def test_predict(self):
mng = MlpManager()
mng.createMlp(self.output, self.factors, self.output, [10])
weights = mng.copyWeights()
# Set weights=0
# then the output must be sigmoid(0)
layers = []
for layer in weights:
shape = layer.shape
layers.append(np.zeros(shape))
mng.setMlpWeights(layers)
mng._predict(self.output, self.factors)
# Prediction ( the output must be sigmoid(0) )
raster = mng.getPrediction(self.output, self.factors, calcTransitions=True)
assert_array_equal(raster.getBand(1), sigmoid(0)*np.ones((3,3)))
# Confidence is zero
confid = mng.getConfidence()
self.assertEquals(confid.getBand(1).dtype, np.uint8)
assert_array_equal(confid.getBand(1), np.zeros((3,3)))
# Transition Potentials (is (sigmoid(0) - sigmin)/sigrange )
potentials = mng.getTransitionPotentials()
cats = self.output.getBandGradation(1)
for cat in cats:
map = potentials[cat]
self.assertEquals(map.getBand(1).dtype, np.uint8)
assert_array_equal(map.getBand(1), 50*np.ones((3,3)))
def test_predict(self):
mng = MlpManager()
mng.createMlp(self.output, self.factors, self.output, [10])
weights = mng.copyWeights()
# Set weights=0
# then the output must be sigmoid(0)
layers = []
for layer in weights:
shape = layer.shape
layers.append(np.zeros(shape))
mng.setMlpWeights(layers)
mng._predict(self.output, self.factors)
# Prediction
raster = mng.getPrediction(self.output, self.factors)
assert_array_equal(raster.getBand(1), sigmoid(0)*np.zeros((3,3)))
# Confidence
confid = mng.getConfidence()
assert_array_equal(confid.getBand(1), sigmoid(0)*np.zeros((3,3)))
