Utility functions¶

climin.util.
empty_with_views
(shapes, empty_func=<builtin function empty>)¶ Create an array and views shaped according to
shapes
.The
shapes
parameter is a list of tuples of ints. Each tuple represents a desired shape for an array which will be allocated in a bigger memory region. This memory region will be represented by an array as well.For example, the shape speciciation
[2, (3, 2)]
will create an arrayflat
of size 8. The first view will have a size of(2,)
and point to the first two entries, i.e.flat`[:2]`, while the second array will have a shape of ``(3, 2)
and point to the elementsflat[2:8]
.Parameters: spec : list of tuples of ints
Specification of the desired shapes.
empty_func : callable
function that returns a memory region given an integer of the desired size. (Examples include
numpy.empty
, which is the default,gnumpy.empty
andtheano.tensor.empty
.Returns: flat : array_like (depending on
empty_func
)Memory region containing all the views.
views : list of array_like
Variable number of results. Each contains a view into the array
flat
.Examples
>>> from climin.util import empty_with_views >>> flat, (w, b) = empty_with_views([(3, 2), 2]) >>> w[...] = 1 >>> b[...] = 2 >>> flat array([ 1., 1., 1., 1., 1., 1., 2., 2.]) >>> flat[0] = 3 >>> w array([[ 3., 1.], [ 1., 1.], [ 1., 1.]])

climin.util.
shaped_from_flat
(flat, shapes)¶ Given a one dimensional array
flat
, return a list of views of shapesshapes
on that array.Each view will point to a distinct memory region, consecutively allocated in flat.
Parameters: flat : array_like
Array of one dimension.
shapes : list of tuples of ints
Each entry of this list specifies the shape of the corresponding view into
flat
.Returns: views : list of arrays
Each entry has the shape given in
shapes
and points as a view intoflat
.

climin.util.
minibatches
(arr, batch_size, d=0)¶ Return a list of views of the given arr.
Each view represents a mini bach of the data.
Parameters: arr : array_like
Array to obtain batches from. Needs to be slicable. If
d > 0
, needs to have a.shape
attribute from which the number of samples can be obtained.batch_size : int
Size of a batch. Last batch might be smaller if
batch_size
is not a divisor ofarr
.d : int, optional, default: 0
Dimension along which the data samples are separated and thus slicing should be done.
Returns: mini_batches : list
Each item of the list is a view of
arr
. Views are ordered.

climin.util.
iter_minibatches
(lst, batch_size, dims, n_cycles=None, random_state=None, discard_illsized_batch=False)¶ Return an iterator that successively yields tuples containing aligned minibatches of size batch_size from slicable objects given in lst, in random order without replacement. Because different containers might require slicing over different dimensions, the dimension of each container has to be givens as a list dims.
Parameters: lst : list of array_like
Each item of the list will be sliced into mini batches in alignment with the others.
batch_size : int
Size of each batch. Last batch might be smaller.
dims : list
Aligned with
lst
, gives the dimension along which the data samples are separated.n_cycles : int, optional [default: None]
Number of cycles after which to stop the iterator. If
None
, will yield forever.random_state : a numpy.random.RandomState object, optional [default
Random number generator that will act as a seed for the minibatch order.
discard_illsized_batch : bool, optional [default
If
True
and the length of the sliced dimension is not divisible bybatch_size
, the leftover samples are discarded.Returns: batches : iterator
Infinite iterator of mini batches in random order (without replacement).