Other classes

These are smaller, simpler classes that are included in combi.

MapSpace

class MapSpace(function, sequence)

A space of a function applied to a sequence.

This is similar to Python’s built-in map(), except that it behaves like a sequence rather than an iterable. (Though it’s also iterable.) You can access any item by its index number.

Example:

>>> map_space = MapSpace(lambda x: x ** 2, range(7))
>>> map_space
MapSpace(<function <lambda> at 0x00000000030C1510>, range(0, 7))
>>> len(map_space)
7
>>> map_space[3]
9
>>> tuple(map_space)
(0, 1, 4, 9, 16, 25, 36)

ProductSpace

class ProductSpace(sequences)

A product space between sequences.

This is similar to Python’s itertools.product(), except that it behaves like a sequence rather than an iterable. (Though it’s also iterable.) You can access any item by its index number.

Example:

>>> product_space = ProductSpace(('abc', range(4)))
>>> product_space
<ProductSpace: 3 * 4>
>>> product_space.length
12
>>> product_space[10]
('c', 2)
>>> tuple(product_space)
(('a', 0), ('a', 1), ('a', 2), ('a', 3), ('b', 0), ('b', 1), ('b', 2),
 ('b', 3), ('c', 0), ('c', 1), ('c', 2), ('c', 3))

index(given_sequence)

Get the index number of given_sequence in this product space.

ChainSpace

class ChainSpace(sequences)

A space of sequences chained together.

This is similar to Python’s itertools.chain(), except that items can be fetched by index number rather than just iteration.

Example:

>>> chain_space = ChainSpace(('abc', (1, 2, 3)))
>>> chain_space
<ChainSpace: 3+3>
>>> chain_space[4]
2
>>> tuple(chain_space)
('a', 'b', 'c', 1, 2, 3)
>>> chain_space.index(2)
4

index(item)

Get the index number of item in this space.

SelectionSpace

class SelectionSpace(sequence)

Space of possible selections of any number of items from sequence.

For example:

>>> tuple(SelectionSpace(range(2)))
(set(), {1}, {0}, {0, 1})

The selections (which are sets) can be for any number of items, from zero to the length of the sequence.

Of course, this is a smart object that doesn’t really create all these sets in advance, but rather on demand. So you can create a SelectionSpace like this:

>>> selection_space = SelectionSpace(range(10**4))

And take a random selection from it:

>>> selection_space.take_random()
{0, 3, 4, ..., 9996, 9997}

Even though the length of this space is around 10 ** 3010, which is much bigger than the number of particles in the universe.

index(selection)

Find the index number of set selection in this SelectionSpace.