README.md in svmkit-0.1.3 vs README.md in svmkit-0.2.0

@@ -28,22 +28,21 @@
 
 Training phase:
 
 ```ruby
 require 'svmkit'
-require 'libsvmloader'
 
-samples, labels = LibSVMLoader.load_libsvm_file('pendigits', stype: :dense)
+samples, labels = SVMKit::Dataset.load_libsvm_file('pendigits')
 
 normalizer = SVMKit::Preprocessing::MinMaxScaler.new
 normalized = normalizer.fit_transform(samples)
 
 transformer = SVMKit::KernelApproximation::RBF.new(gamma: 2.0, n_components: 1024, random_seed: 1)
 transformed = transformer.fit_transform(normalized)
 
 base_classifier =
-  SVMKit::LinearModel::PegasosSVC.new(reg_param: 1.0, max_iter: 50, batch_size: 20, random_seed: 1)
+  SVMKit::LinearModel::SVC.new(reg_param: 1.0, max_iter: 1000, batch_size: 20, random_seed: 1)
 classifier = SVMKit::Multiclass::OneVsRestClassifier.new(estimator: base_classifier)
 classifier.fit(transformed, labels)
 
 File.open('trained_normalizer.dat', 'wb') { |f| f.write(Marshal.dump(normalizer)) }
 File.open('trained_transformer.dat', 'wb') { |f| f.write(Marshal.dump(transformer)) }
@@ -52,12 +51,11 @@
 
 Testing phase:
 
 ```ruby
 require 'svmkit'
-require 'libsvmloader'
 
-samples, labels = LibSVMLoader.load_libsvm_file('pendigits.t', stype: :dense)
+samples, labels = SVMKit::Dataset.load_libsvm_file('pendigits.t')
 
 normalizer = Marshal.load(File.binread('trained_normalizer.dat'))
 transformer = Marshal.load(File.binread('trained_transformer.dat'))
 classifier = Marshal.load(File.binread('trained_classifier.dat'))