GlobalContrastNormalizingEstimator Class
Definition
Important
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Normalizes (scales) vectors in the input column applying the global contrast normalization.
public sealed class GlobalContrastNormalizingEstimator : Microsoft.ML.Transforms.LpNormNormalizingEstimatorBasetype GlobalContrastNormalizingEstimator = class
inherit LpNormNormalizingEstimatorBasePublic NotInheritable Class GlobalContrastNormalizingEstimator
Inherits LpNormNormalizingEstimatorBase- Inheritance
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GlobalContrastNormalizingEstimator
Remarks
Estimator Characteristics
| Does this estimator need to look at the data to train its parameters? | No |
| Input column data type | Vector of Single |
| Output column data type | Vector of Single |
| Exportable to ONNX | Yes |
The resulting LpNormNormalizingTransformer normalizes vectors in the input column individually, rescaling them by applying global contrast normalization. The transform performs the following operation on each input vector $x$: $y = \frac{s * x - \mu(x)}{L(x)}$. Where $s$ is a user provided scaling factor, $\mu(x)$ is the mean of the elements of vector $x$, and $L(x)$ is the $L_2$ norm or the standard deviation of the elements of vector $x$. These settings can be specified by the user when the GlobalContrastNormalizingEstimator is initialized.
Check the See Also section for links to usage examples.
Methods
| Fit(IDataView) | (Inherited from TrivialEstimator<TTransformer>) |
| GetOutputSchema(SchemaShape) |
Returns the SchemaShape of the schema which will be produced by the transformer. Used for schema propagation and verification in a pipeline. (Inherited from LpNormNormalizingEstimatorBase) |
Extension Methods
| AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment) |
Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes. |
| WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>) |
Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called. |