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)
