def test_simpleboxcar():
data = np.atleast_2d(np.arange(10)).T
sp = np.arange(10)
# check if stupid thing don't work
assert_raises(ValueError, BoxcarMapper, sp, 0)
# now do an identity transformation
bcm = BoxcarMapper(sp, 1)
trans = bcm.forward(data)
# ,0 is a feature below, so we get explicit 2D out of 1D
assert_array_equal(trans[:, 0], data)
# now check for illegal boxes
if __debug__:
# condition is checked only in __debug__
assert_raises(ValueError, BoxcarMapper(sp, 2).train, data)
# now something that should work
nbox = 9
boxlength = 2
sp = np.arange(nbox)
bcm = BoxcarMapper(sp, boxlength)
trans = bcm.forward(data)
# check that is properly upcasts the dimensionality
assert_equal(trans.shape, (nbox, boxlength) + data.shape[1:])
# check actual values, squeezing the last dim for simplicity
assert_array_equal(trans.squeeze(),
np.vstack((np.arange(9), np.arange(9) + 1)).T)
# now test for proper data shape
data = np.ones((10, 3, 4, 2))
sp = [2, 4, 3, 5]
trans = BoxcarMapper(sp, 4).forward(data)
assert_equal(trans.shape, (4, 4, 3, 4, 2))
# test reverse
data = np.arange(240).reshape(10, 3, 4, 2)
sp = [2, 4, 3, 5]
boxlength = 2
m = BoxcarMapper(sp, boxlength)
m.train(data)
mp = m.forward(data)
assert_equal(mp.shape, (4, 2, 3, 4, 2))
# try full reconstruct
mr = m.reverse(mp)
# shape has to match
assert_equal(mr.shape, (len(sp) * boxlength, ) + data.shape[1:])
# only known samples are part of the results
assert_true((mr >= 24).all())
assert_true((mr < 168).all())
# check proper reconstruction of non-conflicting sample
assert_array_equal(mr[0].ravel(), np.arange(48, 72))
# check proper reconstruction of samples being part of multiple
# mapped samples
assert_array_equal(mr[1].ravel(), np.arange(72, 96))
# test reverse of a single sample
singlesample = np.arange(48).reshape(2, 3, 4, 2)
assert_array_equal(singlesample, m.reverse1(singlesample))
# now in a dataset
ds = Dataset([singlesample])
assert_equal(ds.shape, (1, ) + singlesample.shape)
# after reverse mapping the 'sample axis' should vanish and the original 3d
# shape of the samples should be restored
assert_equal(ds.shape[1:], m.reverse(ds).shape)
# multiple samples should just be concatenated along the samples axis
ds = Dataset([singlesample, singlesample])
assert_equal((np.prod(ds.shape[:2]), ) + singlesample.shape[1:],
m.reverse(ds).shape)
# should not work for shape mismatch, but it does work and is useful when
# reverse mapping sample attributes
#assert_raises(ValueError, m.reverse, singlesample[0])
# check broadcasting of 'raw' samples into proper boxcars on forward()
bc = m.forward1(np.arange(24).reshape(3, 4, 2))
assert_array_equal(bc, np.array(2 * [np.arange(24).reshape(3, 4, 2)]))
def test_simpleboxcar():
data = np.atleast_2d(np.arange(10)).T
sp = np.arange(10)
# check if stupid thing don't work
assert_raises(ValueError, BoxcarMapper, sp, 0)
# now do an identity transformation
bcm = BoxcarMapper(sp, 1)
trans = bcm.forward(data)
# ,0 is a feature below, so we get explicit 2D out of 1D
assert_array_equal(trans[:,0], data)
# now check for illegal boxes
if __debug__:
# condition is checked only in __debug__
assert_raises(ValueError, BoxcarMapper(sp, 2).train, data)
# now something that should work
nbox = 9
boxlength = 2
sp = np.arange(nbox)
bcm = BoxcarMapper(sp, boxlength)
trans = bcm.forward(data)
# check that is properly upcasts the dimensionality
assert_equal(trans.shape, (nbox, boxlength) + data.shape[1:])
# check actual values, squeezing the last dim for simplicity
assert_array_equal(trans.squeeze(), np.vstack((np.arange(9), np.arange(9)+1)).T)
# now test for proper data shape
data = np.ones((10,3,4,2))
sp = [ 2, 4, 3, 5 ]
trans = BoxcarMapper(sp, 4).forward(data)
assert_equal(trans.shape, (4,4,3,4,2))
# test reverse
data = np.arange(240).reshape(10, 3, 4, 2)
sp = [ 2, 4, 3, 5 ]
boxlength = 2
m = BoxcarMapper(sp, boxlength)
m.train(data)
mp = m.forward(data)
assert_equal(mp.shape, (4, 2, 3, 4, 2))
# try full reconstruct
mr = m.reverse(mp)
# shape has to match
assert_equal(mr.shape, (len(sp) * boxlength,) + data.shape[1:])
# only known samples are part of the results
assert_true((mr >= 24).all())
assert_true((mr < 168).all())
# check proper reconstruction of non-conflicting sample
assert_array_equal(mr[0].ravel(), np.arange(48, 72))
# check proper reconstruction of samples being part of multiple
# mapped samples
assert_array_equal(mr[1].ravel(), np.arange(72, 96))
# test reverse of a single sample
singlesample = np.arange(48).reshape(2, 3, 4, 2)
assert_array_equal(singlesample, m.reverse1(singlesample))
# now in a dataset
ds = Dataset([singlesample])
assert_equal(ds.shape, (1,) + singlesample.shape)
# after reverse mapping the 'sample axis' should vanish and the original 3d
# shape of the samples should be restored
assert_equal(ds.shape[1:], m.reverse(ds).shape)
# multiple samples should just be concatenated along the samples axis
ds = Dataset([singlesample, singlesample])
assert_equal((np.prod(ds.shape[:2]),) + singlesample.shape[1:],
m.reverse(ds).shape)
# should not work for shape mismatch, but it does work and is useful when
# reverse mapping sample attributes
#assert_raises(ValueError, m.reverse, singlesample[0])
# check broadcasting of 'raw' samples into proper boxcars on forward()
bc = m.forward1(np.arange(24).reshape(3, 4, 2))
assert_array_equal(bc, np.array(2 * [np.arange(24).reshape(3, 4, 2)]))
