Normalizer class abstract

A class that performs normalization of data.

Normalization is a process aimed to make all values in a vector vary within the range from 0.0 to 1.0 - this makes it possible to treat all the values equally disregard their units.

Normalization is applied row-wise.

Example:

import 'package:ml_dataframe/ml_dataframe.dart';
import 'package:ml_preprocessing/ml_preprocessing.dart';

void main() {
  final data = DataFrame([
    ['feature_1', 'feature_2', 'label'],
    [         10,        33.2,       2],
    [         20,          -1,       4],
    [         40,         -10,       5],
    [         55,         100,      10],
  ]);
  final normalizer = Normalizer();
  final processed = normalizer.process(data);

  print(processed);
  // DataFrame (4 x 3)
  //         feature_1                feature_2                 label
  // 0.287927508354187       0.9559193253517151   0.05758550018072128
  // 0.9794042110443115   -0.048970211297273636   0.19588084518909454
  // 0.9630868434906006    -0.24077171087265015   0.12038585543632507
  // 0.4800793528556824      0.8728715777397156   0.08728715777397156
}

Constructors

Normalizer([Norm norm, DType dtype])
factory

Properties

hashCode int
The hash code for this object.
no setterinherited
runtimeType Type
A representation of the runtime type of the object.
no setterinherited

Methods

noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
process(DataFrame input) → DataFrame
inherited
toString() String
A string representation of this object.
inherited

Operators

operator ==(Object other) bool
The equality operator.
inherited