def grow(self, min_depth=1, max_depth=3):
"""It creates a random tree based on the GROW algorithm.
References:
S. Lukw. Two Fast Tree-Creation Algorithms for Genetic Programming.
IEEE Transactions on Evolutionary Computation (2000).
Args:
min_depth (int): Minimum depth of the tree.
max_depth (int): Maximum depth of the tree.
Returns:
A random tree based on the GROW algorithm.
"""
# Re-initialize the terminals to provide diversity
self._initialize_terminals()
# If minimum depth equals the maximum depth
if min_depth == max_depth:
# Generates a terminal identifier
terminal_id = r.generate_integer_random_number(0, self.n_terminals)
# Return the terminal node with its id and corresponding position
return Node(terminal_id, 'TERMINAL', self.terminals[terminal_id].position)
# Generates a node identifier
node_id = r.generate_integer_random_number(0, len(self.functions) + self.n_terminals)
# If the identifier is a terminal
if node_id >= len(self.functions):
# Gathers its real identifier
terminal_id = node_id - len(self.functions)
# Return the terminal node with its id and corresponding position
return Node(terminal_id, 'TERMINAL', self.terminals[terminal_id].position)
# Generates a new function node
function_node = Node(self.functions[node_id], 'FUNCTION')
# For every possible function argument
for i in range(c.N_ARGS_FUNCTION[self.functions[node_id]]):
# Calls recursively the grow function and creates a temporary node
node = self.grow(min_depth + 1, max_depth)
# If it is not the root
if not i:
# The left child receives the temporary node
function_node.left = node
# If it is the first node
else:
# The right child receives the temporary node
function_node.right = node
# Flag to identify whether the node is a left child
node.flag = False
# The parent of the temporary node is the function node
node.parent = function_node
return function_node
import numpy as np
from opytimizer.core.node import Node
# Creating two new Nodes
n1 = Node(name='0', type='TERMINAL', value=np.array(1))
n2 = Node(name='1', type='TERMINAL', value=np.array(2))
# Outputting information about one of the nodes
print(n1)
print(f'Post Order: {n1.post_order} | Size: {n1.n_nodes}.')
# Additionally, one can stack nodes to create a tree
t = Node(name='SUM', type='FUNCTION', left=n1, right=n2)
# Defining `n1` and `n2` parent as `t`
n1.parent = t
n2.parent = t
# Outputting information about the tree
print(t)
print(
f'Post Order: {t.post_order} | Size: {t.n_nodes} | Minimum Depth: {t.min_depth} | Maximum Depth: {t.max_depth}.'
)
import numpy as np
from opytimizer.core.node import Node
# Creates two new Nodes
n1 = Node(name="0", category="TERMINAL", value=np.array(1))
n2 = Node(name="1", category="TERMINAL", value=np.array(2))
# Outputting information about one of the nodes
print(n1)
print(f"Post Order: {n1.post_order} | Size: {n1.n_nodes}.")
# Additionally, one can stack nodes to create a tree
t = Node(name="SUM", category="FUNCTION", left=n1, right=n2)
# Defines `n1` and `n2` parent as `t`
n1.parent = t
n2.parent = t
# Outputting information about the tree
print(t)
print(
f"Post Order: {t.post_order} | Size: {t.n_nodes} | Minimum Depth: {t.min_depth} | Maximum Depth: {t.max_depth}."
)