DexGroves

library("mlbench")
library("gbm")
library("ggplot2")


get_spiral_df <- function(N, cycles, sd) {
  spiral_data <- mlbench.spirals(N, cycles, sd)
  data.frame(x = spiral_data$x[, 1],
             y = spiral_data$x[, 2],
             class = as.numeric(spiral_data$classes) - 1)

DexGroves

import itertools
import numpy as np 
from collections import Counter


def person_says_no(possibilities):
    uniqueness = Counter([d.values()[0] 
                         for d in possibilities]).items()
    impossibilities = [x for (x, count) in uniqueness if count == 1]

DexGroves

strip_glm <- function(cm) {
  cm$y = c()
  cm$model = c()
  
  cm$residuals = c()
  cm$fitted.values = c()
  cm$effects = c()
  cm$qr$qr = c()  
  cm$linear.predictors = c()
  cm$weights = c()

DexGroves

strip_glm <- function(cm) {
  cm$y = c()
  cm$model = c()
  
  cm$residuals = c()
  cm$fitted.values = c()
  cm$effects = c()
  cm$qr$qr = c()  
  cm$linear.predictors = c()
  cm$weights = c()

DexGroves

library("data.table")


fread("a,b\n1,T\n1,T\n1,T\n1,T\n1,T\n2,C\n")
#    a b
# 1: 1 1
# 2: 1 1
# 3: 1 1
# 4: 1 1
# 5: 1 1

DexGroves

library("data.table")
library("xgboost")
library("Matrix")


generate_data <- function(N) {
  data.table(
    response = as.numeric(runif(N) > 0.8),
    float1   = rnorm(N, 3, 3))
}

DexGroves

library("data.table")
library("xgboost")
library("Matrix")


generate_data <- function(N) {
  data.table(
    response = as.numeric(runif(N) > 0.8),
    int1 = round(rnorm(N, 3, 3))
  )

DexGroves

library("data.table")
library("xgboost")
library("Matrix")


generate_data <- function(N) {
  data.table(
    response = as.numeric(runif(N) > 0.8),
    int1 = round(rnorm(N, 3, 3)),
    int2 = round(rnorm(N, 3, 3)),

DexGroves

library("igraph")
library("stringi")
library("magrittr")


generate_bridge_df <- function(nrow, ncol) {
  this_layer <- generate_layer(ncol)
  first_connections <- data.frame(from = "Northside", 
                                  to = get_layer_vertices(this_layer))
  all_bridges <- rbind(first_connections, this_layer)

DexGroves

library("Rcpp")
library("microbenchmark")

simulate_throws <- cppFunction('
  double simulate_throw(NumericVector start, int nthrows, int trials) {
      
      int trials_so_far = 0;
      int final_throw_successes_so_far = 0;
      int hits_so_far = 0;
      int nstart = start.size();