainingInputData=trainingInputData,
trainingOutputData=trainingOutputData,
validationOutputData=validationData,
initialInputSeedForValidation=initialSeedForValidation,
from sklearn import preprocessing as pp
from reservoir import Utility as util
from performance import ErrorMetrics as rmse
# 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
results = open(outputFolderName+"/OptimalParameters.res", 'w')
startTime = time()
#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[:6000].reshape((6000, 1))
# Split the data into training, validation and testing
trainingData, validationData, testingData = util.splitData(data, 0.5, 0.25, 0.25)
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]
# Error function
error = metrics.MeanSquareError()
# Number of iterations
iterations = 1
