public class GaussianMixtureModel extends Model<GaussianMixtureModel> implements GaussianMixtureParams, MLWritable, HasTrainingSummary<GaussianMixtureSummary>
param: weights Weight for each Gaussian distribution in the mixture.
This is a multinomial probability distribution over the k Gaussians,
where weights(i) is the weight for Gaussian i, and weights sum to 1.
param: gaussians Array of MultivariateGaussian where gaussians(i) represents
the Multivariate Gaussian (Normal) Distribution for Gaussian i
| Modifier and Type | Method and Description |
|---|---|
IntParam |
aggregationDepth()
Param for suggested depth for treeAggregate (>= 2).
|
GaussianMixtureModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
Param<String> |
featuresCol()
Param for features column name.
|
MultivariateGaussian[] |
gaussians() |
Dataset<Row> |
gaussiansDF()
Retrieve Gaussian distributions as a DataFrame.
|
IntParam |
k()
Number of independent Gaussians in the mixture model.
|
static GaussianMixtureModel |
load(String path) |
IntParam |
maxIter()
Param for maximum number of iterations (>= 0).
|
int |
numFeatures() |
int |
predict(Vector features) |
Param<String> |
predictionCol()
Param for prediction column name.
|
Vector |
predictProbability(Vector features) |
Param<String> |
probabilityCol()
Param for Column name for predicted class conditional probabilities.
|
static MLReader<GaussianMixtureModel> |
read() |
LongParam |
seed()
Param for random seed.
|
GaussianMixtureModel |
setFeaturesCol(String value) |
GaussianMixtureModel |
setPredictionCol(String value) |
GaussianMixtureModel |
setProbabilityCol(String value) |
GaussianMixtureSummary |
summary()
Gets summary of model on training set.
|
DoubleParam |
tol()
Param for the convergence tolerance for iterative algorithms (>= 0).
|
String |
toString() |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
Param<String> |
weightCol()
Param for weight column name.
|
double[] |
weights() |
MLWriter |
write()
Returns a
MLWriter instance for this ML instance. |
transform, transform, transformparamsgetK, validateAndTransformSchemagetMaxItergetFeaturesColgetPredictionColgetWeightColgetProbabilityColgetAggregationDepthclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwnsavehasSummary, setSummary$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitializepublic static MLReader<GaussianMixtureModel> read()
public static GaussianMixtureModel load(String path)
public final IntParam k()
GaussianMixtureParamsk in interface GaussianMixtureParamspublic final IntParam aggregationDepth()
HasAggregationDepthaggregationDepth in interface HasAggregationDepthpublic final DoubleParam tol()
HasTolpublic final Param<String> probabilityCol()
HasProbabilityColprobabilityCol in interface HasProbabilityColpublic final Param<String> weightCol()
HasWeightColweightCol in interface HasWeightColpublic final Param<String> predictionCol()
HasPredictionColpredictionCol in interface HasPredictionColpublic final LongParam seed()
HasSeedpublic final Param<String> featuresCol()
HasFeaturesColfeaturesCol in interface HasFeaturesColpublic final IntParam maxIter()
HasMaxItermaxIter in interface HasMaxIterpublic String uid()
Identifiableuid in interface Identifiablepublic double[] weights()
public MultivariateGaussian[] gaussians()
public int numFeatures()
public GaussianMixtureModel setFeaturesCol(String value)
public GaussianMixtureModel setPredictionCol(String value)
public GaussianMixtureModel setProbabilityCol(String value)
public GaussianMixtureModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<GaussianMixtureModel>extra - (undocumented)public Dataset<Row> transform(Dataset<?> dataset)
Transformertransform in class Transformerdataset - (undocumented)public StructType transformSchema(StructType schema)
PipelineStage
We check validity for interactions between parameters during transformSchema and
raise an exception if any parameter value is invalid. Parameter value checks which
do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
transformSchema in class PipelineStageschema - (undocumented)public int predict(Vector features)
public Dataset<Row> gaussiansDF()
root
|-- mean: vector (nullable = true)
|-- cov: matrix (nullable = true)
public MLWriter write()
MLWriter instance for this ML instance.
For GaussianMixtureModel, this does NOT currently save the training summary.
An option to save summary may be added in the future.
write in interface MLWritablepublic String toString()
toString in interface IdentifiabletoString in class Objectpublic GaussianMixtureSummary summary()
hasSummary is false.summary in interface HasTrainingSummary<GaussianMixtureSummary>