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Update comment for the comparison table to use measured results rather than predicted.

This commit is contained in:
Raymond Hettinger 2014-04-10 01:18:01 -06:00
parent bed54b569a
commit 6ed7c20ce5
1 changed files with 10 additions and 9 deletions
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19
Lib/heapq.py
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@ -202,16 +202,17 @@ def _heapify_max(x):
# Number of comparisons for n random inputs, keeping the k smallest values:
# -----------------------------------------------------------
# Step Comparisons Action
# 1 2*k heapify the first k-inputs
# 2 n-k compare new input elements to top of heap
# 3 k*lg2(k)*(ln(n)-lg(k)) add new extreme values to the heap
# 1 1.66*k heapify the first k-inputs
# 2 n - k compare new input elements to top of heap
# 3 k*lg2(k)*(ln(n)-ln(k)) add new extreme values to the heap
# 4 k*lg2(k) final sort of the k most extreme values
#
# n-random inputs k-extreme values number of comparisons % more than min()
# --------------- ---------------- ------------------- -----------------
# 10,000 100 13,634 36.3%
# 100,000 100 105,163 5.2%
# 1,000,000 100 1,006,694 0.7%
# number of comparisons
# n-random inputs k-extreme values average of 5 trials % more than min()
# --------------- ---------------- ------------------- -----------------
# 10,000 100 14,046 40.5%
# 100,000 100 105,749 5.7%
# 1,000,000 100 1,007,751 0.8%
#
# Computing the number of comparisons for step 3:
# -----------------------------------------------
@ -234,7 +235,7 @@ def _heapify_max(x):
# comparisons = k * log(k, 2) * (log(n,e) - log(k, e))
#
# Worst-case for step 3:
# ---------------------
# ----------------------
# In the worst case, the input data is reversed sorted so that every new element
# must be inserted in the heap:
# comparisons = log(k, 2) * (n - k)