本文整理汇总了Python中mvpa2.datasets.Dataset.get_mapped方法的典型用法代码示例。如果您正苦于以下问题：Python Dataset.get_mapped方法的具体用法？Python Dataset.get_mapped怎么用？Python Dataset.get_mapped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.get_mapped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_datasetmapping
# 需要导入模块: from mvpa2.datasets import Dataset [as 别名]
# 或者: from mvpa2.datasets.Dataset import get_mapped [as 别名]
def test_datasetmapping():
# 6 samples, 4X2 features
data = np.arange(48).reshape(6,4,2)
ds = Dataset(data,
sa={'timepoints': np.arange(6),
'multidim': data.copy()},
fa={'fid': np.arange(4)})
# with overlapping and non-overlapping boxcars
startpoints = [0, 1, 4]
boxlength = 2
bm = BoxcarMapper(startpoints, boxlength, space='boxy')
# train is critical
bm.train(ds)
mds = bm.forward(ds)
assert_equal(len(mds), len(startpoints))
assert_equal(mds.nfeatures, boxlength)
# all samples attributes remain, but the can rotated/compressed into
# multidimensional attributes
assert_equal(sorted(mds.sa.keys()), ['boxy_onsetidx'] + sorted(ds.sa.keys()))
assert_equal(mds.sa.multidim.shape,
(len(startpoints), boxlength) + ds.shape[1:])
assert_equal(mds.sa.timepoints.shape, (len(startpoints), boxlength))
assert_array_equal(mds.sa.timepoints.flatten(),
np.array([(s, s+1) for s in startpoints]).flatten())
assert_array_equal(mds.sa.boxy_onsetidx, startpoints)
# feature attributes also get rotated and broadcasted
assert_array_equal(mds.fa.fid, [ds.fa.fid, ds.fa.fid])
# and finally there is a new one
assert_array_equal(mds.fa.boxy_offsetidx, range(boxlength))
# now see how it works on reverse()
rds = bm.reverse(mds)
# we got at least something of all original attributes back
assert_equal(sorted(rds.sa.keys()), sorted(ds.sa.keys()))
assert_equal(sorted(rds.fa.keys()), sorted(ds.fa.keys()))
# it is not possible to reconstruct the full samples array
# some samples even might show up multiple times (when there are overlapping
# boxcars
assert_array_equal(rds.samples,
np.array([[[ 0, 1], [ 2, 3], [ 4, 5], [ 6, 7]],
[[ 8, 9], [10, 11], [12, 13], [14, 15]],
[[ 8, 9], [10, 11], [12, 13], [14, 15]],
[[16, 17], [18, 19], [20, 21], [22, 23]],
[[32, 33], [34, 35], [36, 37], [38, 39]],
[[40, 41], [42, 43], [44, 45], [46, 47]]]))
assert_array_equal(rds.sa.timepoints, [0, 1, 1, 2, 4, 5])
assert_array_equal(rds.sa.multidim, ds.sa.multidim[rds.sa.timepoints])
# but feature attributes should be fully recovered
assert_array_equal(rds.fa.fid, ds.fa.fid)
# popular dataset configuration (double flatten + boxcar)
cm= ChainMapper([FlattenMapper(), bm, FlattenMapper()])
cm.train(ds)
bflat = ds.get_mapped(cm)
assert_equal(bflat.shape, (len(startpoints), boxlength * np.prod(ds.shape[1:])))
# add attributes
bflat.fa['testfa'] = np.arange(bflat.nfeatures)
bflat.sa['testsa'] = np.arange(bflat.nsamples)
# now try to go back
bflatrev = bflat.mapper.reverse(bflat)
# data should be same again, as far as the boxcars match
assert_array_equal(ds.samples[:2], bflatrev.samples[:2])
assert_array_equal(ds.samples[-2:], bflatrev.samples[-2:])
# feature axis should match
assert_equal(ds.shape[1:], bflatrev.shape[1:])