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bart6114 / mpg_API.R
Created November 24, 2015 08:33
mpg_fit<-
readRDS("mpg_fit.Rdata")
predict_mpg<-function(disp, wt, hp){
new_data<-data.frame(disp=as.numeric(disp),
wt=as.numeric(wt),
hp=as.numeric(hp))
predict(mpg_fit, newdata = new_data)
}
@bart6114
bart6114 / train.R
Created November 24, 2015 08:34
library(randomForest)
mpg_fit<-
randomForest(mpg~disp+wt+hp, mtcars)
saveRDS(mpg_fit, "mpg_fit.Rdata")
@bart6114
bart6114 / OECDcareresources.csv
Created January 19, 2014 12:44
Evolution hospital beds, resources and consumption
Variable Unit Country Year Value Flags
Total hospital beds Number Australia 1960 99191
Total hospital beds Number Australia 1961 99670
Total hospital beds Number Australia 1962 100413
Total hospital beds Number Australia 1963 102109
Total hospital beds Number Australia 1964 102618
Total hospital beds Number Australia 1965 103580
Total hospital beds Number Australia 1966 103636
Total hospital beds Number Australia 1967 104262
Total hospital beds Number Australia 1968 103397
@bart6114
bart6114 / analysis.Rmd
Last active January 3, 2016 18:49
Evolution hospital beds, resources and consumption
========================================================
```{r setup, echo=FALSE, message=FALSE, warning=FALSE}
imgur_insecure<-function (file, key = "75ace4c8e871b86")
{
if (!is.character(key))
stop("The Imgur API Key must be a character string!")
res = RCurl::postForm("https://api.imgur.com/3/image.xml",
image = RCurl::fileUpload(file), .opts = RCurl::curlOptions(httpheader = c(Authorization = paste("Client-ID",
key)),ssl.verifypeer = FALSE))
@bart6114
bart6114 / definition2.json
Created May 3, 2016 07:34
{
"env": {
"resources": {
"nurse": {
"capacity": 1
},
"doctor": {
"capacity": 1
}
},
@bart6114
bart6114 / definition1.json
Created May 3, 2016 07:34
{
"env": {
"resources": {
"nurse": {
"capacity": 1
},
"doctor": {
"capacity": 1
}
},
@bart6114
bart6114 / lpsolve_example.r
Created February 19, 2014 08:54
library(lpSolveAPI)
#used for result visualization
library(ggplot2)
library(reshape)
library(gridExtra)
#define the datasets
train<-data.frame(wagon=c('w1','w2','w3'), weightcapacity=c(10,8,12), spacecapacity=c(5000,4000,8000))
@bart6114
bart6114 / simmer_gate_test.R
Created June 15, 2016 13:22
library(R6)
library(simmer)
BatchGate <- R6Class("BatchGate",
public = list(
queue_length = 0,
opens_at_length = NA,
open = FALSE,
allowed_to_pass = 0,
initialize = function(opens_at_length){
@bart6114
bart6114 / bpnn.py
Created December 13, 2014 19:34
adjusted bpnn.py
# Back-Propagation Neural Networks
#
# Written in Python. See http://www.python.org/
# Placed in the public domain.
# Neil Schemenauer <nas@arctrix.com>
# Adapted for instructional purposes by Bart Smeets <bartsmeets86@gmail.com>
import math
import random
@bart6114
bart6114 / temp.ipynb
Created November 21, 2017 10:13
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