Taken from http://thedarnedestthing.com/vimwiki%20cheatsheet
[n] is relative wiki order as defined in .vimrc, default 1.
keys | action |
---|---|
[n] <leader>ww | open wiki index file |
[n] \wt | open wiki index file in new tab |
import getpass | |
import os | |
import bs4 | |
from langchain import hub | |
# from langchain_community.document_loaders import WebBaseLoader | |
from langchain_chroma import Chroma | |
from langchain_core.output_parsers import StrOutputParser | |
from langchain_core.runnables import RunnablePassthrough | |
from langchain_openai import ChatOpenAI, OpenAIEmbeddings | |
from langchain_text_splitters import RecursiveCharacterTextSplitter |
Service used for data extraction from unstructured HTML: https://www.kadoa.com | |
Data source: http://clhs.lisp.se/Body/f_map.htm | |
Resulting JSON: | |
[ | |
{ | |
"id": "661a5232c700f85cf0e07c91", | |
"data": { |
import gradio as gr | |
from PIL import Image | |
import torch | |
from transformers import AutoProcessor, AutoModelForCausalLM | |
processor = AutoProcessor.from_pretrained("microsoft/git-base-textvqa") | |
model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-textvqa", cache_dir='/tmp') | |
def doit(image1, image2, question): |
/** | |
* @param <T> the type of elements in the list | |
*/ | |
public sealed interface SizedList<T, LENGTH extends Nat> { | |
/** | |
* The empty list. | |
*/ | |
final class Nil<T> implements SizedList<T, Zero> { | |
private static final Nil<?> NIL = new Nil<>(); |
<!DOCTYPE html> | |
<html lang="en"> | |
<head> | |
<meta charset="UTF-8"> | |
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
<title>IRC Message Parser</title> | |
<style> | |
body { | |
font-family: Arial, sans-serif; | |
} |
Taken from http://thedarnedestthing.com/vimwiki%20cheatsheet
[n] is relative wiki order as defined in .vimrc, default 1.
keys | action |
---|---|
[n] <leader>ww | open wiki index file |
[n] \wt | open wiki index file in new tab |
# Chapter 17 of HPMOR | |
# The experiment with prime numbers | |
# python doesn't support tail recursion, so do this with a while loop | |
def run(x, y): | |
while True: | |
# paper 2 is blank | |
if x is None and y is None: | |
x, y = 101, 101 |
MAX_LENGTH = 12 | |
def snip(request): | |
""" | |
request - str | |
""" | |
snipped_req = "" | |
lines = list(filter(lambda l: len(l) > 0, request.split('\n'))) |
(4) Another (and a more universal approach) to get rid of the dependence on $\theta$ in (2) is to estimate $s$ via the sample | |
standard error and use approximation of $\bar{X}$ via Student t-distribution; see details in Ross textbook on statistics or in the lecture notes | |
```{r} | |
for (n in c(100, 1000, 10000)) { | |
cat("FOR SAMPLE SIZE = ", n, ":\n") | |
sample_exp = rexp(n*m, lambda) # creating a single sample of exponential distribution | |
sample_means = colMeans(matrix(sample_exp, nrow=n)) # creating a sample of sample means | |