[ot][spam]gpt-4 completions

[Undescribed Horrific Abuse, One Victim & Survivor of Many]

https://arweave.net/SiWAWmYuzv3tH6QmzcWGlRMB3SLunH19AC79PST0nYc#minblockheight=1142624&minblockhash=qbL-on09lB2RU8M33XbFzLJgM3jq
WYY4k21GFXDYpwza75jxbQzZ6yUihQCdwc_r

{"time": "2023-03-21T17:18:13.607220", "lm": "openai", "model":
"gpt-4", "chat": true, "kwparams": {"temperature": 0, "top_p": 0},
"input": "Write a resumable tree explorer. Explore branches in order
of priority product, such that the exploration priority of each node
is the product of its priority with that of all its parents. The
priorities of branches can change during exploration. Take a
configuration flag as to whether to use ancestor priorities seen at
time of discovery or instead to update them when changed. Use
optimization techniques to prevent explosive looping if needed.",
"output": "To create a resumable tree explorer, we can use a priority
queue to store the nodes to be explored, sorted by their priority
product. We can also use a configuration flag to determine whether to
use ancestor priorities seen at the time of discovery or to update
them when changed. Here's a Python implementation of the resumable
tree explorer:\n\n```python\nimport heapq\n\nclass Node:\n    def
__init__(self, value, priority, children=None):\n        self.value =
value\n        self.priority = priority\n        self.children =
children if children is not None else []\n\n    def __lt__(self,
other):\n        return self.priority > other.priority\n\nclass
TreeExplorer:\n    def __init__(self, root,
use_ancestor_priorities_at_discovery=True):\n        self.root =
root\n        self.use_ancestor_priorities_at_discovery =
use_ancestor_priorities_at_discovery\n        self.priority_queue =
[(root.priority, root)]\n\n    def explore(self):\n        while
self.priority_queue:\n            priority, node =
heapq.heappop(self.priority_queue)\n            print(f\"Exploring
node {node.value} with priority {priority}\")\n\n            for child
in node.children:\n                if
self.use_ancestor_priorities_at_discovery:\n
child_priority = priority * child.priority\n                else:\n
                child_priority = self.calculate_priority(child)\n\n
            heapq.heappush(self.priority_queue, (child_priority,
child))\n\n    def calculate_priority(self, node):\n        if node ==
self.root:\n            return node.priority\n\n
parent_priority = self.calculate_priority(node.parent)\n        return
parent_priority * node.priority\n\n    def update_priority(self, node,
new_priority):\n        node.priority = new_priority\n\n        if not
self.use_ancestor_priorities_at_discovery:\n            for idx,
(priority, n) in enumerate(self.priority_queue):\n                if n
== node:\n                    self.priority_queue[idx] =
(self.calculate_priority(node), node)\n
heapq.heapify(self.priority_queue)\n
break\n```\n\nHere's an example of how to use the `TreeExplorer`
class:\n\n```python\n# Create a tree\nroot = Node(\"A\", 1)\nb =
Node(\"B\", 2)\nc = Node(\"C\", 3)\nd = Node(\"D\", 4)\ne =
Node(\"E\", 5)\nf = Node(\"F\", 6)\n\nroot.children = [b,
c]\nb.children = [d, e]\nc.children = [f]\n\n# Initialize the tree
explorer\nexplorer = TreeExplorer(root,
use_ancestor_priorities_at_discovery=True)\n\n# Explore the
tree\nexplorer.explore()\n\n# Update the priority of a
node\nexplorer.update_priority(d, 10)\n\n# Resume
exploration\nexplorer.explore()\n```\n\nThis implementation uses a
priority queue to efficiently explore nodes in order of their priority
product. The `update_priority` method allows updating the priority of
a node during exploration, and the exploration will be resumed
accordingly based on the configuration flag."}