def test_train(self):
mng = MlpManager()
mng.createMlp(self.output, self.factors, self.output, [10])
mng.setTrainingData(self.output, self.factors, self.output)
mng.train(1, valPercent=50)
val = mng.getMinValError()
tr = mng.getTrainError()
mng.train(20, valPercent=50, continue_train=True)
self.assertGreaterEqual(val, mng.getMinValError())
mng = MlpManager(ns=1)
mng.createMlp(self.state1, self.factors1, self.output1, [10])
mng.setTrainingData(self.state1, self.factors1, self.output1)
mng.train(1, valPercent=20)
predict = mng.getPrediction(self.state1, self.factors1)
mask = predict.getBand(1).mask
self.assertTrue(not all(mask.flatten()))
def main(initRaster, finalRaster, factors):
print 'Start Reading Init Data...', clock()
initRaster = Raster(initRaster)
finalRaster = Raster(finalRaster)
factors = [Raster(rasterName) for rasterName in factors]
print 'Finish Reading Init Data', clock(), '\n'
print "Start Making CrossTable...", clock()
crosstab = CrossTableManager(initRaster, finalRaster)
#print crosstab.getTransitionStat()
print "Finish Making CrossTable", clock(), '\n'
# Create and Train Analyst
print 'Start creating AreaAnalyst...', clock()
analyst = AreaAnalyst(initRaster, finalRaster)
print 'Finish creating AreaAnalyst ...', clock(), '\n'
print 'Start Making Change Map...', clock()
analyst = AreaAnalyst(initRaster, finalRaster)
changeMap = analyst.getChangeMap()
print 'Finish Making Change Map', clock(), '\n'
#~ # Create and Train ANN Model
model = MlpManager(ns=1)
model.createMlp(initRaster, factors, changeMap, [10])
print 'Start Setting MLP Trainig Data...', clock()
model.setTrainingData(initRaster,
factors,
changeMap,
mode='Stratified',
samples=1000)
print 'Finish Setting Trainig Data', clock(), '\n'
print 'Start MLP Training...', clock()
model.train(20, valPercent=20)
print 'Finish Trainig', clock(), '\n'
# print 'Start ANN Prediction...', clock()
# predict = model.getPrediction(initRaster, factors, calcTransitions=True)
# confidence = model.getConfidence()
# potentials = model.getTransitionPotentials()
#~ # Create and Train LR Model
#~ model = LR(ns=0)
#~ print 'Start Setting LR Trainig Data...', clock()
#~ model.setState(initRaster)
#~ model.setFactors(factors)
#~ model.setOutput(changeMap)
#~ model.setMode('Stratified')
#~ model.setSamples(100)
#~ model.setTrainingData()
#~ print 'Finish Setting Trainig Data', clock(), '\n'
#~ print 'Start LR Training...', clock()
#~ model.train()
#~ print 'Finish Trainig', clock(), '\n'
#~
#~ print 'Start LR Prediction...', clock()
#~ predict = model.getPrediction(initRaster, factors, calcTransitions=True)
#~ print 'Finish LR Prediction...', clock(), '\n'
# Create and Train WoE Model
# print 'Start creating AreaAnalyst...', clock()
# analyst = AreaAnalyst(initRaster, finalRaster)
# print 'Finish creating AreaAnalyst ...', clock(), '\n'
# print 'Start creating WoE model...', clock()
# bins = {0: [[1000, 3000]], 1: [[200, 500, 1500]]}
# model = WoeManager(factors, analyst, bins= bins)
# model.train()
# print 'Finish creating WoE model...', clock(), '\n'
#~ # Create and Train MCE Model
#~ print 'Start creating MCE model...', clock()
#~ matrix = [
#~ [1, 6],
#~ [1.0/6, 1]
#~ ]
#~ model = MCE(factors, matrix, 2, 3, analyst)
#~ print 'Finish creating MCE model...', clock(), '\n'
# predict = model.getPrediction(initRaster, factors, calcTransitions=True)
# confidence = model.getConfidence()
# potentials = model.getTransitionPotentials()
# filename = 'predict.tif'
# confname = 'confidence.tif'
# trans_prefix='trans_'
# try:
# predict.save(filename)
# confidence.save(confname)
# if potentials != None:
# for k,v in potentials.iteritems():
# map = v.save(trans_prefix+str(k) + '.tif')
# finally:
# os.remove(filename)
# #pass
# print 'Finish Saving...', clock(), '\n'
# simulation
print 'Start Simulation...', clock()
simulator = Simulator(initRaster, factors, model, crosstab)
# Make 1 cycle of simulation:
simulator.setIterationCount(1)
simulator.simN()
monteCarloSim = simulator.getState() # Result of MonteCarlo simulation
errors = simulator.errorMap(finalRaster) # Risk class validation
riskFunct = simulator.getConfidence() # Risk function
try:
monteCarloSim.save('simulation_result.tiff')
errors.save('risk_validation.tiff')
riskFunct.save('risk_func.tiff')
finally:
pass
# os.remove('simulation_result.tiff')
# os.remove('risk_validation.tiff')
# os.remove('risk_func.tiff')
print 'Finish Simulation', clock(), '\n'
print 'Done', clock()
def main(initRaster, finalRaster, factors):
print 'Start Reading Init Data...', clock()
initRaster = Raster(initRaster)
finalRaster = Raster(finalRaster)
factors = [Raster(rasterName) for rasterName in factors]
print 'Finish Reading Init Data', clock(), '\n'
print "Start Making CrossTable...", clock()
crosstab = CrossTableManager(initRaster, finalRaster)
#print crosstab.getTransitionStat()
print "Finish Making CrossTable", clock(), '\n'
# Create and Train Analyst
print 'Start creating AreaAnalyst...', clock()
analyst = AreaAnalyst(initRaster, finalRaster)
print 'Finish creating AreaAnalyst ...', clock(), '\n'
print 'Start Making Change Map...', clock()
analyst = AreaAnalyst(initRaster,finalRaster)
changeMap = analyst.getChangeMap()
print 'Finish Making Change Map', clock(), '\n'
#~ # Create and Train ANN Model
model = MlpManager(ns=1)
model.createMlp(initRaster, factors, changeMap, [10])
print 'Start Setting MLP Trainig Data...', clock()
model.setTrainingData(initRaster, factors, changeMap, mode='Stratified', samples=1000)
print 'Finish Setting Trainig Data', clock(), '\n'
print 'Start MLP Training...', clock()
model.train(20, valPercent=20)
print 'Finish Trainig', clock(), '\n'
# print 'Start ANN Prediction...', clock()
# predict = model.getPrediction(initRaster, factors, calcTransitions=True)
# confidence = model.getConfidence()
# potentials = model.getTransitionPotentials()
#~ # Create and Train LR Model
#~ model = LR(ns=0)
#~ print 'Start Setting LR Trainig Data...', clock()
#~ model.setState(initRaster)
#~ model.setFactors(factors)
#~ model.setOutput(changeMap)
#~ model.setMode('Stratified')
#~ model.setSamples(100)
#~ model.setTrainingData()
#~ print 'Finish Setting Trainig Data', clock(), '\n'
#~ print 'Start LR Training...', clock()
#~ model.train()
#~ print 'Finish Trainig', clock(), '\n'
#~
#~ print 'Start LR Prediction...', clock()
#~ predict = model.getPrediction(initRaster, factors, calcTransitions=True)
#~ print 'Finish LR Prediction...', clock(), '\n'
# Create and Train WoE Model
# print 'Start creating AreaAnalyst...', clock()
# analyst = AreaAnalyst(initRaster, finalRaster)
# print 'Finish creating AreaAnalyst ...', clock(), '\n'
# print 'Start creating WoE model...', clock()
# bins = {0: [[1000, 3000]], 1: [[200, 500, 1500]]}
# model = WoeManager(factors, analyst, bins= bins)
# model.train()
# print 'Finish creating WoE model...', clock(), '\n'
#~ # Create and Train MCE Model
#~ print 'Start creating MCE model...', clock()
#~ matrix = [
#~ [1, 6],
#~ [1.0/6, 1]
#~ ]
#~ model = MCE(factors, matrix, 2, 3, analyst)
#~ print 'Finish creating MCE model...', clock(), '\n'
# predict = model.getPrediction(initRaster, factors, calcTransitions=True)
# confidence = model.getConfidence()
# potentials = model.getTransitionPotentials()
# filename = 'predict.tif'
# confname = 'confidence.tif'
# trans_prefix='trans_'
# try:
# predict.save(filename)
# confidence.save(confname)
# if potentials != None:
# for k,v in potentials.iteritems():
# map = v.save(trans_prefix+str(k) + '.tif')
# finally:
# os.remove(filename)
# #pass
# print 'Finish Saving...', clock(), '\n'
# simulation
print 'Start Simulation...', clock()
simulator = Simulator(initRaster, factors, model, crosstab)
# Make 1 cycle of simulation:
simulator.setIterationCount(1)
simulator.simN()
monteCarloSim = simulator.getState() # Result of MonteCarlo simulation
errors = simulator.errorMap(finalRaster) # Risk class validation
riskFunct = simulator.getConfidence() # Risk function
try:
monteCarloSim.save('simulation_result.tiff')
errors.save('risk_validation.tiff')
riskFunct.save('risk_func.tiff')
finally:
pass
# os.remove('simulation_result.tiff')
# os.remove('risk_validation.tiff')
# os.remove('risk_func.tiff')
print 'Finish Simulation', clock(), '\n'
print 'Done', clock()
