Table Of Contents

Classify Custom Samples (G Dataflow)

Last Modified: October 26, 2017

Classifies the feature vector.

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classifier session in

Reference to the classifier session on which the node operates.

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error in

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

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feature vector

User-defined vector that uniquely describes the sample.

All feature vectors added to a custom session must have the same dimensions.

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class results

Array with one element for every class in the classifier session.

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class

One of the classes in classifier session in.

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distance

Distance from the closest sample in class to the input sample when performing nearest neighbor and k-nearest neighbor classification.

distance is the distance between the input sample and the center of each class when performing minimum mean distance classification.

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classifier session out

Reference to the classifier session the node creates.

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sample results

Array with information about the closest samples.

Every sample in the array contains what class the sample belongs to, the distance of the class to each other class, and the index of the sample.

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class

Class into which the classifier session categorizes the input sample.

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distance

Distance from the closest sample in class to the input sample when performing nearest neighbor and k-nearest neighbor classification.

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index

Location of the sample in the classifier session among the entire trained sample.

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class

Class into which the classifier session categorizes the input sample.

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error out

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.
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scores

Estimations of how well the classifier session classified the input.

The score can vary from 0 to 1000, where 1000 represents the best possible score.

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classification score

Score that indicates how much better the assigned class represents the input sample than other classes represent the input.

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identification score

Score that indicates the similarity of the input and the assigned class.

Use identification score only when you cannot reach a decision about the class of a sample using classification score alone.

Where This Node Can Run:

Desktop OS: Windows

FPGA: Not supported

Web Server: Not supported in VIs that run in a web application


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