notwa/backyard
1
0
Fork 0
Code Issues Pull Requests Releases Activity

direct: update birect.py

Browse Source
This commit is contained in:
Connor Olding 2022-06-13 06:15:08 +02:00
parent 993d3fde72
commit 4c97502e84
1 changed files with 79 additions and 43 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

122
direct/birect.py
View File

@ -1,18 +1,32 @@
#!/usr/bin/env python3
# Copyright (C) 2022 Connor Olding
# Permission to use, copy, modify, and/or distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in all copies.
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
def birect(
obj,
lo,
hi,
obj, # objective function to find the minimum value of
lo, # list of lower bounds for each problem dimension
hi, # list of upper bounds for each problem dimension
*,
min_diag,
min_error=None,
max_evals=None,
max_iters=None,
by_longest=False,
min_diag, # never subdivide hyper-rectangles below this length
min_error=None, # exit when the objective function achieves this error
max_evals=None, # exit when the objective function has been run this many times
max_iters=None, # exit when the optimization procedure iterates this many times
by_longest=False, # measure by rects by longest edge instead of their diagonal
pruning=0,
F=float,
F=float, # can be float, np.float32, np.float64, decimal, or anything float-like
):
assert len(lo) == len(hi), "dimensional mismatch"
@ -26,31 +40,43 @@ def birect(
# aside: xmin should actually be called v_xmin, but it's not!
def fun(w_t):
# xs = [l + (h - l) * t for t in w_t]
# final conversion from exact fraction to possibly-inexact F-type:
v_x = [F(num) / F(den) for num, den in w_t]
# linearly interpolate within the bounds of the function:
v_x = [l * (1 - t) + h * t for l, h, t in zip(lo, hi, v_x)]
res = obj(v_x)
return res
def ab_to_lu(w_a, w_b):
def ab_to_lu(w_a, w_b): # converts corner points to midpoints, also denominators
# (2 * a + b) / (den * 3) = point halfway between corner "a" and the center
w_l = [(a[0] + a[0] + b[0], (1 << a[1]) * 3) for a, b in zip(w_a, w_b)]
# (a + 2 * b) / (den * 3) = point halfway between corner "b" and the center
w_u = [(a[0] + b[0] + b[0], (1 << b[1]) * 3) for a, b in zip(w_a, w_b)]
return w_l, w_u
dims = len(lo)
dims = len(lo) # already asserted that len(lo) == len(hi)
# initial corner points:
# note that the denominators are encoded as the exponents of a power of two.
# therefore, coordinate = pair[0] / (1 << pair[1]).
w_a, w_b = [(0, 0)] * dims, [(1, 0)] * dims
# initial points halfway between the each of the two corners and the center:
# note that the denominators are identity here.
# therefore, coordinate = pair[0] / pair[1].
w_l, w_u = ab_to_lu(w_a, w_b)
# initial function evaluations:
fl = fun(w_l)
fu = fun(w_u)
# initial minimum of all evaluated points so far:
if fl <= fu:
xmin, fmin = w_l, fl
else:
xmin, fmin = w_u, fu
imin = 0 # index of the minimum -- only one point so far, so it's that one.
# sample coordinates and their values:
# construct lists to hold all sample coordinates and their values:
vw_a, vw_b = [w_a], [w_b]
v_fl = [fl] # remember that "l" and "u" are arbitrary shorthand used by the paper,
v_fu = [fu] # and one isn't necessarily above or below the other.
@ -60,19 +86,19 @@ def birect(
del w_a, w_b, w_l, w_u, fl, fu # prevent accidental re-use
def precision_met():
def precision_met(): # returns True when the optimization procedure should exit
return min_error is not None and fmin <= min_error
def no_more_evals():
def no_more_evals(): # returns True when the optimization procedure should exit
return max_evals is not None and n + 1 >= max_evals
def gather_potential(v_i):
# crappy algorithm for finding the convex hull of the plot, where
# x = diameter of hyper-rectangle, and
# y = minimum loss of the two points (v_fl, v_fu) within it.
# crappy algorithm for finding the convex hull of a line plot where
# the x axis is the diameter of hyper-rectangle, and
# the y axis is the minimum loss of the two points (v_fl, v_fu) within it.
# TODO: make this faster. use a sorted queue and peek at the best for each depth.
# start by finding the arg-minimum for each set of equal-diameter rects.
# TODO: make this faster. use a sorted queue and peek at the best for each depth.
bests = {} # mapping of depth to arg-minimum value (i.e. its index)
for i in v_i:
fl, fu = v_fl[i], v_fu[i]
@ -86,7 +112,7 @@ def birect(
if best is None or f < best[1]:
bests[depth] = (i, f)
if len(bests) == 1: # nothing to compare
if len(bests) == 1: # nothing to compare it to
return [i for i, f in bests.values()]
asc = sorted(bests.items(), key=lambda t: -t[0]) # sort by length, ascending
@ -170,6 +196,19 @@ def birect(
return longest
# Hyper-rectangle subdivision demonstration: (2D example)
#
# Initial Split Once Split Twice Split Both
# ↓b ↓b a↓b a↓
# ┌───────────┐←b ┌─────╥─────┐ ┌─────╥─────┐ ┌─────╥─────┐←a
# │ │ │ ║ │ │ l ║ │ │ l ║ l │
# │ ① u │ ⇒ │ u ② ║ u │ ⇒ │ u ③ ║ u │ ⇒ │ u ║ u │
# │ │ │ ║ │ b→╞═════╣←a │ b→╞═════╬═════╡←a
# │ l │ ⇒ │ l ║ l │ ⇒ │ l ║ l │ ⇒ │ l ║ l │
# │ │ │ ║ │ │ u ║ │ │ u ║ u │
# a→└───────────┘ └─────╨─────┘ b→└─────╨─────┘ b→└─────╨─────┘
# ↑a ↑a ↑a ↑b
def split_it(i, which, *, w_a, w_b, d):
nonlocal n, xmin, fmin
new, n = n, n + 1 # designate an index for the new hyper-rectangle
@ -223,6 +262,16 @@ def birect(
assert len(v_depth) == n, "internal error: v_depth has invalid length"
return v_new
def _arbitrary_subdivision(w_a, w_b, d):
# shrink the coordinates as if they were subdivided and a single
# subdivision was selected. which subdivision is chosen doesn't matter.
a_d = w_a[d]
b_d = w_b[d]
large = max(a_d[0], b_d[0])
small = min(a_d[0], b_d[0])
w_a[d] = (large * 2 - 0, a_d[1] + 1)
w_b[d] = (small * 2 + 1, b_d[1] + 1)
def precompute_diagonals_by_limit(limit):
diags = []
w_a = vw_a[0].copy()
@ -233,17 +282,10 @@ def birect(
delta = b[0] - a[0]
sq_dist += (delta * delta) << (2 * (limit - a[1]))
diags.append(sq_dist)
d = depth % dims
a_d = w_a[d]
b_d = w_b[d]
large = max(a_d[0], b_d[0])
small = min(a_d[0], b_d[0])
w_a[d] = (large * 2 - 0, a_d[1] + 1)
w_b[d] = (small * 2 + 1, b_d[1] + 1)
_arbitrary_subdivision(w_a, w_b, depth % dims)
return [F(diag) ** F(0.5) / F(1 << limit) for diag in diags]
def precompute_diagonals_by_length(limit):
def precompute_diagonals_by_length(minlen):
diags, longests = [], []
w_a = vw_a[0].copy()
w_b = vw_b[0].copy()
@ -255,19 +297,12 @@ def birect(
sq_dist += (delta * delta) << (2 * (bits - a[1]))
longest = max(longest, abs(delta) << (bits - a[1]))
diag = F(sq_dist) ** F(0.5) / F(1 << bits)
if diag < limit:
if diag < minlen:
break
longest = F(longest) / F(1 << bits)
diags.append(diag)
longests.append(longest)
d = depth % dims
a_d = w_a[d]
b_d = w_b[d]
large = max(a_d[0], b_d[0])
small = min(a_d[0], b_d[0])
w_a[d] = (large * 2 - 0, a_d[1] + 1)
w_b[d] = (small * 2 + 1, b_d[1] + 1)
_arbitrary_subdivision(w_a, w_b, depth % dims)
return diags, longests
diagonal_cache, longest_cache = precompute_diagonals_by_length(min_diag)
@ -275,17 +310,18 @@ def birect(
diagonal_cache = precompute_diagonals_by_limit(depth_limit)
for outer in range(1_000_000 if max_iters is None else max_iters):
if precision_met() or no_more_evals():
if precision_met() or no_more_evals(): # check stopping conditions
break
# perform the actual *di*viding *rect*angles algorithm:
v_potential = gather_potential(v_active)
v_new = split_rectangles(v_potential)
for j in v_potential:
del v_active[j]
del v_active[j] # these were just split a moment ago, so remove them
for j in v_new:
if v_depth[j] < depth_limit:
v_active[j] = True
if v_depth[j] < depth_limit: # TODO: is checking this late wasting evals?
v_active[j] = True # these were just created, so add them
tmin = [F(x[0]) / F(x[1]) for x in xmin]
argmin = [l * (1 - t) + h * t for l, h, t in zip(lo, hi, tmin)]