0.25.0
·
83 commits
to main
since this release
rumale-linear_model
Breaking change
- Add new SGDClassfier and SGDRegressor by extracting stochastic gradient descent solver from each linear model.
- Change the optimization method of ElasticNet and Lasso to use the coordinate descent algorithm.
- Change the optimization method of SVC and SVR to use the L-BFGS method.
- Change the loss function of SVC to the squared hinge loss.
- Change the loss function of SVR to the squared epsilon-insensitive loss.
- Change not to use random vector for initialization of weights.
- From the above changes, keyword arguments such as learning_rate, decay, momentum, batch_size,
and random_seed for LinearModel estimators have been removed.
- From the above changes, keyword arguments such as learning_rate, decay, momentum, batch_size,
- Fix the column and row vectors of weight matrix are reversed in LinearRegression, Ridge, and NNLS.
rumale-decomposition
- Fix missing require method to load Rumale::Utils in PCA class.
It is needed to initialize the principal components when optimizing with fixed-point algorithm.
rumale-evaluation_measure
- Apply automatic correction for Style/ZeroLengthPredicate of RuboCop to ROCAUC class.
others
- No changes, or only modifications in test code or configuration.