Compare sorting algorithms' performance
You are encouraged to solve this task according to the task description, using any language you may know.
Measure a relative performance of sorting algorithms implementations.
Plot execution time vs. input sequence length dependencies for various implementation of sorting algorithm and different input sequence types (example figures).
Consider three type of input sequences:
- ones: sequence of all 1's. Example: {1, 1, 1, 1, 1}
- range: ascending sequence, i.e. already sorted. Example: {1, 2, 3, 10, 15}
- shuffled range: sequence with elements randomly distributed. Example: {5, 3, 9, 6, 8}
Consider at least two different sorting functions (different algorithms or/and different implementation of the same algorithm). For example, consider Bubble Sort, Insertion sort, Quicksort or/and implementations of Quicksort with different pivot selection mechanisms. Where possible, use existing implementations.
Preliminary subtask:
- Bubble Sort, Insertion sort, Quicksort, Radix sort, Shell sort
- Query Performance
- Write float arrays to a text file
- Plot x, y arrays
- Polynomial Fitting
General steps:
- Define sorting routines to be considered.
- Define appropriate sequence generators and write timings.
- Plot timings.
- What conclusions about relative performance of the sorting routines could be made based on the plots?
Python
Examples of sorting routines
<lang python> def builtinsort(x):
x.sort()
def partition(seq, pivot):
low, middle, up = [], [], []
for x in seq:
if x < pivot:
low.append(x)
elif x == pivot:
middle.append(x)
else:
up.append(x)
return low, middle, up
import random
def qsortranpart(seq):
size = len(seq)
if size < 2: return seq
low, middle, up = partition(seq, seq[random.randrange(size)])
return qsortranpart(low) + middle + qsortranpart(up)</lang>
Sequence generators
<lang python> def ones(n):
return [1]*n
def reversedrange(n):
x = range(n)
x.reverse()
return x
def shuffledrange(n):
x = range(n)
random.shuffle(x)
return x</lang>
Write timings
<lang python> def write_timings(npoints=10, maxN=10**4, sort_functions=(builtinsort,insertion_sort, qsort),
sequence_creators = (ones, range, shuffledrange)):
Ns = range(2, maxN, maxN//npoints)
for sort in sort_functions:
for make_seq in sequence_creators:
Ts = map(lambda n: usec(sort, (make_seq(n),)), Ns)
writedat('%s-%s-%d-%d.xy' % (sort.__name__, make_seq.__name__, len(Ns), max(Ns)), Ns, Ts)</lang>
Where writedat() is defined in the Write float arrays to a text file, usec() - Query Performance, insertion_sort() - Insertion sort, qsort - Quicksort subtasks, correspondingly.
Plot timings
<lang python> import operator
import numpy, pylab
def plotdd(dictplotdict):
"""See ``plot_timings()`` below."""
symbols = ('o', '^', 'v', '<', '>', 's', '+', 'x', 'D', 'd',
'1', '2', '3', '4', 'h', 'H', 'p', '|', '_')
colors = map(None, 'bgrcmyk') # split string on distinct characters
for npoints, plotdict in dictplotdict.iteritems():
for ttle, lst in plotdict.iteritems():
pylab.hold(False)
for i, (label, polynom, x, y) in enumerate(sorted(lst,key=operator.itemgetter(0))):
pylab.plot(x, y, colors[i % len(colors)] + symbols[i % len(symbols)], label='%s %s' % (polynom, label))
pylab.hold(True)
y = numpy.polyval(polynom, x)
pylab.plot(x, y, colors[i % len(colors)], label= '_nolegend_')
pylab.legend(loc='upper left')
pylab.xlabel(polynom.variable)
pylab.ylabel('log2( time in microseconds )')
pylab.title(ttle, verticalalignment='bottom')
figname = '_%(npoints)03d%(ttle)s' % vars()
pylab.savefig(figname+'.png')
pylab.savefig(figname+'.pdf')
print figname</lang>
See Plot x, y arrays and Polynomial Fitting subtasks for a basic usage of pylab.plot() and numpy.polyfit().
<lang python> import collections, itertools, glob, re
import numpy
def plot_timings():
makedict = lambda: collections.defaultdict(lambda: collections.defaultdict(list))
df = makedict()
ds = makedict()
# populate plot dictionaries
for filename in glob.glob('*.xy'):
m = re.match(r'([^-]+)-([^-]+)-(\d+)-(\d+)\.xy', filename)
print filename
assert m, filename
funcname, seqname, npoints, maxN = m.groups()
npoints, maxN = int(npoints), int(maxN)
a = numpy.fromiter(itertools.imap(float, open(filename).read().split()), dtype='f')
Ns = a[::2] # sequences lengths
Ts = a[1::2] # corresponding times
assert len(Ns) == len(Ts) == npoints
assert max(Ns) <= maxN
#
logsafe = numpy.logical_and(Ns>0, Ts>0)
Ts = numpy.log2(Ts[logsafe])
Ns = numpy.log2(Ns[logsafe])
coeffs = numpy.polyfit(Ns, Ts, deg=1)
poly = numpy.poly1d(coeffs, variable='log2(N)')
#
df[npoints][funcname].append((seqname, poly, Ns, Ts))
ds[npoints][seqname].append((funcname, poly, Ns, Ts))
# actual plotting
plotdd(df)
plotdd(ds) # see ``plotdd()`` above</lang>
Figures: log2( time in microseconds ) vs. log2( sequence length )
<lang python> sort_functions = [
builtinsort, # see implementation above
insertion_sort, # see Insertion sort
insertion_sort_lowb, # insertion_sort, where sequential search is replaced
# by lower_bound() function
qsort, # see Quicksort
qsortranlc, # qsort with randomly choosen pivot
# and the filtering via list comprehension
qsortranpart, # qsortranlc with filtering via partition function
qsortranpartis, # qsortranpart, where for a small input sequence lengths
] # insertion_sort is called
if __name__=="__main__":
import sys
sys.setrecursionlimit(10000)
write_timings(npoints=100, maxN=1024, # 1 <= N <= 2**10 an input sequence length
sort_functions=sort_functions,
sequence_creators = (ones, range, shuffledrange))
plot_timings()</lang>
Executing above script we get belowed figures.
ones
ones.png (143KiB)
builtinsort - O(N) insertion_sort - O(N) qsort - O(N**2) qsortranpart - O(N)
range
range.png (145KiB)
builtinsort - O(N) insertion_sort - O(N) qsort - O(N**2) qsortranpart - O(N*log(N))
shuffled range
shuffledrange.png (152KiB)
builtinsort - O(N) insertion_sort - O(N**4) ??? qsort - O(N*log(N)) qsortranpart - O(N) ???