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import random  ## to generate the items
from pulp import * ## import pulp-or functions


	
class MasterProblem:
	def __init__(self, maxValue, itemLengths, itemDemands, initialPatterns, problemname):
		
		self.maxValue=maxValue
		self.itemLengths=itemLengths

bart6114

bart6114

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

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))

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# Back-Propagation Neural Networks
#
# Written in Python.  See http://www.python.org/
# Placed in the public domain.
# Neil Schemenauer <[email protected]>

# Adapted for instructional purposes by Bart Smeets <[email protected]>

import math
import random

bart6114

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)
}

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library(randomForest)

mpg_fit<-
  randomForest(mpg~disp+wt+hp, mtcars)

saveRDS(mpg_fit, "mpg_fit.Rdata")

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{
  "env": {
    "resources": {
      "nurse": {
        "capacity": 1
      },
      "doctor": {
        "capacity": 1
      }
    },

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{
  "env": {
    "resources": {
      "nurse": {
        "capacity": 1
      },
      "doctor": {
        "capacity": 1
      }
    },