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Fixing arrange and normalize. #103

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Apr 29, 2023
Merged

Fixing arrange and normalize. #103

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e896f08
Fixing arrange and normalize.
dmdunla Apr 29, 2023
63019ae
Merge main (#104)
dmdunla Apr 29, 2023
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4 changes: 1 addition & 3 deletions .github/workflows/regression-tests.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,4 @@ jobs:
coverage run --source pyttb -m pytest tests/
coverage report
- name: Upload coverage to Coveralls
run: coveralls --service=github
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
uses: coverallsapp/github-action@v2
86 changes: 56 additions & 30 deletions pyttb/ktensor.py
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Expand Up @@ -495,25 +495,27 @@ def from_vector(cls, data, shape, contains_weights):

def arrange(self, weight_factor=None, permutation=None):
"""
Arrange the rank-1 components of a :class:`pyttb.ktensor`. The
columns are permuted in place, so you must make a copy before calling
this method if you want to store the original :class:`pyttb.ktensor`.
One of the parameters, either `weight_factor` or `permutation`
must be passed, and passing both parameters leads to an error.
Arrange the rank-1 components of a :class:`pyttb.ktensor` in place.
If `permutation` is passed, the columns of `self.factor_matrices` are
arranged using the provided permutation, so you must make a copy
before calling this method if you want to store the original
:class:`pyttb.ktensor`. If `weight_factor` is passed, then the values
in `self.weights` are absorbed into
`self.factor_matrices[weight_factor]`. If no parameters are passed,
then the columns of `self.factor_matrices` are normalized and then
permuted such that the resulting `self.weights` are sorted by
magnitude, greatest to least. Passing both parameters leads to an
error.

Parameters
----------
weight_factor: int, optional
The index of the factor that the weights will be absorbed into
permutation: :class:`tuple`, :class:`list`, :class:`numpy.ndarray`, optional
The index of the factor matrix that the weights will be absorbed into.
permutation: :class:`tuple`, :class:`list`, or :class:`numpy.ndarray`, optional
The new order of the components of the :class:`pyttb.ktensor`
into which to permute. The permutation must be of length equal to
the number of components of the :class:`pyttb.ktensor`, N, and
must be a permutation of 1 to N.

Returns
-------
:class:`pyttb.ktensor`
the number of components of the :class:`pyttb.ktensor`, `self.ncomponents`
and must be a permutation of [0,...,`self.ncomponents`-1].

Examples
--------
Expand Down Expand Up @@ -546,13 +548,40 @@ def arrange(self, weight_factor=None, permutation=None):
factor_matrices[1] =
[[6. 5.]
[8. 7.]]
"""

Normalize and permute columns such that `weights` are sorted in
decreasing order:

>>> K.arrange()
>>> print(K) # doctest: +ELLIPSIS
ktensor of shape 2 x 2
weights=[89.4427... 27.2029...]
factor_matrices[0] =
[[0.4472... 0.3162...]
[0.8944... 0.9486...]]
factor_matrices[1] =
[[0.6... 0.5812...]
[0.8... 0.8137...]]

Absorb the weights into the second factor:

>>> K.arrange(weight_factor=1)
>>> print(K) # doctest: +ELLIPSIS
ktensor of shape 2 x 2
weights=[1. 1.]
factor_matrices[0] =
[[0.4472... 0.3162...]
[0.8944... 0.9486...]]
factor_matrices[1] =
[[53.6656... 15.8113...]
[71.5541... 22.1359...]]
"""
if permutation is not None and weight_factor is not None:
assert (
False
), "Weighting and permuting the ktensor at the same time is not allowed."

# arrange columns of factor matrices using the permutation provided
if permutation is not None and isinstance(
permutation, (tuple, list, np.ndarray)
):
Expand All @@ -566,8 +595,8 @@ def arrange(self, weight_factor=None, permutation=None):
False
), "Number of elements in permutation does not match number of components in ktensor."

# TODO there is a relationship here between normalize and arrange that repeats tasks. Can this be made to be more efficient?
# ensure that factor matrices are normalized
# TODO there is a relationship here between normalize and arrange that repeats tasks.
# Can this be made to be more efficient? ensure that factor matrices are normalized
self.normalize()

# sort
Expand All @@ -576,16 +605,13 @@ def arrange(self, weight_factor=None, permutation=None):
for i in range(self.ndims):
self.factor_matrices[i] = self.factor_matrices[i][:, p]

# TODO is this necessary? Matlab only absorbs into the last factor, not factor N as is documented
# absorb weight into one factor if requested
# absorb the weights into one factor, optional
if weight_factor is not None:
pass
# if exist('foo','var')
# r = length(X.lambda);
# X.u{end} = full(X.u{end} * spdiags(X.lambda,0,r,r));
# X.lambda = ones(size(X.lambda));
# end
return self
r = len(self.weights)
self.factor_matrices[weight_factor] *= self.weights
self.weights = np.ones_like(self.weights)

return

def copy(self):
"""
Expand Down Expand Up @@ -1260,7 +1286,7 @@ def norm(self):
def normalize(self, weight_factor=None, sort=False, normtype=2, mode=None):
"""
Normalize the columns of the factor matrices of a
:class:`pyttb.ktensor`.
:class:`pyttb.ktensor` in place.

Parameters
----------
Expand All @@ -1273,7 +1299,7 @@ def normalize(self, weight_factor=None, sort=False, normtype=2, mode=None):
normtype: {non-negative int, -1, -2, np.inf, -np.inf}, optional
Order of the norm (see :func:`numpy.linalg.norm` for possible
values).
mode: {int, None}, optional
mode: int, optional
Index of factor matrix to normalize. A value of `None` means
normalize all factor matrices.

Expand All @@ -1284,7 +1310,7 @@ def normalize(self, weight_factor=None, sort=False, normtype=2, mode=None):
Examples
--------
>>> K = ttb.ktensor.from_function(np.ones, (2, 3, 4), 2)
>>> print(K.normalize()) # doctest: +ELLIPSIS
>>> print(K.normalize()) # doctest: +ELLIPSIS
ktensor of shape 2 x 3 x 4
weights=[4.898... 4.898...]
factor_matrices[0] =
Expand All @@ -1310,7 +1336,7 @@ def normalize(self, weight_factor=None, sort=False, normtype=2, mode=None):
1.0 / tmp * self.factor_matrices[mode][:, r]
)
self.weights[r] = self.weights[r] * tmp
return
return self
else:
assert (
False
Expand Down Expand Up @@ -1349,7 +1375,7 @@ def normalize(self, weight_factor=None, sort=False, normtype=2, mode=None):
if self.ncomponents > 1:
# indices of srting in descending order
p = np.argsort(self.weights)[::-1]
self = self.arrange(permutation=p)
self.arrange(permutation=p)

return self

Expand Down