hex.schemas

Class KMeansV3.KMeansParametersV3

java.lang.Object

water.Iced

water.api.Schema<I,S>

water.api.schemas3.SchemaV3<P,S>

water.api.schemas3.ModelParametersSchemaV3<P,S>

water.api.schemas3.ClusteringModelParametersSchemaV3<KMeansModel.KMeansParameters,KMeansV3.KMeansParametersV3>

hex.schemas.KMeansV3.KMeansParametersV3

All Implemented Interfaces:

java.io.Externalizable, java.io.Serializable, java.lang.Cloneable, water.Freezable

Enclosing class:

KMeansV3

public static final class KMeansV3.KMeansParametersV3
extends water.api.schemas3.ClusteringModelParametersSchemaV3<KMeansModel.KMeansParameters,KMeansV3.KMeansParametersV3>

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class water.api.schemas3.SchemaV3

water.api.schemas3.SchemaV3.Meta

Nested classes/interfaces inherited from class water.api.Schema

water.api.Schema.AutoParseable

Field Summary

Fields

Modifier and Type	Field and Description
int[]	cluster_size_constraints
boolean	estimate_k
static java.lang.String[]	fields
KMeans.Initialization	init
int	max_iterations
long	seed
boolean	standardize
water.api.schemas3.KeyV3.FrameKeyV3	user_points

Fields inherited from class water.api.schemas3.ClusteringModelParametersSchemaV3

k, own_fields

Fields inherited from class water.api.schemas3.ModelParametersSchemaV3

auc_type, categorical_encoding, checkpoint, custom_distribution_func, custom_metric_func, distribution, export_checkpoints_dir, fold_assignment, fold_column, gainslift_bins, huber_alpha, ignore_const_cols, ignored_columns, keep_cross_validation_fold_assignment, keep_cross_validation_models, keep_cross_validation_predictions, max_categorical_levels, max_runtime_secs, model_id, nfolds, offset_column, parallelize_cross_validation, quantile_alpha, response_column, score_each_iteration, stopping_metric, stopping_rounds, stopping_tolerance, training_frame, tweedie_power, validation_frame, weights_column

Fields inherited from class water.api.schemas3.SchemaV3

__meta

Constructor Summary

Constructors

Constructor and Description
KMeansParametersV3()

Method Summary

Methods inherited from class water.api.schemas3.ModelParametersSchemaV3

append_field_arrays, extractDeclaredApiParameters, fields, fillFromImpl, fillImpl, getAdditionalParameters, writeParametersJSON

Methods inherited from class water.api.schemas3.SchemaV3

writeJSON

Methods inherited from class water.api.Schema

createAndFillImpl, createImpl, extractVersionFromSchemaName, fillFromAny, fillFromBody, fillFromImpl, fillFromImpl, fillFromParms, fillFromParms, fillFromParms, fillImpl, getImplClass, getImplClass, getSchemaName, getSchemaType, getSchemaVersion, init_meta, markdown, markdown, newInstance, newInstance, setField, setSchemaType_doNotCall

Methods inherited from class water.Iced

asBytes, clone, copyOver, frozenType, read, readExternal, readJSON, reloadFromBytes, toJsonBytes, toJsonString, write, writeExternal

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

fields

public static java.lang.String[] fields

user_points

@API(help="This option allows you to specify a dataframe, where each row represents an initial cluster center. The user-specified points must have the same number of columns as the training observations. The number of rows must equal the number of clusters",
     required=false,
     level=expert)
public water.api.schemas3.KeyV3.FrameKeyV3 user_points

max_iterations

@API(help="Maximum training iterations (if estimate_k is enabled, then this is for each inner Lloyds iteration)",
     gridable=true)
public int max_iterations

standardize

@API(help="Standardize columns before computing distances",
     level=critical,
     gridable=true)
public boolean standardize

seed

@API(help="RNG Seed",
     level=secondary,
     gridable=true)
public long seed

init

@API(help="Initialization mode",
     values={"Random","PlusPlus","Furthest","User"},
     gridable=true)
public KMeans.Initialization init

estimate_k

@API(help="Whether to estimate the number of clusters (<=k) iteratively and deterministically.",
     level=critical,
     gridable=true)
public boolean estimate_k

cluster_size_constraints

@API(help="An array specifying the minimum number of points that should be in each cluster. The length of the constraints array has to be the same as the number of clusters.",
     level=expert)
public int[] cluster_size_constraints

Constructor Detail

KMeansParametersV3

public KMeansParametersV3()