From 46fe0c0cc53c35011758a74fc9f981d78f46f8db Mon Sep 17 00:00:00 2001
From: Sean Paeglis <s.paeglis@student.unimelb.edu.au>
Date: Sun, 19 May 2019 21:48:55 +1000
Subject: [PATCH] ML
---
__pycache__/visualize.cpython-36.pyc | Bin 0 -> 5377 bytes
ai-part-b-master | 1 +
config | 64 +++++++++
tensTest.py | 39 ++++++
visualize.py | 197 +++++++++++++++++++++++++++
5 files changed, 301 insertions(+)
create mode 100644 __pycache__/visualize.cpython-36.pyc
create mode 160000 ai-part-b-master
create mode 100644 config
create mode 100644 tensTest.py
create mode 100644 visualize.py
diff --git a/__pycache__/visualize.cpython-36.pyc b/__pycache__/visualize.cpython-36.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..820f6e2409a620aa5c5826b4e14a0adc6e2499b4
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diff --git a/ai-part-b-master b/ai-part-b-master
new file mode 160000
index 0000000..7d1282b
--- /dev/null
+++ b/ai-part-b-master
@@ -0,0 +1 @@
+Subproject commit 7d1282b7852f205d0eab5ad6c1a48222e04d42ab
diff --git a/config b/config
new file mode 100644
index 0000000..e2c73de
--- /dev/null
+++ b/config
@@ -0,0 +1,64 @@
+# NEAT configuration for the bit-sequence memory experiment.
+
+# The `NEAT` section specifies parameters particular to the NEAT algorithm
+# or the experiment itself. This is the only required section.
+[NEAT]
+fitness_criterion = max
+fitness_threshold = 600.0
+pop_size = 100
+reset_on_extinction = 0
+
+[DefaultGenome]
+num_inputs = 4
+num_hidden = 1
+num_outputs = 1
+initial_connection = partial_direct 0.5
+feed_forward = True
+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
+activation_default = sigmoid
+activation_options = sigmoid
+activation_mutate_rate = 0.0
+aggregation_default = sum
+aggregation_options = sum
+aggregation_mutate_rate = 0.0
+bias_init_mean = 0.0
+bias_init_stdev = 1.0
+bias_replace_rate = 0.1
+bias_mutate_rate = 0.7
+bias_mutate_power = 0.5
+bias_max_value = 30.0
+bias_min_value = -30.0
+response_init_mean = 1.0
+response_init_stdev = 0.0
+response_replace_rate = 0.0
+response_mutate_rate = 0.0
+response_mutate_power = 0.0
+response_max_value = 30.0
+response_min_value = -30.0
+
+weight_max_value = 30
+weight_min_value = -30
+weight_init_mean = 0.0
+weight_init_stdev = 1.0
+weight_mutate_rate = 0.8
+weight_replace_rate = 0.1
+weight_mutate_power = 0.5
+enabled_default = True
+enabled_mutate_rate = 0.01
+
+[DefaultSpeciesSet]
+compatibility_threshold = 3.0
+
+[DefaultStagnation]
+species_fitness_func = max
+max_stagnation = 20
+
+[DefaultReproduction]
+elitism = 2
+survival_threshold = 0.2
+
diff --git a/tensTest.py b/tensTest.py
new file mode 100644
index 0000000..88916fd
--- /dev/null
+++ b/tensTest.py
@@ -0,0 +1,39 @@
+from itertools import cycle
+from numpy.random import randint,choice
+import sys
+import neat
+import os
+import random
+
+GENERATION = 0
+MAX_FITNESS = 0
+BEST_GENOME = 0
+
+def eval_genomes(genomes, config):
+ i = 0
+ global SCORE
+ global GENERATION, MAX_FITNESS, BEST_GENOME
+
+ GENERATION += 1
+ for genome_id, genome in genomes:
+
+ genome.fitness = random.randint(1,10)
+ print("Gen : %d Genome # : %d Fitness : %f Max Fitness : %f"%(GENERATION,i,genome.fitness, MAX_FITNESS))
+ if genome.fitness >= MAX_FITNESS:
+ MAX_FITNESS = genome.fitness
+ BEST_GENOME = genome
+ SCORE = 0
+ i+=1
+
+
+config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
+ neat.DefaultSpeciesSet, neat.DefaultStagnation,
+ 'config')
+
+pop = neat.Population(config)
+stats = neat.StatisticsReporter()
+pop.add_reporter(stats)
+
+winner = pop.run(eval_genomes, 30)
+
+print(winner)
\ No newline at end of file
diff --git a/visualize.py b/visualize.py
new file mode 100644
index 0000000..3bae1a2
--- /dev/null
+++ b/visualize.py
@@ -0,0 +1,197 @@
+from __future__ import print_function
+
+import copy
+import warnings
+
+import graphviz
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def plot_stats(statistics, ylog=False, view=False, filename='avg_fitness.svg'):
+ """ Plots the population's average and best fitness. """
+ if plt is None:
+ warnings.warn("This display is not available due to a missing optional dependency (matplotlib)")
+ return
+
+ generation = range(len(statistics.most_fit_genomes))
+ best_fitness = [c.fitness for c in statistics.most_fit_genomes]
+ avg_fitness = np.array(statistics.get_fitness_mean())
+ stdev_fitness = np.array(statistics.get_fitness_stdev())
+
+ plt.plot(generation, avg_fitness, 'b-', label="average")
+ plt.plot(generation, avg_fitness - stdev_fitness, 'g-.', label="-1 sd")
+ plt.plot(generation, avg_fitness + stdev_fitness, 'g-.', label="+1 sd")
+ plt.plot(generation, best_fitness, 'r-', label="best")
+
+ plt.title("Population's average and best fitness")
+ plt.xlabel("Generations")
+ plt.ylabel("Fitness")
+ plt.grid()
+ plt.legend(loc="best")
+ if ylog:
+ plt.gca().set_yscale('symlog')
+
+ plt.savefig(filename)
+ if view:
+ plt.show()
+
+ plt.close()
+
+
+def plot_spikes(spikes, view=False, filename=None, title=None):
+ """ Plots the trains for a single spiking neuron. """
+ t_values = [t for t, I, v, u, f in spikes]
+ v_values = [v for t, I, v, u, f in spikes]
+ u_values = [u for t, I, v, u, f in spikes]
+ I_values = [I for t, I, v, u, f in spikes]
+ f_values = [f for t, I, v, u, f in spikes]
+
+ fig = plt.figure()
+ plt.subplot(4, 1, 1)
+ plt.ylabel("Potential (mv)")
+ plt.xlabel("Time (in ms)")
+ plt.grid()
+ plt.plot(t_values, v_values, "g-")
+
+ if title is None:
+ plt.title("Izhikevich's spiking neuron model")
+ else:
+ plt.title("Izhikevich's spiking neuron model ({0!s})".format(title))
+
+ plt.subplot(4, 1, 2)
+ plt.ylabel("Fired")
+ plt.xlabel("Time (in ms)")
+ plt.grid()
+ plt.plot(t_values, f_values, "r-")
+
+ plt.subplot(4, 1, 3)
+ plt.ylabel("Recovery (u)")
+ plt.xlabel("Time (in ms)")
+ plt.grid()
+ plt.plot(t_values, u_values, "r-")
+
+ plt.subplot(4, 1, 4)
+ plt.ylabel("Current (I)")
+ plt.xlabel("Time (in ms)")
+ plt.grid()
+ plt.plot(t_values, I_values, "r-o")
+
+ if filename is not None:
+ plt.savefig(filename)
+
+ if view:
+ plt.show()
+ plt.close()
+ fig = None
+
+ return fig
+
+
+def plot_species(statistics, view=False, filename='speciation.svg'):
+ """ Visualizes speciation throughout evolution. """
+ if plt is None:
+ warnings.warn("This display is not available due to a missing optional dependency (matplotlib)")
+ return
+
+ species_sizes = statistics.get_species_sizes()
+ num_generations = len(species_sizes)
+ curves = np.array(species_sizes).T
+
+ fig, ax = plt.subplots()
+ ax.stackplot(range(num_generations), *curves)
+
+ plt.title("Speciation")
+ plt.ylabel("Size per Species")
+ plt.xlabel("Generations")
+
+ plt.savefig(filename)
+
+ if view:
+ plt.show()
+
+ plt.close()
+
+
+def draw_net(config, genome, view=False, filename=None, node_names=None, show_disabled=True, prune_unused=False,
+ node_colors=None, fmt='svg'):
+ """ Receives a genome and draws a neural network with arbitrary topology. """
+ # Attributes for network nodes.
+ if graphviz is None:
+ warnings.warn("This display is not available due to a missing optional dependency (graphviz)")
+ return
+
+ if node_names is None:
+ node_names = {}
+
+ assert type(node_names) is dict
+
+ if node_colors is None:
+ node_colors = {}
+
+ assert type(node_colors) is dict
+
+ node_attrs = {
+ 'shape': 'circle',
+ 'fontsize': '9',
+ 'height': '0.2',
+ 'width': '0.2'}
+
+ dot = graphviz.Digraph(format=fmt, node_attr=node_attrs)
+
+ inputs = set()
+ for k in config.genome_config.input_keys:
+ inputs.add(k)
+ name = node_names.get(k, str(k))
+ 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', 'fillcolor': node_colors.get(k, 'lightblue')}
+
+ dot.node(name, _attributes=node_attrs)
+
+ if prune_unused:
+ connections = set()
+ for cg in genome.connections.values():
+ if cg.enabled or show_disabled:
+ connections.add((cg.in_node_id, cg.out_node_id))
+
+ used_nodes = copy.copy(outputs)
+ pending = copy.copy(outputs)
+ while pending:
+ new_pending = set()
+ for a, b in connections:
+ if b in pending and a not in used_nodes:
+ new_pending.add(a)
+ used_nodes.add(a)
+ pending = new_pending
+ else:
+ used_nodes = set(genome.nodes.keys())
+
+ for n in used_nodes:
+ if n in inputs or n in outputs:
+ continue
+
+ attrs = {'style': 'filled',
+ 'fillcolor': node_colors.get(n, 'white')}
+ dot.node(str(n), _attributes=attrs)
+
+ for cg in genome.connections.values():
+ if cg.enabled or show_disabled:
+ #if cg.input not in used_nodes or cg.output not in used_nodes:
+ # continue
+ input, output = cg.key
+ a = node_names.get(input, str(input))
+ b = node_names.get(output, str(output))
+ style = 'solid' if cg.enabled else 'dotted'
+ color = 'green' if cg.weight > 0 else 'red'
+ width = str(0.1 + abs(cg.weight / 5.0))
+ dot.edge(a, b, _attributes={'style': style, 'color': color, 'penwidth': width})
+
+ dot.render(filename, view=view)
+
+ return dot
--
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