This function restores an ENVIMachineLearningModel object, which specifies the MachineLearning model used for machine learning.
Example
e = ENVI(/HEADLESS)
Model = ENVIMachineLearningModel('model_file.json', ERROR=error)
print, 'Number of Bands: ', Strtrim(Model.nBands, 2)
print, 'Numer of Classes: ', Strtrim(Model.nClasses, 2)
print, 'Class Labels: ', Model.Labels
ModelStats = Model.Statistics
print, ModelStats, /IMPLIED
end
Syntax
Result = ENVIMachineLearningModel(Input_File [, Properties=value] [, ERROR=value])
Return Value
This routine returns a reference to an ENVIMachineLearningModel object.
Arguments
Input_File
Specify a fully qualified filename and path to an ENVIMachineLearningModel file in HDF5 format.
Methods
Close
Dehydrate
Hydrate
Properties
Properties marked as "Set" are those that you can set to specific values. You can also retrieve their current values any time. Properties marked as "Get" are those whose values you can retrieve but not set.
COLORS (Get)
An array of RGB triplets, one per label.
DESCRIPTION (Get)
A description of the model's capabilities.
LABELS (Get)
An array of string labels, one per class.
MODEL_TYPE (Get)
The string reference for the model type.
NAME (Get)
The name of the model.
NBANDS (Get)
The number of bands in the training rasters (minus the mask band).
NCLASSES (Get)
The number of classes (minus the background class).
SCALE_FACTOR (Get)
A user-defined scale factor for normalizing the data.
SCHEMA (Get)
The schema reference.
STATISTICS (Get)
OrderedHash providing per-class metrics, global metrics, and a confusion matrix.
URI (Get)
A string that is a fully-qualified raster file path.
Keywords
ERROR (optional)
Set this keyword to a named variable that will contain any error message issued during execution of this routine. If no error occurs, the ERROR variable will be set to a null string (''). If an error occurs and the routine is a function, then the function result will be undefined.
When this keyword is not set and an error occurs, ENVI returns to the caller and execution halts. In this case, the error message is contained within !ERROR_STATE and can be caught using IDL's CATCH routine. See IDL Help for more information on !ERROR_STATE and CATCH.
See the Manage Errors topic in ENVI Help for more information on error handling.
Version History
Deep Learning 2.0
|
Introduced |
See Also
MachineLearningClassification Task