Exemplos de GAUtility.tuneTrainPredictGA em Python

Exemplo n.º 1

Arquivo: testGAOptimizer.py Projeto: rajcscw/echo-state-networks

# 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,
                                            testingData=actualOutputData
                                            )


predictedOutputData = minMax.inverse_transform(predictedOutputData)

#Plotting of the prediction output and error
outputFolderName = "Outputs/Outputs" + datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
os.mkdir(outputFolderName)
outplot = outputPlot.OutputPlot(outputFolderName + "/Prediction.html", "Mackey-Glass Time Series - GA Optimization", "Predicted vs Actual", "Time", "Output")
outplot.setXSeries(np.arange(1, nTesting + 1))
outplot.setYSeries('Actual Output', actualOutputData)
outplot.setYSeries('Predicted Output', predictedOutputData)
outplot.createOutput()

Exemplo n.º 2

# 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,
    testingData=actualOutputData)

predictedOutputData = minMax.inverse_transform(predictedOutputData)

#Plotting of the prediction output and error
outputFolderName = "Outputs/Outputs" + datetime.now().strftime(
    "%Y_%m_%d_%H_%M_%S")
os.mkdir(outputFolderName)
outplot = outputPlot.OutputPlot(outputFolderName + "/Prediction.html",
                                "Mackey-Glass Time Series - GA Optimization",
                                "Predicted vs Actual", "Time", "Output")
outplot.setXSeries(np.arange(1, nTesting + 1))
outplot.setYSeries('Actual Output', actualOutputData)
outplot.setYSeries('Predicted Output', predictedOutputData)