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 LR Model
model = LR(ns=1)
print 'Start Setting LR Trainig Data...', clock()
model.setTrainingData(initRaster,
factors,
finalRaster,
mode='Stratified',
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)
print 'Finish LR Prediction...', clock(), '\n'
# simulation
print 'Start Simulation...', clock()
simulator = Simulator(initRaster, factors, model, crosstab)
# Make 1 cycle of simulation:
simulator.simN(1)
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()
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 LR Model
model = LR(ns=1)
print 'Start Setting LR Trainig Data...', clock()
model.setTrainingData(initRaster, factors, finalRaster, mode='Stratified', 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)
print 'Finish LR Prediction...', clock(), '\n'
# simulation
print 'Start Simulation...', clock()
simulator = Simulator(initRaster, factors, model, crosstab)
# Make 1 cycle of simulation:
simulator.simN(1)
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()
def test_LR(self):
#~ data = [
#~ [3.0, 1.0, 3.0],
#~ [3.0, 1.0, 3.0],
#~ [0, 3.0, 1.0]
#~ ]
#~ result = np.ma.array(data = data, mask = (data==0))
lr = LR(ns=0) # 3-class problem
lr.setState(self.state)
lr.setFactors(self.factors)
lr.setOutput(self.output)
lr.setTrainingData()
lr.train()
predict = lr.getPrediction(self.state, self.factors)
predict = predict.getBand(1)
assert_array_equal(predict, self.output.getBand(1))
lr = LR(ns=1) # Two-class problem (it's because of boundary effect)
lr.setState(self.state1)
lr.setFactors(self.factors1)
lr.setOutput(self.output1)
lr.setTrainingData()
lr.train()
predict = lr.getPrediction(self.state1,
self.factors1,
calcTransitions=True)
predict = predict.getBand(1)
self.assertEquals(predict.dtype, np.uint8)
data = [
[0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 2.0, 0.0],
[0.0, 2.0, 2.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
]
result = np.ma.array(data=data, mask=(data == 0))
assert_array_equal(predict, result)
# Confidence is zero
confid = lr.getConfidence()
self.assertEquals(confid.getBand(1).dtype, np.uint8)
# Transition Potentials
potentials = lr.getTransitionPotentials()
cats = self.output.getBandGradation(1)
for cat in [1.0, 2.0]:
map = potentials[cat]
self.assertEquals(map.getBand(1).dtype, np.uint8)
def test_LR(self):
#~ data = [
#~ [3.0, 1.0, 3.0],
#~ [3.0, 1.0, 3.0],
#~ [0, 3.0, 1.0]
#~ ]
#~ result = np.ma.array(data = data, mask = (data==0))
lr = LR(ns=0) # 3-class problem
lr.setState(self.state)
lr.setFactors(self.factors)
lr.setOutput(self.output)
lr.setTrainingData()
lr.train()
predict = lr.getPrediction(self.state, self.factors)
predict = predict.getBand(1)
assert_array_equal(predict, self.output.getBand(1))
lr = LR(ns=1) # Two-class problem (it's because of boundary effect)
lr.setState(self.state1)
lr.setFactors(self.factors1)
lr.setOutput(self.output1)
lr.setTrainingData()
lr.train()
predict = lr.getPrediction(self.state1, self.factors1, calcTransitions=True)
predict = predict.getBand(1)
self.assertEquals(predict.dtype, np.uint8)
data = [
[0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 2.0, 0.0],
[0.0, 2.0, 2.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
]
result = np.ma.array(data = data, mask = (data==0))
assert_array_equal(predict, result)
# Confidence is zero
confid = lr.getConfidence()
self.assertEquals(confid.getBand(1).dtype, np.uint8)
# Transition Potentials
potentials = lr.getTransitionPotentials()
cats = self.output.getBandGradation(1)
for cat in [1.0, 2.0]:
map = potentials[cat]
self.assertEquals(map.getBand(1).dtype, np.uint8)
def test_LR(self):
data = [
[3.0, 1.0, 3.0],
[3.0, 1.0, 3.0],
[0, 3.0, 1.0]
]
result = np.ma.array(data = data, mask = (data==0))
lr = LR(ns=0) # 3-class problem
lr.setTrainingData(self.state, self.factors, self.output)
lr.train()
predict = lr.getPrediction(self.state, self.factors)
predict = predict.getBand(1)
assert_array_equal(predict, result)
lr = LR(ns=1) # Two-class problem (it's because of boundary effect)
lr.setTrainingData(self.state1, self.factors1, self.output1)
lr.train()
predict = lr.getPrediction(self.state1, self.factors1)
predict = predict.getBand(1)
data = [
[0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 2.0, 0.0],
[0.0, 2.0, 2.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
]
result = np.ma.array(data = data, mask = (data==0))
assert_array_equal(predict, result)
