Nothing
## ---- eval = F, echo = T, warning = F, message = F, cache = T-----------------
#
# # using system('wget..') on a linux OS
#
# system("wget https://raw.githubusercontent.com/mlampros/DataSets/master/mnist.zip")
#
# mnist <- read.table(unz("mnist.zip", "mnist.csv"), nrows = 70000, header = T,
#
# quote = "\"", sep = ",")
#
## ---- eval = F, cache = T-----------------------------------------------------
# X = mnist[, -ncol(mnist)]
# dim(X)
#
# ## [1] 70000 784
#
# # the KernelKnn function requires that the labels are numeric and start from 1 : Inf
#
# y = mnist[, ncol(mnist)] + 1
# table(y)
#
# ## y
# ## 1 2 3 4 5 6 7 8 9 10
# ## 6903 7877 6990 7141 6824 6313 6876 7293 6825 6958
#
## ---- eval = T, echo = F------------------------------------------------------
knitr::kable(data.frame(irlba_singlular_vectors = 40, k = 8, method = 'braycurtis', kernel = 'biweight_tricube_MULT'), align = 'l')
## ---- eval = F, cache = T-----------------------------------------------------
#
# library(irlba)
#
# svd_irlb = irlba(as.matrix(X), nv = 40, nu = 40, verbose = F) # irlba truncated svd
#
# new_x = as.matrix(X) %*% svd_irlb$v # new_x using the 40 right singular vectors
#
## ---- eval = F, cache = T, warning = FALSE, message = FALSE, results = 'hide'----
#
# library(KernelKnn)
#
# fit = KernelKnnCV(as.matrix(new_x), y, k = 8, folds = 4, method = 'braycurtis',
#
# weights_function = 'biweight_tricube_MULT', regression = F,
#
# threads = 6, Levels = sort(unique(y)))
#
#
# # str(fit)
#
#
# # evaluation metric
#
# acc = function (y_true, preds) {
#
# out = table(y_true, max.col(preds, ties.method = "random"))
#
# acc = sum(diag(out))/sum(out)
#
# acc
# }
#
## ---- eval = F, cache = F-----------------------------------------------------
#
# acc_fit = unlist(lapply(1:length(fit$preds),
#
# function(x) acc(y[fit$folds[[x]]],
#
# fit$preds[[x]])))
# acc_fit
#
# ## [1] 0.9742857 0.9749143 0.9761143 0.9741143
#
# cat('mean accuracy using cross-validation :', mean(acc_fit), '\n')
#
# ## mean accuracy using cross-validation : 0.9748571
#
## ---- eval = F, cache = T-----------------------------------------------------
#
# library(OpenImageR)
#
# hog = HOG_apply(X, cells = 6, orientations = 9, rows = 28, columns = 28, threads = 6)
#
# ##
# ## time to complete : 1.802997 secs
#
# dim(hog)
#
# ## [1] 70000 324
#
## ---- eval = F, cache = T, warning = FALSE, message = FALSE, results = 'hide'----
#
# fit_hog = KernelKnnCV(hog, y, k = 20, folds = 4, method = 'braycurtis',
#
# weights_function = 'biweight_tricube_MULT', regression = F,
#
# threads = 6, Levels = sort(unique(y)))
#
#
# #str(fit_hog)
#
## ---- eval = F, cache = F-----------------------------------------------------
#
# acc_fit_hog = unlist(lapply(1:length(fit_hog$preds),
#
# function(x) acc(y[fit_hog$folds[[x]]],
#
# fit_hog$preds[[x]])))
# acc_fit_hog
#
# ## [1] 0.9833714 0.9840571 0.9846857 0.9838857
#
# cat('mean accuracy for hog-features using cross-validation :', mean(acc_fit_hog), '\n')
#
# ## mean accuracy for hog-features using cross-validation : 0.984
#
## ---- eval = F, echo = T, warning = F, message = F, cache = T-----------------
#
# # using system('wget..') on a linux OS
#
# system("wget https://raw.githubusercontent.com/mlampros/DataSets/master/cifar_10.zip")
#
# cifar_10 <- read.table(unz("cifar_10.zip", "cifar_10.csv"), nrows = 60000, header = T,
#
# quote = "\"", sep = ",")
#
## ---- eval = F, cache = T-----------------------------------------------------
# X = cifar_10[, -ncol(cifar_10)]
# dim(X)
#
# ## [1] 60000 1024
#
# # the KernelKnn function requires that the labels are numeric and start from 1 : Inf
#
# y = cifar_10[, ncol(cifar_10)]
# table(y)
#
# ## y
# ## 1 2 3 4 5 6 7 8 9 10
# ## 6000 6000 6000 6000 6000 6000 6000 6000 6000 6000
#
## ---- eval = T, echo = F------------------------------------------------------
knitr::kable(data.frame(irlba_singlular_vectors = 40, k = 8, method = 'braycurtis',
kernel = 'biweight_tricube_MULT'), align = 'l')
## ---- eval = F, cache = T-----------------------------------------------------
#
# svd_irlb = irlba(as.matrix(X), nv = 40, nu = 40, verbose = F) # irlba truncated svd
#
# new_x = as.matrix(X) %*% svd_irlb$v # new_x using the 40 right singular vectors
#
## ---- eval = F, cache = T, warning = FALSE, message = FALSE, results = 'hide'----
#
# fit = KernelKnnCV(as.matrix(new_x), y, k = 8, folds = 4, method = 'braycurtis',
#
# weights_function = 'biweight_tricube_MULT', regression = F,
#
# threads = 6, Levels = sort(unique(y)))
#
#
# # str(fit)
#
## ---- eval = F, cache = F-----------------------------------------------------
#
# acc_fit = unlist(lapply(1:length(fit$preds),
#
# function(x) acc(y[fit$folds[[x]]],
#
# fit$preds[[x]])))
# acc_fit
#
# ## [1] 0.4080667 0.4097333 0.4040000 0.4102667
#
# cat('mean accuracy using cross-validation :', mean(acc_fit), '\n')
#
# ## mean accuracy using cross-validation : 0.4080167
#
## ---- eval = F, cache = T-----------------------------------------------------
#
# hog = HOG_apply(X, cells = 6, orientations = 9, rows = 32,
#
# columns = 32, threads = 6)
#
# ##
# ## time to complete : 3.394621 secs
#
# dim(hog)
#
# ## [1] 60000 324
#
## ---- eval = F, cache = T, warning = FALSE, message = FALSE, results = 'hide'----
#
# fit_hog = KernelKnnCV(hog, y, k = 20, folds = 4, method = 'braycurtis',
#
# weights_function = 'biweight_tricube_MULT', regression = F,
#
# threads = 6, Levels = sort(unique(y)))
#
#
# # str(fit_hog)
#
## ---- eval = F, cache = F-----------------------------------------------------
#
# acc_fit_hog = unlist(lapply(1:length(fit_hog$preds),
#
# function(x) acc(y[fit_hog$folds[[x]]],
#
# fit_hog$preds[[x]])))
# acc_fit_hog
#
# ## [1] 0.5807333 0.5884000 0.5777333 0.5861333
#
# cat('mean accuracy for hog-features using cross-validation :', mean(acc_fit_hog), '\n')
#
# ## mean accuracy for hog-features using cross-validation : 0.58325
#
## ---- eval = F, echo = F------------------------------------------------------
#
# # remove cache and mnist.zip once vignettes are built
#
# # unlink("image_classification_using_MNIST_CIFAR_data_cache", recursive = TRUE) # USE this chunk in case of 'eval = TRUE'
# # unlink("mnist.zip", recursive = TRUE)
# # unlink("cifar_10.zip", recursive = TRUE)
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