def __init__(self, size, initialTransient, trainingInputData, trainingOutputData, initialSeed, validationOutputData, reservoirConnectivityBound=(0.1, 1.0), minimizer=Minimizer.DifferentialEvolution, initialGuess=0.5, spectralRadius=0.79, inputScaling=0.5, reservoirScaling=0.5, leakingRate=0.3): self.size = size self.initialTransient = initialTransient self.trainingInputData = trainingInputData self.trainingOutputData = trainingOutputData self.initialSeed = initialSeed self.validationOutputData = validationOutputData self.reservoirConnectivityBound = reservoirConnectivityBound self.horizon = self.validationOutputData.shape[0] self.minimizer = minimizer self.initialGuess = np.array([initialGuess]) # Input-to-reservoir is of Classic Type - Fully connected and maintained as constant self.inputN, self.inputD = self.trainingInputData.shape self.inputWeight = topology.ClassicInputTopology( self.inputD, self.size).generateWeightMatrix() # Other reservoir parameters are also kept constant self.spectralRadius = spectralRadius self.inputScaling = inputScaling self.reservoirScaling = reservoirScaling self.leakingRate = leakingRate
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 tuneTrainPredictGA(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) 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] return predictedOutputData
# Step 3 - Rescale the series normalizedSeries = util.scaleSeriesStandard(resampledSeries) del resampledSeries # Step 4 - Form feature and target vectors depth = 1 featureVectors, targetVectors = util.formContinousFeatureAndTargetVectorsInOrder( normalizedSeries, depth) n = featureVectors.shape[0] # Input-to-reservoir fully connected size = 100 inputWeight = topology.ClassicInputTopology( inputSize=featureVectors.shape[1], reservoirSize=size).generateWeightMatrix() # Reservoir-to-reservoir fully connected #reservoirWeight = topology.ClassicReservoirTopology(size=size).generateWeightMatrix() reservoirWeight = topology.SmallWorldGraphs(size=size, meanDegree=int(size / 2), beta=0.8).generateWeightMatrix() res = ESN.Reservoir(size=size, inputData=featureVectors, outputData=targetVectors, spectralRadius=1.5, inputScaling=0.1, reservoirScaling=0.5, leakingRate=0.7,
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, inputScaling=0.5, reservoirScaling=0.5, leakingRate=0.3, initialTransient=initialTransient, inputWeightRandom=inputWeight,
# Split training and testing horizon = 300 training_data, testing_data = split_into_training_and_testing(data, horizon) # Form feature vectors input_training, output_training = form_feature_vectors(training_data) # Train an echo state network size = 500 initial_transient = 50 reg_factor = 1e-4 leaking_rate = 0.3 spectral_radius = 0.79 # Input-to-reservoir fully connected input_weight = topology.ClassicInputTopology(inputSize=input_training.shape[1], reservoirSize=size).generateWeightMatrix(scaling=1.0) # Reservoir-to-reservoir fully connected reservoir_weight = topology.ClassicReservoirTopology(size=size).generateWeightMatrix(scaling=1.0) # Plot variables to hold data for comparison plot_names = [] predicted = [] plot_names.append("actual") predicted.append(testing_data) # Train full batch - classic closed form solution res = ESN.Reservoir(size=size, input_data=torch.from_numpy(input_training.T), output_data=torch.from_numpy(output_training.T),
def tuneTrainPredictConnectivityGA(trainingInputData, trainingOutputData, validationOutputData, initialInputSeedForValidation, horizon, noOfBest, size=256,initialTransient=50, resTopology = Topology.Random, popSize=100, maxGeneration=100): # Other reservoir parameters spectralRadius = 0.79 inputScaling = 0.5 reservoirScaling = 0.5 leakingRate = 0.3 if(resTopology == Topology.Random): resTuner = rgTuner.RandomGraphTuner(size=size, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, initialSeed=initialInputSeedForValidation, validationOutputData=validationOutputData, noOfBest=noOfBest, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate, populationSize=popSize, maxGeneration=maxGeneration) resTuner.__tune__() reservoirConnectivityOptimum = resTuner.getOptimalParameters() bestPopulation = resTuner.getBestPopulation() inputWeightMatrix = topology.ClassicInputTopology(inputSize=trainingInputData.shape[1], reservoirSize=size).generateWeightMatrix() reservoirWeightMatrix = topology.RandomReservoirTopology(size=size, connectivity=reservoirConnectivityOptimum).generateWeightMatrix() elif(resTopology == Topology.ErdosRenyi): resTuner = rgTuner.ErdosRenyiTuner(size=size, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, initialSeed=initialInputSeedForValidation, validationOutputData=validationOutputData, noOfBest=noOfBest, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate, populationSize=popSize, maxGeneration=maxGeneration) resTuner.__tune__() probabilityOptimum = resTuner.getOptimalParameters() bestPopulation = resTuner.getBestPopulation() inputWeightMatrix = topology.ClassicInputTopology(inputSize=trainingInputData.shape[1], reservoirSize=size).generateWeightMatrix() reservoirWeightMatrix = topology.ErdosRenyiTopology(size=size, probability=probabilityOptimum).generateWeightMatrix() elif(resTopology == Topology.ScaleFreeNetworks): resTuner = rgTuner.ScaleFreeNetworksTuner(size=size, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, initialSeed=initialInputSeedForValidation, validationOutputData=validationOutputData, noOfBest=noOfBest, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate, populationSize=popSize, maxGeneration=maxGeneration) resTuner.__tune__() attachmentOptimum = resTuner.getOptimalParameters() bestPopulation = resTuner.getBestPopulation() inputWeightMatrix = topology.ClassicInputTopology(inputSize=trainingInputData.shape[1], reservoirSize=size).generateWeightMatrix() reservoirWeightMatrix = topology.ScaleFreeNetworks(size=size, attachmentCount=attachmentOptimum).generateWeightMatrix() elif(resTopology == Topology.SmallWorldGraphs): resTuner = rgTuner.SmallWorldNetworksTuner(size=size, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, initialSeed=initialInputSeedForValidation, validationOutputData=validationOutputData, noOfBest=noOfBest, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate, populationSize=popSize, maxGeneration=maxGeneration) resTuner.__tune__() meanDegreeOptimum, betaOptimum = resTuner.getOptimalParameters() bestPopulation = resTuner.getBestPopulation() inputWeightMatrix = topology.ClassicInputTopology(inputSize=trainingInputData.shape[1], reservoirSize=size).generateWeightMatrix() reservoirWeightMatrix = topology.SmallWorldGraphs(size=size, meanDegree=int(meanDegreeOptimum), beta=betaOptimum).generateWeightMatrix() #Train network = esn.Reservoir(size=size, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate, initialTransient=initialTransient, inputData=trainingInputData, outputData=trainingOutputData, inputWeightRandom=inputWeightMatrix, reservoirWeightRandom=reservoirWeightMatrix) network.trainReservoir() warmupFeatureVectors, warmTargetVectors = formFeatureVectors(validationOutputData) predictedWarmup = network.predict(warmupFeatureVectors[-initialTransient:]) initialInputSeedForTesting = validationOutputData[-1] predictedOutputData = predictFuture(network, initialInputSeedForTesting, horizon)[:,0] return predictedOutputData, bestPopulation
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 tuneConnectivity(trainingInputData, trainingOutputData, validationOutputData, initialInputSeedForValidation, horizon, testingActualOutputData, size=256, initialTransient=50, resTopology=Topology.Classic): # Other reservoir parameters spectralRadius = 0.79 inputScaling = 0.5 reservoirScaling = 0.5 leakingRate = 0.3 # Optimal Parameters List optimalParameters = {} if (resTopology == Topology.Classic): # Run 100 times and get the average regression error iterations = 1000 cumulativeError = 0.0 for i in range(iterations): inputWeightMatrix = topology.ClassicInputTopology( inputSize=trainingInputData.shape[1], reservoirSize=size).generateWeightMatrix() reservoirWeightMatrix = topology.ClassicReservoirTopology( size=size).generateWeightMatrix() error = trainAndGetError( size=size, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, inputWeightMatrix=inputWeightMatrix, reservoirWeightMatrix=reservoirWeightMatrix, validationOutputData=validationOutputData, horizon=horizon, testingActualOutputData=testingActualOutputData) # Calculate the error cumulativeError += error return cumulativeError / iterations, optimalParameters elif (resTopology == Topology.Random): resTuner = tuner.RandomConnectivityBruteTuner( size=size, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, initialSeed=initialInputSeedForValidation, validationOutputData=validationOutputData, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate) reservoirConnectivityOptimum = resTuner.getOptimalParameters() optimalParameters[ "Optimal_Reservoir_Connectivity"] = reservoirConnectivityOptimum # Run 100 times and get the average regression error iterations = 1000 cumulativeError = 0.0 for i in range(iterations): inputWeightMatrix = topology.ClassicInputTopology( inputSize=trainingInputData.shape[1], reservoirSize=size).generateWeightMatrix() reservoirWeightMatrix = topology.RandomReservoirTopology( size=size, connectivity=reservoirConnectivityOptimum ).generateWeightMatrix() error = trainAndGetError( size=size, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, inputWeightMatrix=inputWeightMatrix, reservoirWeightMatrix=reservoirWeightMatrix, validationOutputData=validationOutputData, horizon=horizon, testingActualOutputData=testingActualOutputData) # Calculate the error cumulativeError += error return cumulativeError / iterations, optimalParameters elif (resTopology == Topology.ErdosRenyi): resTuner = tuner.ErdosRenyiConnectivityBruteTuner( size=size, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, initialSeed=initialInputSeedForValidation, validationOutputData=validationOutputData, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate) probabilityOptimum = resTuner.getOptimalParameters() optimalParameters[ "Optimal_Connectivity_Probability"] = probabilityOptimum # Run 100 times and get the average regression error iterations = 1000 cumulativeError = 0.0 for i in range(iterations): inputWeightMatrix = topology.ClassicInputTopology( inputSize=trainingInputData.shape[1], reservoirSize=size).generateWeightMatrix() reservoirWeightMatrix = topology.ErdosRenyiTopology( size=size, probability=probabilityOptimum).generateWeightMatrix() error = trainAndGetError( size=size, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, inputWeightMatrix=inputWeightMatrix, reservoirWeightMatrix=reservoirWeightMatrix, validationOutputData=validationOutputData, horizon=horizon, testingActualOutputData=testingActualOutputData) # Calculate the error cumulativeError += error return cumulativeError / iterations, optimalParameters elif (resTopology == Topology.ScaleFreeNetworks): resTuner = tuner.ScaleFreeNetworksConnectivityBruteTuner( size=size, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, initialSeed=initialInputSeedForValidation, validationOutputData=validationOutputData, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate) attachmentOptimum = resTuner.getOptimalParameters() optimalParameters[ "Optimal_Preferential_Attachment"] = attachmentOptimum # Run 100 times and get the average regression error iterations = 1000 cumulativeError = 0.0 for i in range(iterations): inputWeightMatrix = topology.ClassicInputTopology( inputSize=trainingInputData.shape[1], reservoirSize=size).generateWeightMatrix() reservoirWeightMatrix = topology.ScaleFreeNetworks( size=size, attachmentCount=attachmentOptimum).generateWeightMatrix() error = trainAndGetError( size=size, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, inputWeightMatrix=inputWeightMatrix, reservoirWeightMatrix=reservoirWeightMatrix, validationOutputData=validationOutputData, horizon=horizon, testingActualOutputData=testingActualOutputData) # Calculate the error cumulativeError += error return cumulativeError / iterations, optimalParameters elif (resTopology == Topology.SmallWorldGraphs): resTuner = tuner.SmallWorldGraphsConnectivityBruteTuner( size=size, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, initialSeed=initialInputSeedForValidation, validationOutputData=validationOutputData, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate) meanDegreeOptimum, betaOptimum = resTuner.getOptimalParameters() optimalParameters["Optimal_MeanDegree"] = meanDegreeOptimum optimalParameters["Optimal_Beta"] = betaOptimum # Run 100 times and get the average regression error iterations = 1000 cumulativeError = 0.0 for i in range(iterations): inputWeightMatrix = topology.ClassicInputTopology( inputSize=trainingInputData.shape[1], reservoirSize=size).generateWeightMatrix() reservoirWeightMatrix = topology.SmallWorldGraphs( size=size, meanDegree=int(meanDegreeOptimum), beta=betaOptimum).generateWeightMatrix() error = trainAndGetError( size=size, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, inputWeightMatrix=inputWeightMatrix, reservoirWeightMatrix=reservoirWeightMatrix, validationOutputData=validationOutputData, horizon=horizon, testingActualOutputData=testingActualOutputData) # Calculate the error cumulativeError += error return cumulativeError / iterations, optimalParameters
def tuneTrainPredictConnectivityNonBrute(trainingInputData, trainingOutputData, validationOutputData, initialInputSeedForValidation, horizon, size=256, initialTransient=50, resTopology=Topology.Random): # Other reservoir parameters spectralRadius = 0.79 inputScaling = 0.5 reservoirScaling = 0.5 leakingRate = 0.3 if (resTopology == Topology.Random): resTuner = tuner.RandomConnectivityTuner( size=size, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, initialSeed=initialInputSeedForValidation, validationOutputData=validationOutputData, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate) reservoirConnectivityOptimum = resTuner.getOptimalParameters() inputWeightMatrix = topology.ClassicInputTopology( inputSize=trainingInputData.shape[1], reservoirSize=size).generateWeightMatrix() reservoirWeightMatrix = topology.RandomReservoirTopology( size=size, connectivity=reservoirConnectivityOptimum).generateWeightMatrix() elif (resTopology == Topology.SmallWorldGraphs): resTuner = tuner.SmallWorldGraphsConnectivityNonBruteTuner( size=size, initialTransient=initialTransient, trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, initialSeed=initialInputSeedForValidation, validationOutputData=validationOutputData, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate) meanDegreeOptimum, betaOptimum = resTuner.getOptimalParameters() inputWeightMatrix = topology.ClassicInputTopology( inputSize=trainingInputData.shape[1], reservoirSize=size).generateWeightMatrix() reservoirWeightMatrix = topology.SmallWorldGraphs( size=size, meanDegree=int(meanDegreeOptimum), beta=betaOptimum).generateWeightMatrix() #Train network = ESN.Reservoir(size=size, spectralRadius=spectralRadius, inputScaling=inputScaling, reservoirScaling=reservoirScaling, leakingRate=leakingRate, 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, horizon)[:, 0] return predictedOutputData