tests/perf_bench: Add some benchmarks from python-performance.

From https://github.com/python/pyperformance commit
6690642ddeda46fc5ee6e97c3ef4b2f292348ab8
This commit is contained in:
Damien George 2019-06-26 14:24:13 +10:00
parent e92c9aa9c9
commit 127714c3af
6 changed files with 1182 additions and 0 deletions

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# Source: https://github.com/python/pyperformance
# License: MIT
# create chaosgame-like fractals
# Copyright (C) 2005 Carl Friedrich Bolz
import math
import random
class GVector(object):
def __init__(self, x=0, y=0, z=0):
self.x = x
self.y = y
self.z = z
def Mag(self):
return math.sqrt(self.x ** 2 + self.y ** 2 + self.z ** 2)
def dist(self, other):
return math.sqrt((self.x - other.x) ** 2
+ (self.y - other.y) ** 2
+ (self.z - other.z) ** 2)
def __add__(self, other):
if not isinstance(other, GVector):
raise ValueError("Can't add GVector to " + str(type(other)))
v = GVector(self.x + other.x, self.y + other.y, self.z + other.z)
return v
def __sub__(self, other):
return self + other * -1
def __mul__(self, other):
v = GVector(self.x * other, self.y * other, self.z * other)
return v
__rmul__ = __mul__
def linear_combination(self, other, l1, l2=None):
if l2 is None:
l2 = 1 - l1
v = GVector(self.x * l1 + other.x * l2,
self.y * l1 + other.y * l2,
self.z * l1 + other.z * l2)
return v
def __str__(self):
return "<%f, %f, %f>" % (self.x, self.y, self.z)
def __repr__(self):
return "GVector(%f, %f, %f)" % (self.x, self.y, self.z)
class Spline(object):
"""Class for representing B-Splines and NURBS of arbitrary degree"""
def __init__(self, points, degree, knots):
"""Creates a Spline.
points is a list of GVector, degree is the degree of the Spline.
"""
if len(points) > len(knots) - degree + 1:
raise ValueError("too many control points")
elif len(points) < len(knots) - degree + 1:
raise ValueError("not enough control points")
last = knots[0]
for cur in knots[1:]:
if cur < last:
raise ValueError("knots not strictly increasing")
last = cur
self.knots = knots
self.points = points
self.degree = degree
def GetDomain(self):
"""Returns the domain of the B-Spline"""
return (self.knots[self.degree - 1],
self.knots[len(self.knots) - self.degree])
def __call__(self, u):
"""Calculates a point of the B-Spline using de Boors Algorithm"""
dom = self.GetDomain()
if u < dom[0] or u > dom[1]:
raise ValueError("Function value not in domain")
if u == dom[0]:
return self.points[0]
if u == dom[1]:
return self.points[-1]
I = self.GetIndex(u)
d = [self.points[I - self.degree + 1 + ii]
for ii in range(self.degree + 1)]
U = self.knots
for ik in range(1, self.degree + 1):
for ii in range(I - self.degree + ik + 1, I + 2):
ua = U[ii + self.degree - ik]
ub = U[ii - 1]
co1 = (ua - u) / (ua - ub)
co2 = (u - ub) / (ua - ub)
index = ii - I + self.degree - ik - 1
d[index] = d[index].linear_combination(d[index + 1], co1, co2)
return d[0]
def GetIndex(self, u):
dom = self.GetDomain()
for ii in range(self.degree - 1, len(self.knots) - self.degree):
if u >= self.knots[ii] and u < self.knots[ii + 1]:
I = ii
break
else:
I = dom[1] - 1
return I
def __len__(self):
return len(self.points)
def __repr__(self):
return "Spline(%r, %r, %r)" % (self.points, self.degree, self.knots)
def write_ppm(im, w, h, filename):
with open(filename, "wb") as f:
f.write(b'P6\n%i %i\n255\n' % (w, h))
for j in range(h):
for i in range(w):
val = im[j * w + i]
c = val * 255
f.write(b'%c%c%c' % (c, c, c))
class Chaosgame(object):
def __init__(self, splines, thickness, subdivs):
self.splines = splines
self.thickness = thickness
self.minx = min([p.x for spl in splines for p in spl.points])
self.miny = min([p.y for spl in splines for p in spl.points])
self.maxx = max([p.x for spl in splines for p in spl.points])
self.maxy = max([p.y for spl in splines for p in spl.points])
self.height = self.maxy - self.miny
self.width = self.maxx - self.minx
self.num_trafos = []
maxlength = thickness * self.width / self.height
for spl in splines:
length = 0
curr = spl(0)
for i in range(1, subdivs + 1):
last = curr
t = 1 / subdivs * i
curr = spl(t)
length += curr.dist(last)
self.num_trafos.append(max(1, int(length / maxlength * 1.5)))
self.num_total = sum(self.num_trafos)
def get_random_trafo(self):
r = random.randrange(int(self.num_total) + 1)
l = 0
for i in range(len(self.num_trafos)):
if r >= l and r < l + self.num_trafos[i]:
return i, random.randrange(self.num_trafos[i])
l += self.num_trafos[i]
return len(self.num_trafos) - 1, random.randrange(self.num_trafos[-1])
def transform_point(self, point, trafo=None):
x = (point.x - self.minx) / self.width
y = (point.y - self.miny) / self.height
if trafo is None:
trafo = self.get_random_trafo()
start, end = self.splines[trafo[0]].GetDomain()
length = end - start
seg_length = length / self.num_trafos[trafo[0]]
t = start + seg_length * trafo[1] + seg_length * x
basepoint = self.splines[trafo[0]](t)
if t + 1 / 50000 > end:
neighbour = self.splines[trafo[0]](t - 1 / 50000)
derivative = neighbour - basepoint
else:
neighbour = self.splines[trafo[0]](t + 1 / 50000)
derivative = basepoint - neighbour
if derivative.Mag() != 0:
basepoint.x += derivative.y / derivative.Mag() * (y - 0.5) * \
self.thickness
basepoint.y += -derivative.x / derivative.Mag() * (y - 0.5) * \
self.thickness
else:
# can happen, especially with single precision float
pass
self.truncate(basepoint)
return basepoint
def truncate(self, point):
if point.x >= self.maxx:
point.x = self.maxx
if point.y >= self.maxy:
point.y = self.maxy
if point.x < self.minx:
point.x = self.minx
if point.y < self.miny:
point.y = self.miny
def create_image_chaos(self, w, h, iterations, rng_seed):
# Always use the same sequence of random numbers
# to get reproductible benchmark
random.seed(rng_seed)
im = bytearray(w * h)
point = GVector((self.maxx + self.minx) / 2,
(self.maxy + self.miny) / 2, 0)
for _ in range(iterations):
point = self.transform_point(point)
x = (point.x - self.minx) / self.width * w
y = (point.y - self.miny) / self.height * h
x = int(x)
y = int(y)
if x == w:
x -= 1
if y == h:
y -= 1
im[(h - y - 1) * w + x] = 1
return im
###########################################################################
# Benchmark interface
bm_params = {
(100, 50): (0.25, 100, 50, 50, 50, 1234),
(1000, 1000): (0.25, 200, 400, 400, 1000, 1234),
(5000, 1000): (0.25, 400, 500, 500, 7000, 1234),
}
def bm_setup(params):
splines = [
Spline([
GVector(1.597, 3.304, 0.0),
GVector(1.576, 4.123, 0.0),
GVector(1.313, 5.288, 0.0),
GVector(1.619, 5.330, 0.0),
GVector(2.890, 5.503, 0.0),
GVector(2.373, 4.382, 0.0),
GVector(1.662, 4.360, 0.0)],
3, [0, 0, 0, 1, 1, 1, 2, 2, 2]),
Spline([
GVector(2.805, 4.017, 0.0),
GVector(2.551, 3.525, 0.0),
GVector(1.979, 2.620, 0.0),
GVector(1.979, 2.620, 0.0)],
3, [0, 0, 0, 1, 1, 1]),
Spline([
GVector(2.002, 4.011, 0.0),
GVector(2.335, 3.313, 0.0),
GVector(2.367, 3.233, 0.0),
GVector(2.367, 3.233, 0.0)],
3, [0, 0, 0, 1, 1, 1])
]
chaos = Chaosgame(splines, params[0], params[1])
image = None
def run():
nonlocal image
_, _, width, height, iter, rng_seed = params
image = chaos.create_image_chaos(width, height, iter, rng_seed)
def result():
norm = params[4]
# Images are not the same when floating point behaviour is different,
# so return percentage of pixels that are set (rounded to int).
#write_ppm(image, params[2], params[3], 'out-.ppm')
pix = int(100 * sum(image) / len(image))
return norm, pix
return run, result

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# Source: https://github.com/python/pyperformance
# License: MIT
# The Computer Language Benchmarks Game
# http://benchmarksgame.alioth.debian.org/
# Contributed by Sokolov Yura, modified by Tupteq.
def fannkuch(n):
count = list(range(1, n + 1))
max_flips = 0
m = n - 1
r = n
check = 0
perm1 = list(range(n))
perm = list(range(n))
perm1_ins = perm1.insert
perm1_pop = perm1.pop
while 1:
if check < 30:
check += 1
while r != 1:
count[r - 1] = r
r -= 1
if perm1[0] != 0 and perm1[m] != m:
perm = perm1[:]
flips_count = 0
k = perm[0]
while k:
perm[:k + 1] = perm[k::-1]
flips_count += 1
k = perm[0]
if flips_count > max_flips:
max_flips = flips_count
while r != n:
perm1_ins(r, perm1_pop(0))
count[r] -= 1
if count[r] > 0:
break
r += 1
else:
return max_flips
###########################################################################
# Benchmark interface
bm_params = {
(50, 10): (5,),
(100, 10): (6,),
(500, 10): (7,),
(1000, 10): (8,),
(5000, 10): (9,),
}
def bm_setup(params):
state = None
def run():
nonlocal state
state = fannkuch(params[0])
def result():
return params[0], state
return run, result

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# Source: https://github.com/python/pyperformance
# License: MIT
# Artificial, floating point-heavy benchmark originally used by Factor.
from math import sin, cos, sqrt
class Point(object):
__slots__ = ('x', 'y', 'z')
def __init__(self, i):
self.x = x = sin(i)
self.y = cos(i) * 3
self.z = (x * x) / 2
def __repr__(self):
return "<Point: x=%s, y=%s, z=%s>" % (self.x, self.y, self.z)
def normalize(self):
x = self.x
y = self.y
z = self.z
norm = sqrt(x * x + y * y + z * z)
self.x /= norm
self.y /= norm
self.z /= norm
def maximize(self, other):
self.x = self.x if self.x > other.x else other.x
self.y = self.y if self.y > other.y else other.y
self.z = self.z if self.z > other.z else other.z
return self
def maximize(points):
next = points[0]
for p in points[1:]:
next = next.maximize(p)
return next
def benchmark(n):
points = [None] * n
for i in range(n):
points[i] = Point(i)
for p in points:
p.normalize()
return maximize(points)
###########################################################################
# Benchmark interface
bm_params = {
(50, 25): (1, 150),
(100, 100): (1, 250),
(1000, 1000): (10, 1500),
(5000, 1000): (20, 3000),
}
def bm_setup(params):
state = None
def run():
nonlocal state
for _ in range(params[0]):
state = benchmark(params[1])
def result():
return params[0] * params[1], 'Point(%.4f, %.4f, %.4f)' % (state.x, state.y, state.z)
return run, result

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# Source: https://github.com/python/pyperformance
# License: MIT
# Solver of Hexiom board game.
# Benchmark from Laurent Vaucher.
# Source: https://github.com/slowfrog/hexiom : hexiom2.py, level36.txt
# (Main function tweaked by Armin Rigo.)
##################################
class Dir(object):
def __init__(self, x, y):
self.x = x
self.y = y
DIRS = [Dir(1, 0),
Dir(-1, 0),
Dir(0, 1),
Dir(0, -1),
Dir(1, 1),
Dir(-1, -1)]
EMPTY = 7
##################################
class Done(object):
MIN_CHOICE_STRATEGY = 0
MAX_CHOICE_STRATEGY = 1
HIGHEST_VALUE_STRATEGY = 2
FIRST_STRATEGY = 3
MAX_NEIGHBORS_STRATEGY = 4
MIN_NEIGHBORS_STRATEGY = 5
def __init__(self, count, empty=False):
self.count = count
self.cells = None if empty else [
[0, 1, 2, 3, 4, 5, 6, EMPTY] for i in range(count)]
def clone(self):
ret = Done(self.count, True)
ret.cells = [self.cells[i][:] for i in range(self.count)]
return ret
def __getitem__(self, i):
return self.cells[i]
def set_done(self, i, v):
self.cells[i] = [v]
def already_done(self, i):
return len(self.cells[i]) == 1
def remove(self, i, v):
if v in self.cells[i]:
self.cells[i].remove(v)
return True
else:
return False
def remove_all(self, v):
for i in range(self.count):
self.remove(i, v)
def remove_unfixed(self, v):
changed = False
for i in range(self.count):
if not self.already_done(i):
if self.remove(i, v):
changed = True
return changed
def filter_tiles(self, tiles):
for v in range(8):
if tiles[v] == 0:
self.remove_all(v)
def next_cell_min_choice(self):
minlen = 10
mini = -1
for i in range(self.count):
if 1 < len(self.cells[i]) < minlen:
minlen = len(self.cells[i])
mini = i
return mini
def next_cell_max_choice(self):
maxlen = 1
maxi = -1
for i in range(self.count):
if maxlen < len(self.cells[i]):
maxlen = len(self.cells[i])
maxi = i
return maxi
def next_cell_highest_value(self):
maxval = -1
maxi = -1
for i in range(self.count):
if (not self.already_done(i)):
maxvali = max(k for k in self.cells[i] if k != EMPTY)
if maxval < maxvali:
maxval = maxvali
maxi = i
return maxi
def next_cell_first(self):
for i in range(self.count):
if (not self.already_done(i)):
return i
return -1
def next_cell_max_neighbors(self, pos):
maxn = -1
maxi = -1
for i in range(self.count):
if not self.already_done(i):
cells_around = pos.hex.get_by_id(i).links
n = sum(1 if (self.already_done(nid) and (self[nid][0] != EMPTY)) else 0
for nid in cells_around)
if n > maxn:
maxn = n
maxi = i
return maxi
def next_cell_min_neighbors(self, pos):
minn = 7
mini = -1
for i in range(self.count):
if not self.already_done(i):
cells_around = pos.hex.get_by_id(i).links
n = sum(1 if (self.already_done(nid) and (self[nid][0] != EMPTY)) else 0
for nid in cells_around)
if n < minn:
minn = n
mini = i
return mini
def next_cell(self, pos, strategy=HIGHEST_VALUE_STRATEGY):
if strategy == Done.HIGHEST_VALUE_STRATEGY:
return self.next_cell_highest_value()
elif strategy == Done.MIN_CHOICE_STRATEGY:
return self.next_cell_min_choice()
elif strategy == Done.MAX_CHOICE_STRATEGY:
return self.next_cell_max_choice()
elif strategy == Done.FIRST_STRATEGY:
return self.next_cell_first()
elif strategy == Done.MAX_NEIGHBORS_STRATEGY:
return self.next_cell_max_neighbors(pos)
elif strategy == Done.MIN_NEIGHBORS_STRATEGY:
return self.next_cell_min_neighbors(pos)
else:
raise Exception("Wrong strategy: %d" % strategy)
##################################
class Node(object):
def __init__(self, pos, id, links):
self.pos = pos
self.id = id
self.links = links
##################################
class Hex(object):
def __init__(self, size):
self.size = size
self.count = 3 * size * (size - 1) + 1
self.nodes_by_id = self.count * [None]
self.nodes_by_pos = {}
id = 0
for y in range(size):
for x in range(size + y):
pos = (x, y)
node = Node(pos, id, [])
self.nodes_by_pos[pos] = node
self.nodes_by_id[node.id] = node
id += 1
for y in range(1, size):
for x in range(y, size * 2 - 1):
ry = size + y - 1
pos = (x, ry)
node = Node(pos, id, [])
self.nodes_by_pos[pos] = node
self.nodes_by_id[node.id] = node
id += 1
def link_nodes(self):
for node in self.nodes_by_id:
(x, y) = node.pos
for dir in DIRS:
nx = x + dir.x
ny = y + dir.y
if self.contains_pos((nx, ny)):
node.links.append(self.nodes_by_pos[(nx, ny)].id)
def contains_pos(self, pos):
return pos in self.nodes_by_pos
def get_by_pos(self, pos):
return self.nodes_by_pos[pos]
def get_by_id(self, id):
return self.nodes_by_id[id]
##################################
class Pos(object):
def __init__(self, hex, tiles, done=None):
self.hex = hex
self.tiles = tiles
self.done = Done(hex.count) if done is None else done
def clone(self):
return Pos(self.hex, self.tiles, self.done.clone())
##################################
def constraint_pass(pos, last_move=None):
changed = False
left = pos.tiles[:]
done = pos.done
# Remove impossible values from free cells
free_cells = (range(done.count) if last_move is None
else pos.hex.get_by_id(last_move).links)
for i in free_cells:
if not done.already_done(i):
vmax = 0
vmin = 0
cells_around = pos.hex.get_by_id(i).links
for nid in cells_around:
if done.already_done(nid):
if done[nid][0] != EMPTY:
vmin += 1
vmax += 1
else:
vmax += 1
for num in range(7):
if (num < vmin) or (num > vmax):
if done.remove(i, num):
changed = True
# Computes how many of each value is still free
for cell in done.cells:
if len(cell) == 1:
left[cell[0]] -= 1
for v in range(8):
# If there is none, remove the possibility from all tiles
if (pos.tiles[v] > 0) and (left[v] == 0):
if done.remove_unfixed(v):
changed = True
else:
possible = sum((1 if v in cell else 0) for cell in done.cells)
# If the number of possible cells for a value is exactly the number of available tiles
# put a tile in each cell
if pos.tiles[v] == possible:
for i in range(done.count):
cell = done.cells[i]
if (not done.already_done(i)) and (v in cell):
done.set_done(i, v)
changed = True
# Force empty or non-empty around filled cells
filled_cells = (range(done.count) if last_move is None
else [last_move])
for i in filled_cells:
if done.already_done(i):
num = done[i][0]
empties = 0
filled = 0
unknown = []
cells_around = pos.hex.get_by_id(i).links
for nid in cells_around:
if done.already_done(nid):
if done[nid][0] == EMPTY:
empties += 1
else:
filled += 1
else:
unknown.append(nid)
if len(unknown) > 0:
if num == filled:
for u in unknown:
if EMPTY in done[u]:
done.set_done(u, EMPTY)
changed = True
# else:
# raise Exception("Houston, we've got a problem")
elif num == filled + len(unknown):
for u in unknown:
if done.remove(u, EMPTY):
changed = True
return changed
ASCENDING = 1
DESCENDING = -1
def find_moves(pos, strategy, order):
done = pos.done
cell_id = done.next_cell(pos, strategy)
if cell_id < 0:
return []
if order == ASCENDING:
return [(cell_id, v) for v in done[cell_id]]
else:
# Try higher values first and EMPTY last
moves = list(reversed([(cell_id, v)
for v in done[cell_id] if v != EMPTY]))
if EMPTY in done[cell_id]:
moves.append((cell_id, EMPTY))
return moves
def play_move(pos, move):
(cell_id, i) = move
pos.done.set_done(cell_id, i)
def print_pos(pos, output):
hex = pos.hex
done = pos.done
size = hex.size
for y in range(size):
print(" " * (size - y - 1), end="", file=output)
for x in range(size + y):
pos2 = (x, y)
id = hex.get_by_pos(pos2).id
if done.already_done(id):
c = done[id][0] if done[id][0] != EMPTY else "."
else:
c = "?"
print("%s " % c, end="", file=output)
print(end="\n", file=output)
for y in range(1, size):
print(" " * y, end="", file=output)
for x in range(y, size * 2 - 1):
ry = size + y - 1
pos2 = (x, ry)
id = hex.get_by_pos(pos2).id
if done.already_done(id):
c = done[id][0] if done[id][0] != EMPTY else "."
else:
c = "?"
print("%s " % c, end="", file=output)
print(end="\n", file=output)
OPEN = 0
SOLVED = 1
IMPOSSIBLE = -1
def solved(pos, output, verbose=False):
hex = pos.hex
tiles = pos.tiles[:]
done = pos.done
exact = True
all_done = True
for i in range(hex.count):
if len(done[i]) == 0:
return IMPOSSIBLE
elif done.already_done(i):
num = done[i][0]
tiles[num] -= 1
if (tiles[num] < 0):
return IMPOSSIBLE
vmax = 0
vmin = 0
if num != EMPTY:
cells_around = hex.get_by_id(i).links
for nid in cells_around:
if done.already_done(nid):
if done[nid][0] != EMPTY:
vmin += 1
vmax += 1
else:
vmax += 1
if (num < vmin) or (num > vmax):
return IMPOSSIBLE
if num != vmin:
exact = False
else:
all_done = False
if (not all_done) or (not exact):
return OPEN
print_pos(pos, output)
return SOLVED
def solve_step(prev, strategy, order, output, first=False):
if first:
pos = prev.clone()
while constraint_pass(pos):
pass
else:
pos = prev
moves = find_moves(pos, strategy, order)
if len(moves) == 0:
return solved(pos, output)
else:
for move in moves:
# print("Trying (%d, %d)" % (move[0], move[1]))
ret = OPEN
new_pos = pos.clone()
play_move(new_pos, move)
# print_pos(new_pos)
while constraint_pass(new_pos, move[0]):
pass
cur_status = solved(new_pos, output)
if cur_status != OPEN:
ret = cur_status
else:
ret = solve_step(new_pos, strategy, order, output)
if ret == SOLVED:
return SOLVED
return IMPOSSIBLE
def check_valid(pos):
hex = pos.hex
tiles = pos.tiles
# fill missing entries in tiles
tot = 0
for i in range(8):
if tiles[i] > 0:
tot += tiles[i]
else:
tiles[i] = 0
# check total
if tot != hex.count:
raise Exception(
"Invalid input. Expected %d tiles, got %d." % (hex.count, tot))
def solve(pos, strategy, order, output):
check_valid(pos)
return solve_step(pos, strategy, order, output, first=True)
# TODO Write an 'iterator' to go over all x,y positions
def read_file(file):
lines = [line.strip("\r\n") for line in file.splitlines()]
size = int(lines[0])
hex = Hex(size)
linei = 1
tiles = 8 * [0]
done = Done(hex.count)
for y in range(size):
line = lines[linei][size - y - 1:]
p = 0
for x in range(size + y):
tile = line[p:p + 2]
p += 2
if tile[1] == ".":
inctile = EMPTY
else:
inctile = int(tile)
tiles[inctile] += 1
# Look for locked tiles
if tile[0] == "+":
# print("Adding locked tile: %d at pos %d, %d, id=%d" %
# (inctile, x, y, hex.get_by_pos((x, y)).id))
done.set_done(hex.get_by_pos((x, y)).id, inctile)
linei += 1
for y in range(1, size):
ry = size - 1 + y
line = lines[linei][y:]
p = 0
for x in range(y, size * 2 - 1):
tile = line[p:p + 2]
p += 2
if tile[1] == ".":
inctile = EMPTY
else:
inctile = int(tile)
tiles[inctile] += 1
# Look for locked tiles
if tile[0] == "+":
# print("Adding locked tile: %d at pos %d, %d, id=%d" %
# (inctile, x, ry, hex.get_by_pos((x, ry)).id))
done.set_done(hex.get_by_pos((x, ry)).id, inctile)
linei += 1
hex.link_nodes()
done.filter_tiles(tiles)
return Pos(hex, tiles, done)
def solve_file(file, strategy, order, output):
pos = read_file(file)
solve(pos, strategy, order, output)
LEVELS = {}
LEVELS[2] = ("""
2
. 1
. 1 1
1 .
""", """\
1 1
. . .
1 1
""")
LEVELS[10] = ("""
3
+.+. .
+. 0 . 2
. 1+2 1 .
2 . 0+.
.+.+.
""", """\
. . 1
. 1 . 2
0 . 2 2 .
. . . .
0 . .
""")
LEVELS[20] = ("""
3
. 5 4
. 2+.+1
. 3+2 3 .
+2+. 5 .
. 3 .
""", """\
3 3 2
4 5 . 1
3 5 2 . .
2 . . .
. . .
""")
LEVELS[25] = ("""
3
4 . .
. . 2 .
4 3 2 . 4
2 2 3 .
4 2 4
""", """\
3 4 2
2 4 4 .
. . . 4 2
. 2 4 3
. 2 .
""")
LEVELS[30] = ("""
4
5 5 . .
3 . 2+2 6
3 . 2 . 5 .
. 3 3+4 4 . 3
4 5 4 . 5 4
5+2 . . 3
4 . . .
""", """\
3 4 3 .
4 6 5 2 .
2 5 5 . . 2
. . 5 4 . 4 3
. 3 5 4 5 4
. 2 . 3 3
. . . .
""")
LEVELS[36] = ("""
4
2 1 1 2
3 3 3 . .
2 3 3 . 4 .
. 2 . 2 4 3 2
2 2 . . . 2
4 3 4 . .
3 2 3 3
""", """\
3 4 3 2
3 4 4 . 3
2 . . 3 4 3
2 . 1 . 3 . 2
3 3 . 2 . 2
3 . 2 . 2
2 2 . 1
""")
###########################################################################
# Benchmark interface
bm_params = {
(100, 100): (1, 10, DESCENDING, Done.FIRST_STRATEGY),
(1000, 1000): (1, 25, DESCENDING, Done.FIRST_STRATEGY),
(5000, 1000): (10, 25, DESCENDING, Done.FIRST_STRATEGY),
}
def bm_setup(params):
try:
import uio as io
except ImportError:
import io
loops, level, order, strategy = params
board, solution = LEVELS[level]
board = board.strip()
expected = solution.rstrip()
output = None
def run():
nonlocal output
for _ in range(loops):
stream = io.StringIO()
solve_file(board, strategy, order, stream)
output = stream.getvalue()
stream = None
def result():
norm = params[0] * params[1]
out = '\n'.join(line.rstrip() for line in output.splitlines())
return norm, ((out == expected), out)
return run, result

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@ -0,0 +1,62 @@
# Source: https://github.com/python/pyperformance
# License: MIT
# Simple, brute-force N-Queens solver.
# author: collinwinter@google.com (Collin Winter)
# n_queens function: Copyright 2009 Raymond Hettinger
# Pure-Python implementation of itertools.permutations().
def permutations(iterable, r=None):
"""permutations(range(3), 2) --> (0,1) (0,2) (1,0) (1,2) (2,0) (2,1)"""
pool = tuple(iterable)
n = len(pool)
if r is None:
r = n
indices = list(range(n))
cycles = list(range(n - r + 1, n + 1))[::-1]
yield tuple(pool[i] for i in indices[:r])
while n:
for i in reversed(range(r)):
cycles[i] -= 1
if cycles[i] == 0:
indices[i:] = indices[i + 1:] + indices[i:i + 1]
cycles[i] = n - i
else:
j = cycles[i]
indices[i], indices[-j] = indices[-j], indices[i]
yield tuple(pool[i] for i in indices[:r])
break
else:
return
# From http://code.activestate.com/recipes/576647/
def n_queens(queen_count):
"""N-Queens solver.
Args: queen_count: the number of queens to solve for, same as board size.
Yields: Solutions to the problem, each yielded value is a N-tuple.
"""
cols = range(queen_count)
for vec in permutations(cols):
if (queen_count == len(set(vec[i] + i for i in cols))
== len(set(vec[i] - i for i in cols))):
yield vec
###########################################################################
# Benchmark interface
bm_params = {
(50, 25): (1, 5),
(100, 25): (1, 6),
(1000, 100): (1, 7),
(5000, 100): (1, 8),
}
def bm_setup(params):
res = None
def run():
nonlocal res
for _ in range(params[0]):
res = len(list(n_queens(params[1])))
def result():
return params[0] * 10 ** (params[1] - 3), res
return run, result

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# Source: https://github.com/python/pyperformance
# License: MIT
# Calculating some of the digits of π.
# This benchmark stresses big integer arithmetic.
# Adapted from code on: http://benchmarksgame.alioth.debian.org/
def compose(a, b):
aq, ar, as_, at = a
bq, br, bs, bt = b
return (aq * bq,
aq * br + ar * bt,
as_ * bq + at * bs,
as_ * br + at * bt)
def extract(z, j):
q, r, s, t = z
return (q * j + r) // (s * j + t)
def gen_pi_digits(n):
z = (1, 0, 0, 1)
k = 1
digs = []
for _ in range(n):
y = extract(z, 3)
while y != extract(z, 4):
z = compose(z, (k, 4 * k + 2, 0, 2 * k + 1))
k += 1
y = extract(z, 3)
z = compose((10, -10 * y, 0, 1), z)
digs.append(y)
return digs
###########################################################################
# Benchmark interface
bm_params = {
(50, 25): (1, 35),
(100, 100): (1, 65),
(1000, 1000): (2, 250),
(5000, 1000): (3, 350),
}
def bm_setup(params):
state = None
def run():
nonlocal state
nloop, ndig = params
ndig = params[1]
for _ in range(nloop):
state = None # free previous result
state = gen_pi_digits(ndig)
def result():
return params[0] * params[1], ''.join(str(d) for d in state)
return run, result