def __reservoirTrain__(self, x): #Extract the parameters reservoirConnectivity = float(x) # To get rid off the randomness in assigning weights, run it 10 times and take the average error times = 10 cumulativeError = 0 for i in range(times): # Input and weight connectivity Matrix inputWeightMatrix = topology.ClassicInputTopology( self.inputD, self.size).generateWeightMatrix() reservoirWeightMatrix = topology.RandomReservoirTopology( size=self.size, connectivity=reservoirConnectivity).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 __collectOutputs__(self, featureIndices): # Form the feature and target vectors featureVectors = self.__formFeaturevectors__(self.inputData, featureIndices) targetVectors = self.outputData # Append bias featureVectors = np.hstack((np.ones( (featureVectors.shape[0], 1)), featureVectors)) # Input weight matrix inputSize = featureVectors.shape[1] reservoirSize = self.lowerLayerParameters['size'] inputWeightRandom = topology.RandomInputTopology( inputSize, reservoirSize, self. lowerLayerParameters['inputConnectivity']).generateWeightMatrix() reservoirWeightRandom = topology.RandomReservoirTopology( reservoirSize, self.lowerLayerParameters['reservoirConnectivity'] ).generateWeightMatrix() # Generate the reservoir res = esn.Reservoir( size=self.lowerLayerParameters['size'], spectralRadius=self.lowerLayerParameters['spectralRadius'], inputScaling=self.lowerLayerParameters['inputScaling'], reservoirScaling=self.lowerLayerParameters['reservoirScaling'], leakingRate=self.lowerLayerParameters['leakingRate'], initialTransient=self.lowerLayerParameters['initialTransient'], inputData=featureVectors, outputData=targetVectors, inputWeightRandom=inputWeightRandom, reservoirWeightRandom=reservoirWeightRandom, reservoirActivationFunction=self. lowerLayerParameters['reservoirActivation'], outputActivationFunction=self. lowerLayerParameters['outputActivation']) # Train the reservoir res.trainReservoir() # Just assign the weights randomly # Store the reservoir self.lowerLayerNetworks.append(res) # Collect the outputs outputs = res.predict(featureVectors) return outputs
def trainReservoir(self): # Features for the network in the higher layer features = None # Collect outputs from the lower layer for i in range(self.lowerLayerCount): if (features is None): # First time features = self.__collectOutputs__(self.featureIndicesList[i]) else: features = np.hstack( (features, self.__collectOutputs__(self.featureIndicesList[i]))) # Append bias features = np.hstack((np.ones((features.shape[0], 1)), features)) # Generate the higher layer reservoir # where features are the outputs of the lower layer networks inputSize = features.shape[1] reservoirSize = self.higherLayerParameters['size'] inputWeightRandom = topology.RandomInputTopology( inputSize, reservoirSize, self. higherLayerParameters['inputConnectivity']).generateWeightMatrix() reservoirWeightRandom = topology.RandomReservoirTopology( self.higherLayerParameters['size'], self.higherLayerParameters['reservoirConnectivity'] ).generateWeightMatrix() self.higherLayerReservoir = esn.Reservoir( size=self.higherLayerParameters['size'], spectralRadius=self.higherLayerParameters['spectralRadius'], inputScaling=self.higherLayerParameters['inputScaling'], reservoirScaling=self.higherLayerParameters['reservoirScaling'], leakingRate=self.higherLayerParameters['leakingRate'], initialTransient=self.higherLayerParameters['initialTransient'], inputData=features, outputData=self.outputData, inputWeightRandom=inputWeightRandom, reservoirWeightRandom=reservoirWeightRandom, reservoirActivationFunction=self. higherLayerParameters['reservoirActivation'], outputActivationFunction=self. higherLayerParameters['outputActivation']) # Train the composite network self.higherLayerReservoir.trainReservoir()
def __createTransformer__(self, featureVectors): # Append the bias #featureVectors = np.hstack((np.ones((featureVectors.shape[0],1)),featureVectors)) featureVectors = featureVectors targetVectors = self.outputData # Input weight matrix inputSize = featureVectors.shape[1] reservoirSize = self.featureTransformerParameters['size'] inputWeightRandom = topology.RandomInputTopology( inputSize, reservoirSize, self.featureTransformerParameters['inputConnectivity'] ).generateWeightMatrix() reservoirWeightRandom = topology.RandomReservoirTopology( reservoirSize, self.featureTransformerParameters['reservoirConnectivity'] ).generateWeightMatrix() # Generate the reservoir self.transformer = esn.Reservoir( size=self.featureTransformerParameters['size'], spectralRadius=self.featureTransformerParameters['spectralRadius'], inputScaling=self.featureTransformerParameters['inputScaling'], reservoirScaling=self. featureTransformerParameters['reservoirScaling'], leakingRate=self.featureTransformerParameters['leakingRate'], initialTransient=self. featureTransformerParameters['initialTransient'], inputData=featureVectors, outputData=targetVectors, inputWeightRandom=inputWeightRandom, reservoirWeightRandom=reservoirWeightRandom, reservoirActivationFunction=self. featureTransformerParameters['reservoirActivation'], outputActivationFunction=self. featureTransformerParameters['outputActivation']) # Collect and return the internal state internalStates = self.transformer.collectInternalStates(featureVectors) return internalStates
def getNetworkStats(bestPopulation, type, size): log = [] for item in bestPopulation: averageDegree = 0.0 averagePathLength = 0.0 averageDiameter = 0.0 averageClusteringCoefficient = 0.0 # Run many times to get the average stats times = 1 for i in range(times): if(type == Topology.Random): connectivity = item[0][0,0] network = topology.RandomReservoirTopology(size=size, connectivity=connectivity) elif(type == Topology.ErdosRenyi): probability = item[0][0,0] network = topology.ErdosRenyiTopology(size=size, probability=probability) elif(type == Topology.ScaleFreeNetworks): attachment = int(item[0][0,0]) network = topology.ScaleFreeNetworks(size=size, attachmentCount=attachment) elif(type == Topology.SmallWorldGraphs): meanDegree = item[0][0,0] beta = item[0][1,0] network = topology.SmallWorldGraphs(size=size, meanDegree=meanDegree, beta=beta) averageDegree += network.networkStats.getAverageDegree() averagePathLength += network.networkStats.getAveragePathLenth() averageDiameter += network.networkStats.getDiameter() averageClusteringCoefficient += network.networkStats.getAverageClusteringCoefficient() stats = {} stats["averageDegree"] = averageDegree/times stats["averagePathLength"] = averagePathLength/times stats["averageDiameter"] = averageDiameter/times stats["averageClusteringCoefficient"] = averageClusteringCoefficient/times log.append((item[0], item[1], stats)) return log
def trainReservoir(self): # Feature transformation features = self.__createTransformer__(self.inputData) # Append bias features = np.hstack((np.ones((features.shape[0], 1)), features)) # Generate the predictor # where features are transformed using transformer(esn) inputSize = features.shape[1] reservoirSize = self.predictorParameters['size'] inputWeightRandom = topology.RandomInputTopology( inputSize, reservoirSize, self. predictorParameters['inputConnectivity']).generateWeightMatrix() reservoirWeightRandom = topology.RandomReservoirTopology( self.predictorParameters['size'], self.predictorParameters['reservoirConnectivity'] ).generateWeightMatrix() self.predictor = esn.Reservoir( size=self.predictorParameters['size'], spectralRadius=self.predictorParameters['spectralRadius'], inputScaling=self.predictorParameters['inputScaling'], reservoirScaling=self.predictorParameters['reservoirScaling'], leakingRate=self.predictorParameters['leakingRate'], initialTransient=self.predictorParameters['initialTransient'], inputData=features, outputData=self.outputData, inputWeightRandom=inputWeightRandom, reservoirWeightRandom=reservoirWeightRandom, reservoirActivationFunction=self. predictorParameters['reservoirActivation'], outputActivationFunction=self. predictorParameters['outputActivation']) # Train the predictor network self.predictor.trainReservoir()
# Form feature vectors inputTrainingData, outputTrainingData = util.formFeatureVectors(trainingData) # Tune the network size = 256 initialTransient = 50 # Input-to-reservoir 60% connected inputWeight = topology.RandomInputTopology( inputSize=inputTrainingData.shape[1], reservoirSize=size, inputConnectivity=1.0).generateWeightMatrix() # Reservoir-to-reservoir 50% connected reservoirWeight = topology.RandomReservoirTopology( size=size, connectivity=0.92).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, reservoirWeightRandom=reservoirWeight) res.trainReservoir() #Warm up predictedTrainingOutputData = res.predict(inputTrainingData)
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 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
testingData=testingData) classicESNPredicted = minMax.inverse_transform(predictedOutputData) classicESNError += error classicESNError = classicESNError / iterations # Run random ESN Tuner randomESNError = 0 connectivityOptimum = 0.72999999999999998 print("\n Running Random ESN Tuner..") for i in range(iterations): predictedOutputData, error = util.tuneTrainPredict( trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, validationOutputData=validationData, initialInputSeedForValidation=initialSeedForValidation, reservoirTopology=topology.RandomReservoirTopology( size=reservoirSize, connectivity=connectivityOptimum), testingData=testingData) randomESNPredicted = minMax.inverse_transform(predictedOutputData) randomESNError += error randomESNError = randomESNError / iterations # Run Erdos ESN Tuner erdosESNError = 0 probabilityOptimum = 1.0 print("\n Running Erdos ESN Tuner..") for i in range(iterations): print("\n Iteration:" + str(i + 1)) predictedOutputData, error = util.tuneTrainPredict( trainingInputData=trainingInputData, trainingOutputData=trainingOutputData, validationOutputData=validationData,