def teach(version = 2, verbose = False):
completed = 1
if version == 1:
NN1 = NN()
inputFilePath = generateFilename(description='minmaxinputDataset')
outputFilePath = generateFilename(description='minmaxoutputDataset')
loadData = NN1.loadData(inputFilePath,outputFilePath)
train = NN1.teachPyBrain(verbose=False)
saveResults = NN1.saveResults()
fileIn = generateFilename(description="networkResults")
fileOut_x_t = generateFilename(description="NNoutput_x_t")
fileOut_y_t = generateFilename(description="NNoutput_y_t")
fileOut_z_t = generateFilename(description="NNoutput_z_t")
dataset = Dataset()
sepX = dataset.separate(filepathOut=fileOut_x_t,filepathIn=fileIn,columns=[0,3])
sepY = dataset.separate(filepathOut=fileOut_y_t,filepathIn=fileIn,columns=[1,3])
sepZ = dataset.separate(filepathOut=fileOut_z_t,filepathIn=fileIn,columns=[2,3])
errorFilepath = generateFilename(description="networkError")
saveErrors = NN1.saveError(errorFilepath)
#plotData(fileOut_x_t)
#plotData(fileOut_y_t)
#plotData(fileOut_z_t)
completed = 0
if version == 2:
NN1 = NN()
#Load Dataset in Neural Network
inputFilePath = generateFilename(description='normInputDataset')
outputFilePath = generateFilename(description='normOutputDataset')
loadData = NN1.loadData(inputFilePath,outputFilePath)
#Teach using Pybrain
epochs=5
hiddenLayers=4
teachNN = NN1.teachPyBrain(epochs=epochs,hiddenLayers=hiddenLayers,verbose=False)
#Save Results (Save outputs for given inputs)
saveResultsPath = generateFilename(description='resultsNN')
saveResults = NN1.saveResults(saveResultsPath)
#Separate Results
sepResultsFilepathX = generateFilename(description='resultsNN_x_t')
sepResultsFilepathY = generateFilename(description='resultsNN_y_t')
sepResultsFilepathZ = generateFilename(description='resultsNN_z_t')
columnsX_T = [0,3]
columnsY_T = [1,3]
columnsZ_T = [2,3]
sepResultsX = Dataset().separate(sepResultsFilepathX,saveResultsPath,columnsX_T)
sepResultsY = Dataset().separate(sepResultsFilepathY,saveResultsPath,columnsY_T)
sepResultsZ = Dataset().separate(sepResultsFilepathZ,saveResultsPath,columnsZ_T)
#Denormalize Results
outputFilePath = generateFilename(description='outputDataset')
minmaxValues = (-1.,1.)
resultsData = Dataset()
resultsData.dataset = NN1.results
outputDataset = np.load(outputFilePath)
resultsData.min_dataset = outputDataset.min(axis=0)
resultsData.max_dataset = outputDataset.max(axis=0)
resultsData.min = minmaxValues[0]
resultsData.max = minmaxValues[1]
denormResults = resultsData.denormalize()
#Save Denormalized Results
denormResultsPath = generateFilename(description='denormResultsNN')
resultsData.saveToFile(denormResultsPath)
#Separate Denormalized Results
sepDenormResultsFilepathX = generateFilename(description='denormResultsNN_x_t')
sepDenormResultsFilepathY = generateFilename(description='denormResultsNN_y_t')
sepDenormResultsFilepathZ = generateFilename(description='denormResultsNN_z_t')
columnsX_T = [0,3]
columnsY_T = [1,3]
columnsZ_T = [2,3]
sepDenormResultsX = Dataset().separate(sepDenormResultsFilepathX,denormResultsPath,columnsX_T)
sepDenormResultsY = Dataset().separate(sepDenormResultsFilepathY,denormResultsPath,columnsY_T)
sepDenormResultsZ = Dataset().separate(sepDenormResultsFilepathZ,denormResultsPath,columnsZ_T)
#Save Errors
saveErrorsPath = generateFilename(description='errorsNN')
saveErrors = NN1.saveError(saveErrorsPath)
#Save Weights
savedWeightsfilepath = generateFilename(description='weightsNN')
savedWeights = NN1.getWeights()
saveWeights = NN1.saveWeights()
print savedWeights
print
for item in savedWeights:
print item
if loadData + \
teachNN + \
saveResults + \
sepResultsX + \
sepResultsY + \
sepResultsZ + \
denormResults + \
sepDenormResultsX + \
sepDenormResultsY + \
sepDenormResultsZ + \
saveErrors == 0:
completed = 0
if version == 3:
inputDataFilepath = generateFilename(description = "stations", extension = ".txt", dateToday = False)
fp = open(inputDataFilepath)
listOfSatellites = []
for i, line in enumerate(fp):
if i%3 == 0:
listOfSatellites.append(line.strip())
for satellites in listOfSatellites:
print satellites
NN1 = NN()
#Load Dataset in Neural Network
inputFilePath = generateFilename(description='normInputDataset%s'%satellites)
outputFilePath = generateFilename(description='normOutputDataset%s'%satellites)
loadData = NN1.loadData(inputFilePath,outputFilePath)
#Teach using Pybrain
epochs=5
hiddenLayers=4
teachNN = NN1.teachPyBrain(epochs=epochs,hiddenLayers=hiddenLayers,verbose=False)
#Save Results (Save outputs for given inputs)
saveResultsPath = generateFilename(description='resultsNN%s'%satellites)
saveResults = NN1.saveResults(saveResultsPath)
#Separate Results
sepResultsFilepathX = generateFilename(description='resultsNN_x_t%s'%satellites)
sepResultsFilepathY = generateFilename(description='resultsNN_y_t%s'%satellites)
sepResultsFilepathZ = generateFilename(description='resultsNN_z_t%s'%satellites)
columnsX_T = [0,3]
columnsY_T = [1,3]
columnsZ_T = [2,3]
sepResultsX = Dataset().separate(sepResultsFilepathX,saveResultsPath,columnsX_T)
sepResultsY = Dataset().separate(sepResultsFilepathY,saveResultsPath,columnsY_T)
sepResultsZ = Dataset().separate(sepResultsFilepathZ,saveResultsPath,columnsZ_T)
#Denormalize Results
outputFilePath = generateFilename(description='outputDataset%s'%satellites)
minmaxValues = (-1.,1.)
resultsData = Dataset()
resultsData.dataset = NN1.results
outputDataset = np.load(outputFilePath)
resultsData.min_dataset = outputDataset.min(axis=0)
resultsData.max_dataset = outputDataset.max(axis=0)
resultsData.min = minmaxValues[0]
resultsData.max = minmaxValues[1]
denormResults = resultsData.denormalize()
#Save Denormalized Results
denormResultsPath = generateFilename(description='denormResultsNN%s'%satellites)
resultsData.saveToFile(denormResultsPath)
#Separate Denormalized Results
sepDenormResultsFilepathX = generateFilename(description='denormResultsNN_x_t%s'%satellites)
sepDenormResultsFilepathY = generateFilename(description='denormResultsNN_y_t%s'%satellites)
sepDenormResultsFilepathZ = generateFilename(description='denormResultsNN_z_t%s'%satellites)
columnsX_T = [0,3]
columnsY_T = [1,3]
columnsZ_T = [2,3]
sepDenormResultsX = Dataset().separate(sepDenormResultsFilepathX,denormResultsPath,columnsX_T)
sepDenormResultsY = Dataset().separate(sepDenormResultsFilepathY,denormResultsPath,columnsY_T)
sepDenormResultsZ = Dataset().separate(sepDenormResultsFilepathZ,denormResultsPath,columnsZ_T)
#Save Errors
saveErrorsPath = generateFilename(description='errorsNN%s'%satellites)
saveErrors = NN1.saveError(saveErrorsPath)
#Save Weights
savedWeightsfilepath = generateFilename(description='weightsNN%s'%satellites)
savedWeights = NN1.getWeights()
saveWeights = NN1.saveWeights()
if loadData + \
teachNN + \
saveResults + \
sepResultsX + \
sepResultsY + \
sepResultsZ + \
denormResults + \
sepDenormResultsX + \
sepDenormResultsY + \
sepDenormResultsZ + \
saveErrors == 0:
completed = 0
pass
if version == 4:
listNumberHiddenLayers =[2,3,4,5]
listNumberEpochs = [5,10,30,60]
for hiddenLayers in listNumberHiddenLayers:
for epochs in listNumberEpochs:
print "layers %d , epochs %d" % (hiddenLayers,epochs)
NN1 = NN()
#Load Dataset in Neural Network
inputFilePath = generateFilename(description='normInputDataset')
outputFilePath = generateFilename(description='normOutputDataset')
loadData = NN1.loadData(inputFilePath,outputFilePath)
#Teach using Pybrain
teachNN = NN1.teachPyBrain(epochs=epochs,hiddenLayers=hiddenLayers,verbose=False)
#Save Results (Save outputs for given inputs)
saveResultsPath = generateFilename(description='resultsNN_%d_h_%d_e_'%(hiddenLayers,epochs))
saveResults = NN1.saveResults(saveResultsPath)
#Separate Results
sepResultsFilepathX = generateFilename(description='resultsNN_x_t_%d_h_%d_e_'%(hiddenLayers,epochs))
sepResultsFilepathY = generateFilename(description='resultsNN_y_t_%d_h_%d_e_'%(hiddenLayers,epochs))
sepResultsFilepathZ = generateFilename(description='resultsNN_z_t_%d_h_%d_e_'%(hiddenLayers,epochs))
columnsX_T = [0,3]
columnsY_T = [1,3]
columnsZ_T = [2,3]
sepResultsX = Dataset().separate(sepResultsFilepathX,saveResultsPath,columnsX_T)
sepResultsY = Dataset().separate(sepResultsFilepathY,saveResultsPath,columnsY_T)
sepResultsZ = Dataset().separate(sepResultsFilepathZ,saveResultsPath,columnsZ_T)
#Denormalize Results
outputFilePath = generateFilename(description='outputDataset')
minmaxValues = (-1.,1.)
resultsData = Dataset()
resultsData.dataset = NN1.results
outputDataset = np.load(outputFilePath)
resultsData.min_dataset = outputDataset.min(axis=0)
resultsData.max_dataset = outputDataset.max(axis=0)
resultsData.min = minmaxValues[0]
resultsData.max = minmaxValues[1]
denormResults = resultsData.denormalize()
#Save Denormalized Results
denormResultsPath = generateFilename(description='denormResultsNN_%d_h_%d_e_'%(hiddenLayers,epochs))
resultsData.saveToFile(denormResultsPath)
#Separate Denormalized Results
sepDenormResultsFilepathX = generateFilename(description='denormResultsNN_x_t_%d_h_%d_e_'%(hiddenLayers,epochs))
sepDenormResultsFilepathY = generateFilename(description='denormResultsNN_y_t_%d_h_%d_e_'%(hiddenLayers,epochs))
sepDenormResultsFilepathZ = generateFilename(description='denormResultsNN_z_t_%d_h_%d_e_'%(hiddenLayers,epochs))
columnsX_T = [0,3]
columnsY_T = [1,3]
columnsZ_T = [2,3]
sepDenormResultsX = Dataset().separate(sepDenormResultsFilepathX,denormResultsPath,columnsX_T)
sepDenormResultsY = Dataset().separate(sepDenormResultsFilepathY,denormResultsPath,columnsY_T)
sepDenormResultsZ = Dataset().separate(sepDenormResultsFilepathZ,denormResultsPath,columnsZ_T)
#Save Errors
saveErrorsPath = generateFilename(description='errorsNN_%d_h_%d_e_'%(hiddenLayers,epochs))
saveErrors = NN1.saveError(saveErrorsPath)
#Save Weights
savedWeightsfilepath = generateFilename(description='weightsNN_%d_h_%d_e_'%(hiddenLayers,epochs))
savedWeights = NN1.getWeights()
saveWeights = NN1.saveWeights()
return completed
