- makeKey() - Static method in class hex.glm.GLMTask.GLMValidationTask
-
- makeKey() - Static method in class hex.glm.GLMTask.GLMXValidationTask
-
- makeKey() - Static method in class hex.glm.GLMValidation
-
- makeNeuronsForTesting(DeepLearningModel.DeepLearningModelInfo) - Static method in class hex.deeplearning.DeepLearningTask
-
- makeNeuronsForTraining(DeepLearningModel.DeepLearningModelInfo) - Static method in class hex.deeplearning.DeepLearningTask
-
- map(Chunk[], NewChunk[]) - Method in class hex.FrameTask
-
Extracts the values, applies regularization to numerics, adds appropriate offsets to categoricals,
and adapts response according to the CaseMode/CaseValue if set.
- map(GLMModel) - Method in class hex.glm.GLMModel.GetScoringModelTask
-
- map(Chunk[]) - Method in class hex.glm.GLMTask.GLMValidationTask
-
- map(Chunk[]) - Method in class hex.glm.GLMTask.GLMXValidationTask
-
- map(Chunk, Chunk) - Method in class hex.glm.GLMValidationTsk
-
- map(Chunk, Chunk) - Method in class hex.utils.MSETsk
-
- max_after_balance_size - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
When classes are balanced, limit the resulting dataset size to the
specified multiple of the original dataset size.
- max_after_balance_size - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
When classes are balanced, limit the resulting dataset size to the
specified multiple of the original dataset size.
- max_confusion_matrix_size - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
For classification models, the maximum size (in terms of classes) of the
confusion matrix for it to be printed.
- max_confusion_matrix_size - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
For classification models, the maximum size (in terms of classes) of the
confusion matrix for it to be printed.
- max_hit_ratio_k - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The maximum number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)
- max_hit_ratio_k - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The maximum number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)
- max_iters - Variable in class hex.schemas.ExampleV2.ExampleParametersV2
-
- max_iters - Variable in class hex.schemas.GLMV2.GLMParametersV2
-
- max_iters - Variable in class hex.schemas.KMeansV2.KMeansParametersV2
-
- max_ver() - Method in class hex.schemas.DeepLearningHandler
-
- max_ver() - Method in class hex.schemas.ExampleHandler
-
- max_ver() - Method in class hex.schemas.GLMHandler
-
- max_ver() - Method in class hex.schemas.KMeansHandler
-
- max_w2 - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
A maximum on the sum of the squared incoming weights into
any one neuron.
- max_w2 - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
A maximum on the sum of the squared incoming weights into
any one neuron.
- maxActivePredictors - Variable in class hex.glm.GLMModel.GLMParameters
-
- maxs - Variable in class hex.schemas.ExampleModelV2.ExampleModelOutputV2
-
- mean_a - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningModelInfo
-
- min_ver() - Method in class hex.schemas.DeepLearningHandler
-
- min_ver() - Method in class hex.schemas.ExampleHandler
-
- min_ver() - Method in class hex.schemas.GLMHandler
-
- min_ver() - Method in class hex.schemas.KMeansHandler
-
- missing_int_value - Static variable in class hex.deeplearning.Neurons
-
- missing_real_value - Static variable in class hex.deeplearning.Neurons
-
- missing_values_handling - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
- missing_values_handling - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
- missingColumnsType() - Method in class hex.deeplearning.DeepLearningModel
-
- model_info() - Method in class hex.deeplearning.DeepLearningModel
-
- model_info() - Method in class hex.deeplearning.DeepLearningTask
-
- model_info() - Method in class hex.deeplearning.DeepLearningTask2
-
Returns the aggregated DeepLearning model that was trained by all nodes (over all the training data)
- momentum() - Method in class hex.deeplearning.Neurons
-
- momentum(long) - Method in class hex.deeplearning.Neurons
-
The momentum - real number in [0, 1)
Can be a linear ramp from momentum_start to momentum_stable, over momentum_ramp training samples
- momentum_ramp - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The momentum_ramp parameter controls the amount of learning for which momentum increases
(assuming momentum_stable is larger than momentum_start).
- momentum_ramp - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The momentum_ramp parameter controls the amount of learning for which momentum increases
(assuming momentum_stable is larger than momentum_start).
- momentum_stable - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The momentum_stable parameter controls the final momentum value reached after momentum_ramp training samples.
- momentum_stable - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The momentum_stable parameter controls the final momentum value reached after momentum_ramp training samples.
- momentum_start - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The momentum_start parameter controls the amount of momentum at the beginning of training.
- momentum_start - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The momentum_start parameter controls the amount of momentum at the beginning of training.
- mse() - Method in class hex.deeplearning.DeepLearningModel
-
- mse - Variable in class hex.schemas.KMeansModelV2.KMeansModelOutputV2
-
- mses - Variable in class hex.schemas.KMeansModelV2.KMeansModelOutputV2
-
- MSETsk - Class in hex.utils
-
Created by tomasnykodym on 9/9/14.
- MSETsk() - Constructor for class hex.utils.MSETsk
-
- mul(double) - Method in class hex.glm.Gram
-
- mul(double[]) - Method in class hex.glm.Gram
-
- mul(double[], double[]) - Method in class hex.glm.Gram
-
- mustart(double, double) - Method in class hex.glm.GLMModel.GLMParameters
-
- n_folds - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
- n_folds - Variable in class hex.glm.GLMModel.GLMParameters
-
- n_folds - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
- N_THRESHOLDS - Static variable in class hex.glm.GLMTask.GLMIterationTask
-
- name() - Method in class hex.glm.LSMSolver.ADMMSolver
-
- name() - Method in class hex.glm.LSMSolver
-
- name() - Method in class hex.glm.LSMSolver.ProxSolver
-
- nclasses() - Method in class hex.glm.GLMModel.GLMOutput
-
- needLineSearch(GLMTask.GLMIterationTask) - Method in class hex.glm.GLM.GLMLambdaTask
-
- needLineSearch(GLMTask.GLMIterationTask, double) - Method in class hex.glm.GLM.GLMLambdaTask
-
- needLineSearch(double[], double, double) - Method in class hex.glm.GLM.GLMLambdaTask
-
- nesterov_accelerated_gradient - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The Nesterov accelerated gradient descent method is a modification to
traditional gradient descent for convex functions.
- nesterov_accelerated_gradient - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The Nesterov accelerated gradient descent method is a modification to
traditional gradient descent for convex functions.
- Neurons - Class in hex.deeplearning
-
This class implements the concept of a Neuron layer in a Neural Network
During training, every MRTask2 F/J thread is expected to create these neurons for every map call (Cheap to make).
- Neurons.DenseColMatrix - Class in hex.deeplearning
-
Dense column matrix implementation
- Neurons.DenseRowMatrix - Class in hex.deeplearning
-
Dense row matrix implementation
- Neurons.DenseVector - Class in hex.deeplearning
-
Dense vector implementation
- Neurons.Input - Class in hex.deeplearning
-
Input layer of the Neural Network
This layer is different from other layers as it has no incoming weights,
but instead gets its activation values from the training points.
- Neurons.Linear - Class in hex.deeplearning
-
Output neurons for regression - Softmax
- Neurons.Linear(int) - Constructor for class hex.deeplearning.Neurons.Linear
-
- Neurons.Matrix - Interface in hex.deeplearning
-
Abstract matrix interface
- Neurons.Maxout - Class in hex.deeplearning
-
Maxout neurons
- Neurons.Maxout(int) - Constructor for class hex.deeplearning.Neurons.Maxout
-
- Neurons.MaxoutDropout - Class in hex.deeplearning
-
Maxout neurons with dropout
- Neurons.MaxoutDropout(int) - Constructor for class hex.deeplearning.Neurons.MaxoutDropout
-
- Neurons.Output - Class in hex.deeplearning
-
Abstract class for Output neurons
- Neurons.Rectifier - Class in hex.deeplearning
-
Rectifier linear unit (ReLU) neurons
- Neurons.Rectifier(int) - Constructor for class hex.deeplearning.Neurons.Rectifier
-
- Neurons.RectifierDropout - Class in hex.deeplearning
-
Rectifier linear unit (ReLU) neurons with dropout
- Neurons.RectifierDropout(int) - Constructor for class hex.deeplearning.Neurons.RectifierDropout
-
- Neurons.Softmax - Class in hex.deeplearning
-
Output neurons for classification - Softmax
- Neurons.Softmax(int) - Constructor for class hex.deeplearning.Neurons.Softmax
-
- Neurons.SparseRowMatrix - Class in hex.deeplearning
-
Sparse row matrix implementation
- Neurons.SparseVector - Class in hex.deeplearning
-
Sparse vector implementation
- Neurons.SparseVector.Iterator - Class in hex.deeplearning
-
Iterator over a sparse vector
- Neurons.Tanh - Class in hex.deeplearning
-
Tanh neurons - most common, most stable
- Neurons.Tanh(int) - Constructor for class hex.deeplearning.Neurons.Tanh
-
- Neurons.TanhDropout - Class in hex.deeplearning
-
Tanh neurons with dropout
- Neurons.TanhDropout(int) - Constructor for class hex.deeplearning.Neurons.TanhDropout
-
- Neurons.Vector - Interface in hex.deeplearning
-
Abstract vector interface
- nfeatures() - Method in class hex.kmeans.KMeansModel.KMeansOutput
-
- nlambdas - Variable in class hex.glm.GLMModel.GLMParameters
-
- nnz() - Method in class hex.deeplearning.Neurons.SparseVector
-
- normalize() - Method in class hex.glm.LSMSolver.ADMMSolver
-
- normalize - Variable in class hex.schemas.GLMV2.GLMParametersV2
-
- normalize - Variable in class hex.schemas.KMeansV2.KMeansParametersV2
-
- normMul() - Method in class hex.FrameTask
-
- normRespMul() - Method in class hex.FrameTask
-
- normRespSub() - Method in class hex.FrameTask
-
- normSub() - Method in class hex.FrameTask
-
- nullDeviance() - Method in class hex.glm.GLMValidation
-
- nullDOF() - Method in class hex.glm.GLMValidation
-
- nullModelBeta(FrameTask.DataInfo, double) - Method in class hex.glm.GLMModel.GLMParameters
-
- numStart() - Method in class hex.FrameTask.DataInfo
-
- randomlySparsifyActivation(Neurons.Vector, long) - Method in class hex.deeplearning.Dropout
-
- rank() - Method in class hex.glm.GLMModel.GLMOutput
-
- rank(double) - Method in class hex.glm.GLMModel.GLMOutput
-
- rank(double) - Method in class hex.glm.GLMModel
-
- rate - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
When adaptive learning rate is disabled, the magnitude of the weight
updates are determined by the user specified learning rate
(potentially annealed), and are a function of the difference
between the predicted value and the target value.
- rate(long) - Method in class hex.deeplearning.Neurons
-
The learning rate
- rate - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
When adaptive learning rate is disabled, the magnitude of the weight
updates are determined by the user specified learning rate
(potentially annealed), and are a function of the difference
between the predicted value and the target value.
- rate_annealing - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
Learning rate annealing reduces the learning rate to "freeze" into
local minima in the optimization landscape.
- rate_annealing - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
Learning rate annealing reduces the learning rate to "freeze" into
local minima in the optimization landscape.
- rate_decay - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The learning rate decay parameter controls the change of learning rate across layers.
- rate_decay - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The learning rate decay parameter controls the change of learning rate across layers.
- raw() - Method in class hex.deeplearning.Neurons.DenseColMatrix
-
- raw() - Method in class hex.deeplearning.Neurons.DenseRowMatrix
-
- raw() - Method in class hex.deeplearning.Neurons.DenseVector
-
- raw() - Method in interface hex.deeplearning.Neurons.Matrix
-
- raw() - Method in class hex.deeplearning.Neurons.SparseRowMatrix
-
- raw() - Method in class hex.deeplearning.Neurons.SparseVector
-
- raw() - Method in interface hex.deeplearning.Neurons.Vector
-
- reduce(DeepLearningTask) - Method in class hex.deeplearning.DeepLearningTask
-
- reduce(DeepLearningTask2) - Method in class hex.deeplearning.DeepLearningTask2
-
Reduce between worker ndoes, with network traffic (if greater than 1 nodes)
After all reduce()'s are done, postGlobal() will be called
- reduce(GLMTask.GLMIterationTask) - Method in class hex.glm.GLMTask.GLMIterationTask
-
- reduce(GLMTask.GLMLineSearchTask) - Method in class hex.glm.GLMTask.GLMLineSearchTask
-
- reduce(GLMTask.GLMValidationTask) - Method in class hex.glm.GLMTask.GLMValidationTask
-
- reduce(GLMTask.GLMXValidationTask) - Method in class hex.glm.GLMTask.GLMXValidationTask
-
- reduce(GLMValidationTsk) - Method in class hex.glm.GLMValidationTsk
-
- reduce(Gram.GramTask) - Method in class hex.glm.Gram.GramTask
-
- reduce(MSETsk) - Method in class hex.utils.MSETsk
-
- regression_stop - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The stopping criteria in terms of regression error (MSE) on the training
data scoring dataset.
- regression_stop - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The stopping criteria in terms of regression error (MSE) on the training
data scoring dataset.
- replicate_training_data - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
Replicate the entire training dataset onto every node for faster training on small datasets.
- replicate_training_data - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
Replicate the entire training dataset onto every node for faster training on small datasets.
- resDOF() - Method in class hex.glm.GLMValidation
-
- residualDeviance() - Method in class hex.glm.GLMValidation
-
- rho - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The first of two hyper parameters for adaptive learning rate (ADADELTA).
- rho - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The first of two hyper parameters for adaptive learning rate (ADADELTA).
- rms_weight - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningModelInfo
-
- rows() - Method in class hex.deeplearning.Neurons.DenseColMatrix
-
- rows() - Method in class hex.deeplearning.Neurons.DenseRowMatrix
-
- rows() - Method in interface hex.deeplearning.Neurons.Matrix
-
- rows() - Method in class hex.deeplearning.Neurons.SparseRowMatrix
-
- rows - Variable in class hex.schemas.KMeansModelV2.KMeansModelOutputV2
-
- sanityCheckParameters() - Method in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
- sanityCheckParameters() - Method in class hex.example.ExampleModel.ExampleParameters
-
- sanityCheckParameters() - Method in class hex.glm.GLMModel.GLMParameters
-
- sanityCheckParameters() - Method in class hex.kmeans.KMeansModel.KMeansParameters
-
- schema(int) - Method in class hex.api.DeepLearningBuilderHandler
-
- schema(int) - Method in class hex.api.ExampleBuilderHandler
-
- schema(int) - Method in class hex.api.GLMBuilderHandler
-
- schema(int) - Method in class hex.api.KMeansBuilderHandler
-
- schema() - Method in class hex.deeplearning.DeepLearning
-
- schema() - Method in class hex.deeplearning.DeepLearningModel
-
- schema() - Method in class hex.example.Example
-
- schema() - Method in class hex.example.ExampleModel
-
- schema() - Method in class hex.glm.GLM
-
- schema() - Method in class hex.glm.GLMModel
-
- schema() - Method in class hex.kmeans.KMeans
-
- schema() - Method in class hex.kmeans.KMeansModel
-
- schema(int) - Method in class hex.schemas.DeepLearningHandler
-
- schema(int) - Method in class hex.schemas.ExampleHandler
-
- schema(int) - Method in class hex.schemas.GLMHandler
-
- schema(int) - Method in class hex.schemas.KMeansHandler
-
- score(Frame) - Method in class hex.deeplearning.DeepLearningModel
-
This is an overridden version of Model.score().
- score0(double[], float[]) - Method in class hex.deeplearning.DeepLearningModel
-
Predict from raw double values representing the data
- score0(double[], float[]) - Method in class hex.example.ExampleModel
-
- score0(Chunk[], int, double[], float[]) - Method in class hex.glm.GLMModel
-
- score0(double[], float[]) - Method in class hex.glm.GLMModel
-
- score0(Chunk[], int, double[], float[]) - Method in class hex.kmeans.KMeansModel
-
Bulk scoring API for one row.
- score0(double[], float[]) - Method in class hex.kmeans.KMeansModel
-
- score_duty_cycle - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
Maximum fraction of wall clock time spent on model scoring on training and validation samples,
and on diagnostics such as computation of feature importances (i.e., not on training).
- score_duty_cycle - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
Maximum fraction of wall clock time spent on model scoring on training and validation samples,
and on diagnostics such as computation of feature importances (i.e., not on training).
- score_interval - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The minimum time (in seconds) to elapse between model scoring.
- score_interval - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The minimum time (in seconds) to elapse between model scoring.
- score_training_samples - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The number of training dataset points to be used for scoring.
- score_training_samples - Variable in class hex.deeplearning.DeepLearningModel.Errors
-
- score_training_samples - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The number of training dataset points to be used for scoring.
- score_validation_samples - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The number of validation dataset points to be used for scoring.
- score_validation_samples - Variable in class hex.deeplearning.DeepLearningModel.Errors
-
- score_validation_samples - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The number of validation dataset points to be used for scoring.
- score_validation_sampling - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
Method used to sample the validation dataset for scoring, see Score Validation Samples above.
- score_validation_sampling - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
Method used to sample the validation dataset for scoring, see Score Validation Samples above.
- scoreAutoEncoder(Frame) - Method in class hex.deeplearning.DeepLearningModel
-
Score auto-encoded reconstruction (on-the-fly, without allocating the reconstruction as done in Frame score(Frame fr))
- scoring_history() - Method in class hex.deeplearning.DeepLearningModel
-
- scoring_time - Variable in class hex.deeplearning.DeepLearningModel.Errors
-
- seed - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
The random seed controls sampling and initialization.
- seed - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
The random seed controls sampling and initialization.
- seed - Variable in class hex.schemas.GLMV2.GLMParametersV2
-
- seed - Variable in class hex.schemas.KMeansV2.KMeansParametersV2
-
- set(int, int, float) - Method in class hex.deeplearning.Neurons.DenseColMatrix
-
- set(int, int, float) - Method in class hex.deeplearning.Neurons.DenseRowMatrix
-
- set(int, float) - Method in class hex.deeplearning.Neurons.DenseVector
-
- set(int, int, float) - Method in interface hex.deeplearning.Neurons.Matrix
-
- set(int, int, float) - Method in class hex.deeplearning.Neurons.SparseRowMatrix
-
- set(int, float) - Method in class hex.deeplearning.Neurons.SparseVector
-
- set(int, float) - Method in interface hex.deeplearning.Neurons.Vector
-
- set_processed_global(long) - Method in class hex.deeplearning.DeepLearningModel.DeepLearningModelInfo
-
- set_processed_local(long) - Method in class hex.deeplearning.DeepLearningModel.DeepLearningModelInfo
-
- set_unstable() - Method in class hex.deeplearning.DeepLearningModel.DeepLearningModelInfo
-
- setInput(long, double[]) - Method in class hex.deeplearning.Neurons.Input
-
One of two methods to set layer input values.
- setInput(long, double[], int, int[]) - Method in class hex.deeplearning.Neurons.Input
-
The second method used to set input layer values.
- setSPD(boolean) - Method in class hex.glm.Gram.Cholesky
-
- setSubmodel(H2O.H2OCountedCompleter, Key, double, double[], double[], int, long, boolean, GLMValidation) - Static method in class hex.glm.GLMModel
-
- setSubmodelIdx(int) - Method in class hex.glm.GLMModel.GLMOutput
-
- setupLocal() - Method in class hex.deeplearning.DeepLearningTask
-
- setupLocal() - Method in class hex.deeplearning.DeepLearningTask2
-
Do the local computation: Perform one DeepLearningTask (with run_local=true) iteration.
- setXvalidation(H2O.H2OCountedCompleter, Key, double, GLMValidation) - Static method in class hex.glm.GLMModel
-
- shrinkage(double, double) - Static method in class hex.glm.LSMSolver
-
- shuffle_training_data - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
Enable shuffling of training data (on each node).
- shuffle_training_data - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
Enable shuffling of training data (on each node).
- single_node_mode - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
Run on a single node for fine-tuning of model parameters.
- single_node_mode - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
Run on a single node for fine-tuning of model parameters.
- size() - Method in class hex.deeplearning.DeepLearningModel.DeepLearningModelInfo
-
- size() - Method in class hex.deeplearning.Neurons.DenseColMatrix
-
- size() - Method in class hex.deeplearning.Neurons.DenseRowMatrix
-
- size() - Method in class hex.deeplearning.Neurons.DenseVector
-
- size() - Method in interface hex.deeplearning.Neurons.Matrix
-
- size() - Method in class hex.deeplearning.Neurons.SparseRowMatrix
-
- size() - Method in class hex.deeplearning.Neurons.SparseVector
-
- size() - Method in interface hex.deeplearning.Neurons.Vector
-
- skipMissing() - Method in class hex.FrameTask
-
- softMaxCategoricals(float[], float[]) - Method in class hex.FrameTask.DataInfo
-
Normalize horizontalized categoricals to become probabilities per factor level.
- solve(double[]) - Method in class hex.glm.Gram.Cholesky
-
Find solution to A*x = y.
- solve(Gram, double[], double, double[]) - Method in class hex.glm.LSMSolver.ADMMSolver
-
- solve(Gram, double[], double, double[], double) - Method in class hex.glm.LSMSolver.ADMMSolver
-
- solve(Gram, double[], double, double[]) - Method in class hex.glm.LSMSolver.ProxSolver
-
- solve(Gram, double[], double, double[]) - Method in class hex.glm.LSMSolver
-
- solve(int, L_BFGS.GradientSolver, L_BFGS.L_BFGS_Params) - Static method in class hex.optimization.L_BFGS
-
Solve the optimization problem defined by the user-supplied gradient function using L-BFGS algorithm.
- solve(L_BFGS.GradientSolver, L_BFGS.L_BFGS_Params, L_BFGS.History, double[]) - Static method in class hex.optimization.L_BFGS
-
Solve the optimization problem defined by the user-supplied gradient function using L-BFGS algorithm.
- sparse - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
- sparse - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
- sparseness() - Method in class hex.glm.Gram.Cholesky
-
- sparseness() - Method in class hex.glm.Gram
-
- sparsity_beta - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
- sparsity_beta - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
- step(long, Neurons[], DeepLearningModel.DeepLearningModelInfo, boolean, double[]) - Static method in class hex.deeplearning.DeepLearningTask
-
- subgrad(double, double, double[], double[]) - Static method in class hex.glm.LSMSolver
-
- submodelForLambda(double) - Method in class hex.glm.GLMModel.GLMOutput
-
- submodelIdForLambda(double) - Method in class hex.glm.GLMModel.GLMOutput
-
- valid_confusion_matrix - Variable in class hex.deeplearning.DeepLearningModel.Errors
-
- valid_err - Variable in class hex.deeplearning.DeepLearningModel.Errors
-
- valid_hitratio - Variable in class hex.deeplearning.DeepLearningModel.Errors
-
- valid_mse - Variable in class hex.deeplearning.DeepLearningModel.Errors
-
- validation() - Method in class hex.glm.GLMModel
-
- validation_rows - Variable in class hex.deeplearning.DeepLearningModel
-
- validAUC - Variable in class hex.deeplearning.DeepLearningModel.Errors
-
- valueOf(String) - Static method in enum hex.deeplearning.DeepLearningModel.DeepLearningParameters.Activation
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum hex.deeplearning.DeepLearningModel.DeepLearningParameters.ClassSamplingMethod
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum hex.deeplearning.DeepLearningModel.DeepLearningParameters.InitialWeightDistribution
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum hex.deeplearning.DeepLearningModel.DeepLearningParameters.Loss
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum hex.deeplearning.DeepLearningModel.DeepLearningParameters.MissingValuesHandling
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum hex.FrameTask.DataInfo.TransformType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum hex.glm.GLMModel.GLMParameters.Family
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum hex.glm.GLMModel.GLMParameters.Link
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum hex.glm.LSMSolver.LSMSolverType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum hex.kmeans.KMeans.Initialization
-
Returns the enum constant of this type with the specified name.
- values() - Static method in enum hex.deeplearning.DeepLearningModel.DeepLearningParameters.Activation
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum hex.deeplearning.DeepLearningModel.DeepLearningParameters.ClassSamplingMethod
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum hex.deeplearning.DeepLearningModel.DeepLearningParameters.InitialWeightDistribution
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum hex.deeplearning.DeepLearningModel.DeepLearningParameters.Loss
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum hex.deeplearning.DeepLearningModel.DeepLearningParameters.MissingValuesHandling
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum hex.FrameTask.DataInfo.TransformType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum hex.glm.GLMModel.GLMParameters.Family
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum hex.glm.GLMModel.GLMParameters.Link
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum hex.glm.LSMSolver.LSMSolverType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum hex.kmeans.KMeans.Initialization
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- variable_importances - Variable in class hex.deeplearning.DeepLearningModel.DeepLearningParameters
-
Whether to compute variable importances for input features.
- variable_importances - Variable in class hex.schemas.DeepLearningV2.DeepLearningParametersV2
-
Whether to compute variable importances for input features.
- variance(double) - Method in class hex.glm.GLMModel.GLMParameters
-
- varimp() - Method in class hex.deeplearning.DeepLearningModel
-