classifier_libsvm_minimal_modular with some extra comments

gistfile1.rb

require 'modshogun'
require 'narray'

# For the purposes of the example this is a helper file that's bundled in
# the examples directory.  It does some things like loading data and 
# checking some of the math.  It's not necessary to include in your own 
# projects
require 'load'

# Here we're setting our parameters for the SVM
@num = 1000
@dist = 1
@width = 2.1
C = 1

# We're just generating random data.  gen_rand_ary is in the load.rb file.
# you'll find that all it does is creating an 2 by @num
puts "generating training data"
traindata_real = gen_rand_ary @num
testdata_real = gen_rand_ary @num

# just like the above methods this is just creating random labels for
# the training set and the test set.
puts "generating labels"
trainlab = gen_ones_vec @num
testlab = gen_ones_vec @num

# Assign the data to the feature matrix and then apply a particular
# kernel method to it.
# 
puts "doing feature stuff"
feats_train = Modshogun::RealFeatures.new
feats_train.set_feature_matrix traindata_real
feats_test = Modshogun::RealFeatures.new
feats_test.set_feature_matrix testdata_real
kernel = Modshogun::GaussianKernel.new feats_train, feats_train, @width

# set the labels, the particular type of SVM we want to use and start training!
#
puts "labeling stuff"
labels = Modshogun::Labels.new
labels.set_labels trainlab
svm = Modshogun::LibSVM.new C, kernel, labels
svm.train

# With other ruby svm libs will typically use the method 'predict' to actually 
# make the prediction.  In shogun theres two ways to make a prediction.  The first
# is a little weird in that we can use the kernel object and add both the training
# set and the test set to it.  From there you can call 'svm.apply' and get the Label
# object.  The second is a bit more manual, but you can init the training set on the
# kernel and then pass in a features object when calling 'svm.apply'.  this is more 
# analogous to the 'predict' method call in other SVM ruby libs.
#
puts "the grand finale"
kernel.init feats_train, feats_test
out = svm.apply.get_labels
testerr = mean out.sign.eql_items? testlab
puts testerr