eVision.EasyDeepLearning.EROCPoint Class Reference

The structure representing a point on the ROC (Receiver Operating Characteristic) curve. More...

Public Member Functions
None	__init__ (self)
	Constructs a eVision.EasyDeepLearning.EROCPoint.
None	__init__ (self, float threshold, int truePositiveCount, int positiveCount, int falsePositiveCount, int negativeCount)
	Constructs a eVision.EasyDeepLearning.EROCPoint.
None	__init__ (self, float TPR)
None	__init__ (self, float TPR, int TP)
None	__init__ (self, float TPR, int TP, int P)
None	__init__ (self, float TPR, int TP, int P, float FPR)
None	__init__ (self, float TPR, int TP, int P, float FPR, int FP)
None	__init__ (self, float TPR, int TP, int P, float FPR, int FP, int N)
None	__init__ (self, float TPR, int TP, int P, float FPR, int FP, int N, float Threshold)
None	__init__ (self, eVision.EasyDeepLearning.EROCPoint other)
None	Load (self, str path)
	Loads the ROC point.
None	Load (self, eVision.ESerializer|None serializer)
	Loads the ROC point.
None	Save (self, str path)
	Saves the ROC point.
None	Save (self, eVision.ESerializer|None serializer)
	Saves the ROC point.
float	TPR (self)
	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.
	TPR (self, float value)
	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.
int	TP (self)
	The True Positive count: it is the number of defective images classified as defective.
	TP (self, int value)
	The True Positive count: it is the number of defective images classified as defective.
int	P (self)
	The Positive count: it is the number of defective images.
	P (self, int value)
	The Positive count: it is the number of defective images.
float	FPR (self)
	The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.
	FPR (self, float value)
	The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.
int	FP (self)
	The False Positive count: it is the number of good image classified as defective.
	FP (self, int value)
	The False Positive count: it is the number of good image classified as defective.
int	N (self)
	The Negative count: it is the number of good images.
	N (self, int value)
	The Negative count: it is the number of good images.
float	Threshold (self)
	The threshold associated with the true and false positive rates (see eVision.EasyDeepLearning.EUnsupervisedSegmenter.ClassificationThreshold).
	Threshold (self, float value)
	The threshold associated with the true and false positive rates (see eVision.EasyDeepLearning.EUnsupervisedSegmenter.ClassificationThreshold).

Detailed Description

The structure representing a point on the ROC (Receiver Operating Characteristic) curve.

Remarks:

License(s): None

Constructor & Destructor Documentation

__init__() [1/10]

None eVision.EasyDeepLearning.EROCPoint.__init__

(

self

)

Constructs a eVision.EasyDeepLearning.EROCPoint.

__init__() [2/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	threshold,
		int	truePositiveCount,
		int	positiveCount,
		int	falsePositiveCount,
		int	negativeCount )

Constructs a eVision.EasyDeepLearning.EROCPoint.

Parameters

threshold	Threshold corresponding to the ROC point.
truePositiveCount	Number of defective images classified as defective.
positiveCount	Total number of defective images.
falsePositiveCount	Number of good images classified as defective.
negativeCount	Total number of good images.

__init__() [3/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

__init__() [4/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.

__init__() [5/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP,
		int	P )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.
P	The Positive count: it is the number of defective images.

__init__() [6/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP,
		int	P,
		float	FPR )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.
P	The Positive count: it is the number of defective images.
FPR	The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.

__init__() [7/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP,
		int	P,
		float	FPR,
		int	FP )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.
P	The Positive count: it is the number of defective images.
FPR	The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.
FP	The False Positive count: it is the number of good image classified as defective.

__init__() [8/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP,
		int	P,
		float	FPR,
		int	FP,
		int	N )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.
P	The Positive count: it is the number of defective images.
FPR	The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.
FP	The False Positive count: it is the number of good image classified as defective.
N	The Negative count: it is the number of good images.

__init__() [9/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		float	TPR,
		int	TP,
		int	P,
		float	FPR,
		int	FP,
		int	N,
		float	Threshold )

Parameters

TPR	The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

Parameters

TP	The True Positive count: it is the number of defective images classified as defective.
P	The Positive count: it is the number of defective images.
FPR	The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.
FP	The False Positive count: it is the number of good image classified as defective.
N	The Negative count: it is the number of good images.
Threshold	The threshold associated with the true and false positive rates (see eVision.EasyDeepLearning.EUnsupervisedSegmenter.ClassificationThreshold).

__init__() [10/10]

None eVision.EasyDeepLearning.EROCPoint.__init__	(		self,
		eVision.EasyDeepLearning.EROCPoint	other )

Parameters

other

Other instance

Member Function Documentation

FP() [1/2]

int eVision.EasyDeepLearning.EROCPoint.FP

(

self

)

The False Positive count: it is the number of good image classified as defective.

FP() [2/2]

eVision.EasyDeepLearning.EROCPoint.FP	(		self,
		int	value )

The False Positive count: it is the number of good image classified as defective.

FPR() [1/2]

float eVision.EasyDeepLearning.EROCPoint.FPR

(

self

)

The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.

FPR() [2/2]

eVision.EasyDeepLearning.EROCPoint.FPR	(		self,
		float	value )

The False Positive Rate: it is the number of good image classified as defective, normalized by the number of good images.

Load() [1/2]

None eVision.EasyDeepLearning.EROCPoint.Load	(		self,
		eVision.ESerializer | None	serializer )

Loads the ROC point.

The given eVision.ESerializer must have been created for reading.

Parameters

serializer

The serializer.

Load() [2/2]

None eVision.EasyDeepLearning.EROCPoint.Load	(		self,
		str	path )

Loads the ROC point.

The given eVision.ESerializer must have been created for reading.

Parameters

path	The file path.

N() [1/2]

int eVision.EasyDeepLearning.EROCPoint.N

(

self

)

The Negative count: it is the number of good images.

N() [2/2]

eVision.EasyDeepLearning.EROCPoint.N	(		self,
		int	value )

The Negative count: it is the number of good images.

P() [1/2]

int eVision.EasyDeepLearning.EROCPoint.P

(

self

)

The Positive count: it is the number of defective images.

P() [2/2]

eVision.EasyDeepLearning.EROCPoint.P	(		self,
		int	value )

The Positive count: it is the number of defective images.

Save() [1/2]

None eVision.EasyDeepLearning.EROCPoint.Save	(		self,
		eVision.ESerializer | None	serializer )

Saves the ROC point.

The given eVision.ESerializer must have been created for writing.

Parameters

serializer

The serializer.

Save() [2/2]

None eVision.EasyDeepLearning.EROCPoint.Save	(		self,
		str	path )

Saves the ROC point.

The given eVision.ESerializer must have been created for writing.

Parameters

path	The file path.

Threshold() [1/2]

float eVision.EasyDeepLearning.EROCPoint.Threshold

(

self

)

The threshold associated with the true and false positive rates (see eVision.EasyDeepLearning.EUnsupervisedSegmenter.ClassificationThreshold).

Threshold() [2/2]

eVision.EasyDeepLearning.EROCPoint.Threshold	(		self,
		float	value )

The threshold associated with the true and false positive rates (see eVision.EasyDeepLearning.EUnsupervisedSegmenter.ClassificationThreshold).

TP() [1/2]

int eVision.EasyDeepLearning.EROCPoint.TP

(

self

)

The True Positive count: it is the number of defective images classified as defective.

TP() [2/2]

eVision.EasyDeepLearning.EROCPoint.TP	(		self,
		int	value )

The True Positive count: it is the number of defective images classified as defective.

TPR() [1/2]

float eVision.EasyDeepLearning.EROCPoint.TPR

(

self

)

The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.

TPR() [2/2]

eVision.EasyDeepLearning.EROCPoint.TPR	(		self,
		float	value )

The True Positive Rate: it is the number of defective images classified as defective, normalized by the number of defective images.

The TPR can be NaN (Not A Number) if the metrics were computed using only good images.