本文整理汇总了Python中molusce.algorithms.dataprovider.Raster.getBandGradation方法的典型用法代码示例。如果您正苦于以下问题：Python Raster.getBandGradation方法的具体用法？Python Raster.getBandGradation怎么用？Python Raster.getBandGradation使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类molusce.algorithms.dataprovider.Raster的用法示例。
在下文中一共展示了Raster.getBandGradation方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: TestMlpManager
# 需要导入模块: from molusce.algorithms.dataprovider import Raster [as 别名]
# 或者: from molusce.algorithms.dataprovider.Raster import getBandGradation [as 别名]
#.........这里部分代码省略.........
assert_array_equal(data[0]['output'], mng.data[0]['output'])
assert_array_equal(data[0]['state'], mng.data[0]['state'])
# Multiband input
mng = MlpManager(ns=1)
mng.createMlp(self.output, self.factors3, self.output, [10])
stat1 = self.factors3[0].getBandStat(1) # mean & std
m1,s1 = stat1['mean'], stat1['std']
stat2 = self.factors3[0].getBandStat(2) # mean & std
m2,s2 = stat2['mean'], stat2['std']
mng.setTrainingData(self.output, self.factors3, self.output)
data = [
{
'factors': np.array([ (1.-m1)/s1, (2.-m1)/s1, (1.-m1)/s1, (1.-m1)/s1, (2.-m1)/s1, (1.-m1)/s1, (0.-m1)/s1, (1.-m1)/s1, (2.-m1)/s1,
(1.-m2)/s2, (1.-m2)/s2, (3.-m2)/s2, (3.-m2)/s2, (2.-m2)/s2, (1.-m2)/s2, (0.-m2)/s2, (3.-m2)/s2, (1.-m2)/s2]),
'output': np.array([min, min, max]),
'state': np.array([0,1, 0,0, 0,1, 0,1, 0,0, 0,1, 1,0, 0,1, 0,0])
}
]
self.assertEqual(mng.data.shape, (1,))
assert_array_equal(data[0]['factors'], mng.data[0]['factors'])
assert_array_equal(data[0]['output'], mng.data[0]['output'])
assert_array_equal(data[0]['state'], mng.data[0]['state'])
# Complex case:
mng = MlpManager(ns=1)
mng.createMlp(self.output, self.factors4, self.output, [10])
stat1 = self.factors4[0].getBandStat(1) # mean & std
m1,s1 = stat1['mean'], stat1['std']
stat2 = self.factors4[0].getBandStat(2) # mean & std
m2,s2 = stat2['mean'], stat2['std']
stat3 = self.factors4[1].getBandStat(1)
m3,s3 = stat3['mean'], stat2['std']
mng.setTrainingData(self.output, self.factors4, self.output)
data = [
{
'factors': np.array([ (1.-m1)/s1, (2.-m1)/s1, (1.-m1)/s1, (1.-m1)/s1, (2.-m1)/s1, (1.-m1)/s1, (0.-m1)/s1, (1.-m1)/s1, (2.-m1)/s1,
(1.-m2)/s2, (1.-m2)/s2, (3.-m2)/s2, (3.-m2)/s2, (2.-m2)/s2, (1.-m2)/s2, (0.-m2)/s2, (3.-m2)/s2, (1.-m2)/s2,
(1.-m3)/s3, (1.-m3)/s3, (3.-m3)/s3, (3.-m3)/s3, (2.-m3)/s3, (1.-m3)/s3, (0.-m3)/s3, (3.-m3)/s3, (1.-m3)/s3]),
'output': np.array([min, min, max]),
'state': np.array([0,1, 0,0, 0,1, 0,1, 0,0, 0,1, 1,0, 0,1, 0,0])
}
]
self.assertEqual(mng.data.shape, (1,))
assert_array_equal(data[0]['factors'], mng.data[0]['factors'])
assert_array_equal(data[0]['output'], mng.data[0]['output'])
assert_array_equal(data[0]['state'], mng.data[0]['state'])
def test_train(self):
mng = MlpManager()
mng.createMlp(self.output, self.factors, self.output, [10])
mng.setTrainingData(self.output, self.factors, self.output)
mng.train(1, valPercent=50)
val = mng.getMinValError()
tr = mng.getTrainError()
mng.train(20, valPercent=50, continue_train=True)
self.assertGreaterEqual(val, mng.getMinValError())
mng = MlpManager(ns=1)
mng.createMlp(self.state1, self.factors1, self.output1, [10])
mng.setTrainingData(self.state1, self.factors1, self.output1)
mng.train(1, valPercent=20)
predict = mng.getPrediction(self.state1, self.factors1)
mask = predict.getBand(1).mask
self.assertTrue(not all(mask.flatten()))
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)))示例2: test_create
# 需要导入模块: from molusce.algorithms.dataprovider import Raster [as 别名]
# 或者: from molusce.algorithms.dataprovider.Raster import getBandGradation [as 别名]
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]))示例3: TestLRManager
# 需要导入模块: from molusce.algorithms.dataprovider import Raster [as 别名]
# 或者: from molusce.algorithms.dataprovider.Raster import getBandGradation [as 别名]
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)示例4: TestRaster
# 需要导入模块: from molusce.algorithms.dataprovider import Raster [as 别名]
# 或者: from molusce.algorithms.dataprovider.Raster import getBandGradation [as 别名]
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')
#.........这里部分代码省略.........