validationOutputData = trainingOutputData
#Tune the reservoir
spectralRadius = 0.5
inputScaling = 0.5
reservoirScaling = 0.5
leakingRate = 0.3
size = 500
initialTransient = 50
#Train the reservoir with the optimal parameters
res1 = reservoir.Reservoir(size=size,
spectralRadius=spectralRadius,
inputScaling=inputScaling,
reservoirScaling=reservoirScaling,
leakingRate=leakingRate,
initialTransient=initialTransient,
inputData=trainingInputData,
outputData=trainingOutputData)
res1.trainReservoir()
# To predict the test data, predict training and valiadation data as a warm up
trainingPredictedOutputData = res1.predict(trainingInputData)
#Now, start predicting the future
xAxis = []
predictedOutput = []
lastDayIndex = rawData.shape[0] -1
lastDay = date(int(availableData[lastDayIndex, 0]), int(availableData[lastDayIndex, 1]), int(availableData[lastDayIndex, 2]))
trainingOutputData=targetVectors,
validationInputData=featureVectors,
validationOutputData=targetVectors,
spectralRadiusBound=spectralRadiusBound,
inputScalingBound=inputScalingBound,
reservoirScalingBound=reservoirScalingBound,
leakingRateBound=leakingRateBound)
spectralRadiusOptimum, inputScalingOptimum, reservoirScalingOptimum, leakingRateOptimum, inputWeightOptimum, reservoirWeightOptimum = resTuner.getOptimalParameters(
)
#Train the reservoir with the optimal parameters
res = reservoir.Reservoir(size=size,
spectralRadius=spectralRadiusOptimum,
inputScaling=inputScalingOptimum,
reservoirScaling=reservoirScalingOptimum,
leakingRate=leakingRateOptimum,
initialTransient=initialTransient,
inputData=featureVectors,
outputData=targetVectors,
inputWeightRandom=inputWeightOptimum,
reservoirWeightRandom=reservoirWeightOptimum)
res.trainReservoir()
#Predict for the training data as a warmup
trainingPredictedOutputData = res.predict(featureVectors)
#Now, start predicted the future
nextDate = series.last_valid_index()
predicted = []
xAxis = []
trainingOutputData=trainingOutputData,
validationInputData=validationInputData,
validationOutputData=validationOutputData,
spectralRadiusBound=spectralRadiusBound,
inputScalingBound=inputScalingBound,
reservoirScalingBound=reservoirScalingBound,
leakingRateBound=leakingRateBound)
spectralRadiusOptimum, inputScalingOptimum, reservoirScalingOptimum, leakingRateOptimum, inputWeightOptimum, reservoirWeightOptimum = resTuner.getOptimalParameters(
)
#Train the reservoir with the optimal parameters
res = reservoir.Reservoir(size=size,
spectralRadius=spectralRadiusOptimum,
inputScaling=inputScalingOptimum,
reservoirScaling=reservoirScalingOptimum,
leakingRate=leakingRateOptimum,
initialTransient=initialTransient,
inputData=trainingInputData,
outputData=trainingOutputData,
inputWeightRandom=inputWeightOptimum,
reservoirWeightRandom=reservoirWeightOptimum)
res.trainReservoir()
# To predict the test data, predict training and valiadation data as a warm up
trainingPredictedOutputData = res.predict(trainingInputData)
#Now, start predicting the future
xAxis = []
predictedOutput = []
lastDayIndex = rawData.shape[0] - 1
lastDay = date(int(availableData[lastDayIndex, 0]),
horizon = 1
tsp = ts.TimeSeriesProcessor(rawData, depth, horizon, 4)
processedData = tsp.getProcessedData()
inputData = np.hstack((np.ones((processedData.shape[0], 1)),
processedData[:processedData.shape[0], :depth]))
outputData = processedData[:processedData.shape[0], depth:depth + horizon]
# Train
inputWeightRandom = np.load("Outputs/inputWeight.npy")
reservoirWeightRandom = np.load("Outputs/reservoirWeight.npy")
res = reservoir.Reservoir(size=600,
spectralRadius=1.25,
inputScaling=0.50,
reservoirScaling=0.50,
leakingRate=0.20,
initialTransient=0,
inputData=inputData,
outputData=outputData,
inputWeightRandom=inputWeightRandom,
reservoirWeightRandom=reservoirWeightRandom)
res.trainReservoir()
#Predict for past
lastDayIndex = 0
lastDayValue = rawData[lastDayIndex, 4]
xAxisPast = []
testPredictedOutputDataPast = []
testActualOutputDataPast = []
numberOfDaysInPast = rawData.shape[0] - 1
for i in range(numberOfDaysInPast):
