本文整理汇总了Python中mvpa2.datasets.base.Dataset.init_origids方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.init_origids方法的具体用法？Python Dataset.init_origids怎么用？Python Dataset.init_origids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.base.Dataset的用法示例。
在下文中一共展示了Dataset.init_origids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_from_wizard
# 需要导入模块: from mvpa2.datasets.base import Dataset [as 别名]
# 或者: from mvpa2.datasets.base.Dataset import init_origids [as 别名]
def test_from_wizard():
samples = np.arange(12).reshape((4, 3)).view(myarray)
labels = range(4)
chunks = [1, 1, 2, 2]
ds = Dataset(samples, sa={'targets': labels, 'chunks': chunks})
ds.init_origids('both')
first = ds.sa.origids
# now do again and check that they get regenerated
ds.init_origids('both')
assert_false(first is ds.sa.origids)
assert_array_equal(first, ds.sa.origids)
ok_(is_datasetlike(ds))
ok_(not is_datasetlike(labels))
# array subclass survives
ok_(isinstance(ds.samples, myarray))
## XXX stuff that needs thought:
# ds.sa (empty) has this in the public namespace:
# add, get, getvalue, has_key, is_set, items, listing, name, names
# owner, remove, reset, setvalue, which_set
# maybe we need some form of leightweightCollection?
assert_array_equal(ds.samples, samples)
assert_array_equal(ds.sa.targets, labels)
assert_array_equal(ds.sa.chunks, chunks)
# same should work for shortcuts
assert_array_equal(ds.targets, labels)
assert_array_equal(ds.chunks, chunks)
ok_(sorted(ds.sa.keys()) == ['chunks', 'origids', 'targets'])
ok_(sorted(ds.fa.keys()) == ['origids'])
# add some more
ds.a['random'] = 'blurb'
# check stripping attributes from a copy
cds = ds.copy() # full copy
ok_(sorted(cds.sa.keys()) == ['chunks', 'origids', 'targets'])
ok_(sorted(cds.fa.keys()) == ['origids'])
ok_(sorted(cds.a.keys()) == ['random'])
cds = ds.copy(sa=[], fa=[], a=[]) # plain copy
ok_(cds.sa.keys() == [])
ok_(cds.fa.keys() == [])
ok_(cds.a.keys() == [])
cds = ds.copy(sa=['targets'], fa=None, a=['random']) # partial copy
ok_(cds.sa.keys() == ['targets'])
ok_(cds.fa.keys() == ['origids'])
ok_(cds.a.keys() == ['random'])
# there is not necessarily a mapper present
ok_(not ds.a.has_key('mapper'))
# has to complain about misshaped samples attributes
assert_raises(ValueError, Dataset.from_wizard, samples, labels + labels)
# check that we actually have attributes of the expected type
ok_(isinstance(ds.sa['targets'], ArrayCollectable))
# the dataset will take care of not adding stupid stuff
assert_raises(ValueError, ds.sa.__setitem__, 'stupid', np.arange(3))
assert_raises(ValueError, ds.fa.__setitem__, 'stupid', np.arange(4))
# or change proper attributes to stupid shapes
try:
ds.sa.targets = np.arange(3)
except ValueError:
pass
else:
ok_(False, msg="Assigning value with improper shape to attribute "
"did not raise exception.")