def __init(init, X, N, rank, dtype):
Uinit = [None for _ in range(N)]
if isinstance(init, list):
Uinit = init
elif init == 'random':
for n in range(1, N):
Uinit[n] = array(rand(X.shape[n], rank), dtype=dtype)
elif init == 'nvecs':
for n in range(1, N):
Uinit[n] = array(nvecs(X, n, rank), dtype=dtype)
else:
raise 'Unknown option (init=%s)' % str(init)
return Uinit
def hosvd(X, rank, dims=None, dtype=None, compute_core=True):
U = [None for _ in xrange(X.ndim)]
if dims is None:
dims = range(X.ndim)
if dtype is None:
dtype = X.dtype
for d in dims:
U[d] = array(nvecs(X, d, rank[d]), dtype=dtype)
if compute_core:
core = X.ttm(U, transp=True)
return U, core
else:
return U
def hooi(X, rank, **kwargs):
"""
Compute Tucker decomposition of a tensor using Higher-Order Orthogonal
Iterations.
Parameters
----------
X : tensor_mixin
The tensor to be decomposed
rank : array_like
The rank of the decomposition for each mode of the tensor.
The length of ``rank`` must match the number of modes of ``X``.
init : {'random', 'nvecs'}, optional
The initialization method to use.
- random : Factor matrices are initialized randomly.
- nvecs : Factor matrices are initialzed via HOSVD.
default : 'nvecs'
Examples
--------
Create dense tensor
>>> T = np.zeros((3, 4, 2))
>>> T[:, :, 0] = [[ 1, 4, 7, 10], [ 2, 5, 8, 11], [3, 6, 9, 12]]
>>> T[:, :, 1] = [[13, 16, 19, 22], [14, 17, 20, 23], [15, 18, 21, 24]]
>>> T = dtensor(T)
Compute Tucker decomposition of ``T`` with n-rank [2, 3, 1] via higher-order
orthogonal iterations
>>> Y = hooi(T, [2, 3, 1], init='nvecs')
Shape of the core tensor matches n-rank of the decomposition.
>>> Y['core'].shape
(2, 3, 1)
>>> Y['U'][1].shape
(3, 2)
References
----------
.. [1] L. De Lathauwer, B. De Moor, J. Vandewalle: On the best rank-1 and
rank-(R_1, R_2, \ldots, R_N) approximation of higher order tensors;
IEEE Trans. Signal Process. 49 (2001), pp. 2262-2271
"""
# init options
ainit = kwargs.pop('init', __DEF_INIT)
maxIter = kwargs.pop('maxIter', __DEF_MAXITER)
conv = kwargs.pop('conv', __DEF_CONV)
dtype = kwargs.pop('dtype', X.dtype)
if not len(kwargs) == 0:
raise ValueError('Unknown keywords (%s)' % (kwargs.keys()))
ndims = X.ndim
if isNumberType(rank):
rank = rank * ones(ndims)
normX = norm(X)
U = __init(ainit, X, ndims, rank, dtype)
fit = 0
exectimes = []
for itr in xrange(maxIter):
tic = time.clock()
fitold = fit
for n in range(ndims):
Utilde = ttm(X, U, n, transp=True, without=True)
U[n] = nvecs(Utilde, n, rank[n])
# compute core tensor to get fit
core = ttm(Utilde, U, n, transp=True)
# since factors are orthonormal, compute fit on core tensor
normresidual = sqrt(normX ** 2 - norm(core) ** 2)
# fraction explained by model
fit = 1 - (normresidual / normX)
fitchange = abs(fitold - fit)
exectimes.append(time.clock() - tic)
_log.debug(
'[%3d] fit: %.5f | delta: %7.1e | secs: %.5f'
% (itr, fit, fitchange, exectimes[-1])
)
if itr > 1 and fitchange < conv:
break
return core, U
