anchor.diff:    /// [dtype] A data type for all the numeric values, used by the algorithm. Can
     /// affect performance or accuracy of the computations. Default value is
     /// [DType.float32]
  -  factory SoftmaxRegressor.gradient(
  -      Matrix trainingFeatures,
  -      Matrix trainingOutcomes, {
  +  factory SoftmaxRegressor.gradient(DataFrame fittingData, {
  +        Iterable<int> targetIndices = const [],
  +        Iterable<String> targetNames = const [],
           int iterationsLimit,
           double initialLearningRate,
           double minWeightsUpdate,
  
positive.code: import 'package:ml_algo/src/classifier/linear/softmax_regressor/gradient_softmax_regressor.dart';
  import 'package:ml_algo/src/classifier/linear/linear_classifier.dart';
  import 'package:ml_algo/src/model_selection/assessable.dart';
  import 'package:ml_algo/src/solver/linear/gradient/learning_rate_generator/learning_rate_type.dart';
  import 'package:ml_linalg/dtype.dart';
  import 'package:ml_linalg/matrix.dart';
  
  
negative.code:   ///
    /// Parameters:
    ///
    /// [fittingData] A [DataFrame] with observations, that will be used by the
    /// classifier to learn coefficients of the hyperplane, which divides the
    /// features space, forming classes of the features. Should contain target
    /// column id (index or name)
    ///
    /// [targetIndices] A collection of indices of encoded target columns (a
    /// column, that contains class labels or outcomes for the associated
    /// features)
    ///
    /// [targetNames] A collection of strings, that serves as names for the
    /// encoded target columns (a column, that contains class labels or outcomes
    /// for the associated features)
    ///
    /// [iterationsLimit] A number of fitting iterations. Default value is 100
    ///
    /// [initialLearningRate] A value, defining velocity of the convergence of the
    /// gradient descent solver. Default value is 1e-3
  