ianjohns

// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadillo.h>
using namespace Rcpp;
using namespace arma;
// [[Rcpp::export]]
arma::mat lm_rcpp(arma::mat X, arma::vec y)
{
    arma::vec b_hat;
    b_hat = (X.t() * X).i() * X.t() * y;
    return (b_hat);

ianjohns

library(lightgbm)
library(caret)
library(fastDummies)
#load("~/R_Cars_19/Data/cars_19.Rdata")
title <- "light gbm"

tmp <- cars_19[, c(4, 6, 7, 9:12)]
tmp1 <- dummy_cols(tmp)
tmp1 <- tmp1[,8:36]
d <- data.frame(cars_19[, c(1:3, 5, 8)], tmp1)

ianjohns

#cars_19 data set
#extreme gradient boosting

#raw data
#https://www.fueleconomy.gov/feg/epadata/19data.zip

library(xgboost)
library(caret)
library(dummies)
library(DiagrammeR)

ianjohns

#cars_19 data set
#neural network with 2 hidden layers (7 neurons and 3 neurons)

#raw data
#https://www.fueleconomy.gov/feg/epadata/19data.zip

library(tfestimators)
library(caret)

#load("~/R_Cars_19/Data/cars_19.Rdata")

ianjohns

#cars_19 data set
#neural network with 2 hidden layers (7 neurons and 3 neurons)

#raw data
#https://www.fueleconomy.gov/feg/epadata/19data.zip

library(neuralnet)
library(caret)

#load("~/R_Cars_19/Data/cars_19.Rdata")

ianjohns

library(tfestimators)
library(caret)

#input_fn for a given subset of data
cars_19_input_fn <- function(data, num_epochs = 1) {
  input_fn(
    data,
    features = colnames(cars_19[c(2:12)]),
    response = "fuel_economy_combined",
    batch_size = 64,

ianjohns

#cars_19 data set
#support vector machine regression

#raw data
#https://www.fueleconomy.gov/feg/epadata/19data.zip
library(e1071)
library(caret)

r <- function(data) {round(data, 1)}

ianjohns

#raw data
# https://www.fueleconomy.gov/feg/epadata/19data.zip

library(gbm)
library(caret)

r <- function(data) {round(data, 1)}

set.seed(123)
indices <- sample(nrow(cars_19), size = .75 * nrow(cars_19))

ianjohns

#raw data
# https://www.fueleconomy.gov/feg/epadata/19data.zip

library(rpart)       # regression trees
library(rpart.plot)  # plotting regression trees
library(ipred)       # bagging
library(caret)       # bagging
library(e1071)
library(randomForest) #random forests

ianjohns

# n= amount of people in room
# assumes non leap year
# returns PDF of n
birthday <- function(n) {
  return(p <- 1 - choose(365, 365 - n) * factorial(n) / 365 ^ n)
}