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  1. # Definition of price.
  2. price <- function(rating, age) {
  3. 	peakAge <- rating- 50
  4. 	price <- rating / 2
  5. 	ret <- sapply(1:length(rating), function(i){
  6. 				if (age[i] > peakAge[i]) {
  7. 					p <- price[i] * (5 - (age[i] - peakAge[i]) / 2)
  8. 				} else {
  9. 					p <- price[i] * 5 * (age[i] + 1) / peakAge[i]
  10. 				}
  11. 				return(ifelse(p<0, 0, p))
  12. 			})
  13. 	return(ret)
  14. }
  15.  
  16. # Create a price dataset.
  17. wineSet1 <- function(n=300) {
  18. 	randVector <- runif(n * 2)
  19. 	rating <- randVector[1:n] * 50 + 50
  20. 	age <- randVector[(n+1):(n*2)] * 50
  21. 	price <- price(rating, age) * (runif(1) * 0.2 + 0.9)
  22. 	data <- cbind(rating, age, price)
  23. 	return(data)
  24. }
  25.  
  26. getDistances <- function(data, v1) {
  27. 	#distances <- sqrt(sum(sweep(data[,1:2],2,v1,FUN="-")^2))
  28. 	distances <- apply(data[,c(1,2)], 1, function(v2) sqrt(sum((v2 - v1) ^ 2)))
  29. 	distanceFrame <- cbind(distances,c(1:row(data)))#data.frame(distance=distances, index=1:nrow(data))
  30. 	ord=order(distanceFrame[,1])
  31. 	return(distanceFrame[ord,])
  32. }
  33.  
  34. # KNN estimate. 
  35. knnEstimate <- function(data, v1, k=3) {
  36. 	v <- unlist(v1)
  37. 	idx <- order(sqrt(colSums((t(data[,1:2]) - v) ^ 2)))[1:k]
  38. 	return(mean(data[idx,3]))
  39. }
  40. # Divide data set into training set and test set.
  41. divideData <- function(data, test = 0.1) {
  42. 	testIndices <- which(runif(nrow(data)) < test)
  43. 	return(list(test=data[testIndices,], train=data[-testIndices,]))
  44. }
  45.  
  46. testAlgorithm <- function(algF, trainSet, testSet) {
  47. 	errorVector <- apply(testSet, 1, function(row) {
  48. 				#print(paste(row[3], algF(trainSet, row[1:2])))
  49. 				return(row[3] - algF(trainSet, row[1:2]))
  50. 			} )
  51. 	return(mean(errorVector ^ 2))
  52. }
  53.  
  54. crossValidate <- function(algF, data, trials=100, test=0.1) {
  55. 	errorVector <- sapply(1:trials, function(i) {
  56. 				#print(paste('Trail', i))
  57. 				dataSets <- divideData(data, test)
  58. 				v <- testAlgorithm(algF, dataSets[['train']], dataSets[['test']])
  59. 				return(v)
  60. 			})
  61. 	return(sum(errorVector) / trials)
  62. }
  63.  
  64. data <- wineSet1()
  65. print(crossValidate(weightedKNN, data))
  66.  
Success #stdin #stdout 0.31s 22832KB
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https://ideone.com/x97yQ
language:
R (R 3.5.2)
created:
12 years ago
visibility:
public

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