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React + Typescript Cheatsheet

Setup

If you use atom... download & install the following packages:

• atom-typescript
• linter-tslint

What are Typescript type definition files? (*.d.ts)

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How to cross compile Go with CGO programs for a different OS/Arch

It is possible to compile Go programs for a different OS, even though go build says otherwise.

You'll need:

• a valid toolchain for the platform/os you're targetting
• Go Tip (works on 1.1 and 1.2rc1 but you might as well be on tip)
• golang-crosscompile helper script https://github.com/davecheney/golang-crosscompile
• the patch provided

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package com.example.app

import org.scalatra._
import servlet.{SizeConstraintExceededException, FileUploadSupport}
import scalate.ScalateSupport
import java.io.ByteArrayInputStream
import org.apache.poi.ss.usermodel.WorkbookFactory
import org.apache.poi.ss.usermodel.Cell
import org.apache.poi.ss.usermodel.DataFormatter
import org.apache.poi.ss.usermodel.DateUtil

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package com.stackoverflow.q3732109;

import java.io.IOException;
import java.io.OutputStream;
import java.net.InetSocketAddress;

import com.sun.net.httpserver.HttpExchange;
import com.sun.net.httpserver.HttpHandler;
import com.sun.net.httpserver.HttpServer;

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:: Launch PyCharm from within conda environment

:: This will ensure that all necessary environment variables are set.
:: Modify the paths and environment name for your installation, if necessary.
:: This configuration is for PyCharm x64 installed with JetBrains Toolbox and 
:: will launch the latest installed version.

@set condaroot="%userprofile%\Anaconda3"
@set pycharmroot="%localappdata%\JetBrains\Toolbox\apps\PyCharm-P\ch-0"
@set env=base

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from numba import cuda

gpus = cuda.gpus.lst

for gpu in gpus:
    with gpu:
        meminfo = cuda.current_context().get_memory_info()
        print("%s, free: %s bytes, total, %s bytes" % (gpu, meminfo[0], meminfo[1]))

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import time
import numba
from numba import jit
import numpy as np

#input matrices
matrix1 = np.random.rand(30,30)
matrix2 = np.random.rand(30,30)
rmatrix = np.zeros(shape=(30,30))

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"""
Hierarchial Clustering.
The goal of gist is to show to use scikit-learn to perform agglomerative clustering when:
1. There is a need for a custom distance metric (like levenshtein distance)
2. Use the distance in sklearn's API.

Adapted from: sklearn's FAQ.
http://scikit-learn.org/stable/faq.html
"""

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extern mod sync;

// str op trait
use std::str::StrSlice;
// for tcp listen
use std::io::{TcpListener, TcpStream};
use std::io::net::ip::SocketAddr;
// for trait
use std::io::{Listener, Writer, Acceptor, Buffer};
// for spawn