From 5dce1a05f115ecd63e604af95fa0bf1bf7fe3297 Mon Sep 17 00:00:00 2001 From: William Song <30965609+Freakwill@users.noreply.github.com> Date: Mon, 4 Dec 2023 20:46:10 +0800 Subject: [PATCH] paper;game --- docs/build/.doctrees/environment.pickle | Bin 2146932 -> 2054630 bytes .../.doctrees/source/Customization.doctree | Bin 22737 -> 22703 bytes docs/build/.doctrees/source/Examples.doctree | Bin 21719 -> 32466 bytes docs/build/.doctrees/source/Helpers.doctree | Bin 4721 -> 10191 bytes docs/build/.doctrees/source/Install.doctree | Bin 8279 -> 8281 bytes docs/build/.doctrees/source/Misc.doctree | Bin 3360 -> 4413 bytes .../source/pyrimidine.benchmarks.doctree | Bin 105040 -> 95183 bytes .../build/.doctrees/source/pyrimidine.doctree | Bin 1006783 -> 962711 bytes .../source/pyrimidine.local_search.doctree | Bin 83228 -> 76932 bytes .../_sources/source/Customization.md.txt | 7 +- docs/build/_sources/source/Examples.md.txt | 233 +- docs/build/_sources/source/Helpers.md.txt | 94 + docs/build/_sources/source/Install.md.txt | 2 +- docs/build/_sources/source/Misc.md.txt | 13 +- docs/build/genindex.html | 607 +++--- docs/build/index.html | 13 +- docs/build/objects.inv | Bin 5460 -> 5405 bytes docs/build/py-modindex.html | 21 +- docs/build/searchindex.js | 2 +- docs/build/source/Customization.html | 15 +- docs/build/source/Examples.html | 252 ++- docs/build/source/Helpers.html | 100 + docs/build/source/Install.html | 2 +- docs/build/source/Misc.html | 33 +- docs/build/source/modules.html | 271 +-- docs/build/source/pyrimidine.benchmarks.html | 181 +- docs/build/source/pyrimidine.html | 1881 ++++++++--------- .../build/source/pyrimidine.local_search.html | 98 +- docs/source/Customization.md | 7 +- docs/source/Examples.md | 93 +- docs/source/Misc.md | 9 +- examples/comparison-trait.py | 10 +- examples/example-game.py | 60 +- examples/example15.py | 23 +- pyrimidine/base.py | 2 +- pyrimidine/benchmarks/neural_network.py | 13 +- pyrimidine/gene.py | 3 + pyrimidine/multipopulation.py | 2 +- 38 files changed, 2201 insertions(+), 1846 deletions(-) diff --git a/docs/build/.doctrees/environment.pickle b/docs/build/.doctrees/environment.pickle index 24b51fb24917cae125bbef5cba8ae7e2df170250..83d001940fa4e3dfc64a9fa35c9c995a40d5b07e 100644 GIT binary patch literal 2054630 zcmeFa37lNVRWEE=%SfYr@fO?UT3%*Gp6OX+S+e2@mLbo#k@4|u>|-*c)$aamzS__fv`UyVF?ft2r=9HLLk5+k3d3r|8uJ9 zR@J>d-974-mB1+Ry*;<~Q>RXyI$OPY{Wt#B+BMhEzi?Mj$>mE6DX(-fUoDr4Ua8gy zcRX0n7qSl-pWfaW`s&7|#$dSCtyCJP!~SeJQ>%JjHeYRodt@E;TE37b^`KE}Jkba@1l0_6B)4fW*Pq**+mhRw+m_p& z+aXdj<h%iL$eAzI00{e#( zhG?!(K@o8E-GxSYvyqkB54=nbT`joNUZLTIeU)n2$EQYad$=i6CaBKl(3xEmV-x#_ zou|b_IA_ZQ{|%@hSH_^W*x&oAUcqw%bYbmyYOE1%t9ivr!Nst6wgHu}KOKE*xZw_O zAycSlJxazv8E|2Lu2w9hYdNooiu%wK7xN2brVix}qDPV49)1K3~du80v1$ znJpLHd?}4@`3z?2#c&&ct_L1{L%tWo>v@K}k~>}S(y`g=bBh514}(PCssRcW7|Awg z%7t<%J>!)!)W57ZQ^?b_0o4lyw<^kPn#p^GtVr0-?Yh}wxs)f6BP)N&7kRd}R6W2b zgVC01K;F%0wOV_wUIx?%7i-o0oL9?L%k^36OHiK)$yZ)Dn9suzLx>6tS1ofmJPL=N=ytUwdzPIP7ivem0J=;Qg> z6n|P@BiNxM=jhF!`ZMLMH!@u)XXa4;QNRXkmQw2Bc8QJq({gQL`Sg`5G!|Y;JAj0k ztKbnJz|n#mpv|$Gw=e+&PRpb>$||hC-&n5KD)n00t$J=F2*TIM1%^4xdDVQ4(1QS3 znk78gaM3N)0U(g6+}%L;wV-K@(_h!frE;UWS^Nlh%Ef?gMhoE@kQag-=N!(BU3vj1eMwgA%z9X13xH9fP@YfE z)SrKTF1=%RjFNP;%W zsks%Ui&%c;S_z9jUG-)>^n*ZG0a=is#>%HE1mRWi54TkoXPIuKgW4hwi?MQ74#c}a z1Uy0%L8%A;velhBk6wX1=-fnZzrgP8idLtmvE5;7IW4|#o%6g(dfu&;cu5Il2zLhc zN`!=E;fJW2VcEpQ&)TNs%n3dDr?bSc8qs#1R`vywqCE3kWb%3r|vq zTLs>x!DHmyAdh{W_t9LrkfmZ0E4T8tI1R3Z$t2jlIi6_#tf=?H4{T&M_nL4WeP;sL z&p8AelKZFJKT)Y&`divqHJm2%=WAGpXobIR=KPXd!tTN!MUT{w8@YM$S?u3v9s4$Sd=xu)(klRfZ0AMN}8FfX!7^)P_c@{_w;-|OY7_!QA&)4=_*z`(Phcw zi7ty|gI^S%cW|9x{xj7)vn~>hJSid(+zm+8G`Q@lnYfcn&VwNV*`!?syLLbL==}4@ z?-skM1Pg2JI-9s@(CacN19xT%f0=PF<^d7;F?S~SIH%^G$ek5G?#mq(`30v8 zs$6yL5nm^&2Ctv4SF$)zVAp0&iklHUZ2&x-#$gMrhNw*x!UoP#dq+F$rlQGf3BGNXiGr2s}#oGg|^4I+Vf$h2T&?5Y{@ zL;^Le60hfW^>@WVh3ir^@Jga;al|3q#4;!ao?Ff2h}Yzvi*{*^sj_lDL;^JyOAys@ zhcwG8hj*js4{;x&9h5E7UaLAR$IltWCM!#d>8Hg&OrDw+0awYfYtC*xb9Qog0>i;8 zfCB;#gEK@lEn-&1zDXON-Q6yHo}kiS?>mAu+$sZt!3c^}w6>IDZz zF&QJpVqqjheM4pmHFY*i0A-tMN)kufj<0=o5G>ZSLk&-KKPsHf2iSxcdC$J~+4Gd? 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an individual, a particle has two "chromosomes", one represents the current position, the other represents the current velocity. While, you can define three or more chromosomes, to include the acceleration. It also has an important attribute, `memory` as its clone, but stores the best position that the particle passed-by. +In the standard definition, as an individual, a particle has two "chromosomes", one represents the current position, the other represents the current velocity. While, you can define three or more chromosomes, to include the acceleration. It also has an important attribute, `memory` storing the best position that the particle passed-by. ## Simulated Annealing Algorithm @@ -45,7 +46,7 @@ class SimulatedAnnealing(FitnessModel): } def init(self): - self.phantom = self.clone(fitness=None) + self.phantom = self.copy(fitness=None) def transition(self, *args, **kwargs): T = self.initT @@ -145,7 +146,7 @@ class SimulatedAnnealing(PhantomIndividual): def init(self): # initialize phantom solution - self.phantom = self.clone(fitness=None) + self.phantom = self.copy(fitness=None) def transit(self, *args, **kwargs): diff --git a/docs/build/_sources/source/Examples.md.txt b/docs/build/_sources/source/Examples.md.txt index 190e85e..4b9aba0 100644 --- a/docs/build/_sources/source/Examples.md.txt +++ b/docs/build/_sources/source/Examples.md.txt @@ -1,11 +1,11 @@ -# Examples and Comparison of Algorithm +# Examples and Comparison of Algorithms [TOC] -## Examples +## Example 1 ### A simple example --- Knapsack problem -One of the famous problem is the knapsack problem. It is a good example for GA. +One of the well-known problem is the knapsack problem. It is a good example for GA. #### Codes @@ -24,13 +24,13 @@ _evaluate = Knapsack.random(n_bags) # : 0-1 array -> float # Define the individual class class MyIndividual(MonoIndividual): - element_class = BinaryChromosome.set(default_size=n_bags) + element_class = BinaryChromosome // n_bags def _fitness(self) -> float: # To evaluate an individual! return _evaluate(self.chromosome) """ Equiv. to - MyIndividual = MonoIndividual[BinaryChromosome.set(default_size=n_bags)].set_fitness(_evaluate) + MyIndividual = MonoIndividual[BinaryChromosome//n_bags].set_fitness(_evaluate) """ # Define the population class @@ -39,9 +39,9 @@ class MyPopulation(HOFPopulation): default_size = 10 """ Equiv. to - MyPopulation = HOFPopulation[MyIndividual].set(default_size=10) + MyPopulation = HOFPopulation[MyIndividual] //10 or, as a population of chromosomes - MyPopulation = HOFPopulation[BinaryChromosome.set(default_size=n_bags).set_fitness(_evaluate)].set(default_size=10) + MyPopulation = HOFPopulation[(BinaryChromosome//n_bags).set_fitness(_evaluate)] //10 """ pop = MyPopulation.random() @@ -153,9 +153,9 @@ iteration & solution & Mean Fitness & Best Fitness & Standard Deviation of Fitne ``` -## Create new algo. +## Example 2 -In the following example, the binary chromosomes should be decoded to floats. We recommend `digit_converter`, created by the author for such purpose, to handle with it. +In the following example, the binary chromosomes should be decoded to floats. We recommend `digit_converter` to handle with it, created by the author for such purpose. ```python #!/usr/bin/env python3 @@ -168,7 +168,7 @@ from digit_converter import * ndim = 10 def evaluate(x): - return -rosenbrock(ndim)(x) + return -rosenbrock(x) class _Chromosome(BinaryChromosome): @@ -184,12 +184,12 @@ class uChromosome(BinaryChromosome): def _fitness(i): return evaluate(i.decode()) -ExampleIndividual = MultiIndividual[_Chromosome].set_fitness(_fitness) +ExampleIndividual = MultiIndividual[_Chromosome].set_fitness(_fitness) // ndim -class MyIndividual(MixIndividual[(_Chromosome,)*ndim + (uChromosome,)].set_fitness(_fitness)): - """my own individual class +class MyIndividual(MixedIndividual[(_Chromosome,)*ndim + (uChromosome,)].set_fitness(_fitness)): + """My own individual class - Method `mate` is overriden. + The method `mate` is overriden. """ ranking = None threshold = 0.25 @@ -217,12 +217,7 @@ class MyIndividual(MixIndividual[(_Chromosome,)*ndim + (uChromosome,)].set_fitne else: return super().mate(other) -class MyPopulation(StandardPopulation[MyIndividual]): - - def transition(self, *args, **kwargs): - self.sort() - super().transition(*args, **kwargs) - +MyPopulation = StandardPopulation[MyIndividual] ``` @@ -237,7 +232,7 @@ ax = fig.add_subplot(111) _Population = StandardPopulation[ExampleIndividual] pop = MyPopulation.random(n_individuals=20, sizes=[8]*ndim+[8]) -cpy = pop.clone(_Population) +cpy = pop.copy(type_=_Population) d = cpy.evolve(stat=stat, n_iter=100, history=True) ax.plot(d.index, d['Mean Fitness'], d.index, d['Best Fitness'], '.-') @@ -247,4 +242,198 @@ ax.legend(('Traditional mean','Traditional best', 'New mean', 'New best')) plt.show() ``` -![](comparison.png) \ No newline at end of file +![](comparison.png) + + +## Example 3 + +### Quantum GA + +It is based on quantum chromosomes. Let use have a look at the source code. + +```python +class QuantumChromosome(CircleChromosome): + + measure_result = None + + def decode(self): + self.measure() + return self.measure_result + + def measure(self): + # measure a QuantumChromosome to get a binary sequence + rs = np.random.random(size=(len(self),)) + self.measure_result = np.cos(self) ** 2 > rs + self.measure_result.astype(np.int_) +``` + +```python +#!/usr/bin/env python3 + +from pyrimidine import * +from pyrimidine.benchmarks.optimization import * + +from pyrimidine.deco import add_memory, fitness_cache + +# generate a knapsack problem randomly +n_bags = 50 +evaluate = Knapsack.random(n=n_bags) + +@fitness_cache +class YourIndividual(BinaryChromosome // n_bags): + + def _fitness(self): + return evaluate(self.decode()) + + +YourPopulation = HOFPopulation[YourIndividual] // 20 + +@fitness_cache +@add_memory({'measure_result': None, 'fitness': None}) +class MyIndividual(QuantumChromosome // n_bags): + + def _fitness(self): + return evaluate(self.decode()) + + def backup(self, check=False): + f = self._fitness() + if not check or (self.memory['fitness'] is None or f > self.memory['fitness']): + self._memory = { + 'measure_result': self.measure_result, + 'fitness': f + } + +class MyPopulation(HOFPopulation): + + element_class = MyIndividual + default_size = 20 + + def init(self): + self.backup() + super().init() + + def backup(self, check=True): + for i in self: + i.backup(check=check) + + def update_hall_of_fame(self, *args, **kwargs): + """ + Update the `hall_of_fame` after each step of evolution + """ + self.backup() + super().update_hall_of_fame(*args, **kwargs) + +``` + +### Visualization and comparison +```python +stat={'Mean Fitness': 'mean_fitness', 'Best Fitness': 'best_fitness'} +mypop = MyPopulation.random() + +yourpop = YourPopulation([YourIndividual(i.decode()) for i in mypop]) +mydata = mypop.evolve(n_iter=100, stat=stat, history=True) +yourdata = yourpop.evolve(n_iter=100, stat=stat, history=True) + +import matplotlib.pyplot as plt +fig = plt.figure() +ax = fig.add_subplot(111) +yourdata[['Mean Fitness', 'Best Fitness']].plot(ax=ax) +mydata[['Mean Fitness', 'Best Fitness']].plot(ax=ax) +ax.legend(('Mean Fitness', 'Best Fitness', 'Mean Fitness(Quantum)', 'Best Fitness(Quantum)')) +ax.set_xlabel('Generations') +ax.set_ylabel('Fitness') +ax.set_title(f'Demo of (Quantum)GA: {n_bags}-Knapsack Problem') +plt.show() + +``` + +![](QGA.png) + +## Game + +```python +#!/usr/bin/env python + + +from random import random, randint + +import numpy as np + +from pyrimidine import BasePopulation + + +class Player: + """ + 'scissors', 'paper', 'stone' = 0, 1, 2 + """ + + params = {'mutate_prob': 0.02} + + def __init__(self, strategy=0, score=0): + self.strategy = strategy # 1,2 + self.score = score + + @classmethod + def random(cls): + return cls(strategy=randint(0, 2), score=0) + + def clone(self, *args, **kwargs): + return self.__class__(self.strategy, self.score) + + def mutate(self): + self.strategy = randint(0, 2) + + def init(self): + pass + + def __lt__(self, other): + return ((self.strategy, other.strategy) == (0, 1) + or (self.strategy, other.strategy) == (1, 2) + or (self.strategy, other.strategy) == (2, 0)) + + +class Game(BasePopulation): + + element_class = Player + default_size = 100 + + def transition(self, *args, **kwargs): + self.compete() + self.duplicate() + self.mutate() + + def compete(self): + k = int(0.5 * self.default_size) + winner = [] + for i, p in enumerate(self[:-1]): + for j, q in enumerate(self[:i]): + if random() < 0.5: + if p < q: + p.score += 1 + q.score -= 1 + elif q < p: + p.score -= 1 + q.score += 1 + winners = np.argsort([p.score for p in self])[-k:] + self.elements = [self.elements[k] for k in winners] + + def duplicate(self): + self.extend(self.clone()) + + +game = Game.random() +stat = {'scissors': lambda game: sum(p.strategy==0 for p in game), +'paper': lambda game: sum(p.strategy==1 for p in game), +'stone': lambda game: sum(p.strategy==2 for p in game) +} +data = game.evolve(stat=stat, history=True) + +import matplotlib.pyplot as plt +fig = plt.figure() +ax = fig.add_subplot(111) +data[['scissors', 'paper', 'stone']].plot(ax=ax) +ax.set_title("Have a zero-sum game") +plt.show() +``` + +![](game.png) \ No newline at end of file diff --git a/docs/build/_sources/source/Helpers.md.txt b/docs/build/_sources/source/Helpers.md.txt index 8f5b494..b775a8c 100644 --- a/docs/build/_sources/source/Helpers.md.txt +++ b/docs/build/_sources/source/Helpers.md.txt @@ -20,3 +20,97 @@ print(solution) ``` OUTPUT: `[-0.0078125 -1. ]` + +## Decorators + + +### Memory +```python +#!/usr/bin/env python3 + +from pyrimidine import * +from pyrimidine.benchmarks.optimization import * + +from pyrimidine.deco import add_memory + +# generate a knapsack problem randomly +n_bags = 50 +evaluate = Knapsack.random(n_bags) + +class YourIndividual(BinaryChromosome // n_bags): + + def _fitness(self): + return evaluate(self.decode()) + + +YourPopulation = HOFPopulation[YourIndividual] // 20 + + +@add_memory({'solution': None, 'fitness': None}) +class MyIndividual(YourIndividual): + # Individual with a memory, recording a best solution + + def backup(self, check=False): + f = self._fitness() + if not check or (self.memory['fitness'] is None or f > self.memory['fitness']): + self._memory = { + 'solution': self.clone(), + 'fitness': f + } + + @property + def solution(self): + if self._memory['solution'] is not None: + return self._memory['solution'] + else: + return self.solution + + +class MyPopulation(HOFPopulation): + + element_class = MyIndividual + default_size = 20 + + def init(self): + self.backup() + super().init() + + def backup(self, check=True): + for i in self: + i.backup(check=check) + + def transition(self, *args, **kwargs): + """ + Update the `hall_of_fame` after each step of evolution + """ + self.backup() + super().transition(*args, **kwargs) + + +stat = {'Mean Fitness': 'mean_fitness', 'Best Fitness': 'best_fitness'} +mypop = MyPopulation.random() + +yourpop = mypop.clone(type_=YourPopulation) +mydata = mypop.evolve(n_iter=200, stat=stat, history=True) +yourdata = yourpop.evolve(n_iter=200, stat=stat, history=True) + +import matplotlib.pyplot as plt +fig = plt.figure() +ax = fig.add_subplot(111) +yourdata[['Mean Fitness', 'Best Fitness']].plot(ax=ax) +mydata[['Mean Fitness', 'Best Fitness']].plot(ax=ax) +ax.legend(('Mean Fitness', 'Best Fitness', 'Mean Fitness(Memory)', 'Best Fitness(Memory)')) +ax.set_xlabel('Generations') +ax.set_ylabel('Fitness') +ax.set_title(f'Demo of GA: {n_bags}-Knapsack Problem') +plt.show() + +``` + +### Cache +Cache the fitness, if the indiviudal dose not change, the fitness will be read from cache by default. + +```python +@fitness_cache +class MyIndividual... +``` \ No newline at end of file diff --git a/docs/build/_sources/source/Install.md.txt b/docs/build/_sources/source/Install.md.txt index 7e3f095..ce6048a 100644 --- a/docs/build/_sources/source/Install.md.txt +++ b/docs/build/_sources/source/Install.md.txt @@ -1,6 +1,6 @@ # Installation and Getting Started -**Pythons**: Python 3.7, 3.8, 3.9, 3.10, PyPy3 +**Pythons**: Python 3.8, 3.9, 3.10, 3.11, PyPy3 **Platforms**: Linux/Unix and Windows diff --git a/docs/build/_sources/source/Misc.md.txt b/docs/build/_sources/source/Misc.md.txt index 58671df..eb37ba0 100644 --- a/docs/build/_sources/source/Misc.md.txt +++ b/docs/build/_sources/source/Misc.md.txt @@ -1,8 +1,19 @@ -# More Algorithms +# Misc +## Pictures + + +## Videos + + \ No newline at end of file diff --git a/docs/build/genindex.html b/docs/build/genindex.html index a5bacd2..dcaf5ce 100644 --- a/docs/build/genindex.html +++ b/docs/build/genindex.html @@ -71,9 +71,7 @@

A

 @@ -173,16 +169,16 @@

A

  • alpine() (in module pyrimidine.benchmarks.special)
    • -
  • apply() (pyrimidine.base.BaseIndividual method) +
  • apply() (pyrimidine.base.BaseCommunity method)
  • argsort() (pyrimidine.benchmarks.optimization.Knapsack method) + +
  • ArrayChromosome (class in pyrimidine.chromosome)
    • @@ -250,6 +246,8 @@

    B

  • BaseChromosome (class in pyrimidine.base) +
    • +
  • BaseCommunity (class in pyrimidine.base)
  • BaseEnvironment (class in pyrimidine.base)
    • @@ -264,34 +262,32 @@

    B

  • BaseParticle (class in pyrimidine.pso)
  • BasePopulation (class in pyrimidine.base) -
    • -
  • BaseProblem (class in pyrimidine.benchmarks)
      • -
    • BaseSpecies (class in pyrimidine.base) +
    • BaseProblem (class in pyrimidine.benchmarks)
    • BaseTabuSearch (class in pyrimidine.local_search.tabu_search)
    • basis() (in module pyrimidine.benchmarks.fitting)
      • -
    • best_fitness (pyrimidine.base.BaseMultiPopulation property) +
    • best_fitness (pyrimidine.population.HOFPopulation property)
      • +
    • best_fitness() (pyrimidine.base.BaseMultiPopulation method) +
    • best_individual (pyrimidine.population.HOFPopulation property)
    • best_position (pyrimidine.pso.BaseParticle property) -
      • -
    • binary_select() (in module pyrimidine.utils)
    • BinaryChromosome (class in pyrimidine.chromosome)
    • BinaryGene (class in pyrimidine.gene) +
      • +
    • binaryIndividual() (in module pyrimidine.individual)
    • boltzmann_select() (in module pyrimidine.utils)
      • @@ -301,11 +297,13 @@

      B

      C

      - + @@ -556,12 +576,6 @@

      F

      + -
    • fitness (pyrimidine.individual.MemoryIndividual property) + +
    • Fitting (class in pyrimidine.benchmarks.fitting)
      • -
    • flatten() (pyrimidine.base.BaseMultiPopulation method) +
      • FloatChromosome (class in pyrimidine.chromosome)
      • FloatGene (class in pyrimidine.gene) @@ -623,6 +617,10 @@

        F

        G

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"pyrimidine.local_search.random_walk.RandomWalk.transit"]], "transition() (pyrimidine.local_search.simulated_annealing.simulatedannealing method)": [[12, "pyrimidine.local_search.simulated_annealing.SimulatedAnnealing.transition"]], "transition() (pyrimidine.local_search.tabu_search.basetabusearch method)": [[12, "pyrimidine.local_search.tabu_search.BaseTabuSearch.transition"]], "type_check() (pyrimidine.local_search.simulated_annealing.simulatedannealing method)": [[12, "pyrimidine.local_search.simulated_annealing.SimulatedAnnealing.type_check"]], "type_check() (pyrimidine.local_search.tabu_search.basetabusearch method)": [[12, "pyrimidine.local_search.tabu_search.BaseTabuSearch.type_check"]], "type_check() (pyrimidine.local_search.tabu_search.simpletabusearch method)": [[12, "pyrimidine.local_search.tabu_search.SimpleTabuSearch.type_check"]], "update_tabu_list() (pyrimidine.local_search.tabu_search.basetabusearch method)": [[12, "pyrimidine.local_search.tabu_search.BaseTabuSearch.update_tabu_list"]]}}) \ No newline at end of file diff --git a/docs/build/source/Customization.html b/docs/build/source/Customization.html index 12a08d4..34cf798 100644 --- a/docs/build/source/Customization.html +++ b/docs/build/source/Customization.html @@ -14,7 +14,7 @@ - + 0.123 return IntervalConverter(-5,5)(self) + class ExampleIndividual(MixedIndividual): """base class of individual You should implement the methods, cross, mute """ + element_class = FloatChromosome, FloatChromosome, FloatChromosome, _Chromosome def decode(self): @@ -34,18 +36,21 @@ def decode(self): def _fitness(self): return evaluate(self.decode()) -class ExampleIndividual2(ExampleIndividual, TraitThresholdIndividual): + +class ExampleIndividual2(ExampleIndividual, SelfAdaptiveIndividual): """base class of individual You should implement the methods, cross, mute """ + element_class = FloatChromosome, FloatChromosome, FloatChromosome, _Chromosome, FloatChromosome + if __name__ == '__main__': MyPopulation = StandardPopulation[ExampleIndividual2] pop = MyPopulation.random(n_individuals=100, sizes=[h*p, h, h, 8, 4]) - pop1 = pop.copy(type_=HOFPopulation) + pop1 = pop.copy(type_=StandardPopulationPopulation) pop2 = pop.copy() pop.mate_prob = pop.mutate_prob = 1 @@ -92,4 +97,3 @@ class ExampleIndividual2(ExampleIndividual, TraitThresholdIndividual): ax2 = fig.add_subplot(122) data[['best mate_prob', 'best mutate_prob', 'best desire', 'worst mate_prob', 'worst mutate_prob', 'worst desire']].plot(ax=ax2) plt.show() - plt.show() diff --git a/examples/example-game.py b/examples/example-game.py index 0523e13..b3c808f 100755 --- a/examples/example-game.py +++ b/examples/example-game.py @@ -9,8 +9,11 @@ class Player: + """ + 'scissors', 'paper', 'stone' = 0, 1, 2 + """ - params = {'mutate_prob': 0.02} + params = {'mutate_prob': 0.1} def __init__(self, strategy=0, score=0): self.strategy = strategy # 1,2 @@ -26,13 +29,23 @@ def clone(self, *args, **kwargs): def mutate(self): self.strategy = randint(0, 2) + def init(self): + pass + + def __lt__(self, other): + return ((self.strategy, other.strategy) == (0, 1) + or (self.strategy, other.strategy) == (1, 2) + or (self.strategy, other.strategy) == (2, 0)) + class Game(BasePopulation): + params = {'compete_prob': 0.5} + element_class = Player - default_size = 50 + default_size = 100 - def transition(self): + def transition(self, *args, **kwargs): self.compete() self.duplicate() self.mutate() @@ -42,25 +55,13 @@ def compete(self): winner = [] for i, p in enumerate(self[:-1]): for j, q in enumerate(self[:i]): - if random() < 0.5: - if (p.strategy, q.strategy) == (0, 1): - p.score -= 1 - q.score += 1 - elif (p.strategy, q.strategy) == (0, 2): + if random() < self.compete_prob: + if p < q: p.score += 1 - q.score -= 1 - elif (p.strategy, q.strategy) == (1, 2): + q.score -= 1 + elif q < p: p.score -= 1 q.score += 1 - elif (p.strategy, q.strategy) == (1, 0): - p.score += 1 - q.score -= 1 - elif (p.strategy, q.strategy) == (2, 0): - p.score -= 1 - q.score += 1 - elif (p.strategy, q.strategy) == (2, 1): - p.score += 1 - q.score -= 1 winners = np.argsort([p.score for p in self])[-k:] self.elements = [self.elements[k] for k in winners] @@ -68,11 +69,16 @@ def duplicate(self): self.extend(self.clone()) -pop = Game.random() -import collections -c = collections.Counter([i.strategy for i in pop]) -print(c) -for _ in range(10): - pop.transition() - c = collections.Counter([i.strategy for i in pop]) - print(c) +game = Game.random() +stat = {'scissors': lambda game: sum(p.strategy==0 for p in game), +'paper': lambda game: sum(p.strategy==1 for p in game), +'stone': lambda game: sum(p.strategy==2 for p in game) +} +data = game.evolve(stat=stat, history=True) + +import matplotlib.pyplot as plt +fig = plt.figure() +ax = fig.add_subplot(111) +data[['scissors', 'paper', 'stone']].plot(ax=ax) +ax.set_title("Have a zero-sum game") +plt.show() diff --git a/examples/example15.py b/examples/example15.py index c74d3b6..472748d 100755 --- a/examples/example15.py +++ b/examples/example15.py @@ -1,23 +1,24 @@ #!/usr/bin/env python3 -from pyrimidine import * -from pyrimidine.benchmarks.fitting import * import numpy as np +from pyrimidine import * +from pyrimidine.benchmarks.fitting import * from digit_converter import colorConverter from PIL import Image -image = Image.open('painting.jpg') +image = Image.open('taichi.jpeg') evaluate = Painting(image=image, size=(100,100)) class _Gene(NaturalGene): - lb, ub = 0, 100 + lb, ub = 0, 90 class _Chromosome(VectorChromosome): - element_class = NaturalGene + + element_class = _Gene default_size = 10 @@ -33,10 +34,10 @@ class MyIndividual(MixedIndividual): def decode(self): c = self.chromosomes[2] c = c.reshape((n_basis, 8)) - d = np.asarray([colorConverter(c[i,:]) for i in range(c.shape[0])]) - a, b, c = self.chromosomes[0][:n_basis], self.chromosomes[0][n_basis:2*n_basis], self.chromosomes[0][2*n_basis:] + c = np.asarray([colorConverter(ci) for ci in c]) + a, d1, d2 = self.chromosomes[0][:n_basis], self.chromosomes[0][n_basis:2*n_basis], self.chromosomes[0][2*n_basis:] t = self.chromosomes[1].reshape((n_basis, 2)) - return a, b, c, t, d + return a, d1, d2, t, c def _fitness(self): params = self.decode() @@ -45,12 +46,10 @@ def _fitness(self): MyPopulation = HOFPopulation[MyIndividual] // 50 +pop = MyPopulation.random() -pop = MyPopulation.random(n_individuals=50) - -pop.ezolve(n_iter=300) +pop.ezolve(n_iter=250) params = pop.solution im = evaluate.toimage(*params) -print(im) im.show() diff --git a/pyrimidine/base.py b/pyrimidine/base.py index e8a1a9b..9aa9381 100755 --- a/pyrimidine/base.py +++ b/pyrimidine/base.py @@ -47,7 +47,7 @@ def _fitness(self): def evaluate(self): return np.dot(n, self.chromosomes[0]), max_repeat(ti for ti, c in zip(t, self.chromosomes[0]) if c==1) -class MyPopulation(SGAPopulation): +class MyPopulation(StandardPopulation): element_class = MyIndividual pop = MyPopulation.random(n_individuals=50, size=100) diff --git a/pyrimidine/benchmarks/neural_network.py b/pyrimidine/benchmarks/neural_network.py index 43e49fe..fa39508 100755 --- a/pyrimidine/benchmarks/neural_network.py +++ b/pyrimidine/benchmarks/neural_network.py @@ -5,19 +5,23 @@ from ..benchmarks import BaseProblem -def relu(x): - return (x > 0) * x +from .utils import max0 + + +relu = max0 def _mlp(X, A1, b1, A2, b2): N = X.shape[0] O1 = relu(np.dot(X, A1)+ np.tile(b1, (N, 1))) return np.dot(O1, A2)+ np.tile(b2, (N, 1)) + class MLP(BaseProblem): """MLP Y = A2f(A1X+b1)+b2 """ + def __init__(self, X, Y): self.X = X self.Y = Y @@ -26,7 +30,6 @@ def __init__(self, X, Y): def random(N=100, p=2): X = np.random.random(size=(N, p)) Y = np.sin(X[:, 0]) + np.cos(X[:, 1]) + np.sin(X[:, 0]*2) + np.cos(X[:, 1]*2) * np.cos(X[:, 2]) - return MLP(X, Y) @@ -34,6 +37,7 @@ def __call__(self, x): E = LA.norm(_mlp(self.X, *x) - self.Y) / LA.norm(self.Y) return -E + def _rnn(X, A1, b1, A2, b2, C1, c1, C2, c2, Z=0): N = X.shape[0] if Z == 0: @@ -43,12 +47,14 @@ def _rnn(X, A1, b1, A2, b2, C1, c1, C2, c2, Z=0): O1 = relu(np.dot(X, A1)+ np.tile(b1, (N, 1)) + np.dot(C1, Z)) return np.dot(O1, A2)+ np.tile(b2, (N, 1)), np.dot(O1, C2)+ np.tile(c2, (H, 1)) + class RNN(BaseProblem): """RNN Yt+1 = A2f(A1Xt+C1Zt+b1)+b2 Zt+1 = C2g(A1X+C1Zt+b1)+c2 """ + def __init__(self, X, Y): self.X = X self.Y = Y @@ -58,7 +64,6 @@ def random(N=100, p=2): X = np.random.random(size=(N, p)) return MLP(X, Y) - def __call__(self, xs, h=1): Z = 0 Xs = [] diff --git a/pyrimidine/gene.py b/pyrimidine/gene.py index 4287ebb..0ccaf7b 100755 --- a/pyrimidine/gene.py +++ b/pyrimidine/gene.py @@ -15,9 +15,11 @@ class NaturalGene(np.int_, BaseGene): def random(cls, *args, **kwargs): return np.random.randint(cls.ub, dtype=cls, *args, **kwargs) + class DigitGene(NaturalGene): pass + class IntegerGene(np.int_, BaseGene): lb, ub = -10, 10 @@ -25,6 +27,7 @@ class IntegerGene(np.int_, BaseGene): def random(cls, *args, **kwargs): return np.random.randint(cls.ub-cls.lb, dtype=cls, *args, **kwargs) + cls.lb + class BinaryGene(np.int_, BaseGene): values = (0, 1) diff --git a/pyrimidine/multipopulation.py b/pyrimidine/multipopulation.py index 2850a38..72a3159 100755 --- a/pyrimidine/multipopulation.py +++ b/pyrimidine/multipopulation.py @@ -5,7 +5,7 @@ import threading from . import BaseMultiPopulation -from .utils import * +from .utils import * class MultiPopulation(BaseMultiPopulation):
      • get_rank() (pyrimidine.base.BasePopulation method) -
        • -
      • get_stem() (in module pyrimidine.meta)
      • GravitySearch (class in pyrimidine.gsa)
        • @@ -717,16 +655,10 @@

        G

        H

        @@ -1843,6 +1706,8 @@

        U

        +
      • update_vilocity_by_fame() (pyrimidine.pso.Particle method) +
        • diff --git a/docs/build/index.html b/docs/build/index.html index 8cb5204..cbb5081 100644 --- a/docs/build/index.html +++ b/docs/build/index.html @@ -73,8 +73,10 @@

        Welcome to pyrimidine’s world of genetic algorithm
      • Examples
      • Customization
          @@ -87,9 +89,14 @@

          Welcome to pyrimidine’s world of genetic algorithm
        • Helpers +
          • +
        • Misc
          • -
        • Misc
        • modules diff --git a/docs/build/objects.inv b/docs/build/objects.inv index b4fe4ba02b8f0810a0c0751d44615ea3b697b50c..cb2d5a1f1fa668a7fff15495ab5fb6f5d313416a 100644 GIT binary patch delta 5333 zcmV;`6e{b~DxE5jcz?Z}OOxBUwa54T6sk&6SxI;7+0D*lw>@2PPdd}SGaFZ_APSOD zO%fcC>W5eP5ZTrb)VpCi8d zVU@|V3i9N=1IP- zx)g^^fHmiTo>>H9#atax<%-Wa%TchDDHa2JK(S;mV^HA~Pa_fHD*Hm>$T&VHB7dbw zQNzF}QAbPD=zkSbxh68pvZYFh@W}IR!o~5l(+E)AiDQC6 zqOV~L7;1bmkdWcU;L#P+gn@>t9ulz9GI*G=>P{c!uOWm?2m)`BAS&-LsZwtP zceO;Grb3T_IbyxX`~92HSf zY$QtQZ7xLaZ75yy9U@5hZEmiMnu$hYVN}Q%0Fi&N@*_pxV38-~%V@kwB}&;sBL#er zu*6)42grx18f%au8M0`bai+o(2pJRQ+C)(gJb$nQiAf_bJr)>{&|?vF80y1Nk$@V^ z3?zNO&4F!MmI__#93{+{u@*Yab{=v#Ct+E%$@zH_o?@mRvkRyFUBc0W|1MQoOC@(Y zyv7H9n>>uvW3`emB5r3B$ny5!n~!xvzWMOZHVUuFCKAaul=1Fj4^B6rSDm>?j_5|J zv45NQXZ6HCb~X@g;$s&-UWkh84J=gd?@;7vW5M`j#Y2}EoCQ{n~P78P~#>)l6`KX*iQ1j;nm zzkXX}L^Iiys@}8wgldx>{UZxSB=XnK?0;3B^Y36{$$r@UBVlRAs`(ha9u0iM)G?DP zU$zPx{=(uk;tN|zwmWvbyo92f`5v9VDLRGkm#WL%=bJ^`N^=)&o;#5|q%2AF!1zUR zzDym&%VNz_qBoH|ru)NEE%dT@Clouhi>g!4!m<+8H??H5J)DY=&ho{EKhprZCVyGV zL&4w^#u~d}QChz!0GiV^;G+`!nI*@^1=uH+WIR~_9EzRmQtXxTj>#~Ka?UhDo%smZ7;)yI(+Co9 zv@=4Vy?;N#r0ltmU}tZ=8_b_Y!haOXNo^R&OC9XYbHXts)~Osk!mAu8$gSQB6-FH7 z0yYff0uC12Uk)D8Uk)^pD%Q>M4b27l!3S{^Imitx7RU>1D%J^XI;dGcMv|eL)nb8~ z)uv*b)utmt%VsXqRrLT%dd_)-o{3}3FlD|gcYu*jW)qRF=CDEC2W^Fg8h^mS0w%}~ z6A@xdb<(daKPwi2ddZ=Iy2<6z=bSDRE3_^j;Rh}in>yhia;+B6oP&>z^VOU`UB3F zO^`*Tq6&zZ^pinB>4!t2QWlSIKB4wwTpTJMT`*{W!$U^( znvy%VE1%OqHLs6|YGE&X?xVPu!2l`sg28&^wE+oc`LKhG*R zcTL0G6n`ne{3Yb+b@Jap9~1eUJm{GT0e0k+E3Av$^&>s=?##0Q^Ot)zh;naU$*LYu zA=Ih^Y@!{cAR8U#LhdoFGo?)BQznGADQByQ{IsU+Ff_mGIDe#8y-jjG^e95R7zA3& zm>^o)m?To`)MvPa)-edQ)-ge})-g$>*10dGL4<_nF%gE;Ts^%gVGleOWC`s!`p5(tnRxT_#{k?LvzKPe_d^OD>m2nxYrDd!}*`Mtq*rHMuu^Kbu#x4mN*3 zq|ceT5z>QTJha7uoYUsb+Y;!J=Iwg)5QHFU8N^+V9*Y`l9Y}UTMk^9=hor|MZH@Fu zPdt)g{q2!i^&@P_TDO?a8bdqA=G~EWXZsL|B1*%|nSTN`2qcBhAa$NH4C06pKFFm+ z`yiAUuY+7t>>5-(Z{2eR@^~8ta(Wj__j`wl@+k*=EPsGw$wB21adG9KL80Yvana?V zQ2}7wEPOntVCc+e-N%rqAaKCORrye^NJI{91G2a{#JKzc zUU!^kIe%!7W;tA(W;y7L=i9EKkzg5}XRlwzz%Z2+FMN*@***)Uu{ISTu@=(aY9RHL z#Rlv3;M+VO++L= z?Sp4O&^3&NMN}V4kNrLm82WuMD)ReONZ?yYTz}m6!Nt5KoA;RSFchXG#YKK7G3m2V zYGP9Xa$+H2u5WSaL23bkQEHJ& zaeqSYvO!Yr0aw2)K*kLdF1wh1EfUDE5lBYX4mzoItJkyoK*F)}CT5TP=LMKVTA`ws zzPeDR89N!T5@_#JT8u7##o=n+=F_{+zNpyUKkdxM;G@&pC;UyMl%x1=&&zCx6dqkT&~O| ze)C)T-+$DWMuKQ=1q=(*UQUBpnmS%XjMa}sLhF@@xOgdw@3$<`ffil= zG{3zwEu&mxn#HNhuw`sdSgI{zc=G&p5$l(yVL9h z5$jhhO%|~~`5E?QtWRCzTxUIJyYqq*9Rpv!;rvDad&=7*vnyd)S!P5Svj-mqMvN^a zMI#oA&wx+8P6=jo!cG#-7I-)tF}2uGGGcR?;bX+$Vxm1_b0MJ~v$(i8+ZNC1UXKsk zGDRe^<>EsRMQQXMblbMGymGr17k|}m$J}>_e#hqb(Qn7>lB(($`!2fbSY2LO9h2Wh zTOF(4LF-W4W4dsJ7oLmUn>+nK<)zlwA4a$N-^(Ot`WHoD{GwwP!TDT|6*IFx=%vV< zmvCFamvcHh|9uf+U$pyC^KWADBIdk_Y+*80XU6Ia!A`+w?#p!#E; 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        		•     pyrimidine.benchmarks.approximation
            - pyrimidine.benchmarks.cluster -
            @@ -94,21 +89,11 @@

        Python Module Index

        		    pyrimidine.benchmarks.matrix
            - pyrimidine.benchmarks.neural_network -
            pyrimidine.benchmarks.optimization
            - pyrimidine.benchmarks.others -
            @@ -182,17 +167,17 @@

        Python Module Index

            - pyrimidine.population + pyrimidine.optimize
            - pyrimidine.pso + pyrimidine.population
            - pyrimidine.species + pyrimidine.pso