| BaseStatsTask |
Class for storing and updating basic column stats (max, min, mean, sigma).
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| ColSummaryTask |
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| ConfusionMatrix |
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| Covariance |
Calculate the covariance and correlation of two variables
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| Covariance.COV_Task |
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| DGLM |
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| DGLM.FamilyIced |
passthrough class around family that properly supports icing
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| DGLM.GLMJob |
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| DGLM.GLMModel |
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| DGLM.GLMModel.GLMValidationTask |
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| DGLM.GLMParams |
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| DGLM.GLMValidation |
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| DGLM.GLMValidationFunc |
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| DGLM.GramMatrixFunc |
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| DGLM.LambdaMax |
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| DGLM.LambdaMaxFunc |
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| DGLM.LinkIced |
passthrough class around Link that supports Icing
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| DLSM |
Distributed least squares solvers
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| DLSM.ADMMSolver |
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| DLSM.GeneralizedGradientSolver |
Generalized gradient solver for solving LSM problem with combination of L1 and L2 penalty.
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| DLSM.LSMSolver |
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| FrameTask<T extends FrameTask<T>> |
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| FrameTask.DataInfo |
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| GLMGrid |
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| GLMGrid.GLMModels |
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| GridSearch |
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| GridSearch.GridSearchProgress |
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| Histogram |
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| Histogram.BinningTask |
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| Histogram.Bins |
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| Histogram.OutlineTask |
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| KMeans |
Scalable K-Means++ (KMeans||)
http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf
http://www.youtube.com/watch?v=cigXAxV3XcY
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| KMeans.Lloyds |
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| KMeans.Sampler |
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| KMeans.Sqr |
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| KMeans2 |
Scalable K-Means++ (KMeans||)
http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf
http://www.youtube.com/watch?v=cigXAxV3XcY
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| KMeans2.KMeans2Model |
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| KMeans2.KMeans2ModelView |
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| KMeans2.KMeans2Progress |
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| KMeans2.Lloyds |
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| KMeans2.Sampler |
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| KMeans2.SumSqr |
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| KMeansModel |
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| KMeansModel.KMeansApply |
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| KMeansModel.KMeansScore |
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| KMeansShared |
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| Layer |
Neural network layer.
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| Layer.Input |
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| Layer.Linear |
Linear output layer is used for regression
Rows with missing values in the response column will be ignored
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| Layer.Maxout |
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| Layer.MaxoutDropout |
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| Layer.Output |
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| Layer.Rectifier |
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| Layer.RectifierDropout |
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| Layer.RectifierPrime |
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| Layer.Softmax |
Softmax output layer is used for classification
Rows with missing values in the response column will be ignored
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| Layer.Tanh |
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| Layer.TanhDropout |
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| Layer.TanhPrime |
Apply tanh to the weights' transpose.
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| Layer.VecLinear |
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| Layer.VecsInput |
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| Layer.VecSoftmax |
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| LinearRegression |
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| LinearRegression.CalcRegressionTask |
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| LinearRegression.CalcSquareErrorsTasks |
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| LinearRegression.CalcSumsTask |
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| LR2 |
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| LR2.CalcRegressionTask |
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| LR2.CalcSquareErrorsTasks |
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| LR2.CalcSumsTask |
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| NeuralNet |
Neural network.
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| NeuralNet.Errors |
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| NeuralNet.NeuralNetModel |
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| NeuralNet.NeuralNetScore |
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| NewRowVecTask<T extends Iced> |
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| NewRowVecTask.DataFrame |
Struct to keep info about our data.
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| NewRowVecTask.RowFunc<T extends Iced> |
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| NodeShuffle |
Shuffle the rows of some dataset, such that the natural placement of the
resulting ValueArray onto Nodes results in some good property.
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| NOPTask |
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| OneHot |
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| ParamsSearch |
Looks for parameters on a set of objects and perform random search.
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| Quantiles |
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| Quantiles.QuantilesTask |
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| RowTask<T extends Freezable> |
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| RowTask.Row<T extends Freezable> |
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| RowTask.RowFunction<T extends Iced> |
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| RowVecTask |
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| RowVecTask.Sampling |
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| ScoreTask |
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| ShuffleTask |
Simple shuffle task based on Fisher&Yates algo.
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| Summary |
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| Summary.ColSummary |
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| Summary2 |
Summary of a column.
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| Summary2.BasicStat |
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| Summary2.PrePass |
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| Summary2.SummaryPerRow |
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| Summary2.SummaryTask2 |
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| Trainer |
Trains a neural network.
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| Trainer.Base |
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| Trainer.Direct |
Trains NN on current thread.
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| Trainer.MapReduce |
Distributed trainer.
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| Trainer.OpenCL |
GPU based trainer.
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| Trainer.Threaded |
Runs several trainers in parallel on the same weights, using threads.
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| VariableImportance |
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