public class DeeplearningMojoModel extends MojoModel
Modifier and Type | Class and Description |
---|---|
static class |
DeeplearningMojoModel.StoreWeightsBias |
Modifier and Type | Field and Description |
---|---|
java.lang.String |
_activation |
double[] |
_all_drop_out_ratios |
java.lang.String[] |
_allActivations |
int[] |
_catNAFill |
int[] |
_catoffsets |
int |
_cats |
int[] |
_catsA |
DistributionFamily |
_family |
boolean |
_imputeMeans |
int |
_mini_batch_size |
double[] |
_normmul |
double[] |
_normrespmul |
double[] |
_normrespsub |
double[] |
_normsub |
int |
_numLayers |
int |
_nums |
double[] |
_numsA |
int[] |
_units |
boolean |
_use_all_factor_levels |
DeeplearningMojoModel.StoreWeightsBias[] |
_weightsAndBias |
_balanceClasses, _category, _defaultThreshold, _h2oVersion, _modelClassDistrib, _mojo_version, _nclasses, _nfeatures, _priorClassDistrib, _supervised, _uuid
_domains, _names, _offsetColumn, _responseColumn
Modifier and Type | Method and Description |
---|---|
double |
calculateReconstructionErrorPerRowData(double[] original,
double[] reconstructed)
Calculates average reconstruction error (MSE).
|
int |
getPredsSize(ModelCategory mc) |
void |
init() |
double[] |
modifyOutputs(double[] out,
double[] preds,
double[] dataRow) |
double[] |
score0(double[] row,
double[] preds)
Subclasses implement the scoring logic.
|
double[] |
score0(double[] dataRow,
double offset,
double[] preds)
This method will be derived from the scoring/prediction function of deeplearning model itself.
|
getModelCategory, getUUID, isSupervised, load, load, nclasses, nfeatures
bitSetContains, bitSetIsInRange, calibrateClassProbabilities, convertDouble2Float, correctProbabilities, createAuxKey, GBM_rescale, getColIdx, getDomainValues, getDomainValues, getDomainValues, getHeader, getModelCategories, getNames, getNumClasses, getNumCols, getNumResponseClasses, getPrediction, getPredsSize, getResponseIdx, getResponseName, GLM_identityInv, GLM_inverseInv, GLM_logInv, GLM_logitInv, GLM_ologitInv, GLM_tweedieInv, img2pixels, isAutoEncoder, isClassifier, KMeans_closest, KMeans_distance, KMeans_distance, KMeans_distances, Kmeans_preprocessData, Kmeans_preprocessData, KMeans_simplex, log_rescale, mapEnum, setCats, setCats, setInput, setInput
public int _mini_batch_size
public int _nums
public int _cats
public int[] _catoffsets
public double[] _normmul
public double[] _normsub
public double[] _normrespmul
public double[] _normrespsub
public boolean _use_all_factor_levels
public java.lang.String _activation
public java.lang.String[] _allActivations
public boolean _imputeMeans
public int[] _units
public double[] _all_drop_out_ratios
public DeeplearningMojoModel.StoreWeightsBias[] _weightsAndBias
public int[] _catNAFill
public int _numLayers
public DistributionFamily _family
public double[] _numsA
public int[] _catsA
public void init()
public final double[] score0(double[] dataRow, double offset, double[] preds)
public double[] modifyOutputs(double[] out, double[] preds, double[] dataRow)
public double[] score0(double[] row, double[] preds)
GenModel
public int getPredsSize(ModelCategory mc)
getPredsSize
in class GenModel
public double calculateReconstructionErrorPerRowData(double[] original, double[] reconstructed)