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import numpy as np
from graphviz import Digraph
import os
import matplotlib.pyplot as plt
import matplotlib.colors as colors
#Note
"visualization functions"
def execute_program(program, input_grid) :
return program.evaluate(input_grid)
def save_tree_as_dot(program, filename):
dot = Digraph(comment='Program Tree')
def add_nodes_edges(node):
if node.children:
node_label = f"{node.value}\nID:{node.id}"
dot.node(str(node.id), node_label, shape='box', style='filled', color='lightblue')
else:
node_label = f"{node.value}\nID:{node.id}"
dot.node(str(node.id), node_label, shape='ellipse', style='filled', color='lightgreen')
for child in node.children:
dot.edge(str(node.id), str(child.id))
add_nodes_edges(child)
add_nodes_edges(program)
dot.render(filename, view=False, format='png')
print(f"Program tree saved as {filename}.png")
def generate_composite_dot(all_generations, filename):
dot = Digraph(comment='All Programs Across Generations', graph_attr={'compound': 'true', 'rankdir': 'TB'})
dot.attr(rankdir='TB')
for gen_idx, generation in enumerate(all_generations):
with dot.subgraph(name=f'cluster_gen_{gen_idx}') as c:
c.attr(label=f'Generation {gen_idx}')
c.attr(style='filled', color='lightgrey')
c.attr(rank='same')
for prog_idx, (program, fitness) in enumerate(generation.programs_with_fitness):
is_selected = program in generation.selected_programs
is_best = program == generation.best_program
if is_best:
node_color = 'gold'
node_shape = 'doublecircle'
elif is_selected:
node_color = 'orange'
node_shape = 'box'
else:
node_color = 'lightblue'
node_shape = 'ellipse'
def add_nodes_edges(node, parent_id: str = None):
label = f"{node.value}\nID:{node.id}"
dot.node(str(node.id), label, shape=node_shape, style='filled', color=node_color)
if parent_id:
dot.edge(parent_id, str(node.id))
for child in node.children:
add_nodes_edges(child, str(node.id))
add_nodes_edges(program)
output_path = dot.render(filename, view=False, format='png')
print(f"All programs across generations saved as {output_path}")
# Visualization Functions
def plot_comparison(input_grid, expected_output, predicted_output, task_number):
fig, axs = plt.subplots(1, 3, figsize=(12, 4))
fig.suptitle(f'Task {task_number}')
cmap = colors.ListedColormap(['#000000', '#0074D9', '#FF4136', '#2ECC40', '#FFDC00', '#AAAAAA', '#F012BE', '#FF851B', '#7FDBFF', '#870C25'])
norm = colors.Normalize(vmin=0, vmax=9)
axs[0].imshow(input_grid, cmap=cmap, norm=norm)
axs[0].set_title("Input Grid")
axs[1].imshow(expected_output, cmap=cmap, norm=norm)
axs[1].set_title("Expected Output")
axs[2].imshow(predicted_output, cmap=cmap, norm=norm)
axs[2].set_title("Predicted Output")
plt.tight_layout()
plt.show()
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