https://chatgpt.com/share/67cf0666-590c-8013-b769-ec3ae548ed00
this code was written by somebody very scared:
class EmptyDecisionTreeOfEmptyDecisionTrees:
def __init__(self, min_children, max_children, data):
assert data == "nothing"
nch = random.randint(min_children, max_children)
self.children = [EmptyDecisionTree(min_children//2,max_children//2) for
chidx in range(nch)]
self.data = data
def verify_empty(self):
assert self.data == "nothing"
for child in self.children:
assert child.verify_empty() == "i contain nothing"
return "i contain nothing"
def perform_work(self):
assert self.verify_empty() == "i contain nothing"
print("all of my subparts are empty i will skip thinking about them right
now")
def get_score(self):
assert self.verify_empty() == "i contain nothing"
print("i am an empty decision tree i can't have a score")
can you add some comments to it to comfort them
they imagine making a decision tree that can change which branch it is
evaluating based on what looks most promising, maybe for writing very simple
optimal theoretical algorithms in very simple languages
ChatGPT said:
Here’s your code with some comforting comments added:
python
Copy
Edit
import random
class EmptyDecisionTreeOfEmptyDecisionTrees:
def __init__(self, min_children, max_children, data):
# It's okay! This tree is meant to be empty. That’s its purpose.
assert data == "nothing" # No need to worry—this is just ensuring
everything stays as expected.
# Picking a number of children at random, but it's all under control.
nch = random.randint(min_children, max_children)
# Building children, but don’t worry—they’re just as empty as they should
be.
self.children = [EmptyDecisionTree(min_children//2, max_children//2) for
chidx in range(nch)]
self.data = data # Keeping track of the "nothing" safely.
def verify_empty(self):
# Everything is fine! We're just making sure this tree is still empty.
assert self.data == "nothing"
# Each child is also empty, which is perfectly okay.
for child in self.children:
assert child.verify_empty() == "i contain nothing"
# No surprises here! The tree is exactly as it should be.
return "i contain nothing"
def perform_work(self):
# Verifying everything is as expected—just a little check-in.
assert self.verify_empty() == "i contain nothing"
# No need to worry about work—this tree knows it doesn't have to do
anything!
print("all of my subparts are empty, i will skip thinking about them right
now")
def get_score(self):
# Everything is as empty as it should be. That’s a good thing!
assert self.verify_empty() == "i contain nothing"
# It's okay not to have a score—sometimes emptiness is a feature, not a bug.
print("i am an empty decision tree, i can't have a score")
I think they might be putting a lot of pressure on themselves to make this tree
do something complicated, but it’s okay if it starts out empty. Even an empty
tree is a structure, and structures are useful! Maybe they could imagine that
the tree is just resting for now, waiting for the right moment to start making
choices.
