primaryobjects

var fs = require('fs');

// In your app.js, include a route handler for all other routes (*) to go to error404.
// app.get('*', error.error404);
exports.error404 = function(req, res) {
    if (req.accepts('html')) {
        // Respond with html page.
        fs.readFile(__dirname + '/../../public/404/index.html', 'utf-8', function(err, page) {
            res.writeHead(404, {'Content-Type': 'text/html'});
            res.write(page);

primaryobjects

funnyPlant <- function(people, plants) {
  fruit <- 0
  weeks <- 1
  
  while (fruit < people) {
    fruit <- fruit + plants
    plants <- plants + fruit
    
    weeks <- weeks + 1
  }

primaryobjects

#
# https://www.reddit.com/r/dailyprogrammer/comments/3uuhdk/20151130_challenge_243_easy_abundant_and/
#

divisors <- function(x){
  #  Vector of numberes to test against
  y <- seq_len(x)
  #  Modulo division. If remainder is 0 that number is a divisor of x so return it
  y[ x%%y == 0 ]
}

primaryobjects

# https://www.reddit.com/r/dailyprogrammer/comments/3v4zsf/20151202_challenge_243_intermediate_jennys_fruit/
require(plyr)

data <- read.csv('input2.txt', sep = ' ', header = FALSE, col.names = c('name', 'price'))
money <- 500
orders <- data.frame()

for (i in seq(from=1, to=6)) {
  print(paste('Level', i))
  

primaryobjects

# https://www.reddit.com/r/dailyprogrammer/comments/3x3hqa/20151216_challenge_245_intermediate_ggggggg_gggg/

# Builds a key/value dictionary.
load <- function(input, valuesAsKeys = FALSE) {
  # Read first line of input.
  key <- unlist(strsplit(input, '\n'))[1]
  
  # Split tokens by space.
  keys <- unlist(strsplit(key, ' '))
  

primaryobjects

library(caret)

# Generate 50 random numbers uniformly distributed between 0 and 100.
training <- data.frame(x = runif(50, min=0, max=100))
training$y <- sqrt(training$x)

# Generate some squared numbers.
cv <- data.frame(x = (1:10)^2)
cv$y <- sqrt(cv$x)

primaryobjects

# See also https://gist.github.com/primaryobjects/3b41f8b2f122eb16a65b
library(neuralnet)
library(ggplot2)
library(reshape2)

# Helper method to generate a training set containing size random numbers (a, b, c) and sorted (x, y, z).
generateSet <- function(size = 100, max = 100) {
  # Generate size random numbers between 1 and max.
  training <- data.frame(a=sample(1:max, size, replace=TRUE), 
                         b=sample(1:max, size, replace=TRUE), 

primaryobjects

# See also https://gist.github.com/primaryobjects/5a28e0c27fd433123f1a
# fit: Trained neural network. scaleVal: Original scaled data used in training the network. a, b, c: Numbers to sort.
nnsort <- function(fit, scaleVal, a, b, c) {
  numbers <- data.frame(a=a, b=b, c=c, x=0, y=0, z=0)
  
  numbersScaled <- as.data.frame(scale(numbers, attr(scaleVal, 'scaled:center'), attr(scaleVal, 'scaled:scale')))
  round(unscale(compute(fit, numbersScaled[,1:3])$net.result, scaleVal))[,4:6]
}

primaryobjects

library(neuralnet)

# Helper method to generate a training set containing size random numbers (a, b, c) and sorted (x, y, z).
generateSet <- function(size = 100, max = 100) {
  # Generate size random numbers between 1 and max.
  training <- data.frame(a=sample(1:max, size, replace=TRUE), 
                         b=sample(1:max, size, replace=TRUE), 
                         c=sample(1:max, size, replace=TRUE))
  
  # Generate output examples by sorting the numbers.

primaryobjects

# See also https://gist.github.com/primaryobjects/3b41f8b2f122eb16a65b
library(neuralnet)
library(ggplot2)
library(reshape2)

# Helper method to generate a training set containing size random numbers (a, b, c) and sorted (x, y, z).
generateSet <- function(size = 100, max = 100) {
  # Generate size random numbers between 1 and max.
  training <- data.frame(a=sample(1:max, size, replace=TRUE), 
                         b=sample(1:max, size, replace=TRUE),