def slice(self, k=None, rows=None, cols=None):
'''
Slice out a part of the SVD results, fewer svals, rows, and/or columns.
k : int - how many svals to keep. None -> keep all.
rows : [obj] - a list of rows to keep. None -> keep all.
cols : [obj] - a list of cols to keep. None -> keep all.
'''
row_labels = self.u.label_list(0)
col_labels = self.v.label_list(0)
if rows is not None:
rows = map(row_labels.index, rows)
row_labels = indexable_set(rows, len(rows))
if cols is not None:
cols = map(col_labels.index, rows)
col_labels = indexable_set(cols, len(cols))
return LabeledSVD2DResults.layer_on(super(LabeledSVD2DResults, self).slice(k, rows, cols), [row_labels, col_labels])
def slice(self, k=None, rows=None, cols=None):
"""
Slice out a part of the SVD results, fewer svals, rows, and/or columns.
k : int - how many svals to keep. None -> keep all.
rows : [obj] - a list of rows to keep. None -> keep all.
cols : [obj] - a list of cols to keep. None -> keep all.
"""
row_labels = self.u.label_list(0)
col_labels = self.v.label_list(0)
if rows is not None:
rows = map(row_labels.index, rows)
row_labels = indexable_set(rows, len(rows))
if cols is not None:
cols = map(col_labels.index, rows)
col_labels = indexable_set(cols, len(cols))
return LabeledSVD2DResults.layer_on(
super(LabeledSVD2DResults, self).slice(k, rows, cols), [row_labels, col_labels]
)
def __init__(self, tensor, label_lists=None):
View.__init__(self, tensor)
shape = tensor.shape
if label_lists is None:
label_lists = [None]*self.tensor.ndim
if tensor.ndim != len(label_lists):
raise IndexError('Number of label lists (%d) does not match number of dimensions (%s)' % (len(label_lists), tensor.ndim))
self._labels = [indexable_set(l, shape[mode])
for mode, l in enumerate(label_lists)]
self.tensor.ensure_index_is_valid(tuple(len(label_list)-1 for label_list in self._labels))
def __init__(self, tensor, label_lists=None):
View.__init__(self, tensor)
shape = tensor.shape
if label_lists is None:
label_lists = [None] * self.tensor.ndim
if tensor.ndim != len(label_lists):
raise IndexError(
"Number of label lists (%d) does not match number of dimensions (%s)" % (len(label_lists), tensor.ndim)
)
self._labels = [indexable_set(l, shape[mode]) for mode, l in enumerate(label_lists)]
self.tensor.ensure_index_is_valid(tuple(len(label_list) - 1 for label_list in self._labels))
