本文整理汇总了Python中mvpa2.datasets.Dataset.sa['opt_reg_const']方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.sa['opt_reg_const']方法的具体用法？Python Dataset.sa['opt_reg_const']怎么用？Python Dataset.sa['opt_reg_const']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mvpa2.datasets.Dataset的用法示例。
在下文中一共展示了Dataset.sa['opt_reg_const']方法的1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: test_polydetrend
# 需要导入模块: from mvpa2.datasets import Dataset [as 别名]
# 或者: from mvpa2.datasets.Dataset import sa['opt_reg_const'] [as 别名]
def test_polydetrend():
samples_forwhole = np.array( [[1.0, 2, 3, 4, 5, 6],
[-2.0, -4, -6, -8, -10, -12]], ndmin=2 ).T
samples_forchunks = np.array( [[1.0, 2, 3, 3, 2, 1],
[-2.0, -4, -6, -6, -4, -2]], ndmin=2 ).T
chunks = [0, 0, 0, 1, 1, 1]
chunks_bad = [ 0, 0, 1, 1, 1, 0]
target_whole = np.array( [[-3.0, -2, -1, 1, 2, 3],
[-6, -4, -2, 2, 4, 6]], ndmin=2 ).T
target_chunked = np.array( [[-1.0, 0, 1, 1, 0, -1],
[2, 0, -2, -2, 0, 2]], ndmin=2 ).T
ds = Dataset(samples_forwhole)
# this one will auto-train the mapper on first use
dm = PolyDetrendMapper(polyord=1, space='police')
mds = dm.forward(ds)
# features are linear trends, so detrending should remove all
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# we get the information where each sample is assumed to be in the
# space spanned by the polynomials
assert_array_equal(mds.sa.police, np.arange(len(ds)))
# hackish way to get the previous regressors into a dataset
ds.sa['opt_reg_const'] = dm._regs[:,0]
ds.sa['opt_reg_lin'] = dm._regs[:,1]
# using these precomputed regressors, we should get the same result as
# before even if we do not generate a regressor for linear
dm_optreg = PolyDetrendMapper(polyord=0,
opt_regs=['opt_reg_const', 'opt_reg_lin'])
mds_optreg = dm_optreg.forward(ds)
assert_array_almost_equal(mds_optreg, np.zeros(mds.shape))
ds = Dataset(samples_forchunks)
# 'constant' detrending removes the mean
mds = PolyDetrendMapper(polyord=0).forward(ds)
assert_array_almost_equal(
mds.samples,
samples_forchunks - np.mean(samples_forchunks, axis=0))
# if there is no GLOBAL linear trend it should be identical to mean removal
# even if trying to remove linear
mds2 = PolyDetrendMapper(polyord=1).forward(ds)
assert_array_almost_equal(mds, mds2)
# chunk-wise detrending
ds = dataset_wizard(samples_forchunks, chunks=chunks)
dm = PolyDetrendMapper(chunks_attr='chunks', polyord=1, space='police')
mds = dm.forward(ds)
# features are chunkswise linear trends, so detrending should remove all
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# we get the information where each sample is assumed to be in the
# space spanned by the polynomials, which is the identical linspace in both
# chunks
assert_array_equal(mds.sa.police, range(3) * 2)
# non-matching number of samples cannot be mapped
assert_raises(ValueError, dm.forward, ds[:-1])
# however, if the dataset knows about the space it is possible
ds.sa['police'] = mds.sa.police
# XXX this should be
#mds2 = dm(ds[1:-1])
#assert_array_equal(mds[1:-1], mds2)
# XXX but right now is
assert_raises(NotImplementedError, dm.forward, ds[1:-1])
# Detrend must preserve the size of dataset
assert_equal(mds.shape, ds.shape)
# small additional test for break points
# although they are no longer there
ds = dataset_wizard(np.array([[1.0, 2, 3, 1, 2, 3]], ndmin=2).T,
targets=chunks, chunks=chunks)
mds = PolyDetrendMapper(chunks_attr='chunks', polyord=1).forward(ds)
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# test of different polyord on each chunk
target_mixed = np.array( [[-1.0, 0, 1, 0, 0, 0],
[2.0, 0, -2, 0, 0, 0]], ndmin=2 ).T
ds = dataset_wizard(samples_forchunks.copy(), targets=chunks, chunks=chunks)
mds = PolyDetrendMapper(chunks_attr='chunks', polyord=[0,1]).forward(ds)
assert_array_almost_equal(mds, target_mixed)
# test irregluar spacing of samples, but with corrective time info
samples_forwhole = np.array( [[1.0, 4, 6, 8, 2, 9],
[-2.0, -8, -12, -16, -4, -18]], ndmin=2 ).T
ds = Dataset(samples_forwhole, sa={'time': samples_forwhole[:,0]})
# linear detrending that makes use of temporal info from dataset
dm = PolyDetrendMapper(polyord=1, space='time')
mds = dm.forward(ds)
assert_array_almost_equal(mds.samples, np.zeros(mds.shape))
# and now the same stuff, but with chunking and ordered by time
samples_forchunks = np.array( [[1.0, 3, 3, 2, 2, 1],
[-2.0, -6, -6, -4, -4, -2]], ndmin=2 ).T
chunks = [0, 1, 0, 1, 0, 1]
time = [4, 4, 12, 8, 8, 12]
ds = Dataset(samples_forchunks.copy(), sa={'chunks': chunks, 'time': time})
mds = PolyDetrendMapper(chunks_attr='chunks', polyord=1, space='time').forward(ds)
#.........这里部分代码省略.........