Files
2023-08-09 08:20:19 -05:00

80 lines
2.1 KiB
Matlab

function foo = main()
addpath('mnistHelper');
train_images = loadMNISTImages('train-images-idx3-ubyte');
train_labels = loadMNISTLabels('train-labels-idx1-ubyte');
test_images = loadMNISTImages('t10k-images-idx3-ubyte');
test_labels = loadMNISTLabels('t10k-labels-idx1-ubyte');
% showData(train_images, 100, 100);
guesses(20, 3, train_images, train_labels, test_images, test_labels);
end
function foo = showData(images, rows, cols)
grid = [];
i = 0;
for x = 1:rows
imgs = [];
for y = 1:cols
i = i + 1;
imgs = [imgs reshape(images(:, i), 28, 28)];
end
grid = [grid; imgs];
end
imshow(grid);
end
function d = distance(train_image, test_image)
v = train_image - test_image;
v = double(v);
d = sqrt(v * v');
end
function result = border(image, value)
image = reshape(image, 28, 28);
result = zeros(28, 28);
result(:, :) = value;
result(2:27, 2:27) = image(2:27, 2:27);
result = reshape(result, 784, 1);
end
function foo = guesses(count, k, train_images, train_labels, test_images, test_labels)
[foo num_train_images] = size(train_images);
[foo num_test_images] = size(test_images);
correct = 0;
grid = [];
for x_ = 1:count
x = floor(rand() * num_test_images);
test_image = test_images(:, x);
correct_label = test_labels(x);
dist = [];
num_train_images = 50000;
for i = 1:num_train_images
train_image = train_images(:, i);
d = distance(train_image', test_image');
dist = [dist; [d i]];
end
sorted_ = (sortrows(dist, 1));
sorted = sorted_(1:k, :);
labels = [];
for i = 1:k
grid = [grid train_images(:, sorted(i, 2))];
labels = [labels train_labels(sorted(i, 2))];
end
guess_label = mode(labels);
if guess_label == correct_label
correct = correct + 1;
grid = [grid test_image];
else
grid = [grid border(test_image, 255)];
end
end
correct
showData(grid, count, k+1);
end