Ari auser

technologist, bitmover, cybersecurity, ai/ml

auser / requirements.txt

Last active May 5, 2021 18:52

auser / gist:c25f0fb8e6e632d9d69a1a93601204c5

Created March 24, 2020 15:47 — forked from digitaljhelms/gist:4287848

Git/GitHub branching standards & conventions

Branching

Description, Instructions, Notes

Instance	Branch

auser / LinkedListNode.js

Created December 11, 2018 06:03

Just saving this for myself... creating a linked list from a list of nodes.

	function LinkedListNode(v) {
	this.val = v;
	this.next = null
	}

	LinkedListNode.prototype.print = function() {
	let node = this;
	while (node) {
	process.stdout.write(`${node.val} -> `)
	node = node.next

auser / reverse_an_integer.js

Created November 30, 2018 16:36

https://leetcode.com/problems/reverse-integer/

auser / simple-middleware.js

Created July 24, 2018 00:55

	// Not the fastest thing in the world
	export default class Middleware {
	constructor (methods) {
	this.methods = methods;
	this.methodsStack = {};
	this.originalMethod = {};
	methods.forEach (method => {
	this.methodsStack[method] = [];
	this.originalMethod[method] = this[method];

auser / splitnum.js

Created April 28, 2018 04:48

	const splitTotal = (total, n) => {
	let arr = [];
	let i = 0;
	while (i < n) {
	arr.push(Math.floor(Math.random() * total) + 1)
	i++;
	}
	const sorted = [0].concat(arr, [total]).sort((a, b) => a - b);

	let out = [];

auser / web3-solc-contract-compile-deploy.js

Created March 2, 2018 20:18 — forked from tomconte/web3-solc-contract-compile-deploy.js

Compiling and deploying an Ethereum Smart Contract, using solc and web3.

	const fs = require('fs');
	const solc = require('solc');
	const Web3 = require('web3');

	// Connect to local Ethereum node
	const web3 = new Web3(new Web3.providers.HttpProvider("http://localhost:8545"));

	// Compile the source code
	const input = fs.readFileSync('Token.sol');
	const output = solc.compile(input.toString(), 1);

auser / keras_model.py

Last active April 16, 2020 18:05

	import matplotlib.pyplot as plt
	from keras.models import Sequential
	from keras.layers import Dense
	from keras.optimizers import Adam

	model = Sequential()
	model.add(Dense(10, input_dim=X_train.shape[1], activation='sigmoid'))
	model.add(Dense(1, activation='sigmoid'))
	model.compile(optimizer=Adam(lr=0.01),
	loss='binary_crossentropy',

auser / model.py

Last active April 16, 2020 18:08

	def nn(X, y, hiddenLayerSize=10, learningRate=0.01, epochs=100, debug=False):
	m = X.shape[1]
	outputSize = y.shape[1]

	# Make our model
	model = dict(
	w0 = np.random.randn(m, hiddenLayerSize),
	w1 = np.random.randn(hiddenLayerSize, outputSize)
	)

auser / data.py

Last active April 16, 2020 18:09

	import numpy as np
	import pandas as pd
	np.random.seed(1)

	# train comes from the titantic dataset provided by
	# kaggle (https://www.kaggle.com/c/titanic/data)
	df = pd.read_csv('./data/titanic-train.csv')

	def preprocess(raw_data):
	# Preprocess data