public class GaussianMixtureModel extends Model<GaussianMixtureModel> implements GaussianMixtureParams, MLWritable
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 |
|---|---|
GaussianMixtureModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
MultivariateGaussian[] |
gaussians() |
Dataset<Row> |
gaussiansDF()
Retrieve Gaussian distributions as a DataFrame.
|
boolean |
hasSummary()
Return true if there exists summary of model.
|
static GaussianMixtureModel |
load(String path) |
static MLReader<GaussianMixtureModel> |
read() |
GaussianMixtureModel |
setFeaturesCol(String value) |
GaussianMixtureModel |
setPredictionCol(String value) |
GaussianMixtureModel |
setProbabilityCol(String value) |
GaussianMixtureSummary |
summary()
Gets summary of model on training set.
|
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
StructType |
transformSchema(StructType schema)
:: DeveloperApi ::
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
double[] |
weights() |
MLWriter |
write()
Returns a
MLWriter instance for this ML instance. |
transform, transform, transformequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetK, k, validateAndTransformSchemagetMaxIter, maxIterfeaturesCol, getFeaturesColgetPredictionCol, predictionColgetProbabilityCol, probabilityColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoStringsaveinitializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarningpublic static MLReader<GaussianMixtureModel> read()
public static GaussianMixtureModel load(String path)
public String uid()
Identifiableuid in interface Identifiablepublic double[] weights()
public MultivariateGaussian[] gaussians()
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)
PipelineStageCheck transform validity and derive the output schema from the input schema.
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 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 boolean hasSummary()
public GaussianMixtureSummary summary()
trainingSummary == None.