/// <summary>
/// KMeans <see cref="ClusteringCatalog"/> extension method.
/// </summary>
/// <param name="catalog">The regression catalog trainer object.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="options">Algorithm advanced settings.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way; it is only a way for the caller to
/// be informed about what was learnt.</param>
/// <returns>The predicted output.</returns>
public static (Vector <float> score, Key <uint> predictedLabel) KMeans(this ClusteringCatalog.ClusteringTrainers catalog,
Vector <float> features, Scalar <float> weights,
KMeansPlusPlusTrainer.Options options,
Action <KMeansModelParameters> onFit = null)
{
Contracts.CheckValueOrNull(onFit);
Contracts.CheckValue(options, nameof(options));
var rec = new TrainerEstimatorReconciler.Clustering(
(env, featuresName, weightsName) =>
{
options.FeatureColumnName = featuresName;
options.ExampleWeightColumnName = weightsName;
var trainer = new KMeansPlusPlusTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, features, weights);
return(rec.Output);
}
/// <summary>
/// KMeans <see cref="ClusteringCatalog"/> extension method.
/// </summary>
/// <param name="catalog">The clustering catalog trainer object.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="clustersCount">The number of clusters to use for KMeans.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way; it is only a way for the caller to
/// be informed about what was learnt.</param>
/// <returns>The predicted output.</returns>
public static (Vector <float> score, Key <uint> predictedLabel) KMeans(this ClusteringCatalog.ClusteringTrainers catalog,
Vector <float> features, Scalar <float> weights = null,
int clustersCount = KMeansPlusPlusTrainer.Defaults.NumberOfClusters,
Action <KMeansModelParameters> onFit = null)
{
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValueOrNull(weights);
Contracts.CheckParam(clustersCount > 1, nameof(clustersCount), "If provided, must be greater than 1.");
Contracts.CheckValueOrNull(onFit);
var rec = new TrainerEstimatorReconciler.Clustering(
(env, featuresName, weightsName) =>
{
var options = new KMeansPlusPlusTrainer.Options
{
FeatureColumnName = featuresName,
NumberOfClusters = clustersCount,
ExampleWeightColumnName = weightsName
};
var trainer = new KMeansPlusPlusTrainer(env, options);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, features, weights);
return(rec.Output);
}
public void KMeansEstimator()
{
string featureColumn = "NumericFeatures";
string weights = "Weights";
var reader = new TextLoader(Env, new TextLoader.Arguments
{
HasHeader = true,
Separator = "\t",
Column = new[]
{
new TextLoader.Column(featureColumn, DataKind.R4, new [] { new TextLoader.Range(1, 784) }),
new TextLoader.Column(weights, DataKind.R4, 0)
}
});
var data = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.mnistTiny28.trainFilename)));
// Pipeline.
var pipeline = new KMeansPlusPlusTrainer(Env, featureColumn, weightColumn: weights,
advancedSettings: s => { s.InitAlgorithm = KMeansPlusPlusTrainer.InitAlgorithm.KMeansParallel; });
TestEstimatorCore(pipeline, data);
Done();
}
/// <summary>
/// KMeans <see cref="ClusteringContext"/> extension method.
/// </summary>
/// <param name="ctx">The regression context trainer object.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="clustersCount">The number of clusters to use for KMeans.</param>
/// <param name="advancedSettings">Algorithm advanced settings.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
/// <see cref="Estimator{TInShape, TOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way; it is only a way for the caller to
/// be informed about what was learnt.</param>
/// <returns>The predicted output.</returns>
public static (Vector <float> score, Key <uint> predictedLabel) KMeans(this ClusteringContext.ClusteringTrainers ctx,
Vector <float> features, Scalar <float> weights = null,
int clustersCount = KMeansPlusPlusTrainer.Defaults.K,
Action <KMeansPlusPlusTrainer.Arguments> advancedSettings = null,
Action <KMeansPredictor> onFit = null)
{
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValueOrNull(weights);
Contracts.CheckParam(clustersCount > 1, nameof(clustersCount), "If provided, must be greater than 1.");
Contracts.CheckValueOrNull(onFit);
Contracts.CheckValueOrNull(advancedSettings);
var rec = new TrainerEstimatorReconciler.Clustering(
(env, featuresName, weightsName) =>
{
var trainer = new KMeansPlusPlusTrainer(env, featuresName, clustersCount, weightsName, advancedSettings);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, features, weights);
return(rec.Output);
}
public void KMeansEstimator()
{
string featureColumn = "NumericFeatures";
string weights = "Weights";
var reader = new TextLoader(Env, new TextLoader.Options
{
HasHeader = true,
Separator = "\t",
Columns = new[]
{
new TextLoader.Column(featureColumn, DataKind.R4, new [] { new TextLoader.Range(1, 784) }),
new TextLoader.Column(weights, DataKind.R4, 0)
},
AllowSparse = true
});
var data = reader.Read(GetDataPath(TestDatasets.mnistTiny28.trainFilename));
// Pipeline.
var pipeline = new KMeansPlusPlusTrainer(Env, new KMeansPlusPlusTrainer.Options
{
FeatureColumn = featureColumn,
WeightColumn = weights,
InitAlgorithm = KMeansPlusPlusTrainer.InitAlgorithm.KMeansParallel,
});
TestEstimatorCore(pipeline, data);
Done();
}
/// <summary>
/// KMeans <see cref="ClusteringContext"/> extension method.
/// </summary>
/// <param name="ctx">The regression context trainer object.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="clustersCount">The number of clusters to use for KMeans.</param>
/// <param name="advancedSettings">Algorithm advanced settings.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}.Fit(DataView{TTupleInShape})"/> method is called on the
/// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained. Note that this action cannot change the result in any way; it is only a way for the caller to
/// be informed about what was learnt.</param>
/// <returns>The predicted output.</returns>
public static (Vector <float> score, Key <uint> predictedLabel) KMeans(this ClusteringContext.ClusteringTrainers ctx,
Vector <float> features, Scalar <float> weights = null,
int clustersCount = KMeansPlusPlusTrainer.Defaults.K,
Action <KMeansPlusPlusTrainer.Arguments> advancedSettings = null,
Action <KMeansPredictor> onFit = null)
{
var rec = new TrainerEstimatorReconciler.Clustering(
(env, featuresName, weightsName) =>
{
var trainer = new KMeansPlusPlusTrainer(env, featuresName, clustersCount, weightsName, advancedSettings);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans => onFit(trans.Model)));
}
else
{
return(trainer);
}
}, features, weights);
return(rec.Output);
}