Changelog #

9.2.1 #

README corrected

9.2.0 #

LinearClassifier.logisticRegressor: numerical stability improved
LinearClassifier.logisticRegressor: probabilityThreshold parameter added
DataFrame.fromCsv: parameter fieldDelimiter added

9.1.0 #

DataFrame: labelName parameter added

9.0.0 #

ml_linalg v6.0.2 supported
Classifier: type of weightsByClasses changed from Map to Matrix
SoftmaxRegressor: more detailed unit tests for softmax regression added
Data preprocessing: DataFrame introduced (former MLData)

8.0.0 #

LinearClassifier.softmaxRegressor implemented
Metric interface refactored (getError renamed to getScore)

7.2.0 #

SoftmaxMapper added (aka Softmax activation function)

7.1.0 #

ConvergenceDetector added (this entity stops the optimizer when it is needed)

7.0.0 #

All the exports packed into ml_algo entry

6.2.0 #

Coefficients in optimizers now are a matrix
InitialWeightsGenerator instantiating fixed: dtype is passed now

6.1.0 #

LinkFunction renamed to ScoreToProbMapper
ScoreToProbMapper accepts vector and returns vector instead of a scalar

6.0.6 #

Pedantic package integration added
Some linter issues fixed

6.0.5 #

Coveralls integration added
dartfm check task added

6.0.4 #

Documentation for linear regression corrected
Documentation for MLData corrected

6.0.3 #

Documentation for logistic regression corrected

6.0.2 #

Tests corrected: removed import test_api.dart

6.0.1 #

Readme corrected

6.0.0 #

Library fully refactored:
- add possibility to set certain data type for numeric computations
- all algorithms code now is more generic
- a lot of unit tests added
- bug fixes

5.2.0 #

Ordinal encoder added
Float32x4CsvMlData significantly extended

5.1.0 #

Real-life example added (black friday dataset)
rows parameter added to Float32x4CsvMlData
Unknown categorical values handling strategy types added

5.0.0 #

One hot encoder integrated into CSV ML data

4.3.3 #

Performance test for one hot encoder added

4.3.2 #

One hot encoder implemented

4.3.1 #

enum for categorical data encoding added

4.3.0 #

Cross validator factory added
README updated

4.2.0 #

csv-parser added

4.1.0 #

ml_linalg removed from export file
README refreshed
General datasets directory created

4.0.0 #

ml_linal ^4.0.0 supported

3.5.4 #

README.md updated
build_runner dependency updated

3.5.3 #

dartfmt tool applied to all necessary files

3.5.2 #

Travis configuration file name corrected

3.5.1 #

Travis integration added

3.5.0 #

Vectorized cost functions applied

3.4.0 #

ml_linalg 2.0.0 supported

3.3.0 #

Matrix-based gradient calculation added for log likelihood cost function

3.2.0 #

Matrix-based gradient calculation added for squared cost function

3.1.2 #

Description corrected

3.1.1 #

dartfm tool applied

3.1.0 #

Get rid of MLVector's deprecated methods

3.0.0 #

Library public release

2.0.0 #

ml_linalg supported

1.2.1 #

subVector -> subvector

1.2.0 #

Matrices support added

1.1.1 #

Examples fixed, dependencies fixed

1.1.0 #

Support of updated linalg package

1.0.1 #

Readme updated, dependencies fixed

1.0.0 #

Migration to dart 2.0

0.38.1 #

issue solved

0.38.0 #

Lasso solution refactored

0.37.0 #

Support of linalg package (former simd_vector)

0.36.0 #

Intercept term considered (fitIntercept and interceptScale parameters)

0.35.1 #

Logistic regression tests improved

0.35.0 #

One versus all refactored, tests for logistic regression added

0.34.0 #

One versus all classifier

0.33.0 #

Gradient descent regressor type enum added

0.32.1 #

Gradient optimizer unit tests

0.32.0 #

Get rid of derivative computation

0.31.0 #

Get rid of di package usage

0.30.1 #

File structure flattened

0.30.0 #

Redundant gradient optimizers removed

0.29.0 #

part ... part of directives removed

0.28.0 #

Coordinate descent optimizer added
Lasso regressor added

0.27.0 #

Gradient calculation changed

0.26.1 #

Code was optimized (removed unnecessary)
Refactoring

0.26.0 #

More distinct modularity was added to the library
Unit tests were fixed

0.25.0 #

Tests for gradient optimizers were added
Gradient calculator was created as a separate entity
Initial weights generator was created as a separate entity
Learning rate generator was created as a separate entity

0.24.0 #

All implementations were hidden

0.23.0 #

findMaxima and findMinima methods were added to Optimizer interface

0.22.0 #

File structure reorganized, predictor classes refactored
README.md updated

0.21.0 #

Logistic regression model added (with example)

0.20.2 #

README.md updated

0.20.1 #

simd_vector dependency url fixed

0.20.0 #

Repository dependency corrected (dart_vector -> simd_vector)

0.19.0 #

Support for Float32x4Vector class was added (from dart_vector library)
Type List for label (target) list replaced with Float32List (in Predictor.train() and Optimizer.optimize())

0.18.0 #

class Vector and enum Norm were extracted to separate library (https://github.com/gyrdym/dart_vector.git)

0.17.0 #

Common interface for loss function was added
Derivative calculation was fixed (common canonical method was used)
Squared loss function was added as a separate class

0.16.0 #

README.md was actualized

0.15.0 #

Tests for gradient optimizers were added
Interfaces (almost for all entities) for DI and IOC mechanism were added
Randomizer class was added
Removed separate classes for k-fold cross validation and lpo cross validation, now it resides in CrossValidation class

0.14.0 #

L1 and L2 regularization added

0.13.0 #

Script for running all unit tests added

0.12.0 #

Vector interface removed
Regular vector implementation removed
TypedVector -> Vector
Implicit vectors constructing replaced with explicit new-instantiation

0.11.0 #

Entity names correction

0.10.0 #

K-fold cross validation added (KFoldCrossValidation)
Leave P out cross validation added (LpoCrossValidation)
DataTrainTestSplitter was removed

0.9.0 #

copy, fill methods were added to Vector

0.8.0 #

Reflection was removed for all cases (Vector instantiation, Optimizer instantiation)

0.7.0 #

Abstract Vector-class was added as a base for typed and regular vector classes

0.6.0 #

Manhattan norm support was added

0.5.2 #

README file was extended and clarified

0.5.1 #

Random interval obtaining for the mini-batch gradient descent was fixed

0.5.0 #

BGDOptimizer, MBGDOptimizer and GradientOptimizer were added

0.4.0 #

OptimizerInterface was added
Stochastic gradient descent optimizer was extracted from the linear regressor class
Line separators changed for all files (CRLF -> LF)

0.3.1 #

tests for sum, abs, fromRange methods of the TypedVector were added
tests for DataTrainTestSplitter was added

0.3.0 #

MAPE cost function was added

0.2.0 #

SGD Regressor refactored (rmse on training removed, estimator added) + example extended

0.1.0 #

Implementation of -, *, / operators and all vectors methods added to the TypedVector

0.0.1 #

Initial version

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