kbroman

#include <Rcpp.h>
using namespace Rcpp;

// [[Rcpp::export]]
void r_message(std::string text)
{
    Rcpp::Function msg("message");
    msg(text);
}

yledu

function writeStuff(n)
    f=open("data.txt","w")
    for el in 1:n
        r=randi(Int32)
        println(f,r)
    end 
    close(f)
end

N = 1000_000

romainfrancois

#include <Rcpp.h>

// thanks to Simon for the macro magic
// problem #1: R internals uses "private" and "class" as 
//             member names. So a C++ compiler is confused
//             when we include Connections.h
//             this workaround uses the preprocessor to rename
//             class as class_name and private as private_ptr
#define class class_name
#define private private_ptr

tc

package imagej;

import ij.*;
import ij.io.*;
import ij.process.*;
import java.io.*;
import javax.imageio.*;
import javax.imageio.stream.*;
import java.awt.image.BufferedImage;

sbodenstein

-- 

require 'cudnn'
require 'cunn'

torch.setdefaulttensortype('torch.FloatTensor')

-- Get weight dim
function checkSums(rnn, seqLength, batch, inputDim, hiddenSize, layerNum, bidirectional)
	rnn:reset()

pssguy

# load requisite libraries
library(shiny)
library(googleVis) 

# load raw data (NB not provided)
tableByGame <- read.csv("../tableByGame.csv",stringsAsFactors=FALSE)

# to create chart need to repeat one column and get negative of league position as hack
tableByGame$game <- tableByGame$seasonGame
tableByGame$lgPos <- -tableByGame$lgPos

smc77

num.iterations <- 1000

# Download South African heart disease data
sa.heart <- read.table("http://www-stat.stanford.edu/~tibs/ElemStatLearn/datasets/SAheart.data", sep=",",head=T,row.names=1)

x <- sa.heart[,c("age", "ldl")]
y <- sa.heart$chd
plot(x, pch=21, bg=c("red","green")[factor(y)])

# Function to standardize input values

jcheng5

library(shiny)
library(ggplot2)

function(input, output) {

  dataset <- reactive({
    diamonds[sample(nrow(diamonds), input$sampleSize),]
  })

  output$plot <- renderPlot({

brendano

# Load the MNIST digit recognition dataset into R
# http://yann.lecun.com/exdb/mnist/
# assume you have all 4 files and gunzip'd them
# creates train$n, train$x, train$y  and test$n, test$x, test$y
# e.g. train$x is a 60000 x 784 matrix, each row is one digit (28x28)
# call:  show_digit(train$x[5,])   to see a digit.
# brendan o'connor - gist.github.com/39760 - anyall.org

load_mnist <- function() {
  load_image_file <- function(filename) {

BenLangmead

class DeBruijnGraph:
    """ A de Bruijn multigraph built from a collection of strings.
        User supplies strings and k-mer length k.  Nodes of the de
        Bruijn graph are k-1-mers and edges correspond to the k-mer
        that joins a left k-1-mer to a right k-1-mer. """

    @staticmethod
    def chop(st, k):
        """ Chop a string up into k mers of given length """
        for i in xrange(0, len(st)-(k-1)):