anchor.diff:        final newFeatures = Matrix.from([
           [2.0, 4.0, 1.0],
         ]);
  -      final probabilities = classifier.predictProbabilities(newFeatures);
  -      final classes = classifier.predictClasses(newFeatures);
  -
  -      expect(
  -          probabilities,
  -          equals([
  -            [0.01798621006309986, 0.0, 0.5]
  -          ]));
  -      expect(classes, equals([2]));
  -    });
  -
  -    test('should evaluate prediction quality, accuracy = 0', () {
  -      final features = Matrix.from([
  -        [5.0, 7.0, 6.0],
  -        [1.0, 2.0, 3.0],
  -        [10.0, 12.0, 31.0],
  -        [9.0, 8.0, 5.0],
  -        [4.0, 0.0, 1.0],
  -      ]);
  -      final labels = Matrix.from([
  -        [0.0],
  -        [1.0],
  -        [1.0],
  -        [2.0],
  -        [0.0],
  -      ]);
  -      classifier.fit(features, labels);
  -
  -      final newFeatures = Matrix.from([
  -        [2.0, 4.0, 1.0],
  -      ]);
         final origLabels = Matrix.from([
           [1.0]
         ]);
  
positive.code:         [3.0, 4.0, 5.0],
        ]);
        final labels = Matrix.from([
          [0.0],
          [1.0],
          [0.0],
        ]);
        classifier.fit(features, labels);
  
  
negative.code:         [4.0, 0.0, 1.0],
        ]);
        final labels = Matrix.from([
          firstClass,
          secondClass,
          secondClass,
          thirdClass,
          firstClass,
        ]);
        classifier.fit(features, labels);
  
  