albertbuchard

# Your init script
#
# Atom will evaluate this file each time a new window is opened. It is run
# after packages are loaded/activated and after the previous editor state
# has been restored.
#
# An example hack to log to the console when each text editor is saved.
#
# atom.workspace.observeTextEditors (editor) ->
#   editor.onDidSave ->

albertbuchard

setup='''
import numpy as np
import torch

V_nat = [[1, 2], [3, 4]]
U_nat = [[2, -1, 0, 0, 0, 0],
         [5, 2, 8, -1, 0, 0]]


def compute_using_fleuret_1():

albertbuchard

import torch
import torch.nn as nn

class Sparsemax(nn.Module):
    def __init__(self, dim=-1):
        super(Sparsemax, self).__init__()
        self.dim = dim

    def forward(self, x):
        # Move the dimension to apply Sparsemax to the front

albertbuchard

class DistributedIdentity:
    """
    Singleton class to hold distributed identity information.
    Handles SLURM, torchrun, and local runs.

    Looks for the following environment variables:
    - RANK
    - WORLD_SIZE
    - LOCAL_RANK

albertbuchard

import math
import torch.optim as optim
import torch
from torch import nn


class MineWrapper(nn.Module):

    def __init__(self, stat_model, moving_average_rate=0.1, unbiased=False):
        super(MineWrapper, self).__init__()

albertbuchard

tailast() {
    # Get the directory from the argument or use the current directory as default
    local dir="${1:-.}"

    # Find the most recently modified file in the directory without descending into subdirectories
    local latest_file=$(find "$dir" -maxdepth 1 -type f -exec stat --format='%Y %n' {} \; | sort -n | tail -1 | awk '{print $2}')

    # Check if a file was found
    if [[ -z "$latest_file" ]]; then
        echo "No files found in $dir"

albertbuchard

from concurrent.futures import ProcessPoolExecutor

import numpy as np
from scipy.signal import convolve2d
from tqdm import tqdm

nsims = int(1e6)
nevents = int(1e5)

albertbuchard

#!/usr/bin/env python3
"""
textrepo — dump a repository into a single text file, optimized for LLM input.

Features
- Always prints a folder tree of the discovered files (after ignores).
- Emits a per-file section for every *text-eligible* file (even if skipped/truncated).
- Respects .gitignore by default (disable with --no-gitignore).
- Supports extra ignore patterns and an additional ignore file (gitignore-ish).
- Detects and omits binary / non-text files from the per-file sections

albertbuchard

import matplotlib.pyplot as plt
import networkx as nx
from causallearn.utils.KCI.KCI import KCI_CInd, KCI_UInd
from npeet_plus import mi, mi_pvalue
from tqdm import tqdm

from src.causal_discovery.static_causal_discovery import (
    run_causal_discovery,
    visualize_causal_graph,
)

albertbuchard

Active learning in one line Keep a model $p_\theta(y\mid x,\mathcal D)$ that already knows something ($\mathcal D$); then ask for the single measurement $(x^*,y^*)$ that, once answered, will help most.

---

1. Value of Information (VoI)

Concept	Formal core	Intuition for the next query
Decision-centric	$\text{VoI}(x)=\mathbb E_{y\sim p_\theta(y\mid x,\mathcal D)}\big[\Delta U(\theta\mid x,y)\big]$	“If I were given the label of $x$, how much would my future utility rise on average?”