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| -rw-r--r-- | Doc/lib/libheapq.tex | 17 |
1 files changed, 12 insertions, 5 deletions
diff --git a/Doc/lib/libheapq.tex b/Doc/lib/libheapq.tex index d196fda..d1aaaae 100644 --- a/Doc/lib/libheapq.tex +++ b/Doc/lib/libheapq.tex @@ -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 index for a node and the indexes for its children slightly less 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 without surprises: \code{\var{heap}[0]} is the smallest item, and \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: @@ -45,6 +48,10 @@ Pop and return the smallest item from the \var{heap}, maintaining the heap invariant. \end{funcdesc} +\begin{funcdesc}{heapify}{x} +Transform list \var{x} into a heap, in-place, in linear time. +\end{funcdesc} + Example of use: \begin{verbatim} @@ -53,17 +60,17 @@ Example of use: >>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0] >>> for item in data: ... heappush(heap, item) -... +... >>> sorted = [] >>> while heap: ... sorted.append(heappop(heap)) -... +... >>> print sorted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] >>> data.sort() >>> print data == sorted True ->>> +>>> \end{verbatim} |