def test_normalize(self):
multifact = [
[1, 1, 3],
[3, 2, 1],
[0, 3, 1],
]
# Normalize using std and mean
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Normalize using min and max
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Two normalization procedures
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
def test_normalize(self):
multifact = [
[1,1,3],
[3,2,1],
[0,3,1],
]
# Normalize using std and mean
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Normalize using min and max
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Two normalization procedures
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
def test_WoeManager(self):
aa = AreaAnalyst(self.sites, self.sites)
w1 = WoeManager([self.factor], aa)
p = w1.getPrediction(self.sites).getBand(1)
assert_array_equal(p, self.sites.getBand(1))
initState = Raster("../../examples/data.tif")
finalState = Raster("../../examples/data1.tif")
aa = AreaAnalyst(initState, finalState)
w = WoeManager([initState], aa)
p = w.getPrediction(initState).getBand(1)
# Calculate by hands:
# 1->1 transition raster:
r11 = [[1, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 1->2 raster:
r12 = [[0, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 1->3 raster:
r13 = [[0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->1
r21 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->2
r22 = [[0, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->3
r23 = [[0, 0, 0, 0], [0, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 0]]
# 3->1
r31 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 0, 0]]
# 3->2
r32 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]
# 3->3
r33 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0]]
geodata = initState.getGeodata()
sites = {"11": r11, "12": r12, "13": r13, "21": r21, "22": r22, "23": r23, "31": r31, "32": r32, "33": r33}
woeDict = {} # WoE of transitions
for k in sites.keys(): #
if k != "21": # !!! r21 is zero
x = Raster()
x.create([np.ma.array(data=sites[k])], geodata)
sites[k] = x
woeDict[k] = woe(initState.getBand(1), x.getBand(1))
# w1max = np.maximum(woeDict['11'], woeDict['12'], woeDict['13'])
# w2max = np.maximum(woeDict['22'], woeDict['23'])
# w3max = np.maximum(woeDict['31'], woeDict['32'], woeDict['33'])
# Answer is index of finalClass that maximizes weights of transiotion initClass -> finalClass
answer = [[1, 1, 1, 1], [1, 1, 3, 3], [3, 3, 3, 3], [1, 1, 1, 1]]
assert_array_equal(p, answer)
w = WoeManager([initState], aa, bins={0: [[2]]})
p = w.getPrediction(initState).getBand(1)
def test_save(self):
try:
filename = 'temp.tiff'
self.r1.save(filename)
r2 = Raster(filename)
self.assertEqual(r2.get_dtype(), self.r1.get_dtype())
self.assertEqual(r2.getBandsCount(), self.r1.getBandsCount())
for i in range(r2.getBandsCount()):
assert_array_equal(r2.getBand(i+1), self.r1.getBand(i+1))
finally:
os.remove(filename)
def test_save(self):
try:
filename = 'temp.tiff'
self.r1.save(filename)
r2 = Raster(filename)
self.assertEqual(r2.get_dtype(), self.r1.get_dtype())
self.assertEqual(r2.getBandsCount(), self.r1.getBandsCount())
for i in range(r2.getBandsCount()):
assert_array_equal(r2.getBand(i + 1), self.r1.getBand(i + 1))
finally:
os.remove(filename)
class TestRaster (unittest.TestCase):
def setUp(self):
self.r1 = Raster('examples/multifact.tif')
self.r2 = Raster('examples/sites.tif')
self.r3 = Raster('examples/two_band.tif')
# r1
data1 = np.array(
[
[1,1,3],
[3,2,1],
[0,3,1]
])
# r2
data2 = np.array(
[
[1,2,1],
[1,2,1],
[0,1,2]
])
mask = [
[False, False, False],
[False, False, False],
[False, False, False]
]
self.data1 = ma.array(data=data1, mask=mask)
self.data2 = ma.array(data=data2, mask=mask)
def test_RasterInit(self):
self.assertEqual(self.r1.getBandsCount(), 1)
band = self.r1.getBand(1)
shape = band.shape
x = self.r1.getXSize()
y = self.r1.getYSize()
self.assertEqual(shape, (x,y))
self.assertEqual(self.r2.getBandsCount(), 1)
band = self.r2.getBand(1)
assert_array_equal(band, self.data2)
self.assertTrue(self.r1.geoDataMatch(self.r2))
self.assertTrue(self.r1.isMetricProj())
def test_getBandStat(self):
stat = self.r1.getBandStat(1)
self.assertAlmostEqual(stat['mean'], 15.0/9)
self.assertAlmostEqual(stat['std'], np.sqrt(10.0/9))
def test_normalize(self):
multifact = [
[1,1,3],
[3,2,1],
[0,3,1],
]
# Normalize using std and mean
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Normalize using min and max
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Two normalization procedures
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
def test_getNeighbours(self):
neighbours = self.r2.getNeighbours(row=1,col=0, size=0)
self.assertEqual(neighbours, [[1]])
neighbours = self.r2.getNeighbours(row=1,col=1, size=1)
assert_array_equal(neighbours, [self.data2])
neighbours = self.r3.getNeighbours(row=1,col=1, size=1)
assert_array_equal(neighbours, [self.data2, self.data1])
# Check pixel on the raster bound and nonzero neighbour size
self.assertRaises(ProviderError, self.r2.getNeighbours, col=1, row=0, size=1)
self.assertRaises(ProviderError, self.r2.getNeighbours, col=1, row=1, size=2)
def test_geodata(self):
geodata = self.r1.getGeodata()
self.r1.setGeoData(geodata)
geodata['xSize'] = geodata['xSize'] + 10
self.assertRaises(ProviderError, self.r1.setGeoData, geodata=geodata)
def test_save(self):
try:
filename = 'temp.tiff'
self.r1.save(filename)
r2 = Raster(filename)
self.assertEqual(r2.get_dtype(), self.r1.get_dtype())
self.assertEqual(r2.getBandsCount(), self.r1.getBandsCount())
for i in range(r2.getBandsCount()):
assert_array_equal(r2.getBand(i+1), self.r1.getBand(i+1))
finally:
os.remove(filename)
def test_WoeManager(self):
aa = AreaAnalyst(self.sites, self.sites)
w1 = WoeManager([self.factor], aa)
w1.train()
p = w1.getPrediction(self.sites).getBand(1)
answer = [[0,3,0], [0,3,0], [9,0,3]]
answer = ma.array(data = answer, mask = self.mask)
assert_array_equal(p, answer)
initState = Raster('../../examples/data.tif')
#~ [1,1,1,1],
#~ [1,1,2,2],
#~ [2,2,2,2],
#~ [3,3,3,3]
finalState = Raster('../../examples/data1.tif')
#~ [1,1,2,3],
#~ [3,1,2,3],
#~ [3,3,3,3],
#~ [1,1,3,2]
aa = AreaAnalyst(initState, finalState)
w = WoeManager([initState], aa)
w.train()
#print w.woe
p = w.getPrediction(initState).getBand(1)
self.assertEquals(p.dtype, np.uint8)
# Calculate by hands:
#1->1 transition raster:
r11 = [
[1, 1, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
#1->2 raster:
r12 = [
[0, 0, 1, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
#1->3 raster:
r13 = [
[0, 0, 0, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
# 2->1
r21 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
# 2->2
r22 = [
[0, 0, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
# 2->3
r23 = [
[0, 0, 0, 0],
[0, 0, 0, 1],
[1, 1, 1, 1],
[0, 0, 0, 0]
]
# 3->1
r31 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 1, 0, 0]
]
# 3->2
r32 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 1]
]
# 3->3
r33 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 1, 0]
]
geodata = initState.getGeodata()
sites = {'11': r11, '12': r12, '13': r13, '21': r21, '22': r22, '23': r23, '31': r31, '32': r32, '33': r33}
woeDict = {} # WoE of transitions
for k in sites.keys(): #
if k !='21' : # !!! r21 is zero
x = Raster()
x.create([np.ma.array(data=sites[k])], geodata)
sites[k] = x
woeDict[k] = woe(initState.getBand(1), x.getBand(1))
#w1max = np.maximum(woeDict['11'], woeDict['12'], woeDict['13'])
#w2max = np.maximum(woeDict['22'], woeDict['23'])
#w3max = np.maximum(woeDict['31'], woeDict['32'], woeDict['33'])
# Answer is a transition code with max weight
answer = [
[0, 0, 0, 0],
[0, 0, 5, 5],
[5, 5, 5, 5],
[6, 6, 6, 6]
]
assert_array_equal(p, answer)
w = WoeManager([initState], aa, bins = {0: [[2], ],})
w.train()
p = w.getPrediction(initState).getBand(1)
self.assertEquals(p.dtype, np.uint8)
c = w.getConfidence().getBand(1)
self.assertEquals(c.dtype, np.uint8)
class TestLRManager(unittest.TestCase):
def setUp(self):
self.output = Raster('../../examples/multifact.tif')
#~ [1,1,3]
#~ [3,2,1]
#~ [0,3,1]
self.output.resetMask([0])
self.state = self.output
self.factors = [
Raster('../../examples/sites.tif'),
Raster('../../examples/sites.tif')
]
#~ [1,2,1],
#~ [1,2,1],
#~ [0,1,2]
self.output1 = Raster('../../examples/data.tif')
self.state1 = self.output1
self.factors1 = [Raster('../../examples/fact16.tif')]
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)
class TestRaster(unittest.TestCase):
def setUp(self):
self.r1 = Raster('examples/multifact.tif')
self.r2 = Raster('examples/sites.tif')
self.r3 = Raster('examples/two_band.tif')
# r1
data1 = np.array([[1, 1, 3], [3, 2, 1], [0, 3, 1]])
# r2
data2 = np.array([[1, 2, 1], [1, 2, 1], [0, 1, 2]])
mask = [[False, False, False], [False, False, False],
[False, False, False]]
self.data1 = ma.array(data=data1, mask=mask)
self.data2 = ma.array(data=data2, mask=mask)
def test_RasterInit(self):
self.assertEqual(self.r1.getBandsCount(), 1)
band = self.r1.getBand(1)
shape = band.shape
x = self.r1.getXSize()
y = self.r1.getYSize()
self.assertEqual(shape, (x, y))
self.assertEqual(self.r2.getBandsCount(), 1)
band = self.r2.getBand(1)
assert_array_equal(band, self.data2)
self.assertTrue(self.r1.geoDataMatch(self.r2))
self.assertTrue(self.r1.isMetricProj())
def test_create(self):
raster = Raster()
raster.create([self.data1], geodata=self.r1.getGeodata())
self.assertTrue(raster.geoDataMatch(self.r1))
self.assertEqual(raster.getBandsCount(), 1)
self.assertEqual(set(raster.getBandGradation(1)), set([0, 1, 2, 3]))
def test_roundBands(self):
rast = Raster('examples/multifact.tif')
rast.bands = rast.bands * 0.1
rast.roundBands()
answer = [[[
0,
0,
0,
], [0, 0, 0], [0, 0, 0]]]
assert_array_equal(answer, rast.bands)
rast = Raster('examples/multifact.tif')
rast.bands = rast.bands * 1.1
rast.roundBands(decimals=1)
answer = np.array([[[1.1, 1.1, 3.3], [3.3, 2.2, 1.1], [0.0, 3.3,
1.1]]])
assert_array_equal(answer, rast.bands)
def test_isContinues(self):
rast = Raster('examples/multifact.tif')
self.assertFalse(rast.isCountinues(bandNo=1))
rast = Raster('examples/dist_roads.tif')
self.assertTrue(rast.isCountinues(bandNo=1))
def test_getBandStat(self):
stat = self.r1.getBandStat(1)
self.assertAlmostEqual(stat['mean'], 15.0 / 9)
self.assertAlmostEqual(stat['std'], np.sqrt(10.0 / 9))
def test_normalize(self):
multifact = [
[1, 1, 3],
[3, 2, 1],
[0, 3, 1],
]
# Normalize using std and mean
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Normalize using min and max
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Two normalization procedures
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
def test_getNeighbours(self):
neighbours = self.r2.getNeighbours(row=1, col=0, size=0)
self.assertEqual(neighbours, [[1]])
neighbours = self.r2.getNeighbours(row=1, col=1, size=1)
assert_array_equal(neighbours, [self.data2])
neighbours = self.r3.getNeighbours(row=1, col=1, size=1)
assert_array_equal(neighbours, [self.data2, self.data1])
# Check pixel on the raster bound and nonzero neighbour size
self.assertRaises(ProviderError,
self.r2.getNeighbours,
col=1,
row=0,
size=1)
self.assertRaises(ProviderError,
self.r2.getNeighbours,
col=1,
row=1,
size=2)
def test_geodata(self):
geodata = self.r1.getGeodata()
self.r1.setGeoData(geodata)
geodata['xSize'] = geodata['xSize'] + 10
self.assertRaises(ProviderError, self.r1.setGeoData, geodata=geodata)
self.assertTrue(self.r1.geoDataMatch(self.r1))
self.assertTrue(
self.r1.geoDataMatch(raster=None, geodata=self.r1.getGeodata()))
self.assertTrue(self.r1.geoTransformMatch(self.r1))
self.assertTrue(
self.r1.geoTransformMatch(raster=None,
geodata=self.r1.getGeodata()))
def test_save(self):
try:
filename = 'temp.tiff'
self.r1.save(filename)
r2 = Raster(filename)
self.assertEqual(r2.get_dtype(), self.r1.get_dtype())
self.assertEqual(r2.getBandsCount(), self.r1.getBandsCount())
for i in range(r2.getBandsCount()):
assert_array_equal(r2.getBand(i + 1), self.r1.getBand(i + 1))
finally:
os.remove(filename)
def test_getBandGradation(self):
self.assertEqual(set(self.r1.getBandGradation(1)), set([0, 1, 2, 3]))
class TestLRManager (unittest.TestCase):
def setUp(self):
self.output = Raster('../../examples/multifact.tif')
#~ [1,1,3]
#~ [3,2,1]
#~ [0,3,1]
self.output.resetMask([0])
self.state = self.output
self.factors = [Raster('../../examples/sites.tif'), Raster('../../examples/sites.tif')]
#~ [1,2,1],
#~ [1,2,1],
#~ [0,1,2]
self.output1 = Raster('../../examples/data.tif')
self.state1 = self.output1
self.factors1 = [Raster('../../examples/fact16.tif')]
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_WoeManager(self):
aa = AreaAnalyst(self.sites, self.sites)
w1 = WoeManager([self.factor], aa)
w1.train()
p = w1.getPrediction(self.sites).getBand(1)
answer = [[0, 3, 0], [0, 3, 0], [9, 0, 3]]
answer = ma.array(data=answer, mask=self.mask)
assert_array_equal(p, answer)
initState = Raster('../../examples/data.tif')
#~ [1,1,1,1],
#~ [1,1,2,2],
#~ [2,2,2,2],
#~ [3,3,3,3]
finalState = Raster('../../examples/data1.tif')
#~ [1,1,2,3],
#~ [3,1,2,3],
#~ [3,3,3,3],
#~ [1,1,3,2]
aa = AreaAnalyst(initState, finalState)
w = WoeManager([initState], aa)
w.train()
#print w.woe
p = w.getPrediction(initState).getBand(1)
self.assertEquals(p.dtype, np.uint8)
# Calculate by hands:
#1->1 transition raster:
r11 = [[1, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
#1->2 raster:
r12 = [[0, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
#1->3 raster:
r13 = [[0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->1
r21 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->2
r22 = [[0, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->3
r23 = [[0, 0, 0, 0], [0, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 0]]
# 3->1
r31 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 0, 0]]
# 3->2
r32 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]
# 3->3
r33 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0]]
geodata = initState.getGeodata()
sites = {
'11': r11,
'12': r12,
'13': r13,
'21': r21,
'22': r22,
'23': r23,
'31': r31,
'32': r32,
'33': r33
}
woeDict = {} # WoE of transitions
for k in sites.keys(): #
if k != '21': # !!! r21 is zero
x = Raster()
x.create([np.ma.array(data=sites[k])], geodata)
sites[k] = x
woeDict[k] = woe(initState.getBand(1), x.getBand(1))
#w1max = np.maximum(woeDict['11'], woeDict['12'], woeDict['13'])
#w2max = np.maximum(woeDict['22'], woeDict['23'])
#w3max = np.maximum(woeDict['31'], woeDict['32'], woeDict['33'])
# Answer is a transition code with max weight
answer = [[0, 0, 0, 0], [0, 0, 5, 5], [5, 5, 5, 5], [6, 6, 6, 6]]
assert_array_equal(p, answer)
w = WoeManager([initState], aa, bins={
0: [
[2],
],
})
w.train()
p = w.getPrediction(initState).getBand(1)
self.assertEquals(p.dtype, np.uint8)
c = w.getConfidence().getBand(1)
self.assertEquals(c.dtype, np.uint8)
