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What is two-hot encoding?

Description

Two hot encoding was introduced in 2017 in "Marc G Bellemare et all "A distributional perspective on reinforcement learning" but the clearest description is in the 2020 paper "Dreamer-v3" by Danijar Hafner et al.) where it is used for reward and value distributions.

two-hot encoding is a generalization of onehot encoding to continuous values. It produces a vector of length |B| where all elements are 0 except for the two entries closest to the encoded continuous number, at positions k and k + 1. These two entries sum up to 1, with more weight given to the entry that is closer to the encoded number

Code samples

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"""
how to wrap a scikit-learn scalar like RobustScaler for pytorch
"""
import torch
import numpy as np
from einops import rearrange
from sklearn.preprocessing import StandardScaler, RobustScaler

class TorchRobustScaler(RobustScaler):

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"""
you cannot display, you need to specify html
- see also https://pandas.pydata.org/docs/user_guide/style.html#Builtin-Styles
"""
import pandas as pd
from IPython.display import display, HTML

df = pd.DataFrame({
    "strings": ["Adam", "Mike"],
    "ints": [1, 3],

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# see https://cheatography.com/linux-china/cheat-sheets/justfile/

# we can set sehll
set shell := ["zsh", "-cu"]

# settings
set dotenv-load

# Export all just variables as environment variables.
set export

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"""
sometimes you want to run or adapt a cli script from jupyter, here a decent way to do it
# https://gist.github.com/wassname/f1c23636cc04b39176bd82f45c6e398a
"""

def jupyter_argparse(s: str) -> list:
    """
    Usage
      s = '''
      --rank 16 

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{
  "🦆": {
    "tags": [
      "Waterfowl",
      "Bird",
      "Quack"
    ],
    "usage": [
      "🦆🌊: swimming duck",
      "🦆🍞: feeding ducks",

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STOP DOING MATH

• NUMBERS WERE NOT SUPPOSED TO BE GIVEN NAMES
• YEARS OF COUNTING yet NO REAL-WORLD USE FOUND for going higher than your FINGERS
• Wanted to go higher anyway for a laugh? We had a tool for that: It was called "GUESSING"
• "Yes please give me ZERO of something. Please give me INFINITE of it" - Statements dreamed up by the utterly Deranged

LOOK at what Mathematicians have been demanding your Respect for all this time, with all the calculators & abacus we built for them

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"""
When splitting text for Language Models, aim for two properties:

 - Limit tokens to a maximum size (e.g., 400)
 - Use natural boundaries for splits (e.g. ".")

Many splitters don't enforce a token size limit, causing errors like "device assert" or "out of memory." Others focus on character length rather than token length. To address these issues:

- Use RecursiveCharacterTextSplitter from the langchain library
- Set the last separator to an empty string '' to ensure there is always a splitting point, thus maintaining token limits

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#!/bin/bash

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

### to verify your gpu is cuda enable check