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static readonly ValueRange< double > | ScaleFactorRangeDefault = new ValueRange<double>(1.0, 1.0) |
| Default value for the scale factor range.
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static readonly ValueRange< double > | SingularValueRangeDefault = new ValueRange<double>(1.0, 1.0) |
| Default value for the singular value range.
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static readonly ValueRange< double > | ScaleFactorRange = new ValueRange<double>(0.25, 10) |
| Acceptable scale factor range for search classifier training.
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const int | NumClassificationStepsMin = 3 |
| Minimum classification step count.
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const int | SampleSizeMin = 1 |
| Absolute minimum value for SampleSize.
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static readonly ValueRange< double > | LambdaRange = new ValueRange<double>(0.0, 10.0) |
| Acceptable scale factor range for search classifier training.
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const double | LambdaDefault = 0.1 |
| Default value for lambda.
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const int | FeatureResolutionDefault = 1 |
| Default value for feature resolution.
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static readonly ValueRange< int > | FeatureResolutionRange = new ValueRange<int>(0, 10) |
| Valid range of feature resolution value.
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const int | PreprocessingMaxLength = InternalExtensions.PreprocessingMaxLength |
| Maximum length of a preprocessing code (excluding the terminating zero).
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const string | PreprocessingValidCharacters = InternalExtensions.PreprocessingValidCharacters |
| characters that a preprocessing string may contain.
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int | SampleSize [get, set] |
| Sample size that has been used in each training set.
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double | ExtractionRadius [get, set] |
| Radius for extracting positive search instances. The unit size is the size of the feature window, i.e. a value of 0.5 means that positive samples can be extracted from withing a range of +/- Feature Window Width and +/- Feature Window Height around each trained instance. Smaller values will necessitate a finer search grid but may yield better results on difficult search tasks.
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ValueRange< Angle > | AngleRange [get, set] |
| Range of angles to be covered during classifier training.
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ValueRange< double > | ScaleRange [get, set] |
| Range of scale factors to be covered during classifier training. Only meaningful if the invariance type InvarianceType.RotationScaleTranslation has been set.
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ValueRange< double > | SingularValueRange [get, set] |
| Range of permissible singular values to be covered during classifier training. Only meaningful if the invariance type InvarianceType.AffineGroup has been set.
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InvarianceType | Invariances [get, set] |
| Invariances to be used for generating the classifier.
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int | NumClassificationSteps [get, set] |
| Number of classification steps during search operation.
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int | FeatureResolutionStep1And2 [get, set] |
| Feature resolution (determines the size of the classification retina) for search step 1 and 2.
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int | FeatureResolutionRest [get, set] |
| Feature resolution (determines the size of the classification retina) for step 3 and up.
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double | Lambda [get, set] |
| Regularization value to be used for generating the object. Possible values range from 0 to 10, good starting values for experiments are usually around 0.01.
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string | Preprocessing [get, set] |
| Preprocessing code with which the object is to be generated.
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static string | FormatPreprocessingCode (string input) |
| Correct a preprocessing code to make sure that no invalid characters are in the code and the code does not exceed the maximum length.
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| PredictorFactoryBase () |
| ctor (internal).
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virtual void | Dispose (bool disposing) |
| Clean-up of managed-to-native data structures.
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virtual void | OnStartProgressReport (int id, string caption, int stepsTotal) |
| Event raiser.
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virtual void | OnProgressReport (int id, ref bool retval) |
| Event raiser.
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virtual void | OnEndProgressReport (int id) |
| Event raiser.
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EventHandler< StartProgressReportEventArgs > | StartProgressReport |
| Event that will be fired when a new progress stage was initiated.
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EventHandler< ProgressReportEventArgs > | ProgressReport |
| Event that informs about a progress step in the given stage.
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EventHandler< EndProgressReportEventArgs > | EndProgressReport |
| Event that informs about the finalization of a given stage.
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Factory class for the generation of search predictors.