fix merge conflict

CodeReclaimers · Dec 13, 2019 · 2ed502b · 2ed502b
2 parents 4359e95 + c2b79c8
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diff --git a/.travis.yml b/.travis.yml
@@ -1,17 +1,14 @@
-dist: precise
 language: python
 python:
-  - 2.7
+  - 3.4
   - 3.5
   - 3.6
-  - 3.3
-  - 3.4
-  - 3.5-dev
-  - 3.6-dev
+  - 3.7
   - 3.7-dev
+  - 3.8
+  - 3.8-dev
   - nightly
-  - pypy
-  - pypy3.5-5.8.0
+  - pypy3
 install:
     - pip install --upgrade nose coveralls coverage
     - if [[ $TRAVIS_PYTHON_VERSION == 'pypy'* ]]; then export TRAVIS_WAIT=45; else export TRAVIS_WAIT=20; fi

diff --git a/LICENSE b/LICENSE
@@ -1,5 +1,5 @@
 Copyright (c) 2007-2011, cesar.gomes and mirrorballu2
-Copyright (c) 2015-2017, CodeReclaimers, LLC
+Copyright (c) 2015-2019, CodeReclaimers, LLC
 
 Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
 following conditions are met:

diff --git a/README.md b/README.md
@@ -1,18 +1,42 @@
 [![Build Status](https://travis-ci.org/CodeReclaimers/neat-python.svg)](https://travis-ci.org/CodeReclaimers/neat-python)
 [![Coverage Status](https://coveralls.io/repos/CodeReclaimers/neat-python/badge.svg?branch=master&service=github)](https://coveralls.io/github/CodeReclaimers/neat-python?branch=master)
 
+## STATUS NOTE ##
+
+This project is currently in maintenance-only mode. I will make bug fixes, do cleanup, and possibly improve sample code
+as I have time, but I will not be adding any new features.  The forks by
+[@drallensmith](https://github.com/drallensmith/neat-python) and [@bennr01](https://github.com/bennr01/neat-python) have
+been extended beyond this implementation a great deal, so those might be better starting points if you need more
+features than what you see here.
+
 ## About ##
 
-NEAT (NeuroEvolution of Augmenting Topologies) is a method developed by Kenneth O. Stanley for evolving arbitrary neural 
-networks. This project is a Python implementation of NEAT.  It was forked from the excellent project by @MattKallada, 
-and is in the process of being updated to provide more features and a (hopefully) simpler and documented API.
+NEAT (NeuroEvolution of Augmenting Topologies) is a method developed by Kenneth O. Stanley for evolving arbitrary neural
+networks. This project is a pure-Python implementation of NEAT with no dependencies beyond the standard library. It was
+forked from the excellent project by @MattKallada, and is in the process of being updated to provide more features and a
+(hopefully) simpler and documented API.
 
-For further information regarding general concepts and theory, please see [Selected Publications](http://www.cs.ucf.edu/~kstanley/#publications) on Stanley's website.
+For further information regarding general concepts and theory, please see
+[Selected Publications](http://www.cs.ucf.edu/~kstanley/#publications) on Stanley's website.
 
 `neat-python` is licensed under the [3-clause BSD license](https://opensource.org/licenses/BSD-3-Clause).
 
 ## Getting Started ##
 
-If you want to try neat-python, please check out the repository, start playing with the examples (`examples/xor` is a good place to start) and then try creating your own experiment.
+If you want to try neat-python, please check out the repository, start playing with the examples (`examples/xor` is
+a good place to start) and then try creating your own experiment.
+
+The documentation, is available on [Read The Docs](http://neat-python.readthedocs.io).
+
+## Citing ##
+
+Here is a Bibtex entry you can use to cite this project in a publication. The listed authors are the maintainers of
+all iterations of the project up to this point. 
 
-The documentation, which is still a work in progress, is available on [Read The Docs](http://neat-python.readthedocs.io).
+```
+@misc{neat-python,
+    Title = {neat-python},
+    Author = {Alan McIntyre and Matt Kallada and Cesar G. Miguel and Carolina Feher da Silva},
+    howpublished = {\url{https://github.com/CodeReclaimers/neat-python}}   
+  }
+```
diff --git a/docs/activation-abs.png b/docs/activation-abs.png
diff --git a/docs/activation-clamped.png b/docs/activation-clamped.png
diff --git a/docs/activation-cube.png b/docs/activation-cube.png
diff --git a/docs/activation-elu.png b/docs/activation-elu.png
diff --git a/docs/activation-exp.png b/docs/activation-exp.png
diff --git a/docs/activation-gauss.png b/docs/activation-gauss.png
diff --git a/docs/activation-hat.png b/docs/activation-hat.png
diff --git a/docs/activation-identity.png b/docs/activation-identity.png
diff --git a/docs/activation-inv.png b/docs/activation-inv.png
diff --git a/docs/activation-lelu.png b/docs/activation-lelu.png
diff --git a/docs/activation-log.png b/docs/activation-log.png
diff --git a/docs/activation-relu.png b/docs/activation-relu.png
diff --git a/docs/activation-selu.png b/docs/activation-selu.png
diff --git a/docs/activation-sigmoid.png b/docs/activation-sigmoid.png
diff --git a/docs/activation-sin.png b/docs/activation-sin.png
diff --git a/docs/activation-softplus.png b/docs/activation-softplus.png
diff --git a/docs/activation-square.png b/docs/activation-square.png
diff --git a/docs/activation-tanh.png b/docs/activation-tanh.png
diff --git a/docs/activation.rst b/docs/activation.rst
@@ -85,6 +85,27 @@ relu
    :scale: 50 %
    :alt: rectified linear function
 
+elu
+----
+
+.. figure:: activation-elu.png
+   :scale: 50 %
+   :alt: exponential rectified linear function
+
+lelu
+----
+
+.. figure:: activation-lelu.png
+   :scale: 50 %
+   :alt: leaky rectified linear function
+
+selu
+----
+
+.. figure:: activation-selu.png
+   :scale: 50 %
+   :alt: scaled exponential linear function
+
 .. _sigmoid-label:
 
 sigmoid

diff --git a/docs/conf.py b/docs/conf.py
@@ -12,9 +12,8 @@
 # All configuration values have a default; values that are commented out
 # serve to show the default.
 
-import sys
 import os
-import shlex
+import sys
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
@@ -68,7 +67,7 @@
 
 # General information about the project.
 project = u'NEAT-Python'
-copyright = u'2015-2017, CodeReclaimers, LLC'
+copyright = u'2015-2019, CodeReclaimers, LLC'
 author = u'CodeReclaimers, LLC'
 
 # The version info for the project you're documenting, acts as replacement for
@@ -81,7 +80,7 @@
 release = '0.92'
 
 if version != release:
-  tags.add('draft')
+    tags.add('draft')
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

diff --git a/examples/circuits/evolve.py b/examples/circuits/evolve.py
@@ -178,10 +178,10 @@ def add_connection(self, config, input_key, output_key):
         self.connections[key] = connection
 
     def mutate_add_connection(self, config):
-        '''
+        """
         Attempt to add a new connection, the only restriction being that the output
         node cannot be one of the network input pins.
-        '''
+        """
         possible_outputs = list(iterkeys(self.nodes))
         out_node = choice(possible_outputs)
 
@@ -273,7 +273,7 @@ def distance(self, other, config):
         return distance
 
     def size(self):
-        '''Returns genome 'complexity', taken to be (number of nodes, number of enabled connections)'''
+        """Returns genome 'complexity', taken to be (number of nodes, number of enabled connections)"""
         num_enabled_connections = sum([1 for cg in self.connections.values() if cg.enabled is True])
         return len(self.nodes), num_enabled_connections
 

diff --git a/examples/circuits/visualize.py b/examples/circuits/visualize.py
@@ -140,17 +140,14 @@ def draw_net(config, genome, view=False, filename=None, node_names=None, show_di
     for k in config.genome_config.input_keys:
         inputs.add(k)
         name = node_names.get(k, str(k))
-        input_attrs = {'style': 'filled',
-                       'shape': 'box'}
-        input_attrs['fillcolor'] = node_colors.get(k, 'lightgray')
+        input_attrs = {'style': 'filled', 'shape': 'box', 'fillcolor': node_colors.get(k, 'lightgray')}
         dot.node(name, _attributes=input_attrs)
 
     outputs = set()
     for k in config.genome_config.output_keys:
         outputs.add(k)
         name = node_names.get(k, str(k))
-        node_attrs = {'style': 'filled'}
-        node_attrs['fillcolor'] = node_colors.get(k, 'lightblue')
+        node_attrs = {'style': 'filled', 'fillcolor': node_colors.get(k, 'lightblue')}
 
         dot.node(name, _attributes=node_attrs)
 
@@ -177,8 +174,7 @@ def draw_net(config, genome, view=False, filename=None, node_names=None, show_di
         if n in inputs or n in outputs:
             continue
 
-        attrs = {'style': 'filled'}
-        attrs['fillcolor'] = node_colors.get(n, 'white')
+        attrs = {'style': 'filled', 'fillcolor': node_colors.get(n, 'white')}
         dot.node(str(n), _attributes=attrs)
 
     for cg in genome.connections.values():

diff --git a/examples/memory-fixed/clean.bat b/examples/memory-fixed/clean.bat
@@ -0,0 +1,2 @@
+del *.csv *.svg *.gv
+del test_save_config.txt
diff --git a/examples/memory-fixed/clean.sh b/examples/memory-fixed/clean.sh
@@ -1,2 +1,3 @@
 #!/usr/bin/env bash
-rm *.csv *.svg *.gv
+rm *.csv *.svg *.gv
+rm test_save_config.txt
diff --git a/examples/memory-fixed/config b/examples/memory-fixed/config
@@ -1,4 +1,4 @@
-# NEAT configuration for the bit-sequence memory experiment.
+# NEAT configuration for the fixed-length bit-sequence memory experiment.
 
 # The `NEAT` section specifies parameters particular to the NEAT algorithm
 # or the experiment itself.  This is the only required section.

diff --git a/examples/memory-fixed/evolve.py b/examples/memory-fixed/evolve.py
@@ -9,12 +9,14 @@
 from __future__ import division, print_function
 
 import math
+import multiprocessing
 import os
 import random
 
-import neat
 import visualize
 
+import neat
+
 
 # Demonstration of how to add your own custom activation function.
 # This sinc function will be available if my_sinc_function is included in the
@@ -26,10 +28,9 @@ def sinc(x):
 
 
 # N is the length of the test sequence.
-N = 3
+N = 4
 # num_tests is the number of random examples each network is tested against.
-num_tests = 2 ** (N+2)
-
+num_tests = 2 ** (N + 2)
 
 
 def eval_genome(genome, config):
@@ -81,12 +82,8 @@ def run():
     pop.add_reporter(stats)
     pop.add_reporter(neat.StdOutReporter(True))
 
-    if 1:
-        pe = neat.ParallelEvaluator(4, eval_genome)
-        winner = pop.run(pe.evaluate, 1000)
-    else:
-        winner = pop.run(eval_genomes, 1000)
-
+    pe = neat.ParallelEvaluator(multiprocessing.cpu_count(), eval_genome)
+    winner = pop.run(pe.evaluate, 1000)
 
     # Log statistics.
     stats.save()
@@ -113,12 +110,13 @@ def run():
         print("OK" if correct else "FAIL")
         num_correct += 1 if correct else 0
 
-    print("{0} of {1} correct {2:.2f}%".format(num_correct, num_tests, num_correct/num_tests))
+    print("{0} of {1} correct {2:.2f}%".format(num_correct, num_tests, 100.0 * num_correct / num_tests))
 
     node_names = {-1: 'input', -2: 'gate', 0: 'output'}
     visualize.draw_net(config, winner, True, node_names=node_names)
     visualize.plot_stats(stats, ylog=False, view=True)
+    visualize.plot_species(stats, view=True)
 
 
 if __name__ == '__main__':
-    run()
+    run()
diff --git a/examples/memory-fixed/visualize.py b/examples/memory-fixed/visualize.py
@@ -141,17 +141,14 @@ def draw_net(config, genome, view=False, filename=None, node_names=None, show_di
     for k in config.genome_config.input_keys:
         inputs.add(k)
         name = node_names.get(k, str(k))
-        input_attrs = {'style': 'filled',
-                       'shape': 'box'}
-        input_attrs['fillcolor'] = node_colors.get(k, 'lightgray')
+        input_attrs = {'style': 'filled', 'shape': 'box', 'fillcolor': node_colors.get(k, 'lightgray')}
         dot.node(name, _attributes=input_attrs)
 
     outputs = set()
     for k in config.genome_config.output_keys:
         outputs.add(k)
         name = node_names.get(k, str(k))
-        node_attrs = {'style': 'filled'}
-        node_attrs['fillcolor'] = node_colors.get(k, 'lightblue')
+        node_attrs = {'style': 'filled', 'fillcolor': node_colors.get(k, 'lightblue')}
 
         dot.node(name, _attributes=node_attrs)
 
@@ -178,8 +175,7 @@ def draw_net(config, genome, view=False, filename=None, node_names=None, show_di
         if n in inputs or n in outputs:
             continue
 
-        attrs = {'style': 'filled'}
-        attrs['fillcolor'] = node_colors.get(n, 'white')
+        attrs = {'style': 'filled', 'fillcolor': node_colors.get(n, 'white')}
         dot.node(str(n), _attributes=attrs)
 
     for cg in genome.connections.values():

diff --git a/examples/memory-variable/clean.bat b/examples/memory-variable/clean.bat
@@ -0,0 +1 @@
+del *.csv *.svg *.gv
diff --git a/examples/memory-variable/config b/examples/memory-variable/config
@@ -1,4 +1,4 @@
-# NEAT configuration for the bit-sequence memory experiment.
+# NEAT configuration for the variable-length bit-sequence memory experiment.
 
 # The `NEAT` section specifies parameters particular to the NEAT algorithm
 # or the experiment itself.  This is the only required section.
@@ -12,14 +12,14 @@ reset_on_extinction   = 0
 num_inputs              = 3
 num_hidden              = 1
 num_outputs             = 1
-initial_connection      = partial 0.5
+initial_connection      = partial_direct 0.5
 feed_forward            = False
 compatibility_disjoint_coefficient    = 1.0
 compatibility_weight_coefficient      = 0.6
-conn_add_prob           = 0.2
-conn_delete_prob        = 0.2
-node_add_prob           = 0.2
-node_delete_prob        = 0.2
+conn_add_prob           = 0.1
+conn_delete_prob        = 0.1
+node_add_prob           = 0.1
+node_delete_prob        = 0.1
 activation_default      = sigmoid
 activation_options      = sigmoid
 activation_mutate_rate  = 0.0