ml_algo 13.3.4 copy "ml_algo: ^13.3.4" to clipboard
ml_algo: ^13.3.4 copied to clipboard

Published 4 years ago • verified publisherml-algo.comDart 3 incompatible• Latest: 16.17.10
outdated
→

Metadata

Machine learning algorithms written in native dart

More...

Changelog #

13.3.4 #

  • LogisticRegressor: classifier instantiating refactored

13.3.3 #

  • KnnClassifierImpl: unit tests for predictProbability method added

13.3.2 #

  • KnnClassifier: classifier instantiating refactored

13.3.1 #

  • readme: KnnRegressor usage example fixed

13.3.0 #

  • KnnClassifier class added

13.2.0 #

  • KNN algorithm: standardization for distance added
  • KnnRegressor:
    • default kernel changed to gaussian
    • k parameter is required now

13.1.1 #

  • KNN regression: documentation for kernel function types added
  • KnnRegressor: finding weighted average using kernel function fixed

13.1.0 #

  • CrossValidator: onDataSplit hook added

13.0.0 #

  • Predictor's API: DataFrame used instead of Matrix
  • DecisionTreeSolver: data splitting logic fixed

12.1.2 #

  • xrange package version locked

12.1.1 #

  • ml_linalg 11.0.0 supported
  • Unit tests: iterable2dAlmostEqualTo and iterableAlmostEqualTo matchers used from ml_tech

12.1.0 #

  • Decision tree classifier added

12.0.2 #

  • ScoreToProbMapperFactory removed
  • ScoreToProbMapperType enum removed
  • ScoreToProbMapper: the entity renamed to LinkFunction

12.0.1 #

  • Cost function factory removed
  • Cost function type removed

12.0.0 #

  • Breaking change: GradientType enum removed
  • Breaking change: OptimizerType enum removed
  • Breaking change, Predictor: fit method removed, fitting is happening while a model is being created
  • Breaking change, Predictor: interface replaced with Assessable, redundant properties removed
  • Breaking change: LinearClassifier reorganized
  • Optimizers now have immutable state
  • InterceptPreprocessor replaced with a helper function addInterceptIf

11.0.1 #

  • Cross validator refactored
  • Data splitters refactored
  • Unit tests for cross validator added

11.0.0 #

  • Added immutable state to all the predictor subclasses

10.3.0 #

  • kernels added:
    • uniform
    • epanechnikov
    • cosine
    • gaussian
  • NoNParametricRegressor.nearestNeighbour: added possibility to specify the kernel function

10.2.1 #

  • test coverage restored

10.2.0 #

  • NoNParametricRegressor class added
  • KNNRegressor class added
  • ml_linalg v9.0.0 supported

10.1.0 #

  • ml_linalg v7.0.0 support

10.0.0 #

  • Data preprocessing: all the entities moved to separate repo - ml_preprocessing

9.2.4 #

  • Data preprocessing: All categorical values are now converted to String type

9.2.3 #

  • Examples for Linear regression and Logistic regression updated (vector's normalize method used)
  • CategoricalDataEncoderType: one-hot encoding documentation corrected

9.2.2 #

  • Softmax regression example added to README

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 now are 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 #

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

← Metadata

103
likes
0
pub points
82%
popularity

Publisher

verified publisherml-algo.com

Metadata

Machine learning algorithms written in native dart

Repository (GitHub)
View/report issues

License

unknown (LICENSE)

Dependencies

injector, ml_dataframe, ml_linalg, quiver, xrange

More

Packages that depend on ml_algo

Back