Fit Normalizer
Normalize samples individually to unit norm. Each sample (i.e. each row of the data matrix) with at least one non zero component is rescaled independently of other samples so that its norm (l1, l2 or inf) equals one.
Parameters
- Transformer [string]
Scikit-learn python code. See Normalizer for information on different parameters.
Default:
from sklearn.preprocessing import Normalizer transformer = Normalizer()
- Raster layer with features [raster]
Raster layer with feature data X used for fitting the transformer. Mutually exclusive with parameter: Training dataset
- Sample size [number]
Approximate number of samples drawn from raster. If 0, whole raster will be used. Note that this is only a hint for limiting the number of rows and columns.
Default: 1000
- Training dataset [file]
Training dataset pickle file used for fitting the transformer. Mutually exclusive with parameter: Raster layer with features
Outputs
- Output transformer [fileDestination]
Pickle file destination.
Command-line usage
>qgis_process help enmapbox:FitNormalizer
:
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Arguments
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transformer: Transformer
Default value: from sklearn.preprocessing import Normalizer
transformer = Normalizer()
Argument type: string
Acceptable values:
- String value
featureRaster: Raster layer with features (optional)
Argument type: raster
Acceptable values:
- Path to a raster layer
sampleSize: Sample size (optional)
Default value: 1000
Argument type: number
Acceptable values:
- A numeric value
dataset: Training dataset (optional)
Argument type: file
Acceptable values:
- Path to a file
outputTransformer: Output transformer
Argument type: fileDestination
Acceptable values:
- Path for new file
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Outputs
----------------
outputTransformer: <outputFile>
Output transformer