jwalsh

Scratchpad

append

Implement append in Scheme, TypeScript, Python, and Kotlin

Here is an implementation of the append function in Scheme:

(define (append lst1 lst2)
  (if (null? lst1)

jwalsh

Keybase proof

I hereby claim:

• I am jwalsh on github.
• I am jwalsh (https://keybase.io/jwalsh) on keybase.
• I have a public key whose fingerprint is 9EC9 549C D2D6 487F 7D2B 41A8 D522 F8F0 E5EF 6663

To claim this, I am signing this object:

jwalsh

// Resolve a dependency graph to sequence
// https://web.stanford.edu/~jurafsky/slp3/15.pdf


const resolve = (sentence, deps) => {
    let ops = [];
    while (sentence.length > 0) {
        let word = sentence.pop();
        if (deps.hasOwnProperty(word)) {
            console.log('Dependencies exist for', word);

jwalsh

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jwalsh

HOST=example.com
npm install -g get-graphql-schema graphql-cli graphql-faker
mkdir graphql-faker-$HOST && cd graphql-faker-$HOST
graphql -p $HOST init
graphql -p $HOST get-schema

# The previous is the same as 
# get-graphql-schema https://$HOST/graphql/ > schema.graphql

graphql-faker schema.graphql

jwalsh

export PATH="/usr/local/sbin:$PATH"

[[ -r "/usr/local/etc/profile.d/bash_completion.sh" ]] && . "/usr/local/etc/profile.d/bash_completion.sh"

alias emacs='/Applications/Emacs.app/Contents/MacOS/Emacs'

if [ -f "$(brew --prefix)/opt/bash-git-prompt/share/gitprompt.sh" ]; then
    __GIT_PROMPT_DIR=$(brew --prefix)/opt/bash-git-prompt/share
    GIT_PROMPT_ONLY_IN_REPO=1
    source "$(brew --prefix)/opt/bash-git-prompt/share/gitprompt.sh"

jwalsh

// Procedure: Each connection should link lower to higher
let layout = [
	[0, 1, 2],
	[7, 8, 3],
	[6, 5, 4],
];

// Encoding
let digits = [
	"0107122334455667", // 0

jwalsh

import numpy as np
import cv2
import time
import datetime

# import the cascade for face detection
face_cascade = cv2.CascadeClassifier("cascades/haarcascade_frontalface_default.xml")

def TakeSnapshotAndSave():
    # access the webcam (every webcam has a number, the default is 0)

jwalsh

import tensorflow as tf

image_size = 28
labels_size = 10
hidden_size = 1024

# Define placeholders
training_data = tf.placeholder(tf.float32, [None, image_size*image_size])
training_images = tf.reshape(training_data, [-1, image_size, image_size, 1])

jwalsh

import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)

from tensorflow.examples.tutorials.mnist import input_data 

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
                
                
image_size = 28
labels_size = 10