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Branching

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Description, Instructions, Notes

Instance	Branch

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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

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/**
 * @param {number} x
 * @return {number}
 */
var reverse = function(x) {
    let y = 0;
    let num = Math.abs(x);
    const intMax = Math.pow(2, 31) + 1;
    
    while(num != 0) {

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// 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];

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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 = [];

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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);

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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',

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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)
    )
    

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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

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Verifying my Blockstack ID is secured with the address 14xYsyu9E2KocyoSUpByuJ6qZ7RSy3WcAv https://explorer.blockstack.org/address/14xYsyu9E2KocyoSUpByuJ6qZ7RSy3WcAv