miku

import { marked } from 'marked'
import hljs from 'highlight.js'
import katex from 'katex'

marked.setOptions({
  langPrefix: 'hljs lang-',
  pedantic: false,
  gfm: true, // 是否启动类似Github样式的Markdown
  breaks: false, // 是否支持Github换行符
  tables: true, // 是否支持Github形式的表格

miku

/***********************************************
 1. save this file as improveEdgeLabels.gvpr
 2. what it does: it allows you to re-position Graphviz edge labels
 3. command line example (works with any Graphviz engine):
   dot myfile.gv |gvpr -cf improveEdgeLabels.gvpr |neato -n2 -Tpng >myfile.png
 4. how to use:
   improveEdgeLabels adds 3 new edge attributes to Graphviz:
   - labelOverlay, labelAdjust, label2Node
   - they can be used separately or in combination
   - if labelOverlay=true

miku

AA
AAA
Aachen
Aachener
Aachenerin
Aachenerinnen
Aachenern
Aacheners
Aachens
Aal

miku

via (https://www.linux.com/learn/tutorials/442438-vim-tips-folding-fun)

• zf#j creates a fold from the cursor down # lines.
• zf/string creates a fold from the cursor to string .
• zj moves the cursor to the next fold.
• zk moves the cursor to the previous fold.
• zo opens a fold at the cursor.
• zO opens all folds at the cursor.
• zm increases the foldlevel by one.
• zM closes all open folds.

miku

package main

import (
	"context"
	"errors"
	"fmt"
	"io"
	"log"
	"os"
	"runtime"

miku

import cv2

def add_gaussian_noise(X_imgs):
    gaussian_noise_imgs = []
    row, col, _ = X_imgs[0].shape
    # Gaussian distribution parameters
    mean = 0
    var = 0.1
    sigma = var ** 0.5
    

miku

# SGR color constants
# rene-d 2018

class Colors:
    """ ANSI color codes """
    BLACK = "\033[0;30m"
    RED = "\033[0;31m"
    GREEN = "\033[0;32m"
    BROWN = "\033[0;33m"
    BLUE = "\033[0;34m"

miku

Learning Python 3

This gist provides the learning-python3.ipynb notebook file, which can be viewed and edited in a Jupyter Notebook server to learn Python 3.

miku

# Reference: kstathou/acl-search-engine

# !pip install faiss-cpu --no-cache
# !pip install sentence_transformers

import faiss
import numpy as np
import pandas as pd 
import pickle
import torch

miku

llmc() {
    local system_prompt='Output a command that I can run in a ZSH terminal on macOS to accomplish the following task. Try to make the command self-documenting, using the long version of flags where possible. Output the command first enclosed in a "```zsh" codeblock followed by a concise explanation of how it accomplishes it.'
    local temp_file=$(mktemp)
    local capturing=true
    local command_buffer=""
    local first_line=true
    local cleaned_up=false # Flag to indicate whether cleanup has been run

    cleanup() {
        # Only run cleanup if it hasn't been done yet