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Load the MNIST handwritten digits dataset into R as a tidy data frame
# modification of https://gist.github.com/brendano/39760
# automatically obtains data from the web
# creates two data frames, test and train
# labels are stored in the y variables of each data frame
# can easily train many models using formula `y ~ .` syntax
# download data from http://yann.lecun.com/exdb/mnist/
download.file("http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz",
"train-images-idx3-ubyte.gz")
download.file("http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz",
"train-labels-idx1-ubyte.gz")
download.file("http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz",
"t10k-images-idx3-ubyte.gz")
download.file("http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz",
"t10k-labels-idx1-ubyte.gz")
# gunzip the files
R.utils::gunzip("train-images-idx3-ubyte.gz")
R.utils::gunzip("train-labels-idx1-ubyte.gz")
R.utils::gunzip("t10k-images-idx3-ubyte.gz")
R.utils::gunzip("t10k-labels-idx1-ubyte.gz")
# helper function for visualization
show_digit = function(arr784, col = gray(12:1 / 12), ...) {
image(matrix(as.matrix(arr784[-785]), nrow = 28)[, 28:1], col = col, ...)
}
# load image files
load_image_file = function(filename) {
ret = list()
f = file(filename, 'rb')
readBin(f, 'integer', n = 1, size = 4, endian = 'big')
n = readBin(f, 'integer', n = 1, size = 4, endian = 'big')
nrow = readBin(f, 'integer', n = 1, size = 4, endian = 'big')
ncol = readBin(f, 'integer', n = 1, size = 4, endian = 'big')
x = readBin(f, 'integer', n = n * nrow * ncol, size = 1, signed = FALSE)
close(f)
data.frame(matrix(x, ncol = nrow * ncol, byrow = TRUE))
}
# load label files
load_label_file = function(filename) {
f = file(filename, 'rb')
readBin(f, 'integer', n = 1, size = 4, endian = 'big')
n = readBin(f, 'integer', n = 1, size = 4, endian = 'big')
y = readBin(f, 'integer', n = n, size = 1, signed = FALSE)
close(f)
y
}
# load images
train = load_image_file("train-images-idx3-ubyte")
test = load_image_file("t10k-images-idx3-ubyte")
# load labels
train$y = as.factor(load_label_file("train-labels-idx1-ubyte"))
test$y = as.factor(load_label_file("t10k-labels-idx1-ubyte"))
# view test image
show_digit(train[10000, ])
# testing classification on subset of training data
fit = randomForest::randomForest(y ~ ., data = train[1:1000, ])
fit$confusion
test_pred = predict(fit, test)
mean(test_pred == test$y)
table(predicted = test_pred, actual = test$y)
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