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Document new heapify() function.

This commit is contained in:
Tim Peters 2002-08-03 18:02:09 +00:00
parent c4c718057e
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1 changed files with 12 additions and 5 deletions
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Doc/lib/libheapq.tex
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@ -25,13 +25,16 @@ The API below differs from textbook heap algorithms in two aspects:
(a) We use zero-based indexing. This makes the relationship between the (a) We use zero-based indexing. This makes the relationship between the
index for a node and the indexes for its children slightly less index for a node and the indexes for its children slightly less
obvious, but is more suitable since Python uses zero-based indexing. obvious, but is more suitable since Python uses zero-based indexing.
(b) Our pop method returns the smallest item, not the largest. (b) Our pop method returns the smallest item, not the largest (called a
"min heap" in textbooks; a "max heap" is more common in texts because
of its suitability for in-place sorting).
These two make it possible to view the heap as a regular Python list These two make it possible to view the heap as a regular Python list
without surprises: \code{\var{heap}[0]} is the smallest item, and without surprises: \code{\var{heap}[0]} is the smallest item, and
\code{\var{heap}.sort()} maintains the heap invariant! \code{\var{heap}.sort()} maintains the heap invariant!
To create a heap, use a list initialized to \code{[]}. To create a heap, use a list initialized to \code{[]}, or you can
transform a populated list into a heap via function \function{heapify()}.
The following functions are provided: The following functions are provided:
@ -45,6 +48,10 @@ Pop and return the smallest item from the \var{heap}, maintaining the
heap invariant. heap invariant.
\end{funcdesc} \end{funcdesc}
\begin{funcdesc}{heapify}{x}
Transform list \var{x} into a heap, in-place, in linear time.
\end{funcdesc}
Example of use: Example of use:
\begin{verbatim} \begin{verbatim}
@ -53,17 +60,17 @@ Example of use:
>>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0] >>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
>>> for item in data: >>> for item in data:
... heappush(heap, item) ... heappush(heap, item)
... ...
>>> sorted = [] >>> sorted = []
>>> while heap: >>> while heap:
... sorted.append(heappop(heap)) ... sorted.append(heappop(heap))
... ...
>>> print sorted >>> print sorted
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> data.sort() >>> data.sort()
>>> print data == sorted >>> print data == sorted
True True
>>> >>>
\end{verbatim} \end{verbatim}