| class LinearRegression { | |
| w: Mat; | |
| constructor() { } | |
| //x: M-by-N matrix. M data with N dimensions. Each row is an N-dim vector | |
| //y: M-by-1 matrix | |
| fit(x_: Mat, y_: Mat) : LinearRegression{ | |
| let y = y_; | |
| if (y_.rows != 1 && y_.cols == 1) {y = y_.T();} //check the dimension of y | |
| var x = x_.resize(x_.rows, x_.cols + 1, 1); //expan x_ with one more column with 1 |
| -- 只扫原生WS,不要太恐慌. | |
| local stdnse = require "stdnse" | |
| local http = require "http" | |
| categories = { "default", "discovery", "safe" } | |
| portrule = function(host, port) | |
| return true | |
| end |
| import requests | |
| import json | |
| import base64 | |
| import sqlite3 | |
| import zlib | |
| import time | |
| from datetime import datetime | |
| from gql import gql, Client | |
| from gql.transport.requests import RequestsHTTPTransport |
~/.bash_aliases file.function gdrive_download () {
CONFIRM=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate "https://docs.google.com/uc?export=download&id=$1" -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$CONFIRM&id=$1" -O $2
rm -rf /tmp/cookies.txt
}
| #!/bin/bash | |
| set -e | |
| # Put | |
| # 1. CUDA installation script (cuda_*.run, see https://developer.nvidia.com/cuda-downloads) | |
| # 2. cuDNN archive (cudnn-*.tgz, see https://developer.nvidia.com/rdp/cudnn-download) | |
| # 3. Intel MKL archive (l_mkl_*.tgz, see https://software.intel.com/en-us/intel-mkl) | |
| # under $HOME | |
| # Also set INTEL_MKL_SN to Intel MKL's serial number (XXXX-XXXXXXXX) |
| git config --global https.proxy http://127.0.0.1:1080 | |
| git config --global https.proxy https://127.0.0.1:1080 | |
| git config --global --unset http.proxy | |
| git config --global --unset https.proxy | |
| npm config delete proxy |
| —– BEGIN LICENSE —– | |
| Anthony Sansone | |
| Single User License | |
| EA7E-878563 | |
| 28B9A648 42B99D8A F2E3E9E0 16DE076E | |
| E218B3DC F3606379 C33C1526 E8B58964 | |
| B2CB3F63 BDF901BE D31424D2 082891B5 | |
| F7058694 55FA46D8 EFC11878 0868F093 | |
| B17CAFE7 63A78881 86B78E38 0F146238 | |
| BAE22DBB D4EC71A1 0EC2E701 C7F9C648 |
一直都喜欢用markdown记录东西，即使平时用的某云笔记并不支持markdown，也还是习惯保持markdown格式的记录。最近Medium比较火，就试着把之前写好的一篇文章发到Medium上去，结果Medium并不支持Markdown，可是在Medium上写作的Geek们自然有办法：
就是在这里看到了Github的Gist原来可以这么玩，忽然好奇Gist是不是还有其他好玩的玩儿法。一发不可收拾，果然找到了各种花样玩儿法：
总得来说还是对Gist写东西比较感兴趣，有版本控制，能够设置成别人可见或不可见，还支持协作。Gist写好之后可以通过roughdraft.io发布出来，读起来很方便。
| ACM大量习题题库 | |
| ACM大量习题题库 | |
| 现在网上有许多题库，大多是可以在线评测，所以叫做Online Judge。除了USACO是为IOI准备外，其余几乎全部是大学的ACM竞赛题库。 | |
| USACO | |
| http://ace.delos.com/usacogate | |
| 美国著名在线题库，专门为信息学竞赛选手准备 |
| #!/usr/bin/env python | |
| """ | |
| ===================================== | |
| PEP 20 (The Zen of Python) by example | |
| ===================================== | |
| Usage: %prog | |
| :Author: Hunter Blanks, hblanks@artifex.org / hblanks@monetate.com |