def test_compute_table(self):
# print self.crosstab.T
# CrossTab:
# [[ 3. 1. 0.]
# [ 0. 1. 0.]
# [ 1. 0. 2.]]
# prediction = self.model.getPrediction()
# prediction = [[2.0 2.0 1.0]
# [1.0 3.0 1.0]
# [-- 1.0 2.0]]
# confidence = self.model.getConfidence()
# confidence = [[1.0 0.5 0.33]
# [0.5 0.33 0.25]
# [-- 0.25 0.2]]
result = np.array([
[2.0, 1.0, 3.0],
[1.0, 2.0, 1.0],
[0, 3.0, 1.0]
])
result = np.ma.array(result, mask = (result==0))
simulator = Simulator(self.raster1, None, self.model, self.crosstab) # The model does't use factors
simulator.sim()
state = simulator.getState().getBand(1)
assert_array_equal(result, state)
result = np.array([
[2.0, 2.0, 1.0],
[1.0, 2.0, 1.0],
[0, 3.0, 1.0]
])
result = np.ma.array(result, mask = (result==0))
simulator = Simulator(self.raster1, None, self.model, self.crosstab)
simulator.simN(2)
state = simulator.getState().getBand(1)
assert_array_equal(result, state)
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 "Finish Making CrossTable", clock(), '\n'
#~ # Create and Train ANN Model
#~ model = MlpManager(ns=0)
#~ model.createMlp(initRaster, factors, finalRaster, [10])
#~ print 'Start Setting MLP Trainig Data...', clock()
#~ model.setTrainingData(initRaster, factors, finalRaster, mode='Balanced', samples=1000)
#~ print 'Finish Setting Trainig Data', clock(), '\n'
#~ print 'Start MLP Training...', clock()
#~ model.train(1000, valPercent=20)
#~ print 'Finish Trainig', clock(), '\n'
#~
#~ print 'Start ANN Prediction...', clock()
#~ predict = model.getPrediction(initRaster, factors)
#~ filename = 'ann_predict.tiff'
#~ try:
#~ predict.save(filename)
#~ finally:
#~ #os.remove(filename)
#~ pass
#~ print 'Finish ANN Prediction...', clock(), '\n'
#~ # Create and Train LR Model
#~ model = LR(ns=1)
#~ print 'Start Setting LR Trainig Data...', clock()
#~ model.setTrainingData(initRaster, factors, finalRaster, mode='Balanced', samples=1000)
#~ 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)
#~ filename = 'lr_predict.tiff'
#~ try:
#~ predict.save(filename)
#~ finally:
#~ #os.remove(filename)
#~ pass
#~ 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, 2000, 3000]], 1: [[200, 500, 1000, 1500]]}
#~ model = WoeManager(factors, analyst, bins= bins)
#~ 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)
print 'Finish creating MCE model...', clock(), '\n'
# simulation
print 'Start Simulation...', clock()
simulator = Simulator(initRaster, factors, model, crosstab)
# Make 1 cycle of simulation:
simulator.sim()
monteCarloSim = simulator.getState() # Result of MonteCarlo simulation
errors = simulator.errorMap(finalRaster) # Risk class validation
riskFunct = simulator.getConfidence() # Risk function
# Make K cycles of simulation:
# simulator.simN(K)
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()
