SearchPredictorFactory Class Reference

Factory class for the generation of search predictors. More...

Inherits PredictorFactoryBase.

Public Member Functions
int	num_classification_steps_min ()
	Minimum classification step count.
int	sample_size_min ()
	Absolute minimum value for sample_size.
cvb.NumberRange	scale_factor_range ()
	Acceptable scale factor range for search classifier training.
cvb.NumberRange	scale_factor_range_default ()
	Default value for the scale factor range.
cvb.NumberRange	singular_value_range_default ()
	Default value for the singular value range.
None	use_settings_from_predictor (self, cvb.polimago.SearchPredictor clf)
	Copy the predictor generation settings from a predictor.
Public Member Functions inherited from PredictorFactoryBase
int	feature_resolution_default ()
	Default value for feature resolution.
cvb.NumberRange	feature_resolution_range ()
	Valid range of feature resolution value.
str	format_preprocessing_code (str 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.
float	lambda_default ()
	Default value for lambda.
cvb.NumberRange	lambda_range ()
	Acceptable scale factor range for search classifier training.
int	preprocessing_max_length ()
	Maximum length of a preprocessing code (excluding the terminating zero).
List[str]	preprocessing_valid_characters ()
	Characters that a preprocessing string may contain.

Properties
	angle_range = property
	cvb.AngleRange: Sets the range of angles to be covered during classifier training.
	extraction_radius = property
	float: The radius for extracting positive search instances.
	feature_resolution_rest = property
	int: The feature resolution (determines the size of the classification retina) for step 3 and up.
	feature_resolution_step_1_and_2 = property
	int: The feature resolution (determines the size of the classification retina) for search step 1 and 2.
	invariances = property
	int: The invariances to be used for generating the classifier (see cvb.polimago.InvarianceType).
	num_classification_steps = property
	int: The number of classification steps during search operation.
	sample_size = property
	int: The sample size that has been used in each training set.
	scale_range = property
	cvb.NumberRange: The range of scale factors to be covered during classifier training.
	singular_value_range = property
	cvb.NumberRange: The range of permissible singular values to be covered during classifier training.
Properties inherited from PredictorFactoryBase
	lambda_ = property
	float: The regularization value to be used for generating the object.
	preprocessing = property
	str: Preprocessing code with which the object is to be generated.

Detailed Description

Factory class for the generation of search predictors.

Instantiates a SearchPredictorFactory object.

Member Function Documentation

num_classification_steps_min()

int num_classification_steps_min

(

)

Minimum classification step count.

Returns

int Minimum classification step count.

sample_size_min()

int sample_size_min

(

)

Absolute minimum value for sample_size.

Returns

int Sample size minimum.

scale_factor_range()

cvb.NumberRange scale_factor_range

(

)

Acceptable scale factor range for search classifier training.

Returns

cvb.NumberRange Acceptable range.

scale_factor_range_default()

cvb.NumberRange scale_factor_range_default

(

)

Default value for the scale factor range.

Returns

cvb.NumberRange Default range.

singular_value_range_default()

cvb.NumberRange singular_value_range_default

(

)

Default value for the singular value range.

Returns

cvb.NumberRange Default range.

use_settings_from_predictor()

None use_settings_from_predictor	(		self,
		cvb.polimago.SearchPredictor	clf )

Copy the predictor generation settings from a predictor.

Parameters

clf : cvb.polimago.SearchPredictor Predictor to take the settings from.

Property Documentation

extraction_radius

extraction_radius = property

static

float: The 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.

scale_range

scale_range = property

static

cvb.NumberRange: The range of scale factors to be covered during classifier training.

Only meaningful if the invariance type RotationScaleTranslation has been set.

singular_value_range

singular_value_range = property

static

cvb.NumberRange: The range of permissible singular values to be covered during classifier training.

Only meaningful if the invariance type AffineGroup has been set.