learn.rb

require 'pycall/import'
require './dataset_reader.rb'

include PyCall::Import
pyfrom :'sklearn.ensemble', import: :RandomForestClassifier

test_labels = DatasetReader.read_labels( "data/t10k-labels.idx1-ubyte" )
test_images = DatasetReader.read_images( "data/t10k-images.idx3-ubyte" )
rows, columns = DatasetReader.read_rows_columns( "data/t10k-images.idx3-ubyte" )

puts "Labels: #{test_labels.size}, Images: #{test_images.size}, Rows: #{rows}, Columns: #{columns}"

train_labels = DatasetReader.read_labels( "data/train-labels.idx1-ubyte" )
train_images = DatasetReader.read_images( "data/train-images.idx3-ubyte" )

puts "Labels: #{train_labels.size}, Images: #{train_images.size}"

# Initialize a RandomForestClassifier
clf = RandomForestClassifier.new()

# Fit with training data
clf.fit(train_images, train_labels)

# Score our fit using the test data
classification_score = clf.score(test_images,test_labels)
puts "Prediction score for Random Forest classifier #{(classification_score*100).round(2)}%"

# Do a prediction for one sample
sample = 8
puts clf.predict([test_images[sample]])
puts clf.predict_proba([test_images[sample]])
puts "Correct label: #{test_labels[sample]}"

# Reshape back to 2 dimmensions and print to console
#reshaped = test_images[sample].each_slice(rows).to_a
#puts test_labels[sample]
#reshaped.each do |r|
#  puts r.inspect
#end