add the bare-ish minimum to get things going
This commit is contained in:
parent
85754a576e
commit
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15 changed files with 3234 additions and 0 deletions
2
.gitignore
vendored
2
.gitignore
vendored
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__pycache__/*
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*.old
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*.bak
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69
generate_lists.py
Executable file
69
generate_lists.py
Executable file
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#!/usr/bin/env python3
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from types import ModuleType
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import solutions_1680721604
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import solutions_1680730206
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import solutions_1680730206
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import solutions_1680975848
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solutions = {}
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loaded = []
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for local_k in tuple(locals().keys()):
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local_v = locals()[local_k]
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if local_k.startswith("solutions_") and isinstance(local_v, ModuleType):
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loaded.append(local_k)
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for k, v in local_v.solutions.items():
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solutions.setdefault(k, {})
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solutions[k] |= v
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dims_seen = set()
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totally_fine = {}
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too_variable = {}
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too_centered = {}
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too_far_away = {}
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too_positive = {}
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for k, v in solutions.items():
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fopt = sorted(v)[0]
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xopt = v[fopt]
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dims = len(xopt)
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dims_seen.add(dims)
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alpha = [0, 256, 200, 100, 64][dims]
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name = k.removeprefix("go_").removesuffix(str(dims)).removesuffix("_on_cube__")
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is_too_centered = all(round(x * alpha) == alpha // 2 for x in xopt)
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is_too_positive = sum(abs(x - sum(xopt) / dims) for x in xopt) / dims <= 1e-3
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is_too_far_away = any(abs(0.5 - x) > 0.49 for x in xopt)
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is_inconsistent = name in ("stochastic", "xinsheyang01")
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if name == "csendes":
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pass # skip; this is basically equivalent to "infinite"
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elif is_inconsistent:
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too_variable.setdefault(dims, []).append(name)
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elif is_too_centered:
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too_centered.setdefault(dims, []).append(name)
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elif is_too_far_away:
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too_far_away.setdefault(dims, []).append(name)
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elif is_too_positive:
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too_positive.setdefault(dims, []).append(name)
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else:
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totally_fine.setdefault(dims, []).append(name)
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def dump_list(name, rows):
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print()
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print(f"{name} = [")
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for row in rows:
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print(f" {repr(row)},")
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print("]")
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print("# this file was automatically generated by generate_lists.py,")
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print("# any changes may be overwritten!")
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for name in loaded:
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print(f"# loaded {name}.py")
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for dims in sorted(dims_seen):
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dump_list(f"too_variable_{dims}", too_variable.get(dims, []))
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dump_list(f"too_centered_{dims}", too_centered.get(dims, []))
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dump_list(f"too_far_away_{dims}", too_far_away.get(dims, []))
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dump_list(f"too_positive_{dims}", too_positive.get(dims, []))
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dump_list(f"totally_fine_{dims}", totally_fine.get(dims, []))
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# print(len(totally_fine))
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# print(len(totally_fine) + len(too_centered) + len(too_positive))
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497
go_benchmark_it.py
Normal file
497
go_benchmark_it.py
Normal file
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from go_benchmark_lists import *
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from go_benchmarks import problems_2d, problems_3d, problems_4d
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from notwacube import book_of_optimizers
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from prog80 import prog
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from utils import OWrap, COWrap, m1, m33, m36
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from utils import perform_another_experimental_scoring_method
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import numpy as np
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all_problems = {
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2: problems_2d,
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3: problems_3d,
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4: problems_4d,
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}
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tiny_offset = 1.1102230246251565e-16
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def make_transform(lo, hi):
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mul = max(lo, hi) - min(lo, hi)
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add = min(lo, hi)
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return mul, add
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def flipit(transformations, flippy):
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flip_dim = flippy - 1
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mul, add = transformations[flip_dim]
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desired_mul, desired_add = -1, 1 # f(g(x)) = f(1 - x)
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mul, add = mul * desired_mul, mul * desired_add + add
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transformations[flip_dim] = (mul, add)
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def make_objective(problem, n_dim, *, fix_stuff=0):
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obj = problem(n_dim)
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name = problem.__name__
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flippy = 0 # when positive, helps removes positive correlations in solutions
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trippy = None # when not None, moves solution away from center: (dim, dir)
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if fix_stuff >= 0:
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if name == "Deb03":
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# this problem has the wrong bounds for some reason, so we have to patch it.
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obj._bounds = list(zip([0.0] * obj.N, [1.0] * obj.N))
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elif name == "Csendes" or name == "Infinity":
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# this problem is weird... let's avoid division by zero, okay?
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# these problems are duplicates of each other. weird.
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replacement = n_dim * (2 + np.sin(1))
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_fun = obj.fun
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obj.fun = lambda x: replacement if np.any(x == 0.0) else _fun(x)
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elif name == "Keane":
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# another problem that may attempt to divide by zero.
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_fun = obj.fun
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obj.fun = lambda x: 0.0 if np.all(x**2 == 0.0) else _fun(x)
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elif name == "Kowalik":
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# this divide by zero actually approaches infinity when not clipped.
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# TODO: there seems to be some confusion about which is `a`
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# and which is `b` between the equations and the code.
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# hmm it seems like the code is right, judging by this:
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# https://www.itl.nist.gov/div898/strd/nls/data/LINKS/DATA/MGH09.dat
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helper = lambda x: np.where(x < 0, -1, 1) * (
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np.abs(x) + tiny_offset
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)
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# 1.0 - 1e-16 == 0.9999999999999999
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# 1.0 + 1e-16 == 1.0
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# 1.0 + 2e-16 == 1.0000000000000002
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# 1 / 1.1102230246251565e-16**2 == 8.112963841460668e+31
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a, b = obj.a, obj.b
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obj.fun = (
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lambda x: sum(
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b - (x[0] * (a**2 + a * x[1]) / helper(a**2 + a * x[2] + x[3]))
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)
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** 2
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)
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elif name == "Gulf":
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# just another division by zero.
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adjust = np.array([tiny_offset, 0.0, 0.0])
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_fun = obj.fun
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obj.fun = lambda x: _fun(x + adjust)
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if fix_stuff >= 1:
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stuff = {2: too_positive_2, 3: too_positive_3, 4: too_positive_4}[n_dim]
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if name.lower() in stuff:
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# too positively correlated, do some evil.
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ind = stuff.index(name.lower())
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flippy = ind % n_dim + 1 # uniformly select a dimension to "flip"
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if fix_stuff >= 2:
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stuff = {2: too_centered_2, 3: too_centered_3, 4: too_centered_4}[n_dim]
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if name.lower() in stuff:
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# uniformly select offsets to "trip".
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ind = stuff.index(name.lower())
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trippy = (ind % n_dim, ind // n_dim % n_dim) # (dim, dir)
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transformations = [make_transform(lo, hi) for lo, hi in obj.bounds]
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if flippy:
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flipit(transformations, flippy)
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def objective(x):
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# assert all(xi >= 0.0 for xi in x), list(float(xi) for xi in x)
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# assert all(xi <= 1.0 for xi in x), list(float(xi) for xi in x)
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if trippy:
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x = list(x) # mostly to create a copy
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ind = trippy[0]
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x[ind] = 1 - (1 - x[ind]) ** 0.5 if trippy[1] else x[ind] ** 0.5
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x = [xi * mul + add for xi, (mul, add) in zip(x, transformations)]
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# if problem.__name__.startswith("Deb"): print(x)
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return obj.fun(np.array(x, copy=False))
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objective.__name__ = f"go_{problem.__name__.lower()}_on_cube"
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objective.__realname__ = problem.__name__
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# objective.__qualname__ = problem.__name__
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return objective
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def make_objectives(n_dim, n_trials=None, fix_stuff=0):
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problems = all_problems[n_dim]
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return [make_objective(problem, n_dim, fix_stuff=fix_stuff) for problem in problems]
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def find_objective(query, n_dim=None):
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results = []
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for p_dim, problems in all_problems.items():
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if n_dim is not None and p_dim != n_dim:
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continue
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for problem in problems:
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if problem.__name__.lower() == query.lower():
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results.append(problem)
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assert results, "no results found for name " + repr(query)
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return results[0]
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λ = lambda q: make_objective(find_objective(q, 2), 2, fix_stuff=2)
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GO_BENCHMARK_2D_PROBLEMS = list(
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map(λ, totally_fine_2 + too_positive_2 + too_centered_2)
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)
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λ = lambda q: make_objective(find_objective(q, 3), 3, fix_stuff=2)
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GO_BENCHMARK_3D_PROBLEMS = list(
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map(λ, totally_fine_3 + too_positive_3 + too_centered_3)
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)
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λ = lambda q: make_objective(find_objective(q, 4), 4, fix_stuff=2)
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GO_BENCHMARK_4D_PROBLEMS = list(
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map(λ, totally_fine_4 + too_positive_4 + too_centered_4)
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)
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GO_BENCHMARKS = {
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2: GO_BENCHMARK_2D_PROBLEMS,
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3: GO_BENCHMARK_3D_PROBLEMS,
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4: GO_BENCHMARK_4D_PROBLEMS,
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}
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for problem_list in GO_BENCHMARKS.values():
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for problem in problem_list:
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# print(problem.__realname__)
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assert (
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problem.__realname__ != "Csendes"
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), "please use Infinity instead; it's basically equivalent"
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if __name__ == "__main__":
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from tqdm import tqdm
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import sys
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def fib(n):
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return pow(2 << n, n + 1, (4 << 2 * n) - (2 << n) - 1) % (2 << n)
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def optimizer_filter(name):
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# worst scoring optimizers: (awards=(5, 3, 2, 1)) (obj=146, opt=389) (dims=2, evals=80)
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# evosax_pbt with (score: 0, price: 593) -593
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# worst scoring optimizers: (awards=(5, 3, 2, 1)) (obj=70, opt=389) (dims=3, evals=1440)
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# evosax_pbt with (score: 0, price: 315) -315
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return not (
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False
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# or name.startswith("freelunch_krillherd_") # too buggy
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or name == "ng_fcma_cube" # WTF HOW ARE YOU NAN'ING ON HYPERSPHERE
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or name == "ngx_fcmas03_cube" # same thing but quintic
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or name == "ngx_zero_cube" # 0 score, high price
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# or name == "ngx_microcma_cube" # 0 score, high price
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# or name == "ng_multiscalecma_cube" # 0 score, high price
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or name == "evosax_pbt_cube" # 0 score, high price
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or name == "evosax_guidedes_cube" # does get *a little* score...
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or name == "freelunch_sa_ps04_cube"
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or name == "ngx_microcma_cube"
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)
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def stfu(please_be_quiet=None):
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if please_be_quiet is None:
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please_be_quiet = (
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"ng_ascmadethird_cube",
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"ng_cmabounded_cube",
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"ng_cmatuning_cube",
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"ng_chaincmapowell_cube",
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"ng_chainnaivetbpsacmapowell_cube",
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"ng_paraportfolio_cube",
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"ng_rescaledcma_cube",
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)
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for optimizer in optimizers:
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name = optimizer.__name__
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if any(obnoxious in name for obnoxious in please_be_quiet):
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import warnings
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from cma.evolution_strategy import InjectionWarning
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warnings.simplefilter("ignore", InjectionWarning) # not our fault
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break
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def mark(opt_name):
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return (
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"\033[95m@"
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if opt_name in ("another_random_cube", "quasirandom_cube")
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else "\033[96m!"
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if "nelder" in opt_name
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else " "
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)
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def fancy_output(opt_name, score, price):
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name = opt_name.removesuffix("_cube")
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if type(score) is float:
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assert type(price) is float, "type mismatch"
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# unweight = 10 # len(optimizers) # sum(place_scores)
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unweight = multiple * np.sqrt(len(optimizers))
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stats = f"(score:{score * unweight:4.0f}, price:{price * unweight:4.0f})"
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else:
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stats = f"(score:{score:4}, price:{price:4})"
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color = 0
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reset = "\033[m"
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# this will need adjusting depending on your terminal colors:
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gradient = (32, 92, 93, 33, 91) # good to bad
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if score == 0 and price == 0:
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pass # wat?
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elif score < 0 and price < 0:
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color = 35 # wat?!
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elif score > 0 and price == 0:
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color = gradient[0] # good
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elif score == 0 and price > 0:
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color = gradient[4] # awful
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elif score > price:
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color = gradient[1] # ok
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elif score == price:
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color = gradient[2] # meh
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elif score < price:
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color = gradient[3] # bad
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color = f"\033[{color}m"
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s = f"{mark(opt_name)} {name:<32}{reset} with {color}{stats}{reset}"
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delta = score - price
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if type(score) is float:
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delta *= unweight
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color = 0
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if delta > 6:
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color = gradient[0]
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elif delta < -6:
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color = gradient[4]
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elif delta > 1:
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color = gradient[1]
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elif delta < -1:
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color = gradient[3]
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else:
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color = gradient[2]
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color = f"\033[{color}m"
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s += f" {color}{float(delta):+.0f}{reset}"
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# s += f" {color}{delta:+}{reset}"
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return s
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def prune_results(results, multiple):
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# if there are more than `multiple` results for one optimizer+objective pair,
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# then trim the bottom and top until there are only `multiple` left.
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new_results = {}
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for obj_name, obj_res in results.items():
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new_res = {}
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for fopt, opt_name in sorted(obj_res):
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new_res.setdefault(opt_name, []).append(fopt)
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for opt_name, fopts in new_res.items():
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# in the event that an odd number of results needs to be trimmed,
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# prefer trimming from the bottom (i.e. worse solutions get removed first).
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down = (len(fopts) - multiple) // 2
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up = len(fopts) - (len(fopts) - multiple + 1) // 2
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# print("asdf", len(fopts), down, up)
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new_res[opt_name] = fopts[down:up]
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for opt_name, fopts in new_res.items():
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if not no_summary:
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assert len(fopts) == multiple, (len(fopts), multiple)
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if len(fopts) == multiple:
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for fopt in fopts:
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new_results.setdefault(obj_name, []).append((fopt, opt_name))
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return results
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reset = "\033[m"
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quieter = True
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please_stop_the_spam = True
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no_summary = True
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if 1:
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multiple = 2
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run_anyway = 3 # run_anyway = 7
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always_run_anyway = True
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else:
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multiple = 1
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run_anyway = 3
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always_run_anyway = False
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#percents = dict(frugal_percent=0.1, greedy_percent=1.5)
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percents = dict(frugal_percent=1.0, greedy_percent=2.0)
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book = book_of_optimizers
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which = book[sys.argv[1]] if len(sys.argv) > 1 else book["standard"]
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n_dim = int(sys.argv[2]) if len(sys.argv) > 2 else -2
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n_trials = int(sys.argv[3]) if len(sys.argv) > 3 else fib(abs(n_dim) + 4) * 10
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place_names = ("1st", "2nd", "3rd", "4th")
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assert n_dim < 0, "unsupported in this version"
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n_dim = abs(n_dim)
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place_scores = (5, 3, 2, 1)
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objectives = GO_BENCHMARKS[n_dim] # * multiple
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optimizers = list(which) # copy
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before = len(optimizers)
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# if which is not book["everything"]:
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optimizers = [opt for opt in optimizers if optimizer_filter(opt.__name__)]
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after = len(optimizers)
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s = "s" if before - after != 1 else ""
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print(f"Pruned {before - after} unwanted optimizer{s}.")
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ms = f" ({multiple} times)" if multiple != 1 else ""
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n_obj = len(objectives)
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n_opt = len(optimizers)
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print(f"Optimizing {n_obj} objectives{ms} with {n_opt} optimizers...")
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stfu()
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pseudo_shuffled = lambda stuff: sorted(stuff, key=lambda obj: hash(repr(obj)))
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results = {}
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for optimizer in prog(pseudo_shuffled(optimizers), pref="m"):
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opt_name = optimizer.__name__
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wrapped = None
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for objective in prog(pseudo_shuffled(objectives), pref="s"):
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obj_name = objective.__name__
|
||||
obj_realname = getattr(objective, "__realname__", obj_name)
|
||||
|
||||
if wrapped is None:
|
||||
wrapped = COWrap(
|
||||
objective,
|
||||
optimizer=optimizer,
|
||||
n_trials=n_trials,
|
||||
n_dim=n_dim,
|
||||
**percents,
|
||||
)
|
||||
else:
|
||||
wrapped.objective = objective # 10+ times faster
|
||||
|
||||
run = 1
|
||||
while (cache := wrapped.cached(run)) is not None:
|
||||
run += 1
|
||||
fopt, xopt = cache
|
||||
results.setdefault(obj_name, []).append((fopt, opt_name))
|
||||
|
||||
note = (lambda s: None) if quieter else m36
|
||||
|
||||
once = False
|
||||
while (
|
||||
run <= multiple
|
||||
or (always_run_anyway or not once)
|
||||
and run_anyway
|
||||
and run <= run_anyway
|
||||
):
|
||||
# assert run == wrapped._run, (run, wrapped._run)
|
||||
if run != (_run := wrapped._run):
|
||||
m33(f"Note: updating local run count from {run} to {_run}.")
|
||||
run = _run
|
||||
continue # check conditions again
|
||||
|
||||
note(
|
||||
f"Using {opt_name} to optimize {obj_realname} ({obj_name}) [{run}] ..."
|
||||
)
|
||||
_ = optimizer(wrapped, n_trials=n_trials, n_dim=n_dim, with_count=False)
|
||||
fopt, xopt = wrapped.finish()
|
||||
results.setdefault(obj_name, []).append((fopt, opt_name))
|
||||
once = True
|
||||
run += 1
|
||||
|
||||
all_results = results
|
||||
results = prune_results(results, multiple)
|
||||
|
||||
scores, prices = {}, {}
|
||||
all_opt_names = set()
|
||||
for obj_name, obj_res in results.items():
|
||||
if not please_stop_the_spam:
|
||||
print()
|
||||
m1(f"{obj_name}:")
|
||||
all_res = {}
|
||||
for fopt, opt_name in obj_res:
|
||||
all_res.setdefault(fopt, []).append(opt_name)
|
||||
all_opt_names.add(opt_name)
|
||||
scores.setdefault(opt_name, 0.0)
|
||||
prices.setdefault(opt_name, 0.0)
|
||||
sorted_res = sorted(all_res)
|
||||
score_insignificance = sum(
|
||||
len(all_res[fopt])
|
||||
for _, fopt in zip(range(len(place_scores)), sorted_res)
|
||||
)
|
||||
price_insignificance = sum(
|
||||
len(all_res[fopt])
|
||||
for _, fopt in zip(range(len(place_scores)), reversed(sorted_res))
|
||||
)
|
||||
# print("score 1/x:", obj_name, score_insignificance)
|
||||
# print("price 1/x:", obj_name, price_insignificance)
|
||||
for i, fopt in enumerate(sorted_res):
|
||||
# if i >= len(place_scores): # TODO: just make this part of the loop.
|
||||
# break
|
||||
mi = len(all_res) - i - 1
|
||||
if i < len(place_scores):
|
||||
for opt_name in all_res[fopt]:
|
||||
scores[opt_name] = (
|
||||
scores[opt_name] + place_scores[i] / score_insignificance
|
||||
)
|
||||
if mi < len(place_scores):
|
||||
for opt_name in all_res[fopt]:
|
||||
prices[opt_name] = (
|
||||
prices[opt_name] + place_scores[mi] / price_insignificance
|
||||
)
|
||||
|
||||
more_scores = perform_another_experimental_scoring_method(results)
|
||||
|
||||
for blah, points in zip(("best", "worst"), (scores, prices)):
|
||||
if not no_summary:
|
||||
print(
|
||||
f"\n\033[1m{blah} scoring optimizers:\033[m"
|
||||
f" (awards={place_scores})"
|
||||
f" (obj={len(objectives)}, opt={len(optimizers)})"
|
||||
f" (dims={n_dim}, evals={n_trials})"
|
||||
)
|
||||
for opt_name, opt_point in sorted(points.items(), key=lambda t: -t[1]):
|
||||
# place = place_names[i] if i < len(place_names) else " "
|
||||
# delta = scores.get(opt_name, 0) - prices.get(opt_name, 0)
|
||||
if not no_summary:
|
||||
print(
|
||||
fancy_output(
|
||||
opt_name, scores.get(opt_name, 0), prices.get(opt_name, 0)
|
||||
)
|
||||
)
|
||||
|
||||
positive, negative = [], []
|
||||
for opt_name in sorted(all_opt_names):
|
||||
delta = scores.get(opt_name, 0) - prices.get(opt_name, 0)
|
||||
# note: this intentionally includes delta == 0 in both positive and negative.
|
||||
if delta >= 0:
|
||||
if opt_name not in positive:
|
||||
positive.append(opt_name)
|
||||
if delta <= 0:
|
||||
if opt_name not in negative:
|
||||
negative.append(opt_name)
|
||||
|
||||
if no_summary:
|
||||
print(
|
||||
f"\n\033[1malternatively scored optimizers:\033[m"
|
||||
f" (awards={place_scores})"
|
||||
f" (obj={len(objectives)}, opt={len(optimizers)})"
|
||||
f" (dims={n_dim}, evals={n_trials})"
|
||||
)
|
||||
for opt_name, opt_score in sorted(more_scores.items(), key=lambda t: -t[1]):
|
||||
# if opt_score < 1: continue
|
||||
stats = f"{opt_score:18.16f}"
|
||||
name = opt_name.removesuffix("_cube")
|
||||
color = "\033[1m" if opt_score > 1.0 else "\033[33m" if opt_score < 1.0 else ""
|
||||
s = f"{mark(opt_name)} {name:<32}{reset} with {color}{stats}{reset}"
|
||||
# s += f" {color}{float(delta):+.0f}{reset}"
|
||||
print(s)
|
||||
|
||||
text = "# this file was automatically generated by go_benchmark_it.py,\n"
|
||||
text += "# any changes may be overwritten!\n"
|
||||
|
||||
text += "PREVIOUSLY_POSITIVE = [\n"
|
||||
text += "".join(f' "{opt_name}",\n' for opt_name in positive)
|
||||
text += "]\n"
|
||||
|
||||
text += "PREVIOUSLY_NEGATIVE = [\n"
|
||||
text += "".join(f' "{opt_name}",\n' for opt_name in negative)
|
||||
text += "]\n"
|
||||
|
||||
if positive or negative:
|
||||
try:
|
||||
__import__("pathlib").Path("previous.py").write_text(text)
|
||||
except PermissionError:
|
||||
print("# failed to write previous.py, ignoring...")
|
||||
|
||||
if len(sys.argv) > 1 and sys.argv[1] in ("positive", "negative"):
|
||||
all_old_opt_names = set(opt.__name__ for opt in optimizers)
|
||||
C = set(("quasirandom_cube", "another_random_cube"))
|
||||
if sys.argv[1] == "positive" and set(positive) - C == all_old_opt_names - C:
|
||||
exit(2) # no changes
|
||||
if sys.argv[1] == "negative" and set(negative) - C == all_old_opt_names - C:
|
||||
exit(2) # no changes
|
||||
371
go_benchmark_lists.py
Normal file
371
go_benchmark_lists.py
Normal file
|
|
@ -0,0 +1,371 @@
|
|||
# this file was automatically generated by generate_lists.py,
|
||||
# any changes may be overwritten!
|
||||
# loaded solutions_1680721604.py
|
||||
# loaded solutions_1680730206.py
|
||||
# loaded solutions_1680975848.py
|
||||
|
||||
too_variable_2 = [
|
||||
'stochastic',
|
||||
'xinsheyang01',
|
||||
]
|
||||
|
||||
too_centered_2 = [
|
||||
'ackley01',
|
||||
'ackley02',
|
||||
'bartelsconn',
|
||||
'bohachevsky2',
|
||||
'bukin04',
|
||||
'bukin06',
|
||||
'cigar',
|
||||
'deflectedcorrugatedspring',
|
||||
'dropwave',
|
||||
'exponential',
|
||||
'griewank',
|
||||
'infinity',
|
||||
'matyas',
|
||||
'needleeye',
|
||||
'pathological',
|
||||
'pinter',
|
||||
'price02',
|
||||
'price04',
|
||||
'qing',
|
||||
'rotatedellipse01',
|
||||
'rotatedellipse02',
|
||||
'salomon',
|
||||
'sargan',
|
||||
'schaffer01',
|
||||
'schaffer02',
|
||||
'schwefel01',
|
||||
'schwefel02',
|
||||
'sphere',
|
||||
'threehumpcamel',
|
||||
'treccani',
|
||||
'trigonometric02',
|
||||
'ursem04',
|
||||
'ventersobiezcczanskisobieski',
|
||||
'wayburnseader02',
|
||||
'xinsheyang02',
|
||||
'xinsheyang03',
|
||||
'xinsheyang04',
|
||||
'yaoliu04',
|
||||
'yaoliu09',
|
||||
]
|
||||
|
||||
too_far_away_2 = [
|
||||
'adjiman',
|
||||
'brent',
|
||||
'crosslegtable',
|
||||
'eggholder',
|
||||
'katsuura',
|
||||
'mishra01',
|
||||
'mishra02',
|
||||
'mishra03',
|
||||
'mishra05',
|
||||
'newfunction01',
|
||||
'newfunction02',
|
||||
'rana',
|
||||
'trigonometric01',
|
||||
'ursemwaves',
|
||||
]
|
||||
|
||||
too_positive_2 = [
|
||||
'amgm',
|
||||
'brown',
|
||||
'cosinemixture',
|
||||
'crossintray',
|
||||
'cube',
|
||||
'deb01',
|
||||
'deb03',
|
||||
'decanomial',
|
||||
'easom',
|
||||
'eggcrate',
|
||||
'giunta',
|
||||
'jennrichsampson',
|
||||
'keane',
|
||||
'levy03',
|
||||
'mishra04',
|
||||
'penalty01',
|
||||
'penalty02',
|
||||
'penholder',
|
||||
'price03',
|
||||
'quintic',
|
||||
'schwefel04',
|
||||
'schwefel20',
|
||||
'schwefel26',
|
||||
'shubert03',
|
||||
'step2',
|
||||
'styblinskitang',
|
||||
'testtubeholder',
|
||||
'vincent',
|
||||
'weierstrass',
|
||||
'zacharov',
|
||||
'zettl',
|
||||
]
|
||||
|
||||
totally_fine_2 = [
|
||||
'ackley03',
|
||||
'alpine01',
|
||||
'alpine02',
|
||||
'beale',
|
||||
'biggsexp02',
|
||||
'bird',
|
||||
'bohachevsky1',
|
||||
'bohachevsky3',
|
||||
'branin01',
|
||||
'branin02',
|
||||
'bukin02',
|
||||
'carromtable',
|
||||
'chichinadze',
|
||||
'crownedcross',
|
||||
'damavandi',
|
||||
'deceptive',
|
||||
'deckkersaarts',
|
||||
'dixonprice',
|
||||
'elattarvidyasagardutta',
|
||||
'exp2',
|
||||
'freudensteinroth',
|
||||
'goldsteinprice',
|
||||
'hansen',
|
||||
'himmelblau',
|
||||
'holdertable',
|
||||
'hosaki',
|
||||
'judge',
|
||||
'langermann',
|
||||
'leon',
|
||||
'levy05',
|
||||
'levy13',
|
||||
'mccormick',
|
||||
'michalewicz',
|
||||
'mishra06',
|
||||
'mishra07',
|
||||
'mishra08',
|
||||
'mishra10',
|
||||
'mishra11',
|
||||
'multimodal',
|
||||
'oddsquare',
|
||||
'parsopoulos',
|
||||
'permfunction01',
|
||||
'permfunction02',
|
||||
'plateau',
|
||||
'price01',
|
||||
'quadratic',
|
||||
'rastrigin',
|
||||
'ripple01',
|
||||
'ripple25',
|
||||
'rosenbrock',
|
||||
'rosenbrockmodified',
|
||||
'schaffer03',
|
||||
'schaffer04',
|
||||
'schwefel06',
|
||||
'schwefel21',
|
||||
'schwefel22',
|
||||
'schwefel36',
|
||||
'shubert01',
|
||||
'shubert04',
|
||||
'sineenvelope',
|
||||
'sixhumpcamel',
|
||||
'sodp',
|
||||
'step',
|
||||
'stretchedv',
|
||||
'trefethen',
|
||||
'tripod',
|
||||
'ursem01',
|
||||
'ursem03',
|
||||
'wavy',
|
||||
'wayburnseader01',
|
||||
'whitley',
|
||||
'zerosum',
|
||||
'zimmerman',
|
||||
'zirilli',
|
||||
]
|
||||
|
||||
too_variable_3 = [
|
||||
'stochastic',
|
||||
'xinsheyang01',
|
||||
]
|
||||
|
||||
too_centered_3 = [
|
||||
'ackley01',
|
||||
'alpine01',
|
||||
'deflectedcorrugatedspring',
|
||||
'exponential',
|
||||
'griewank',
|
||||
'infinity',
|
||||
'mishra11',
|
||||
'multimodal',
|
||||
'pinter',
|
||||
'qing',
|
||||
'rastrigin',
|
||||
'salomon',
|
||||
'schwefel01',
|
||||
'schwefel20',
|
||||
'schwefel21',
|
||||
'schwefel22',
|
||||
'sineenvelope',
|
||||
'sodp',
|
||||
'trigonometric02',
|
||||
'wavy',
|
||||
'weierstrass',
|
||||
'xinsheyang02',
|
||||
'xinsheyang03',
|
||||
'yaoliu04',
|
||||
'yaoliu09',
|
||||
'zerosum',
|
||||
]
|
||||
|
||||
too_far_away_3 = [
|
||||
'cosinemixture',
|
||||
'katsuura',
|
||||
'meyer',
|
||||
'mishra01',
|
||||
'mishra02',
|
||||
'rana',
|
||||
'trigonometric01',
|
||||
'whitley',
|
||||
'wolfe',
|
||||
]
|
||||
|
||||
too_positive_3 = [
|
||||
'amgm',
|
||||
'brown',
|
||||
'cigar',
|
||||
'deb01',
|
||||
'deb03',
|
||||
'deceptive',
|
||||
'needleeye',
|
||||
'penalty01',
|
||||
'schwefel04',
|
||||
'schwefel26',
|
||||
'sphere',
|
||||
'stretchedv',
|
||||
'styblinskitang',
|
||||
'zacharov',
|
||||
]
|
||||
|
||||
totally_fine_3 = [
|
||||
'alpine02',
|
||||
'biggsexp03',
|
||||
'boxbetts',
|
||||
'dixonprice',
|
||||
'eckerle4',
|
||||
'eggholder',
|
||||
'gulf',
|
||||
'hartmann3',
|
||||
'helicalvalley',
|
||||
'mishra07',
|
||||
'mishra09',
|
||||
'penalty02',
|
||||
'permfunction01',
|
||||
'permfunction02',
|
||||
'plateau',
|
||||
'quadratic',
|
||||
'quintic',
|
||||
'ratkowsky02',
|
||||
'rosenbrock',
|
||||
'sargan',
|
||||
'schwefel02',
|
||||
'shubert01',
|
||||
'shubert03',
|
||||
'shubert04',
|
||||
'step2',
|
||||
'step',
|
||||
'vincent',
|
||||
'xinsheyang04',
|
||||
]
|
||||
|
||||
too_variable_4 = [
|
||||
'stochastic',
|
||||
'xinsheyang01',
|
||||
]
|
||||
|
||||
too_centered_4 = [
|
||||
'ackley01',
|
||||
'alpine01',
|
||||
'cigar',
|
||||
'exponential',
|
||||
'griewank',
|
||||
'infinity',
|
||||
'pinter',
|
||||
'qing',
|
||||
'schwefel01',
|
||||
'schwefel02',
|
||||
'schwefel20',
|
||||
'schwefel21',
|
||||
'schwefel22',
|
||||
'sineenvelope',
|
||||
'sphere',
|
||||
'step2',
|
||||
'step',
|
||||
'stretchedv',
|
||||
'trigonometric02',
|
||||
'weierstrass',
|
||||
'whitley',
|
||||
'xinsheyang02',
|
||||
'yaoliu04',
|
||||
'yaoliu09',
|
||||
'zerosum',
|
||||
]
|
||||
|
||||
too_far_away_4 = [
|
||||
'corana',
|
||||
'cosinemixture',
|
||||
'devilliersglasser01',
|
||||
'katsuura',
|
||||
'mielecantrell',
|
||||
'mishra01',
|
||||
'mishra02',
|
||||
'rana',
|
||||
'sargan',
|
||||
'trigonometric01',
|
||||
'xinsheyang03',
|
||||
]
|
||||
|
||||
too_positive_4 = [
|
||||
'amgm',
|
||||
'brown',
|
||||
'colville',
|
||||
'deb03',
|
||||
'penalty01',
|
||||
'penalty02',
|
||||
'powell',
|
||||
'rosenbrock',
|
||||
'schwefel04',
|
||||
'shekel05',
|
||||
'shekel07',
|
||||
'shekel10',
|
||||
'shubert03',
|
||||
'shubert04',
|
||||
'styblinskitang',
|
||||
'vincent',
|
||||
]
|
||||
|
||||
totally_fine_4 = [
|
||||
'alpine02',
|
||||
'biggsexp04',
|
||||
'deb01',
|
||||
'deceptive',
|
||||
'deflectedcorrugatedspring',
|
||||
'dixonprice',
|
||||
'eggholder',
|
||||
'gear',
|
||||
'kowalik',
|
||||
'mishra07',
|
||||
'mishra11',
|
||||
'multimodal',
|
||||
'needleeye',
|
||||
'permfunction01',
|
||||
'permfunction02',
|
||||
'plateau',
|
||||
'powersum',
|
||||
'quadratic',
|
||||
'quintic',
|
||||
'rastrigin',
|
||||
'ratkowsky01',
|
||||
'salomon',
|
||||
'schwefel26',
|
||||
'shubert01',
|
||||
'sodp',
|
||||
'wavy',
|
||||
'xinsheyang04',
|
||||
'zacharov',
|
||||
]
|
||||
49
go_benchmarks.py
Normal file
49
go_benchmarks.py
Normal file
|
|
@ -0,0 +1,49 @@
|
|||
from go_benchmark_functions import *
|
||||
from inspect import getmro, isclass
|
||||
|
||||
_looky_here = False
|
||||
|
||||
problems = []
|
||||
for k in list(locals().keys()):
|
||||
smth = locals()[k]
|
||||
if isclass(smth) and Benchmark in getmro(smth)[1:]:
|
||||
problems.append(smth)
|
||||
|
||||
counts = {}
|
||||
vcount = 0
|
||||
problems_by_dimensions = {}
|
||||
|
||||
for cls in problems:
|
||||
name = cls.__name__
|
||||
obj = cls()
|
||||
n = obj.N
|
||||
if _looky_here:
|
||||
print(n, name, "(variable)" if obj.change_dimensionality else "(fixed)")
|
||||
counts[n] = counts.get(n, 0) + 1
|
||||
if obj.change_dimensionality:
|
||||
vcount += 1
|
||||
dimkey = f"{n}+"
|
||||
else:
|
||||
dimkey = f"{n}"
|
||||
problems_by_dimensions.setdefault(dimkey, []).append(cls)
|
||||
del obj
|
||||
|
||||
counts["variable"] = vcount
|
||||
|
||||
if _looky_here:
|
||||
print(sorted(counts.items(), key=lambda x: f"{x[0]:09}" if isinstance(x[0], int) else str(x[0])))
|
||||
|
||||
p_by_d = problems_by_dimensions
|
||||
|
||||
if _looky_here:
|
||||
for dims in range(2, 6 + 1):
|
||||
dimp = f"{dims}+"
|
||||
print(dims, len(p_by_d.get(str(dims), [])), sep="\t")
|
||||
print(dimp, len(p_by_d.get(dimp, [])), sep="\t")
|
||||
|
||||
at_least_2d = p_by_d.get("2+", [])
|
||||
at_least_3d = p_by_d.get("3+", []) + at_least_2d
|
||||
at_least_4d = p_by_d.get("4+", []) + at_least_3d
|
||||
problems_2d = p_by_d.get("2", []) + at_least_2d
|
||||
problems_3d = p_by_d.get("3", []) + at_least_3d
|
||||
problems_4d = p_by_d.get("4", []) + at_least_4d
|
||||
218
nloptcube2.py
Normal file
218
nloptcube2.py
Normal file
|
|
@ -0,0 +1,218 @@
|
|||
import nlopt
|
||||
import numpy as np
|
||||
|
||||
NLOPTIMIZERS = {
|
||||
# "gd_mlsl": nlopt.GD_MLSL,
|
||||
# "gd_mlsl_lds": nlopt.GD_MLSL_LDS,
|
||||
# "gd_stogo": nlopt.GD_STOGO,
|
||||
# "gd_stogo_rand": nlopt.GD_STOGO_RAND,
|
||||
"gn_ags": nlopt.GN_AGS,
|
||||
"gn_crs2_lm": nlopt.GN_CRS2_LM,
|
||||
"gn_direct": nlopt.GN_DIRECT,
|
||||
"gn_direct_l": nlopt.GN_DIRECT_L,
|
||||
"gn_direct_l_noscal": nlopt.GN_DIRECT_L_NOSCAL,
|
||||
"gn_direct_l_rand": nlopt.GN_DIRECT_L_RAND,
|
||||
"gn_direct_l_rand_noscal": nlopt.GN_DIRECT_L_RAND_NOSCAL,
|
||||
"gn_direct_noscal": nlopt.GN_DIRECT_NOSCAL,
|
||||
"gn_esch": nlopt.GN_ESCH,
|
||||
"gn_isres": nlopt.GN_ISRES,
|
||||
"gn_mlsl": nlopt.GN_MLSL,
|
||||
"gn_mlsl_lds": nlopt.GN_MLSL_LDS,
|
||||
"gn_orig_direct": nlopt.GN_ORIG_DIRECT,
|
||||
"gn_orig_direct_l": nlopt.GN_ORIG_DIRECT_L,
|
||||
"g_mlsl": nlopt.G_MLSL,
|
||||
"g_mlsl_lds": nlopt.G_MLSL_LDS,
|
||||
# "ld_auglag": nlopt.LD_AUGLAG,
|
||||
# "ld_auglag_eq": nlopt.LD_AUGLAG_EQ,
|
||||
# "ld_ccsaq": nlopt.LD_CCSAQ,
|
||||
# "ld_lbfgs": nlopt.LD_LBFGS,
|
||||
# "ld_lbfgs_nocedal": nlopt.LD_LBFGS_NOCEDAL,
|
||||
# "ld_mma": nlopt.LD_MMA,
|
||||
# "ld_slsqp": nlopt.LD_SLSQP,
|
||||
# "ld_tnewton": nlopt.LD_TNEWTON,
|
||||
# "ld_tnewton_precond": nlopt.LD_TNEWTON_PRECOND,
|
||||
# "ld_tnewton_precond_restart": nlopt.LD_TNEWTON_PRECOND_RESTART,
|
||||
# "ld_tnewton_restart": nlopt.LD_TNEWTON_RESTART,
|
||||
# "ld_var1": nlopt.LD_VAR1,
|
||||
# "ld_var2": nlopt.LD_VAR2,
|
||||
"ln_auglag": nlopt.LN_AUGLAG,
|
||||
"ln_auglag_eq": nlopt.LN_AUGLAG_EQ,
|
||||
"ln_bobyqa": nlopt.LN_BOBYQA,
|
||||
"ln_cobyla": nlopt.LN_COBYLA,
|
||||
"ln_neldermead": nlopt.LN_NELDERMEAD,
|
||||
"ln_newuoa": nlopt.LN_NEWUOA,
|
||||
"ln_newuoa_bound": nlopt.LN_NEWUOA_BOUND,
|
||||
"ln_praxis": nlopt.LN_PRAXIS,
|
||||
"ln_sbplx": nlopt.LN_SBPLX,
|
||||
}
|
||||
|
||||
|
||||
def nlopt_cube_factory(objective, n_trials, n_dim, with_count, method):
|
||||
optim = NLOPTIMIZERS[method]
|
||||
|
||||
feval_count = 0
|
||||
best_so_far = None
|
||||
|
||||
def _objective(x, grad):
|
||||
nonlocal feval_count, best_so_far
|
||||
fx = objective(x)
|
||||
feval_count += 1
|
||||
if feval_count <= n_trials:
|
||||
if best_so_far is None or fx < best_so_far[0]:
|
||||
best_so_far = (fx, x)
|
||||
return fx
|
||||
|
||||
opt = nlopt.opt(optim, n_dim)
|
||||
opt.set_lower_bounds([0.0] * n_dim)
|
||||
opt.set_upper_bounds([1.0] * n_dim)
|
||||
opt.set_min_objective(_objective)
|
||||
opt.set_maxeval(n_trials)
|
||||
|
||||
try:
|
||||
opt.optimize([0.5] * n_dim)
|
||||
except nlopt.RoundoffLimited as e:
|
||||
print( # FIXME: de-uglify this!
|
||||
"\033[33m",
|
||||
"nlopt_" + method,
|
||||
" exited early (",
|
||||
type(e).__name__,
|
||||
")",
|
||||
"\033[0m",
|
||||
sep="",
|
||||
)
|
||||
|
||||
assert best_so_far is not None, optimizer.__name__
|
||||
fopt, xopt = best_so_far
|
||||
|
||||
return (fopt, xopt, feval_count) if with_count else (fopt, xopt)
|
||||
|
||||
|
||||
def nlopt_ags_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "gn_ags")
|
||||
|
||||
|
||||
def nlopt_crs2_lm_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "gn_crs2_lm")
|
||||
|
||||
|
||||
def nlopt_direct_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "gn_direct")
|
||||
|
||||
|
||||
def nlopt_direct_l_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "gn_direct_l")
|
||||
|
||||
|
||||
def nlopt_direct_l_noscal_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(
|
||||
objective, n_trials, n_dim, with_count, "gn_direct_l_noscal"
|
||||
)
|
||||
|
||||
|
||||
def nlopt_direct_lr_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(
|
||||
objective, n_trials, n_dim, with_count, "gn_direct_l_rand"
|
||||
)
|
||||
|
||||
|
||||
def nlopt_direct_lr_noscal_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(
|
||||
objective, n_trials, n_dim, with_count, "gn_direct_l_rand_noscal"
|
||||
)
|
||||
|
||||
|
||||
def nlopt_direct_noscal_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(
|
||||
objective, n_trials, n_dim, with_count, "gn_direct_noscal"
|
||||
)
|
||||
|
||||
|
||||
def nlopt_esch_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "gn_esch")
|
||||
|
||||
|
||||
def nlopt_isres_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "gn_isres")
|
||||
|
||||
|
||||
def nlopt_mlsl_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "gn_mlsl")
|
||||
|
||||
|
||||
def nlopt_mlsl_lds_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "gn_mlsl_lds")
|
||||
|
||||
|
||||
def nlopt_orig_direct_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "gn_orig_direct")
|
||||
|
||||
|
||||
def nlopt_orig_direct_l_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(
|
||||
objective, n_trials, n_dim, with_count, "gn_orig_direct_l"
|
||||
)
|
||||
|
||||
|
||||
def nlopt_auglag_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "ln_auglag")
|
||||
|
||||
|
||||
def nlopt_auglag_eq_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "ln_auglag_eq")
|
||||
|
||||
|
||||
def nlopt_bobyqa_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "ln_bobyqa")
|
||||
|
||||
|
||||
def nlopt_cobyla_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "ln_cobyla")
|
||||
|
||||
|
||||
def nlopt_neldermead_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "ln_neldermead")
|
||||
|
||||
|
||||
def nlopt_newuoa_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "ln_newuoa")
|
||||
|
||||
|
||||
def nlopt_newuoa_bound_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "ln_newuoa_bound")
|
||||
|
||||
|
||||
def nlopt_praxis_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "ln_praxis")
|
||||
|
||||
|
||||
def nlopt_sbplx_cube(objective, n_trials, n_dim, with_count):
|
||||
return nlopt_cube_factory(objective, n_trials, n_dim, with_count, "ln_sbplx")
|
||||
|
||||
|
||||
NLOPT_OPTIMIZERS = [
|
||||
# global
|
||||
# nlopt_ags_cube, # ValueError: nlopt invalid argument
|
||||
nlopt_crs2_lm_cube,
|
||||
nlopt_direct_cube,
|
||||
nlopt_direct_l_cube,
|
||||
nlopt_direct_l_noscal_cube,
|
||||
nlopt_direct_lr_cube,
|
||||
nlopt_direct_lr_noscal_cube,
|
||||
nlopt_direct_noscal_cube,
|
||||
nlopt_esch_cube,
|
||||
nlopt_isres_cube,
|
||||
# nlopt_mlsl_cube, # FIXME: Segmentation fault
|
||||
# nlopt_mlsl_lds_cube # FIXME: Segmentation fault,
|
||||
nlopt_orig_direct_cube,
|
||||
nlopt_orig_direct_l_cube,
|
||||
# local
|
||||
# nlopt_auglag_cube, # FIXME: Segmentation fault
|
||||
# nlopt_auglag_eq_cube, # FIXME: Segmentation fault
|
||||
nlopt_bobyqa_cube,
|
||||
nlopt_cobyla_cube,
|
||||
nlopt_neldermead_cube,
|
||||
nlopt_newuoa_cube,
|
||||
# nlopt_newuoa_bound_cube, # sadly this times out on shekel. however it wins in nlopt_classic_d21_n550_armor
|
||||
# nlopt_praxis_cube, # AssertionError: x is not finite (NaN or Inf or -Inf)
|
||||
nlopt_sbplx_cube,
|
||||
]
|
||||
12
notwacube.py
Normal file
12
notwacube.py
Normal file
|
|
@ -0,0 +1,12 @@
|
|||
from nloptcube2 import nlopt_neldermead_cube
|
||||
from randomcube2 import another_random_cube, quasirandom_cube
|
||||
|
||||
BASELINE_OPTIMIZERS = [
|
||||
another_random_cube,
|
||||
nlopt_neldermead_cube,
|
||||
quasirandom_cube,
|
||||
]
|
||||
|
||||
book_of_optimizers = dict(
|
||||
baseline=BASELINE_OPTIMIZERS,
|
||||
)
|
||||
442
prog80.py
Executable file
442
prog80.py
Executable file
|
|
@ -0,0 +1,442 @@
|
|||
import atexit
|
||||
import sys
|
||||
import time
|
||||
|
||||
# TODO: make this a namedtuple? or one of those newfangles dataclasses.
|
||||
options = dict(
|
||||
ascii=False, # TODO
|
||||
color=True,
|
||||
flush=True,
|
||||
patient=True, # TODO: better name?
|
||||
)
|
||||
|
||||
active = []
|
||||
allocated = 0
|
||||
|
||||
DEBUG = False
|
||||
|
||||
|
||||
def get_terminal_size(file=sys.stderr):
|
||||
from os import get_terminal_size as os_gts
|
||||
|
||||
cols, rows = 0, 0
|
||||
try:
|
||||
cols, rows = os_gts(file.fileno())
|
||||
except OSError:
|
||||
pass
|
||||
if cols <= 0 or rows <= 0:
|
||||
from shutil import get_terminal_size as shutil_gts
|
||||
|
||||
# NOTE: this uses sys.stdout instead of our file!
|
||||
cols, rows = shutil_gts()
|
||||
return cols, rows
|
||||
|
||||
|
||||
def extend72(s, mode=""):
|
||||
if "t" in mode or "d" in mode or "a" in mode:
|
||||
s = s[:-1]
|
||||
return ((s[0] + s[1:-1]) * 72)[:71] + s[-1]
|
||||
|
||||
|
||||
@atexit.register # in the absence of a try-except for KeyboardInterrupt...
|
||||
def reset(*, file=sys.stderr):
|
||||
# TODO: flesh this out?
|
||||
# TODO: fancy backspace "\b" stuff to hide potentially unsupported escape sequences.
|
||||
s = "\0337\033[J\033[r\0338"
|
||||
if file is not None:
|
||||
print(end=s, file=file, flush=True)
|
||||
else:
|
||||
return s
|
||||
|
||||
|
||||
def alloc(lines):
|
||||
# first, perform a reset of whatever scrolling region may already be set.
|
||||
# the cursor is (almost) always reset after performing a reset command,
|
||||
# so wrap the command in a cursor-save and cursor-restore.
|
||||
s = "\0337\033[r\0338"
|
||||
|
||||
# now, do some magic to ensure there's enough empty lines for the progress bars
|
||||
# *without* forcing the screen to scroll if there already is.
|
||||
# TODO: clear these lines too? hmm, that would overwrite bars though, wouldn't it?
|
||||
s += "\n" * lines # this part is simple enough; just print some newlines.
|
||||
s += "\0338" # however, that resets the cursor's column, so restore its position.
|
||||
# the position might not be on the same row as the line of text it was on before,
|
||||
s += f"\033[{lines}B\033[{lines}A" # so move it down (clamped) and back up again.
|
||||
# s += "\0337" # that should be everything, so store the adjusted cursor for later.
|
||||
|
||||
return s
|
||||
|
||||
|
||||
class Prog80:
|
||||
# TODO: use different(-ly placed) delimiters when length < 72?
|
||||
styles = dict(
|
||||
# TODO: maybe use namedtuples for these?
|
||||
# TODO: maybe use a separate field for the "cursor" characters?
|
||||
# this ought to eliminate the need for "d", "t" and "P" modes.
|
||||
bar=dict(A=" ", B="█▌", mode="r"),
|
||||
bar2=dict(A=" ▌", B="██", mode="dp"), # TODO: what's the non-'p' version?
|
||||
bracketed=dict(A="[" + " " * 70 + "]", B="[" + "=" * 70 + "]", mode="P"),
|
||||
circumslider=dict(A="o", B="oô", mode="pr"),
|
||||
crossslider=dict(A="†", B="†‡", mode="pr"),
|
||||
dotslider=dict(A="-", B="-÷", mode="pr"),
|
||||
dotted=dict(A=".", B="!?", mode="r"),
|
||||
equally=dict(A=".", B="="),
|
||||
fractional=dict(
|
||||
A="·····¬·····¬·····¼·····¬·····¬·····½·····¬·····¬·····¾·····¬·····¬·····!",
|
||||
B="xƒ",
|
||||
mode="ir",
|
||||
),
|
||||
longdotslider=dict(A="—", B="—÷", mode="pr"),
|
||||
meme=dict( # slightly modified
|
||||
A="According to all known laws of aviation, there is no way a bee should be",
|
||||
B="able to fly. Its wings are too small to get its fat body off the ground.",
|
||||
mode="P",
|
||||
),
|
||||
middledots=dict(A="•", B="◘"),
|
||||
money=dict(A="¢", B="$"),
|
||||
# pacman=dict(A="-", B="#"),
|
||||
# bracketedpacman=dict(A="[" + "-" * 70 + "]", B="[" + "#" * 70 + "]", mode="P"),
|
||||
pacman=dict(A="[" + "-" * 70 + "]", B="[" + "#" * 70 + "]", mode="P"),
|
||||
piping=dict(A="=", B="=|", mode="pr"),
|
||||
ruler=dict(A="90123456780", B="90123456780", mode="P"),
|
||||
slider=dict(A="_", B="_⌂", mode="pr"),
|
||||
smile=dict(A="☺", B="☻"),
|
||||
song=dict(
|
||||
A=" - ♪ - ♪ - ♪ - ♪",
|
||||
B=" ♫ ♫ ♫ ♫ ♫ ♫ ♫ ♫ ♫ ♫ ♫ ♫ ♫ ♫ ♫ ♫ ♫ ♫",
|
||||
),
|
||||
sticks=dict(A="/", B="\\|", mode="p"),
|
||||
virus=dict(A=".▀", B=" TECHNO ▄", mode="ap"), # »Don't touch the keyboard«
|
||||
)
|
||||
|
||||
bands = [ # TODO: come up with a better name for this variable.
|
||||
("*", -4.94065645841246544177e-324, lambda x: "Waiting"),
|
||||
("*", 0.0, lambda x: "Running"),
|
||||
("*", 1.3888888888888887e-08, lambda x: "TooSlow"),
|
||||
("*", 0.00027776388888888886, lambda x: f"{x * 60 * 60:6.4f}/h"[1:]),
|
||||
("*", 0.001666583333333333, lambda x: f"{x * 60 * 60:5.3f}/h"),
|
||||
("s", 0.01666583333333333, lambda x: f"{x * 60:6.4f}/m"[1:]),
|
||||
("m", 0.01666583333333333, lambda x: f"{x * 60:6.4f}/m"[1:]),
|
||||
("s", 0.09995, lambda x: f"{x * 60:5.3f}/m"),
|
||||
("s", 0.9999499999999999, lambda x: f"{x:6.4f}/s"[1:]),
|
||||
("m", 0.16665833333333332, lambda x: f"{x * 60:5.3f}/m"),
|
||||
("m", 1.666583333333333, lambda x: f"{x * 60:5.2f}/m"),
|
||||
("s", 9.999499999999998, lambda x: f"{x:5.3f}/s"),
|
||||
("s", 12.000833333333333, lambda x: f"{x:5.2f}/s"),
|
||||
("m", 16.66583333333333, lambda x: f"{x * 60:5.1f}/m"),
|
||||
("m", 166.6583333333333, lambda x: f"{x * 60:4.0f}./m"),
|
||||
("*", 0.002777638888888889, lambda x: f"{x * 60 * 60:5.3f}/h"),
|
||||
("*", 0.027776388888888885, lambda x: f"{x * 60 * 60:5.2f}/h"),
|
||||
("*", 0.20001388888888888, lambda x: f"{x * 60 * 60:5.1f}/h"),
|
||||
("h", 0.2777638888888888, lambda x: f"{x * 60 * 60:5.1f}/h"),
|
||||
("h", 2.7776388888888883, lambda x: f"{x * 60 * 60:4.0f}./h"),
|
||||
("*", 1.666583333333333, lambda x: f"{x * 60:5.2f}/m"),
|
||||
("*", 12.000833333333333, lambda x: f"{x * 60:5.1f}/m"),
|
||||
("*", 99.99499999999999, lambda x: f"{x:5.2f}/s"),
|
||||
("*", 999.9499999999999, lambda x: f"{x:5.1f}/s"),
|
||||
("*", 9999.499999999998, lambda x: f"{x:4.0f}./s"),
|
||||
("*", 1.7976931348623157e308, lambda x: f"TooFast"),
|
||||
]
|
||||
|
||||
# TODO: default to sys.stderr as well!
|
||||
def __init__(
|
||||
self,
|
||||
it,
|
||||
length,
|
||||
*,
|
||||
printer=print,
|
||||
style=None,
|
||||
display="elapsed",
|
||||
preference="*",
|
||||
):
|
||||
self.i = 0
|
||||
self.it = iter(it)
|
||||
self.length = length
|
||||
self.printer = printer
|
||||
self.depth = 0
|
||||
self.start, self.end = 0, 0
|
||||
self.cols, self.rows = 0, 0
|
||||
self.preference = preference
|
||||
self.previous_update = 0.0
|
||||
self._state = None
|
||||
self.state = "initialized"
|
||||
self.style = style if style in self.styles else "fractional" # TODO: warning
|
||||
# self.style = style if style in self.styles else "slider" # TODO: warning
|
||||
# self.style = style if style in self.styles else list(self.styles)[-1]
|
||||
self.display = display
|
||||
assert self.display in ("elapsed", "eta", "rate")
|
||||
|
||||
@property
|
||||
def state(self):
|
||||
return self._state
|
||||
|
||||
@state.setter
|
||||
def state(self, new_state):
|
||||
if self._state is not None and new_state == self._state:
|
||||
return # just ignore this
|
||||
valid = {
|
||||
"initialized": {None},
|
||||
"prepared": {"initialized"},
|
||||
"began": {"prepared", "progressing"},
|
||||
"progressing": {"began"},
|
||||
"finished": {"began"},
|
||||
}
|
||||
assert new_state in valid, f"unknown state {new_state}"
|
||||
assert (
|
||||
self._state in valid[new_state]
|
||||
), f"invalid state transition from {self._state} to {new_state}"
|
||||
self._state = new_state
|
||||
|
||||
def __len__(self):
|
||||
return self.length
|
||||
|
||||
@property
|
||||
def unfilled(self):
|
||||
return extend72(self.styles[self.style]["A"], self.styling)
|
||||
|
||||
@property
|
||||
def filled(self):
|
||||
return extend72(self.styles[self.style]["B"], self.styling)
|
||||
|
||||
@property
|
||||
def styling(self):
|
||||
return self.styles[self.style].get("mode", "")
|
||||
|
||||
@property
|
||||
def full(self):
|
||||
return self.i * 72 // self.length if self.length > 0 else 72
|
||||
|
||||
@property
|
||||
def needs_update(self):
|
||||
if self.length <= 0:
|
||||
return True
|
||||
w = 72 * 3 if "t" in self.styling else 72 * 2 if "d" in self.styling else 72
|
||||
return self.i * w // self.length != (self.i - 1) * w // self.length
|
||||
|
||||
@property
|
||||
def elapsed(self):
|
||||
now = self.end if self.state == "finished" else time.time()
|
||||
return now - self.start
|
||||
|
||||
@property
|
||||
def rate(self):
|
||||
assert self.state in (
|
||||
"began",
|
||||
"progressing",
|
||||
"finished",
|
||||
), "invalid state for this"
|
||||
elapsed = self.elasped
|
||||
if elapsed == 0.0:
|
||||
return self.bands[-1][1] # closest thing to infinity
|
||||
return self.i / elapsed
|
||||
|
||||
@property
|
||||
def eta(self):
|
||||
raise NotImplementedError("TODO")
|
||||
|
||||
@classmethod
|
||||
def format_rate(cls, rate, preference="*"):
|
||||
for pref, threshold, formater in cls.bands:
|
||||
if (pref == "*" or pref == preference) and rate <= threshold:
|
||||
return formater(rate) + " "
|
||||
return "????????" # normally unreachable (unless perhaps rate is Inf or NaN?)
|
||||
|
||||
@classmethod
|
||||
def format_time(cls, duration, preference="*"):
|
||||
o = "-" if duration < 0.0 else "+"
|
||||
duration = abs(duration)
|
||||
if duration < 60.0:
|
||||
o += f"{max(0.0, duration - 0.005):5.2f}s "
|
||||
elif duration < 3600.0:
|
||||
x = int(duration)
|
||||
o += f"{x // 60:2d}m{x % 60:02d}s "
|
||||
elif duration < 86400.0:
|
||||
x = int(duration)
|
||||
o += f"{x // 3600:2d}h{x // 60 % 60:02d}m "
|
||||
elif duration < 8640000.0:
|
||||
x = int(duration)
|
||||
o += f"{x // 86400:2d}d{x // 3600 % 24:02d}h "
|
||||
else:
|
||||
o += "TooLong"
|
||||
return o
|
||||
|
||||
@property
|
||||
def blurb(self): # only the first 8 characters that don't constitute the bar at all
|
||||
if self.display == "rate":
|
||||
return self.format_rate(self.rate, self.preference)
|
||||
elif self.display == "elapsed":
|
||||
return self.format_time(self.elapsed, self.preference)
|
||||
elif self.display == "eta":
|
||||
return self.format_time(-self.eta, self.preference)
|
||||
|
||||
@property
|
||||
def bar(self): # the full 80 characters available in any state
|
||||
if options["color"]:
|
||||
color1 = "\033[7m"
|
||||
color2 = "\033[m\033[1m"
|
||||
color_reset = "\033[m"
|
||||
else:
|
||||
color1 = ""
|
||||
color2 = ""
|
||||
color_reset = ""
|
||||
|
||||
if self.state in ("began", "finished", "progressing"):
|
||||
s = self.blurb
|
||||
if options["color"] and "i" in self.styling:
|
||||
left = self.unfilled[: self.full]
|
||||
right = self.unfilled[self.full :]
|
||||
if self.state == "progressing" or "p" in self.styling:
|
||||
s += color1 + left + color2 + right[0]
|
||||
s += color_reset + right[1:]
|
||||
else:
|
||||
s += color1 + left
|
||||
s += color_reset + right
|
||||
|
||||
else:
|
||||
filled = self.filled
|
||||
style = self.styles[self.style]
|
||||
progressing = self.state == "progressing" or "p" in self.styling
|
||||
t = (filled[-1], style["A"][-1], style["B"][-1])
|
||||
if "t" in self.styling and self.length > 0:
|
||||
triple = self.i * 72 * 3 // self.length
|
||||
filled = filled[:-1] + t[triple % 3]
|
||||
elif "d" in self.styling and self.length > 0:
|
||||
double = self.i * 72 * 2 // self.length
|
||||
filled = filled[:-1] + t[double % 2 + 1]
|
||||
elif "a" in self.styling and self.length > 0:
|
||||
filled = filled[:-1] + t[self.full % 2 + 1]
|
||||
|
||||
s += color2
|
||||
if progressing:
|
||||
if "r" in self.styling:
|
||||
s += filled[-self.full - 1 :]
|
||||
elif "P" in self.styling:
|
||||
s += filled[: self.full + 1]
|
||||
else:
|
||||
s += filled[: self.full] + filled[-1]
|
||||
s += color_reset + self.unfilled[self.full + 1 :]
|
||||
else:
|
||||
if "r" in self.styling and self.full > 0:
|
||||
s += filled[-self.full :]
|
||||
else:
|
||||
s += filled[: self.full]
|
||||
s += color_reset + self.unfilled[self.full :]
|
||||
elif self.state == "prepared":
|
||||
s = "Ready..." + " " * 72
|
||||
else:
|
||||
s = "watdafuk" + "?" * 72
|
||||
# assert len(s) == 80 + len(color2) + len(color_reset), (self.full, self.state, repr(s))
|
||||
return s
|
||||
|
||||
@property
|
||||
def row(self):
|
||||
assert self.state in ("began", "progressing"), "invalid state for this"
|
||||
return self.rows - allocated + self.depth
|
||||
|
||||
def __iter__(self):
|
||||
# TODO: check env-var override for disabling and colors!
|
||||
if self.length == 0:
|
||||
return self.it
|
||||
if self.i == 0:
|
||||
self.begin()
|
||||
for x in self.it:
|
||||
self.progress(False)
|
||||
yield x
|
||||
self.progress(True)
|
||||
self.progressing = False
|
||||
self.finish()
|
||||
|
||||
# TODO: is there a case where i actually need this?
|
||||
# def __next__(self):
|
||||
# pass
|
||||
|
||||
def prepare(self):
|
||||
self.state = "prepared"
|
||||
return self
|
||||
|
||||
def begin(self):
|
||||
global allocated
|
||||
self.start = time.time()
|
||||
assert self not in active, "what"
|
||||
active.append(self)
|
||||
self.depth = len(active)
|
||||
|
||||
(_cols, _rows) = get_terminal_size()
|
||||
# propagate terminal size to parents (and self) in case it has since changed:
|
||||
for pb in active:
|
||||
pb.cols, pb.rows = _cols, _rows
|
||||
|
||||
# TODO: this should be handleable in most cases; hide the top bars for a while.
|
||||
# need at least 1 blank line for regular output (or just for the cursor to sit)
|
||||
# i guess just act as though only a subset of `active` are active for a while?
|
||||
assert self.rows - 1 > self.depth, "ran out of space for progress bars"
|
||||
|
||||
s = alloc(self.depth) # TODO: only do this as necessary? (based on allocated)
|
||||
|
||||
# keep track of how many lines are available for progress bars.
|
||||
newly = len(active) - allocated if len(active) > allocated else 0
|
||||
# TODO: allow the user to allocate many at once.
|
||||
allocated += newly
|
||||
self.state = "began"
|
||||
|
||||
s += "\0337"
|
||||
if 1:
|
||||
for pb in active:
|
||||
# TODO: respect 30 fps timers?
|
||||
s += f"\033[{pb.row};1H\033[K{pb.bar}"
|
||||
else: # this isn't as flexible, but it works...?
|
||||
# FIXME: progress() is still broken!
|
||||
for pb in active:
|
||||
s += f"\n\033[K{pb.bar}"
|
||||
# prevent the user from accidentally printing over the progress bars.
|
||||
s += f"\033[1;{self.rows - allocated}r"
|
||||
s += "\0338" # finally, return to the user's (properly adjusted) line and column.
|
||||
self.printer(end=s)
|
||||
# time.sleep(2); exit()
|
||||
|
||||
if DEBUG:
|
||||
print(repr(s).replace("\\x1b", "\\033"))
|
||||
time.sleep(0.5)
|
||||
|
||||
def progress(self, finishing=False):
|
||||
if finishing:
|
||||
self.i += 1
|
||||
self.state = "began"
|
||||
else:
|
||||
self.state = "progressing"
|
||||
|
||||
if not options["patient"] or self.needs_update:
|
||||
now = time.time()
|
||||
if now >= self.previous_update + 0.03333333333333333: # limit to 30 fps
|
||||
self.previous_update = now
|
||||
s = f"\0337\033[{self.row};1H\033[K{self.bar}\0338"
|
||||
self.printer(end=s)
|
||||
|
||||
def finish(self):
|
||||
global allocated
|
||||
self.end = time.time()
|
||||
row = self.row # we need this before we transition states
|
||||
assert self.depth == len(active) and active[-1] is self, "what"
|
||||
active.pop()
|
||||
self.state = "finished"
|
||||
s = ""
|
||||
if len(active) == 0:
|
||||
s += "\0337\033[J\033[r\0338"
|
||||
allocated = 0
|
||||
else:
|
||||
s += f"\0337\033[{row};1H\033[K\0338"
|
||||
self.printer(end=s)
|
||||
|
||||
|
||||
def prog(it, *, file=sys.stderr, pref="*"):
|
||||
def printy(*args, **kwargs):
|
||||
# print(repr(kwargs.get("end", "?").replace("\\x1b", "\\033")))
|
||||
# time.sleep(2.5)
|
||||
print(*args, **kwargs, file=file, flush=options["flush"])
|
||||
# time.sleep(2.5)
|
||||
|
||||
try:
|
||||
l = len(it)
|
||||
except TypeError:
|
||||
raise TypeError("prog(iterator) only works with iterators with a known length")
|
||||
return Prog80(it, l, printer=printy, preference=pref).prepare()
|
||||
41
project.py
Normal file
41
project.py
Normal file
|
|
@ -0,0 +1,41 @@
|
|||
import numpy as np
|
||||
|
||||
|
||||
#def project(a, b): # projects b onto the unit cube, using a as an offset origin
|
||||
# a = np.asanyarray(a)
|
||||
# b = np.asanyarray(b)
|
||||
# d = b - a
|
||||
# if all(np.abs(d) <= 1e-8):
|
||||
# return a
|
||||
# inner = -1 / np.where(np.abs(d) > 1e-8, d, np.where(d >= 0, 1e-8, -1e-8))
|
||||
# small = np.minimum(1 - a, a - 1)
|
||||
# large = np.maximum(0 - a, a - 0)
|
||||
# rescale = np.min(np.maximum(inner * small, inner * large))
|
||||
# assert np.isfinite(rescale), rescale
|
||||
# if rescale < 1:
|
||||
# return a + (rescale - 1e-6) * d
|
||||
# else:
|
||||
# return b
|
||||
|
||||
|
||||
def project(p, a, eps=1e-8):
|
||||
# https://www.desmos.com/calculator/gdcu0ivk0i
|
||||
p = np.asanyarray(p)
|
||||
a = np.asanyarray(a)
|
||||
d = p - a
|
||||
if all(np.abs(d) <= eps):
|
||||
# we might still be inching out of bounds, so just to be sure:
|
||||
a[a <= 0] = 0
|
||||
a[a >= 1] = 1
|
||||
return a
|
||||
|
||||
inner = 1 / np.where(np.abs(d) > eps, d, np.where(d >= 0, eps, -eps))
|
||||
small = -np.abs(p - 1) # np.minimum(1 - p, p - 1)
|
||||
large = np.abs(p) # np.maximum(0 - p, p - 0)
|
||||
rescale = np.min(np.maximum(inner * small, inner * large))
|
||||
|
||||
if rescale <= 1:
|
||||
b = p - max(0, rescale - 1e-8) * d
|
||||
return b
|
||||
else:
|
||||
return a
|
||||
35
randomcube2.py
Normal file
35
randomcube2.py
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
from utils import phi
|
||||
import numpy as np
|
||||
|
||||
|
||||
def another_random_cube(objective, n_trials, n_dim, with_count, seed=None):
|
||||
prng = np.random.default_rng(seed)
|
||||
fopt = None
|
||||
xopt = None
|
||||
for i in range(n_trials):
|
||||
x = prng.uniform(size=n_dim)
|
||||
fx = objective(x)
|
||||
if fopt is None or xopt is None or fx < fopt:
|
||||
fopt = fx
|
||||
xopt = x
|
||||
return (fopt, xopt, n_trials) if with_count else (fopt, xopt)
|
||||
|
||||
|
||||
def quasirandom_cube(objective, n_trials, n_dim, with_count):
|
||||
# http://extremelearning.com.au/unreasonable-effectiveness-of-quasirandom-sequences/
|
||||
magic = phi(n_dim)
|
||||
alpha = np.zeros(n_dim)
|
||||
for i in range(n_dim):
|
||||
alpha[i] = pow(1 / magic, i + 1) % 1
|
||||
xs = np.zeros((n_trials, n_dim))
|
||||
xs[0, :] = 0.5 # first point is always dead center
|
||||
for i in range(1, n_trials):
|
||||
xs[i] = (xs[i - 1] + alpha) % 1
|
||||
|
||||
best_so_far = None
|
||||
for i, x in zip(range(n_trials), xs):
|
||||
fx = objective(x)
|
||||
if best_so_far is None or fx < best_so_far[0]:
|
||||
best_so_far = (fx, x)
|
||||
fopt, xopt = best_so_far
|
||||
return (fopt, xopt, n_trials) if with_count else (fopt, xopt)
|
||||
485
solutions_1680721604.py
Normal file
485
solutions_1680721604.py
Normal file
|
|
@ -0,0 +1,485 @@
|
|||
solutions = {
|
||||
"go_ackley01_on_cube__2": {
|
||||
4.440892098500626e-16: [0.5000000000012516, 0.5000000000009395],
|
||||
},
|
||||
"go_ackley02_on_cube__2": {
|
||||
-200.0: [0.5, 0.5],
|
||||
},
|
||||
"go_ackley03_on_cube__2": {
|
||||
-195.62902826227938: [0.4893347317380225, 0.4943640332293448],
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"go_shubert04_on_cube__2": {
|
||||
-29.016015854390076: [0.14821760462818873, 0.22311218016348278],
|
||||
},
|
||||
"go_sineenvelope_on_cube__2": {
|
||||
0.0: [0.4596149239853102, 0.5030264073322085],
|
||||
},
|
||||
"go_sixhumpcamel_on_cube__2": {
|
||||
-1.0316284534898774: [0.8412902456484702, 0.3984249755872633],
|
||||
},
|
||||
"go_sodp_on_cube__2": {
|
||||
0.0: [0.4999999298520932, 0.4949623459343995],
|
||||
},
|
||||
"go_sphere_on_cube__2": {
|
||||
0.0: [0.5, 0.5],
|
||||
},
|
||||
"go_step2_on_cube__2": {
|
||||
0.5: [0.4962750387667936, 0.4979955154520701],
|
||||
},
|
||||
"go_step_on_cube__2": {
|
||||
0.0: [0.49741917962545434, 0.49952460080066174],
|
||||
},
|
||||
"go_stochastic_on_cube__2": {
|
||||
0.0: [0.5999973641998414, 0.5000000044703483],
|
||||
},
|
||||
"go_stretchedv_on_cube__2": {
|
||||
0.0: [0.23227978992486353, 0.5003909127586422],
|
||||
},
|
||||
"go_styblinskitang_on_cube__2": {
|
||||
-78.33233140754285: [0.2096465967930618, 0.20964659707425057],
|
||||
},
|
||||
"go_testtubeholder_on_cube__2": {
|
||||
-10.872300105622747: [0.4050972481936185, 0.4050972481936185],
|
||||
},
|
||||
"go_threehumpcamel_on_cube__2": {
|
||||
0.0: [0.4999999884956858, 0.500000025238776],
|
||||
},
|
||||
"go_treccani_on_cube__2": {
|
||||
0.0: [0.5000000005096527, 0.49999998234850795],
|
||||
},
|
||||
"go_trefethen_on_cube__2": {
|
||||
-3.3068686474752407: [0.4987798460133582, 0.5105306213572468],
|
||||
},
|
||||
"go_trigonometric01_on_cube__2": {
|
||||
0.0: [0.0, 3.469446951953614e-18],
|
||||
},
|
||||
"go_trigonometric02_on_cube__2": {
|
||||
1.0: [0.5002303123236185, 0.5008999999854868],
|
||||
},
|
||||
"go_tripod_on_cube__2": {
|
||||
0.0: [0.5, 0.25],
|
||||
},
|
||||
"go_ursem01_on_cube__2": {
|
||||
-4.816814063734823: [0.7631157194886242, 0.4999999991819998],
|
||||
},
|
||||
"go_ursem03_on_cube__2": {
|
||||
-3.0: [0.49999999999894734, 0.5064942023957556],
|
||||
},
|
||||
"go_ursem04_on_cube__2": {
|
||||
-1.5: [0.49999999999999994, 0.49999999999999994],
|
||||
},
|
||||
"go_ursemwaves_on_cube__2": {
|
||||
-8.553600000000005: [1.0, 1.0],
|
||||
},
|
||||
"go_ventersobiezcczanskisobieski_on_cube__2": {
|
||||
-400.0: [0.4999999999691136, 0.5000000000649738],
|
||||
},
|
||||
"go_vincent_on_cube__2": {
|
||||
-2.0: [0.7647463840583177, 0.7647463860955502],
|
||||
},
|
||||
"go_wavy_on_cube__2": {
|
||||
0.0: [0.5990111627305801, 0.7166822395492828],
|
||||
},
|
||||
"go_wayburnseader01_on_cube__2": {
|
||||
3.865418435582958e-29: [0.5931930653112957, 0.701088291102508],
|
||||
},
|
||||
"go_wayburnseader02_on_cube__2": {
|
||||
1.1093356479670479e-27: [0.5002001389746917, 0.5009999999999929],
|
||||
},
|
||||
"go_weierstrass_on_cube__2": {
|
||||
0.0: [0.48388671875000094, 0.4838867187500006],
|
||||
},
|
||||
"go_whitley_on_cube__2": {
|
||||
0.0: [0.5004834467831857, 0.39297526505649716],
|
||||
},
|
||||
"go_xinsheyang01_on_cube__2": {
|
||||
0.0: [0.49919291830153395, 0.49919291830153395],
|
||||
},
|
||||
"go_xinsheyang02_on_cube__2": {
|
||||
0.0: [0.5000000000002875, 0.4999999999999388],
|
||||
},
|
||||
"go_xinsheyang03_on_cube__2": {
|
||||
-1.0: [0.4999999986162714, 0.5000000059276509],
|
||||
},
|
||||
"go_xinsheyang04_on_cube__2": {
|
||||
-1.0: [0.49999999999999967, 0.4999999892774735],
|
||||
},
|
||||
"go_yaoliu04_on_cube__2": {
|
||||
0.0: [0.5000000035524891, 0.5000000035524891],
|
||||
},
|
||||
"go_yaoliu09_on_cube__2": {
|
||||
0.0: [0.4999999999409883, 0.4999999999993396],
|
||||
},
|
||||
"go_zacharov_on_cube__2": {
|
||||
0.0: [0.3333333333333333, 0.3333333333333333],
|
||||
},
|
||||
"go_zerosum_on_cube__2": {
|
||||
0.0: [0.24019956025564648, 0.7597811926967968],
|
||||
},
|
||||
"go_zettl_on_cube__2": {
|
||||
-0.0037912372204688986: [0.3313423297689443, 0.3329125985152123],
|
||||
},
|
||||
"go_zimmerman_on_cube__2": {
|
||||
8.881784197001252e-16: [0.024979873370145907, 0.05681725730945934],
|
||||
},
|
||||
"go_zirilli_on_cube__2": {
|
||||
-0.3523860738000365: [0.44782232329859883, 0.5004437701361232],
|
||||
},
|
||||
}
|
||||
242
solutions_1680730206.py
Normal file
242
solutions_1680730206.py
Normal file
|
|
@ -0,0 +1,242 @@
|
|||
solutions = {
|
||||
"go_ackley01_on_cube__3": {
|
||||
4.440892098500626e-16: [0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_alpine01_on_cube__3": {
|
||||
0.0: [0.49999999999999956, 0.5000000000000002, 0.5000000000000012],
|
||||
},
|
||||
"go_alpine02_on_cube__3": {
|
||||
-17.212450994658354: [0.7917052684142234, 0.7917052676939672, 0.4815842307193425],
|
||||
},
|
||||
"go_amgm_on_cube__3": {
|
||||
0.0: [0.8245598718496522, 0.8245598800645785, 0.8245598774911281],
|
||||
},
|
||||
"go_biggsexp03_on_cube__3": {
|
||||
0.0: [0.05, 0.5, 0.24999999999999997],
|
||||
},
|
||||
"go_boxbetts_on_cube__3": {
|
||||
0.0: [0.3333333333333335, 0.454545454545454, 0.3333333333333334],
|
||||
},
|
||||
"go_brown_on_cube__3": {
|
||||
0.0: [0.20000001930221908, 0.19999995251347022, 0.19999929921954904],
|
||||
},
|
||||
"go_cigar_on_cube__3": {
|
||||
0.0: [0.5950381095427524, 0.5950381095427524, 0.5950381095427524],
|
||||
},
|
||||
"go_cosinemixture_on_cube__3": {
|
||||
-2.7: [3.659195132255668e-06, 1.5076273613395665e-11, 9.543896963247637e-16],
|
||||
},
|
||||
"go_csendes_on_cube__3": {
|
||||
1.6005299190889717e-95: [0.5000002614943283, 0.49999979737945727, 0.4999991373845789],
|
||||
},
|
||||
"go_deb01_on_cube__3": {
|
||||
-1.0: [0.049649550146846286, 0.05000289851470013, 0.05006420897463226],
|
||||
},
|
||||
"go_deb03_on_cube__3": {
|
||||
-1.0: [0.8488416211971612, 0.8488416208778257, 0.8488416211316454],
|
||||
},
|
||||
"go_deceptive_on_cube__3": {
|
||||
-1.0: [0.7499999999714193, 0.7499999999714193, 0.7499999999714193],
|
||||
},
|
||||
"go_deflectedcorrugatedspring_on_cube__3": {
|
||||
-1.0: [0.5000000007403892, 0.5000000012200565, 0.49999999741084855],
|
||||
},
|
||||
"go_dixonprice_on_cube__3": {
|
||||
1.1931521191467804e-29: [0.55, 0.5353553390593274, 0.470269822124932],
|
||||
},
|
||||
"go_eckerle4_on_cube__3": {
|
||||
0.0014635887487274677: [0.08717603638237367, 0.22786082165419722, 0.7482996827664747],
|
||||
},
|
||||
"go_eggholder_on_cube__3": {
|
||||
-1888.321390893595: [0.970181519670115, 0.9266961632465174, 0.9411871032839637],
|
||||
},
|
||||
"go_exponential_on_cube__3": {
|
||||
-1.0: [0.5003591836784682, 0.500348485292889, 0.5001780115493856],
|
||||
},
|
||||
"go_griewank_on_cube__3": {
|
||||
0.0: [0.5000000000113756, 0.5000000000565452, 0.4999999999274707],
|
||||
},
|
||||
"go_gulf_on_cube__3": {
|
||||
5.915132712320822e-31: [0.1348208391857785, 0.5, 0.03],
|
||||
},
|
||||
"go_hartmann3_on_cube__3": {
|
||||
-3.862782147820756: [0.11461434502192798, 0.5556488498282574, 0.8525469537098168],
|
||||
},
|
||||
"go_helicalvalley_on_cube__3": {
|
||||
0.0: [0.5497725561310041, 0.5048351824540244, 0.5076838383564906],
|
||||
},
|
||||
"go_infinity_on_cube__3": {
|
||||
1.6005299190889717e-95: [0.4999999715839852, 0.5000000247025426, 0.5000000016223345],
|
||||
},
|
||||
"go_katsuura_on_cube__3": {
|
||||
1.0: [5.679137715652871e-14, 6.262039498050598e-13, 5.512708345367656e-13],
|
||||
},
|
||||
"go_meyer_on_cube__3": {
|
||||
3718453685.016923: [0.9999999999999996, 0.9999999999999999, 3.122502256758253e-17],
|
||||
},
|
||||
"go_mishra01_on_cube__3": {
|
||||
1.9999999952274108: [1.0, 0.9999999999999999, 0.009829734043015896],
|
||||
},
|
||||
"go_mishra02_on_cube__3": {
|
||||
1.9999999952274108: [0.9999999999999993, 1.0, 0.9999999999999998],
|
||||
},
|
||||
"go_mishra07_on_cube__3": {
|
||||
0.0: [0.5240408900442571, 0.5079805506782411, 0.6569953605954606],
|
||||
},
|
||||
"go_mishra09_on_cube__3": {
|
||||
0.0: [0.5537573946909716, 0.6002010732109465, 0.6409128191691206],
|
||||
},
|
||||
"go_mishra11_on_cube__3": {
|
||||
0.0: [0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_multimodal_on_cube__3": {
|
||||
0.0: [0.5022237648504779, 0.49970052009979826, 0.5005325922218133],
|
||||
},
|
||||
"go_needleeye_on_cube__3": {
|
||||
1.0: [0.39961640034876017, 0.39961640034876017, 0.39961640034875184],
|
||||
},
|
||||
"go_penalty01_on_cube__3": {
|
||||
1.570544771786639e-32: [0.49, 0.49, 0.49000000000000005],
|
||||
},
|
||||
"go_penalty02_on_cube__3": {
|
||||
1.3497838043956716e-31: [0.5133271718479692, 0.5062564318780072, 0.5146267812010901],
|
||||
},
|
||||
"go_permfunction01_on_cube__3": {
|
||||
9.860761315262648e-31: [0.640860903825709, 0.01929983613560043, 0.6044294128164022],
|
||||
},
|
||||
"go_permfunction02_on_cube__3": {
|
||||
2.2186712959340957e-30: [0.5714578794300258, 0.47532015480578127, 0.49894707267621674],
|
||||
},
|
||||
"go_pinter_on_cube__3": {
|
||||
0.0: [0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_plateau_on_cube__3": {
|
||||
30.0: [0.4046704030611626, 0.4976189802302213, 0.3543757372858257],
|
||||
},
|
||||
"go_qing_on_cube__3": {
|
||||
5.3707622579709536e-27: [0.501, 0.4985857864376269, 0.5017320508075689],
|
||||
},
|
||||
"go_quadratic_on_cube__3": {
|
||||
-3873.7241821862717: [0.5096940087870239, 0.5242566945952262, 0.29760349466562863],
|
||||
},
|
||||
"go_quintic_on_cube__3": {
|
||||
0.0: [0.44999999999999996, 0.60941162109375, 0.45],
|
||||
},
|
||||
"go_rana_on_cube__3": {
|
||||
-998.9595261322422: [4.150441195255471e-09, 0.0009301076431260141, 0.0019640025818469242],
|
||||
},
|
||||
"go_rastrigin_on_cube__3": {
|
||||
0.0: [0.4999999997679357, 0.49999999993791394, 0.4999999999832573],
|
||||
},
|
||||
"go_ratkowsky02_on_cube__3": {
|
||||
8.056522933811229: [0.3289191890500581, 0.4706837418911737, 0.1170595919922324],
|
||||
},
|
||||
"go_rosenbrock_on_cube__3": {
|
||||
5.679798517591285e-28: [0.4991978266453091, 0.5000882128754777, 0.4938730426361304],
|
||||
},
|
||||
"go_salomon_on_cube__3": {
|
||||
0.0: [0.49999999999999994, 0.5000000000000001, 0.5],
|
||||
},
|
||||
"go_sargan_on_cube__3": {
|
||||
0.0: [0.4890611845448031, 0.5152469505181654, 0.4890831221795244],
|
||||
},
|
||||
"go_schwefel01_on_cube__3": {
|
||||
0.0: [0.5000000000000001, 0.5, 0.5],
|
||||
},
|
||||
"go_schwefel02_on_cube__3": {
|
||||
0.0: [0.515625, 0.12989525470348404, 0.5],
|
||||
},
|
||||
"go_schwefel04_on_cube__3": {
|
||||
0.0: [0.3928252864281473, 0.3928252864281473, 0.39282528642814735],
|
||||
},
|
||||
"go_schwefel20_on_cube__3": {
|
||||
0.0: [0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_schwefel21_on_cube__3": {
|
||||
0.0: [0.5, 0.49999999999999994, 0.5],
|
||||
},
|
||||
"go_schwefel22_on_cube__3": {
|
||||
0.0: [0.49999998835820214, 0.49999999999999994, 0.49999999999999956],
|
||||
},
|
||||
"go_schwefel26_on_cube__3": {
|
||||
-8.173014975909609e-07: [0.9204820116767811, 0.9204820113041389, 0.9204820116767811],
|
||||
},
|
||||
"go_shubert01_on_cube__3": {
|
||||
-2709.093505572829: [0.44022055084214423, 0.7741432103584681, 0.7429028439073201],
|
||||
},
|
||||
"go_shubert03_on_cube__3": {
|
||||
-36.09374832650144: [0.7895348359272809, 0.9397543606957739, 0.7896478307580592],
|
||||
},
|
||||
"go_shubert04_on_cube__3": {
|
||||
-43.524023781585115: [0.6640013808576284, 0.14582468028277795, 0.7741432091030084],
|
||||
},
|
||||
"go_sineenvelope_on_cube__3": {
|
||||
0.0: [0.5000009025942251, 0.500000893069357, 0.5000003811955986],
|
||||
},
|
||||
"go_sodp_on_cube__3": {
|
||||
0.0: [0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_sphere_on_cube__3": {
|
||||
0.0: [0.4833130709800822, 0.4833130709800822, 0.4833130709800822],
|
||||
},
|
||||
"go_step2_on_cube__3": {
|
||||
0.75: [0.5016868928445066, 0.5061703854573943, 0.5026760176613241],
|
||||
},
|
||||
"go_step_on_cube__3": {
|
||||
0.0: [0.5081581595102049, 0.49675129077493513, 0.5079476141696376],
|
||||
},
|
||||
"go_stochastic_on_cube__3": {
|
||||
1.5938474175637294e-22: [0.5333327585272383, 0.5333327585272383, 0.5499994072312144],
|
||||
},
|
||||
"go_stretchedv_on_cube__3": {
|
||||
0.0: [0.2500000000000284, 0.24999999999997158, 0.2500000000000284],
|
||||
},
|
||||
"go_styblinskitang_on_cube__3": {
|
||||
-117.49849711131428: [0.2096465969993631, 0.2096465972561869, 0.20964659614351272],
|
||||
},
|
||||
"go_trigonometric01_on_cube__3": {
|
||||
0.0: [1.3877787807814457e-17, 6.938893903907228e-18, 0.0],
|
||||
},
|
||||
"go_trigonometric02_on_cube__3": {
|
||||
1.0: [0.5008999999918231, 0.5008999999997376, 0.5008999999924959],
|
||||
},
|
||||
"go_vincent_on_cube__3": {
|
||||
-3.0: [0.6708629450200313, 0.7647463865513029, 0.7647158663337039],
|
||||
},
|
||||
"go_wavy_on_cube__3": {
|
||||
0.0: [0.499999999997531, 0.5000000000621029, 0.5000000003640678],
|
||||
},
|
||||
"go_weierstrass_on_cube__3": {
|
||||
0.0: [0.4999961853027344, 0.49999999999999967, 0.49999999999999967],
|
||||
},
|
||||
"go_whitley_on_cube__3": {
|
||||
0.0: [0.0057694254008727915, 0.9614112930973773, 0.13985557238051557],
|
||||
},
|
||||
"go_wolfe_on_cube__3": {
|
||||
0.0: [0.0, 0.0, 0.0],
|
||||
},
|
||||
"go_xinsheyang01_on_cube__3": {
|
||||
0.0: [0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_xinsheyang02_on_cube__3": {
|
||||
0.0: [0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_xinsheyang03_on_cube__3": {
|
||||
-1.0: [0.49999999997731115, 0.5000000000034495, 0.49999999998913747],
|
||||
},
|
||||
"go_xinsheyang04_on_cube__3": {
|
||||
-1.0: [0.3429203670388802, 0.6570796326524454, 0.3429203673085393],
|
||||
},
|
||||
"go_yaoliu04_on_cube__3": {
|
||||
0.0: [0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_yaoliu09_on_cube__3": {
|
||||
0.0: [0.5000000000269273, 0.4999999999491878, 0.49999999989083205],
|
||||
},
|
||||
"go_zacharov_on_cube__3": {
|
||||
0.0: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333],
|
||||
},
|
||||
"go_zerosum_on_cube__3": {
|
||||
0.0: [0.4999999775192155, 0.5, 0.5],
|
||||
},
|
||||
}
|
||||
251
solutions_1680975848.py
Normal file
251
solutions_1680975848.py
Normal file
|
|
@ -0,0 +1,251 @@
|
|||
solutions = {
|
||||
"go_ackley01_on_cube__4": {
|
||||
4.440892098500626e-16: [0.5, 0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_alpine01_on_cube__4": {
|
||||
0.0: [0.4952297200093428, 0.4952297200093428, 0.4952297200093428, 0.4952297200093428],
|
||||
},
|
||||
"go_alpine02_on_cube__4": {
|
||||
-48.33482032244272: [0.7917052678377633, 0.7917052674957755, 0.48158423270992656, 0.7917052673588358],
|
||||
},
|
||||
"go_amgm_on_cube__4": {
|
||||
0.0: [0.012306988086844196, 0.012306988086844196, 0.012306988086844196, 0.012306988086844196],
|
||||
},
|
||||
"go_biggsexp04_on_cube__4": {
|
||||
0.0: [0.04996029633691984, 0.04996029633691984, 0.24980148168459912, 0.24980148168459912],
|
||||
},
|
||||
"go_brown_on_cube__4": {
|
||||
0.0: [0.19999999999999812, 0.2000000000000008, 0.20000000000000118, 0.20000000000000062],
|
||||
},
|
||||
"go_cigar_on_cube__4": {
|
||||
0.0: [0.5, 0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_colville_on_cube__4": {
|
||||
0.0: [0.5499999999999998, 0.5500000000000002, 0.5500000000000005, 0.5500000000000003],
|
||||
},
|
||||
"go_corana_on_cube__4": {
|
||||
0.0: [0.16514354301841233, 0.5037790090306642, 0.5812655986635484, 0.9965260213661287],
|
||||
},
|
||||
"go_cosinemixture_on_cube__4": {
|
||||
-3.6: [1.0, 1.0, 1.0, 1.0],
|
||||
},
|
||||
"go_csendes_on_cube__4": {
|
||||
2.1340398921186287e-95: [0.49999999999999994, 0.49999999999999994, 0.49999999999999994, 0.49999999999999994],
|
||||
},
|
||||
"go_deb01_on_cube__4": {
|
||||
-1.0: [0.24998474166810553, 0.2842827054985171, 0.250000000494007, 0.24517076362726262],
|
||||
},
|
||||
"go_deb03_on_cube__4": {
|
||||
-1.0: [0.6814202220739899, 0.6814202214962974, 0.6814202217045495, 0.6814202224584512],
|
||||
},
|
||||
"go_deceptive_on_cube__4": {
|
||||
-1.0: [0.12504729371493664, 0.4, 0.6, 0.8],
|
||||
},
|
||||
"go_deflectedcorrugatedspring_on_cube__4": {
|
||||
-1.0: [0.5293413782227367, 0.4718024985689048, 0.434224546444892, 0.4705267221389782],
|
||||
},
|
||||
"go_devilliersglasser01_on_cube__4": {
|
||||
1.1781353629549932e-24: [0.643599554903393, 0.003164412438938837, 0.021191388101335643, 0.7694639665621702],
|
||||
},
|
||||
"go_dixonprice_on_cube__4": {
|
||||
2.0362472116017367e-29: [0.55, 0.5353553390593274, 0.529730177875068, 0.5272626933166314],
|
||||
},
|
||||
"go_eggholder_on_cube__4": {
|
||||
-2808.1847921823232: [0.9711233598527298, 0.9228135061716958, 0.936490209590872, 0.9652220933524724],
|
||||
},
|
||||
"go_exponential_on_cube__4": {
|
||||
-1.0: [0.5000000247218881, 0.49999998767538123, 0.49999998303134136, 0.5000000097075422],
|
||||
},
|
||||
"go_gear_on_cube__4": {
|
||||
2.7008571488865134e-12: [0.09051675206121865, 0.15129928153389963, 0.6492028326465633, 0.7748019437203442],
|
||||
},
|
||||
"go_griewank_on_cube__4": {
|
||||
0.0: [0.499999999976616, 0.4999999999392863, 0.49999999996604166, 0.4999999988626738],
|
||||
},
|
||||
"go_infinity_on_cube__4": {
|
||||
2.1340398921186287e-95: [0.49804687500000006, 0.49804687500000006, 0.5019531249999999, 0.49804687500000006],
|
||||
},
|
||||
"go_katsuura_on_cube__4": {
|
||||
1.0: [2.646355357072139e-13, 3.995866137973536e-13, 8.982398158607907e-14, 5.381459167175251e-14],
|
||||
},
|
||||
"go_kowalik_on_cube__4": {
|
||||
0.00030748598780560557: [0.5308380971332559, 0.6379070854704445, 0.7895950761936558, 0.5868729032688482],
|
||||
},
|
||||
"go_mielecantrell_on_cube__4": {
|
||||
0.0: [0.5, 1.0, 1.0, 1.0],
|
||||
},
|
||||
"go_mishra01_on_cube__4": {
|
||||
1.9999999928411163: [0.9999999999999993, 0.9999999999999992, 0.9999999999999999, 0.12006013934268739],
|
||||
},
|
||||
"go_mishra02_on_cube__4": {
|
||||
1.9999999928411163: [0.9907412835351275, 0.9172213539800724, 0.9999999999999996, 0.8792695446455677],
|
||||
},
|
||||
"go_mishra07_on_cube__4": {
|
||||
0.0: [0.8727569465722927, 0.9226986283327735, 0.09557227422564515, 0.1391780495360423],
|
||||
},
|
||||
"go_mishra11_on_cube__4": {
|
||||
0.0: [0.49262618729194974, 0.31782158010515016, 0.5494294194887934, 0.43243988862988303],
|
||||
},
|
||||
"go_multimodal_on_cube__4": {
|
||||
0.0: [0.5000000000004408, 0.5000000000000095, 0.6555568806051131, 0.5000000000005456],
|
||||
},
|
||||
"go_needleeye_on_cube__4": {
|
||||
1.0: [0.4999999988366891, 0.4999999979965216, 0.4999999988366891, 0.6747993599890604],
|
||||
},
|
||||
"go_penalty01_on_cube__4": {
|
||||
1.570544771786639e-32: [0.49000000000000216, 0.4900000000045488, 0.49000000000434424, 0.49000000000259214],
|
||||
},
|
||||
"go_penalty02_on_cube__4": {
|
||||
1.3497838043956716e-31: [0.51, 0.51, 0.51, 0.51],
|
||||
},
|
||||
"go_permfunction01_on_cube__4": {
|
||||
0.022942589411403945: [0.5076571662271249, 0.8575457353508119, 0.5843426595258812, 0.8361085666110741],
|
||||
},
|
||||
"go_permfunction02_on_cube__4": {
|
||||
4.525357590326688e-30: [0.5555598137337187, 0.4719642376742956, 0.49939744649300466, 0.4796132767078549],
|
||||
},
|
||||
"go_pinter_on_cube__4": {
|
||||
0.0: [0.5, 0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_plateau_on_cube__4": {
|
||||
30.0: [0.4932734474316645, 0.4932734474316645, 0.4932734474316645, 0.5508581326238648],
|
||||
},
|
||||
"go_powell_on_cube__4": {
|
||||
0.0: [0.44444509150710204, 0.4444437719243386, 0.4444482661673988, 0.4444482661564514],
|
||||
},
|
||||
"go_powersum_on_cube__4": {
|
||||
0.0: [0.6844559265801914, 0.30950792678221917, 0.3343492057195142, 0.6719797392682317],
|
||||
},
|
||||
"go_qing_on_cube__4": {
|
||||
5.3707622579709536e-27: [0.49900000000000005, 0.4985857864376269, 0.49826794919243106, 0.498],
|
||||
},
|
||||
"go_quadratic_on_cube__4": {
|
||||
-3873.7241821862717: [0.5096874319451075, 0.5242667998367262, 0.8136976152333063, 0.18518089676316943],
|
||||
},
|
||||
"go_quintic_on_cube__4": {
|
||||
0.0: [0.5354949699058432, 0.3458348614376502, 0.389003675938885, 0.5867971950589506],
|
||||
},
|
||||
"go_rana_on_cube__4": {
|
||||
-1495.900483081144: [0.19966236719231722, 1.0, 0.001954518905221303, 0.0030862541309490607],
|
||||
},
|
||||
"go_rastrigin_on_cube__4": {
|
||||
0.0: [0.49999999999969436, 0.5000002447154946, 0.549866189132194, 0.5000000000019278],
|
||||
},
|
||||
"go_ratkowsky01_on_cube__4": {
|
||||
8786.404907963095: [0.6996415124695768, 0.22511186067152653, 0.253209795946327, 0.19987261064121364],
|
||||
},
|
||||
"go_rosenbrock_on_cube__4": {
|
||||
1.8427790745962836e-27: [0.5166666666666667, 0.5166666666666668, 0.5166666666666672, 0.5166666666666674],
|
||||
},
|
||||
"go_salomon_on_cube__4": {
|
||||
0.0: [0.4990867028725108, 0.49824612382646793, 0.20357400849962232, 0.5035731475872616],
|
||||
},
|
||||
"go_sargan_on_cube__4": {
|
||||
-35200.00000000005: [0.8999999985946622, 3.469446951953614e-17, 1.0, 0.10000000181447662],
|
||||
},
|
||||
"go_schwefel01_on_cube__4": {
|
||||
0.0: [0.50048828125, 0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_schwefel02_on_cube__4": {
|
||||
0.0: [0.5000005284691311, 0.4999980379339887, 0.50000312482585, 0.4999975956916072],
|
||||
},
|
||||
"go_schwefel04_on_cube__4": {
|
||||
0.0: [0.1, 0.1, 0.1, 0.1],
|
||||
},
|
||||
"go_schwefel20_on_cube__4": {
|
||||
0.0: [0.49999999999999983, 0.5000000000000002, 0.5, 0.49999999995473077],
|
||||
},
|
||||
"go_schwefel21_on_cube__4": {
|
||||
0.0: [0.4999999997893743, 0.49999999985716703, 0.5000000002106256, 0.49999999978937426],
|
||||
},
|
||||
"go_schwefel22_on_cube__4": {
|
||||
0.0: [0.5, 0.5000000000000001, 0.5, 0.5],
|
||||
},
|
||||
"go_schwefel26_on_cube__4": {
|
||||
-1.089735178538831e-06: [0.9305937532533836, 0.9209687464160179, 0.19747734132307387, 0.9209687456292082],
|
||||
},
|
||||
"go_shekel05_on_cube__4": {
|
||||
-10.15319967905823: [0.4000037689925856, 0.4000130672256758, 0.40000279482137596, 0.4000143020086375],
|
||||
},
|
||||
"go_shekel07_on_cube__4": {
|
||||
-10.402940566818666: [0.40005211433664734, 0.4000732914719807, 0.3999500655069954, 0.3999652204984656],
|
||||
},
|
||||
"go_shekel10_on_cube__4": {
|
||||
-10.536409816692046: [0.4000746531076067, 0.400059293286648, 0.39996633980382557, 0.3999509799996026],
|
||||
},
|
||||
"go_shubert01_on_cube__4": {
|
||||
-39303.550054363215: [0.7741440434102077, 0.7741556333290653, 0.7429083697053124, 0.7741396753978015],
|
||||
},
|
||||
"go_shubert03_on_cube__4": {
|
||||
-48.12499776866859: [0.7895897234617342, 0.7895897234965026, 0.7895897234965026, 0.7895897234965026],
|
||||
},
|
||||
"go_shubert04_on_cube__4": {
|
||||
-58.03203170878015: [0.1458246797794856, 0.14582467977948557, 0.14582467959087073, 0.1458246797794856],
|
||||
},
|
||||
"go_sineenvelope_on_cube__4": {
|
||||
0.0: [0.5000000000049838, 0.5000000000180229, 0.4999999999815214, 0.5000000000104797],
|
||||
},
|
||||
"go_sodp_on_cube__4": {
|
||||
0.0: [0.5000003501207233, 0.49934332155107486, 0.5009018689609204, 0.5232120945186667],
|
||||
},
|
||||
"go_sphere_on_cube__4": {
|
||||
0.0: [0.5, 0.5000000000000009, 0.5, 0.5],
|
||||
},
|
||||
"go_step2_on_cube__4": {
|
||||
1.0: [0.5012432021330329, 0.4951491010577182, 0.5059914251045634, 0.502325162192821],
|
||||
},
|
||||
"go_step_on_cube__4": {
|
||||
0.0: [0.4996842002116297, 0.49694494375720955, 0.503280368273251, 0.4951664506412295],
|
||||
},
|
||||
"go_stochastic_on_cube__4": {
|
||||
9.300834900727792e-22: [0.6, 0.3, 0.33684113309205904, 0.5249999995343387],
|
||||
},
|
||||
"go_stretchedv_on_cube__4": {
|
||||
0.0: [0.5, 0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_styblinskitang_on_cube__4": {
|
||||
-156.6646628150857: [0.022117851576171424, 0.022117851595805146, 0.022117851595805146, 0.022117851576171424],
|
||||
},
|
||||
"go_trigonometric01_on_cube__4": {
|
||||
0.0: [0.0, 0.0, 6.938893903907228e-18, 1.734723475976807e-17],
|
||||
},
|
||||
"go_trigonometric02_on_cube__4": {
|
||||
1.0: [0.5009000000000405, 0.5009000000002075, 0.5009000000004369, 0.5008998066497936],
|
||||
},
|
||||
"go_vincent_on_cube__4": {
|
||||
-4.0: [0.3960212450924436, 0.39602124595914034, 0.39602124518164733, 0.39602124530774435],
|
||||
},
|
||||
"go_wavy_on_cube__4": {
|
||||
0.0: [0.49999999994282107, 0.4894464797508772, 0.49999999985754334, 0.5000000001497444],
|
||||
},
|
||||
"go_weierstrass_on_cube__4": {
|
||||
0.0: [0.5000152588472697, 0.5000000000000009, 0.5, 0.5],
|
||||
},
|
||||
"go_whitley_on_cube__4": {
|
||||
0.0: [0.5000697309500134, 0.5000697309500134, 0.5000697309500134, 0.5000697309500134],
|
||||
},
|
||||
"go_xinsheyang01_on_cube__4": {
|
||||
0.0: [0.5000638843834544, 0.499956010089577, 0.5008984704522029, 0.5048778232729327],
|
||||
},
|
||||
"go_xinsheyang02_on_cube__4": {
|
||||
0.0: [0.5, 0.5, 0.5, 0.49999999999999994],
|
||||
},
|
||||
"go_xinsheyang03_on_cube__4": {
|
||||
-1.0: [0.3750000212082758, 1.8722817393679314e-10, 0.9999999995579297, 1.1439725125517164e-08],
|
||||
},
|
||||
"go_xinsheyang04_on_cube__4": {
|
||||
-1.0: [0.344534632972428, 0.3417994205644585, 0.35607999521170186, 0.34602083862333655],
|
||||
},
|
||||
"go_yaoliu04_on_cube__4": {
|
||||
0.0: [0.5, 0.5, 0.5, 0.5],
|
||||
},
|
||||
"go_yaoliu09_on_cube__4": {
|
||||
0.0: [0.5000000008566511, 0.5000000000634754, 0.500000000363694, 0.49999999975360193],
|
||||
},
|
||||
"go_zacharov_on_cube__4": {
|
||||
0.0: [0.4316875852820895, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333],
|
||||
},
|
||||
"go_zerosum_on_cube__4": {
|
||||
0.0: [0.5, 0.5, 0.5, 0.5],
|
||||
},
|
||||
}
|
||||
263
utils.py
Normal file
263
utils.py
Normal file
|
|
@ -0,0 +1,263 @@
|
|||
from dataclasses import dataclass
|
||||
import math
|
||||
|
||||
feps = 2.0**-23.0
|
||||
tiniest = 2.0**-1022.0
|
||||
check = object() # secret "key" to pass to wrap_untrustworthy to extract feval_count
|
||||
final = object() # secret "key" to pass to wrap_untrustworthy to extract results
|
||||
|
||||
color_factory = lambda color: lambda s: print(f"\033[{color}m{s}\033[m")
|
||||
m1 = color_factory(1)
|
||||
m30 = color_factory(30)
|
||||
m31 = color_factory(31)
|
||||
m32 = color_factory(32)
|
||||
m33 = color_factory(33)
|
||||
m34 = color_factory(34)
|
||||
m35 = color_factory(35)
|
||||
m36 = color_factory(36)
|
||||
m37 = color_factory(37)
|
||||
m90 = color_factory(90)
|
||||
m91 = color_factory(91)
|
||||
m92 = color_factory(92)
|
||||
m93 = color_factory(93)
|
||||
m94 = color_factory(94)
|
||||
m95 = color_factory(95)
|
||||
m96 = color_factory(96)
|
||||
m97 = color_factory(97)
|
||||
|
||||
|
||||
class ExhaustedTrialsError(Exception):
|
||||
pass
|
||||
|
||||
|
||||
def scalar_softplus(x):
|
||||
if x >= 33.276435657655455:
|
||||
return float(x)
|
||||
elif x <= -745.13330078125:
|
||||
return 0.0
|
||||
else:
|
||||
return math.log1p(math.exp(x))
|
||||
|
||||
|
||||
def phi(d):
|
||||
# phi(1) = golden ratio
|
||||
# phi(2) = plastic constant
|
||||
# phi(3) = the positive real root of x**4-x-1
|
||||
x = 2.0
|
||||
for i in range(30 if d == 1 else max(10, 28 - d)):
|
||||
x = pow(1 + x, 1 / (d + 1))
|
||||
return x
|
||||
|
||||
|
||||
def wrap_untrustworthy(
|
||||
objective, n_trials, *, raising=False, bounding=None, softplus=False, eps=0.0
|
||||
):
|
||||
# also handles bounding now, so it may be used for other purposes as well. whoops.
|
||||
feval_count = 0
|
||||
best_so_far = None
|
||||
|
||||
def _objective(x):
|
||||
nonlocal feval_count, best_so_far
|
||||
if x is check:
|
||||
return feval_count
|
||||
if x is final:
|
||||
assert best_so_far is not None
|
||||
fopt, xopt = best_so_far
|
||||
return fopt, xopt, feval_count
|
||||
if raising and feval_count >= n_trials:
|
||||
raise ExhaustedTrialsError()
|
||||
if bounding is not None:
|
||||
x = do_bounding(x, bounding)
|
||||
fx = objective(x)
|
||||
feval_count += 1
|
||||
if n_trials is None or feval_count <= n_trials:
|
||||
if best_so_far is None or fx < best_so_far[0]:
|
||||
best_so_far = (fx, x.copy())
|
||||
return scalar_softplus(fx) + eps if softplus else fx
|
||||
|
||||
return _objective
|
||||
|
||||
|
||||
@dataclass
|
||||
class KeyData:
|
||||
key: str
|
||||
d: int
|
||||
n: int
|
||||
opt: str
|
||||
obj: str
|
||||
run: int
|
||||
|
||||
|
||||
def decode_key(key, _filtering=False):
|
||||
# example key:
|
||||
# COWrap_d03_n130_freelunch_qpso_ps16_cube_go_amgm_on_cube
|
||||
k, _, run = key.partition("[")
|
||||
run, _, _ = run.partition("]")
|
||||
k = k.removeprefix("COWrap_")
|
||||
d, _, k = k.partition("_")
|
||||
n, _, k = k.partition("_")
|
||||
opt, _, k = k.partition("_cube_")
|
||||
obj, _, k = k.partition("_on_cube")
|
||||
if not obj:
|
||||
if opt.endswith("_on_cube"):
|
||||
return # fcmaes_biteopt was missing the _cube in its name for a while
|
||||
if _filtering and obj in ("go_stochastic", "go_xinsheyang01"):
|
||||
return # these are random
|
||||
assert not k, k
|
||||
d = int(d.removeprefix("d"), 10)
|
||||
n = int(n.removeprefix("n"), 10)
|
||||
run = int(run, 10)
|
||||
return KeyData(key=key, d=d, n=n, opt=opt, obj=obj, run=run)
|
||||
|
||||
|
||||
class AcquireForWriting:
|
||||
"""
|
||||
A context manager that allows for very crude file-locking-like
|
||||
functionality when the FileLock module is missing.
|
||||
"""
|
||||
|
||||
def __init__(self, filepath, usingfilelock=None):
|
||||
from pathlib import Path
|
||||
|
||||
self.filepath = Path(filepath)
|
||||
if usingfilelock is None:
|
||||
try:
|
||||
from filelock import FileLock
|
||||
except ModuleNotFoundError:
|
||||
self._locking = False
|
||||
self.lock = None
|
||||
else:
|
||||
self._locking = True
|
||||
self.lock = FileLock(self._altpath)
|
||||
elif usingfilelock:
|
||||
from filelock import FileLock
|
||||
|
||||
self._locking = True
|
||||
self.lock = FileLock(self._altpath)
|
||||
else:
|
||||
self._locking = False
|
||||
self.lock = None
|
||||
|
||||
@property
|
||||
def _altpath(self):
|
||||
suffix = ".lock" if self._locking else "~"
|
||||
return self.filepath.with_suffix(self.filepath.suffix + suffix)
|
||||
|
||||
def __enter__(self):
|
||||
if self._locking:
|
||||
self.lock.__enter__()
|
||||
else:
|
||||
from time import sleep
|
||||
|
||||
for _ in range(3):
|
||||
if self._altpath.exists():
|
||||
sleep(1)
|
||||
assert not self._altpath.exists(), f"file is locked: {self.filepath}"
|
||||
if not self._locking:
|
||||
self._altpath.write_bytes(b"")
|
||||
return self.filepath if self._locking else self._altpath
|
||||
|
||||
def __exit__(self, *exc):
|
||||
if self._locking:
|
||||
self.lock.__exit__(*exc)
|
||||
elif exc == (None, None, None):
|
||||
assert self._altpath.exists(), f"file went missing: {self.filepath}"
|
||||
try:
|
||||
data = self._altpath.read_bytes()
|
||||
if data:
|
||||
self.filepath.write_bytes(data)
|
||||
finally:
|
||||
self._altpath.unlink()
|
||||
# from shutil import move
|
||||
# move(self._altpath, self.filepath) # assumes os.rename overwrites files
|
||||
|
||||
|
||||
def perform_another_experimental_scoring_method(results):
|
||||
new_results = {}
|
||||
all_opt_names = set()
|
||||
for obj_name, obj_res in results.items():
|
||||
all_res = {}
|
||||
for fopt, opt_name in obj_res:
|
||||
all_res.setdefault(fopt, []).append(opt_name)
|
||||
all_opt_names.add(opt_name)
|
||||
new_results[obj_name] = dict(sorted(all_res.items()))
|
||||
|
||||
limited_by_floating_point_precision = 53
|
||||
|
||||
best_ranks_and_counts = {}
|
||||
for outer_rank in range(1, limited_by_floating_point_precision + 1):
|
||||
for obj_name, all_res in new_results.items():
|
||||
for fopt, opt_names in all_res.items():
|
||||
dirty = False
|
||||
for opt_name in set(opt_names):
|
||||
if opt_name in best_ranks_and_counts:
|
||||
rank, count = best_ranks_and_counts[opt_name]
|
||||
if rank == outer_rank:
|
||||
best_ranks_and_counts[opt_name] = (rank, count + 1)
|
||||
dirty = True
|
||||
else:
|
||||
best_ranks_and_counts[opt_name] = (outer_rank, 1)
|
||||
dirty = True
|
||||
if dirty:
|
||||
break
|
||||
|
||||
scores = {k: 0.0 for k in all_opt_names}
|
||||
for opt_name, (rank, count) in best_ranks_and_counts.items():
|
||||
points = 2**(1 - rank)
|
||||
count = min(count, limited_by_floating_point_precision)
|
||||
scores[opt_name] = score = sum(points / 2**i for i in range(count))
|
||||
|
||||
return scores
|
||||
|
||||
|
||||
def needs_rerun(key, value):
|
||||
n_dim = len(value["xopt"])
|
||||
ng = []
|
||||
|
||||
if value["duration"] > -1683000000.0:
|
||||
if n_dim == 3:
|
||||
ng += "go_ackley01 go_alpine01 go_deflectedcorrugatedspring go_exponential go_griewank go_infinity go_mishra11 go_multimodal go_pinter go_qing go_rastrigin go_salomon go_schwefel01 go_schwefel20 go_schwefel21 go_schwefel22 go_sineenvelope go_sodp go_trigonometric02 go_wavy go_weierstrass go_xinsheyang01 go_xinsheyang02 go_xinsheyang03 go_yaoliu04 go_yaoliu09 go_zerosum".split()
|
||||
ng += "go_amgm go_brown go_cigar go_deb01 go_deb03 go_deceptive go_needleeye go_penalty01 go_schwefel04 go_schwefel26 go_sphere go_stretchedv go_styblinskitang go_zacharov".split()
|
||||
if n_dim == 4:
|
||||
ng += "go_ackley01 go_alpine01 go_cigar go_exponential go_griewank go_infinity go_pinter go_qing go_schwefel01 go_schwefel02 go_schwefel20 go_schwefel21 go_schwefel22 go_sineenvelope go_sphere go_step2 go_step go_stretchedv go_trigonometric02 go_weierstrass go_whitley go_xinsheyang01 go_xinsheyang02 go_yaoliu04 go_yaoliu09 go_zerosum".split()
|
||||
ng += "go_amgm go_brown go_colville go_deb03 go_penalty01 go_penalty02 go_powell go_rosenbrock go_schwefel04 go_shekel05 go_shekel07 go_shekel10 go_shubert03 go_shubert04 go_styblinskitang go_vincent".split()
|
||||
|
||||
kd = decode_key(key)
|
||||
assert kd is not None, key
|
||||
if kd.obj in ng:
|
||||
# print("filtered", key, file=__import__("sys").stderr)
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def merge_summaries(all_summaries):
|
||||
# i only needed to write this because i effed up my filenames at one point. oh well.
|
||||
if len(all_summaries) == 0:
|
||||
return {}
|
||||
elif len(all_summaries) == 1:
|
||||
return {k: v for k, v in all_summaries[0].items() if not needs_rerun(k, v)}
|
||||
new_summaries = {}
|
||||
for s in all_summaries:
|
||||
for key, value in s.items():
|
||||
if needs_rerun(key, value):
|
||||
continue
|
||||
k, _, run = key.partition("[")
|
||||
run, _, _ = run.partition("]")
|
||||
for i in range(1, 100):
|
||||
new_key = f"{k}[{i}]"
|
||||
if new_key in new_summaries:
|
||||
if new_summaries[new_key] == value: # this works 'cause it's POD
|
||||
break # already exists (probably; duration is fucked)
|
||||
continue
|
||||
new_summaries[new_key] = value
|
||||
break
|
||||
return new_summaries
|
||||
|
||||
|
||||
try:
|
||||
import numpy
|
||||
except ModuleNotFoundError:
|
||||
pass
|
||||
else:
|
||||
from utils_np import *
|
||||
257
utils_np.py
Normal file
257
utils_np.py
Normal file
|
|
@ -0,0 +1,257 @@
|
|||
# i've separated numpy-dependent methods from the rest of the utils.
|
||||
from project import project
|
||||
from utils import AcquireForWriting, merge_summaries, feps, m33, m34, m93
|
||||
import numpy as np
|
||||
|
||||
|
||||
def do_bounding(x, method="clip"):
|
||||
if method == "clip":
|
||||
x = np.clip(x, 0, 1)
|
||||
elif method == "proj":
|
||||
# projects x back into the unit hypercube, poorly.
|
||||
if any(x < 0) or any(x > 1):
|
||||
x = 2 * x - 1
|
||||
x /= np.max(np.abs(x)) + feps
|
||||
x = (x + 1) / 2
|
||||
assert all(x >= 0) and all(x <= 1), x
|
||||
elif method == "pro2":
|
||||
# a little more logical.
|
||||
# FIXME: we need a way to determine the previous (or center) x somehow?
|
||||
if any(x < 0) or any(x > 1):
|
||||
x = project(best_so_far[1], x, eps=feps)
|
||||
elif method == "prcl":
|
||||
# over-engineered clipping with projection-sliding. (yeah don't ask)
|
||||
# FIXME: we need a way to determine the previous (or center) x somehow?
|
||||
from bitten_snes import _project_with
|
||||
|
||||
x = _project_with(x, old, np.array([[0.0, 1.0] * n_dim]), clipping=0.5)
|
||||
elif method == "tria":
|
||||
hp = np.pi / 2
|
||||
x = np.abs(np.arcsin(np.sin(x * hp)) / hp)
|
||||
elif method == "sine":
|
||||
x = np.square(np.sin(0.5 * np.pi * x))
|
||||
elif method == "ssin":
|
||||
x = np.square(np.sin(0.5 * np.pi * (np.arcsinh(x - 0.5) + 0.5)))
|
||||
elif method == "pycma":
|
||||
raise Exception("TODO: workaround this like pycma does.") # old ver or new ver?
|
||||
return x
|
||||
|
||||
|
||||
class OWrap:
|
||||
def __init__(self, objective, n_trials, frugal_percent=1.0, greedy_percent=2.0):
|
||||
self.feval_count = 0
|
||||
self.best_so_far = None
|
||||
self.warning = None
|
||||
self.objective = objective
|
||||
self.n_trials = n_trials
|
||||
self.__name__ = objective.__name__ # for evolopy
|
||||
self.frugal_percent = float(frugal_percent)
|
||||
self.greedy_percent = float(greedy_percent)
|
||||
|
||||
def __str__(self):
|
||||
return (
|
||||
"<OWrap on "
|
||||
+ str(getattr(self.objective, "__name__", str(self.objective)))
|
||||
+ ">"
|
||||
)
|
||||
|
||||
def __call__(self, x, *args, **kwargs):
|
||||
if getattr(x, "get_x", None): # zoopt
|
||||
x = x.get_x()
|
||||
if type(x) is list: # opytimizer
|
||||
x = np.array(x)
|
||||
|
||||
if x.ndim == 2: # flatten column vectors
|
||||
assert x.shape[1] == 1, x.shape
|
||||
x = x.T[0]
|
||||
|
||||
if not self.warning and (any(x < 0) or any(x > 1.00000001)):
|
||||
self.warning = "bounds"
|
||||
# assert False, x
|
||||
|
||||
if not all(np.isfinite(x)):
|
||||
if not self.warning:
|
||||
m33("x is not finite (NaN or Inf or -Inf)")
|
||||
self.warning = "finite"
|
||||
x[~np.isfinite(x)] = 0.5
|
||||
x = np.clip(x, 0, 1)
|
||||
# assert all(np.isfinite(x)), "x is not finite (NaN or Inf or -Inf)"
|
||||
fx = self.objective(x)
|
||||
assert np.isfinite(fx), "f(x) is not finite (NaN or Inf or -Inf)"
|
||||
self.feval_count += 1
|
||||
|
||||
if self.feval_count <= self.n_trials:
|
||||
if self.best_so_far is None or fx < self.best_so_far[0]:
|
||||
self.best_so_far = (fx, x)
|
||||
|
||||
return float(fx)
|
||||
|
||||
def finish(self, optimizer_name):
|
||||
if self.warning == "bounds":
|
||||
m33(f"{optimizer_name} did not abide to bounds")
|
||||
if self.warning == "finite":
|
||||
m33(f"{optimizer_name} passed a non-finite value")
|
||||
if self.feval_count >= self.n_trials * self.greedy_percent:
|
||||
m33(f"{optimizer_name} got greedy ({self.feval_count}>{self.n_trials})")
|
||||
# if self.feval_count <= self.n_trials * 0.95:
|
||||
if self.feval_count < self.n_trials * self.frugal_percent:
|
||||
m34(f"{optimizer_name} was frugal ({self.feval_count}<{self.n_trials})")
|
||||
return self.best_so_far
|
||||
|
||||
@property
|
||||
def fopt(self):
|
||||
return None if self.best_so_far is None else self.best_so_far[0]
|
||||
|
||||
@property
|
||||
def xopt(self):
|
||||
return None if self.best_so_far is None else self.best_so_far[1]
|
||||
|
||||
|
||||
class COWrap:
|
||||
def __init__(self, objective, *, optimizer, n_trials, n_dim, **kwargs):
|
||||
self._objective = objective
|
||||
self.optimizer = optimizer
|
||||
self.n_trials = n_trials
|
||||
self.n_dim = n_dim
|
||||
self.kwargs = kwargs
|
||||
self._dirty = False
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
# self.cache_dir = Path("./cache")
|
||||
self.cache_dir = Path("~/thursday-cache").expanduser()
|
||||
self._cached_summaries = None
|
||||
self.reset_objective()
|
||||
|
||||
def __str__(self):
|
||||
return (
|
||||
"<COWrap on "
|
||||
+ str(getattr(self.ow.objective, "__name__", str(self.ow.objective)))
|
||||
+ ">"
|
||||
)
|
||||
|
||||
def __name__(self):
|
||||
return str(getattr(self.ow.objective, "__name__", str(self.ow.objective)))
|
||||
|
||||
def __call__(self, x, *args, **kwargs):
|
||||
assert not self._ran, "please run .finish() before continuing!"
|
||||
result = self.ow.__call__(x, *args, **kwargs)
|
||||
self._dirty = True
|
||||
return result
|
||||
|
||||
@property
|
||||
def objective(self):
|
||||
return self._objective
|
||||
|
||||
@objective.setter
|
||||
def objective(self, new_objective):
|
||||
# don't do this or it defeats the purpose: self._cached_summaries = None
|
||||
self._objective = new_objective
|
||||
self.reset_objective()
|
||||
|
||||
@property
|
||||
def cache_name(self):
|
||||
opt_name = self.optimizer.__name__
|
||||
return f"COWrap_d{self.n_dim:02}_n{self.n_trials:03}_{opt_name}"
|
||||
|
||||
@property
|
||||
def cache_key(self):
|
||||
opt_name = self.optimizer.__name__
|
||||
obj_name = self._objective.__name__
|
||||
return f"{self.cache_name}_{obj_name}[{self._run}]"
|
||||
|
||||
@property
|
||||
def cache_file(self):
|
||||
opt_name = self.optimizer.__name__
|
||||
return self.cache_dir / f"{self.cache_name}.json"
|
||||
|
||||
@property
|
||||
def cache_file_fucked(self):
|
||||
opt_name = self.optimizer.__name__
|
||||
return self.cache_dir / f"{self.cache_name}_{opt_name}.json"
|
||||
|
||||
@property # TODO: write a setter as well?
|
||||
def cached_summaries(self):
|
||||
if self._cached_summaries is not None:
|
||||
return self._cached_summaries
|
||||
from json import loads
|
||||
|
||||
if not self.cache_dir.exists() or not (
|
||||
self.cache_file.exists() or self.cache_file_fucked.exists()
|
||||
):
|
||||
return {}
|
||||
# text = self.cache_file.read_text()
|
||||
# if not text:
|
||||
# return {}
|
||||
# summaries = loads(text)
|
||||
# self._cached_summaries = summaries
|
||||
all_summaries = []
|
||||
for cf in (self.cache_file, self.cache_file_fucked):
|
||||
if cf.exists(): # at least one exists at this point...
|
||||
if text := cf.read_text(): # ...but not every file contains anything
|
||||
all_summaries.append(loads(text))
|
||||
self._cached_summaries = merge_summaries(all_summaries)
|
||||
return self._cached_summaries
|
||||
|
||||
def reset_objective(self):
|
||||
self._dirty = False
|
||||
self.ow = OWrap(self._objective, self.n_trials, **self.kwargs)
|
||||
self._check_cache()
|
||||
|
||||
def _check_cache(self):
|
||||
# assert not self._dirty # useless for a private method
|
||||
self._run = 1
|
||||
self._ran = False
|
||||
while self.cache_key in self.cached_summaries:
|
||||
self._run += 1
|
||||
|
||||
def cached(self, run):
|
||||
assert not self._dirty
|
||||
old_run = self._run
|
||||
self._run = run
|
||||
summary = self.cached_summaries.get(self.cache_key, None)
|
||||
self._run = old_run
|
||||
if summary is None:
|
||||
return None
|
||||
assert "fopt" in summary, summary
|
||||
assert "xopt" in summary, summary
|
||||
assert "duration" in summary, summary
|
||||
fopt = float(summary["fopt"])
|
||||
xopt = np.array(summary["xopt"], np.float64)
|
||||
duration = float(summary["duration"])
|
||||
return fopt, xopt
|
||||
|
||||
def finish(self, opt_name=None):
|
||||
from json import dumps
|
||||
from time import time
|
||||
|
||||
assert self._dirty
|
||||
self._ran = True
|
||||
if opt_name is not None and opt_name != self.optimizer.__name__:
|
||||
m93("Warning: opt_name mistmatch")
|
||||
|
||||
assert self.ow.best_so_far is not None
|
||||
# fopt, xopt = self.ow.best_so_far
|
||||
fopt, xopt = self.ow.finish(self.optimizer.__name__)
|
||||
summary = dict(
|
||||
fopt=float(fopt),
|
||||
xopt=[float(x) for x in xopt],
|
||||
# duration=float(-1), # old, bad for uniqueness
|
||||
duration=float(-time()),
|
||||
)
|
||||
|
||||
with AcquireForWriting(self.cache_file) as fp:
|
||||
self._cached_summaries = None # force reload
|
||||
self._check_cache() # refresh ._run and thereby .cache_key
|
||||
summaries = self.cached_summaries
|
||||
summaries[self.cache_key] = summary
|
||||
text = dumps(summaries, separators=(",", ":"))
|
||||
fp.write_text(text)
|
||||
|
||||
if self.cache_file_fucked.exists():
|
||||
# safe to delete now that i've written and tested merge_summaries.
|
||||
self.cache_file_fucked.unlink()
|
||||
|
||||
self._cached_summaries = None # force reload in case of other writes
|
||||
self.reset_objective()
|
||||
return fopt, xopt
|
||||
Loading…
Reference in a new issue