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# nipype.interfaces.afni.svm module

AFNI’s svm interfaces.

## SVMTest

Link to code

> Bases: AFNICommand

> Wrapped executable: `3dsvm`.

> Temporally predictive modeling with the support vector machine SVM Test Only For complete details, see the 3dsvm Documentation.

> Examples

>     >>> from nipype.interfaces import afni as afni
>     >>> svmTest = afni.SVMTest()
>     >>> svmTest.inputs.in_file= 'run2+orig'
>     >>> svmTest.inputs.model= 'run1+orig_model'
>     >>> svmTest.inputs.testlabels= 'run2_categories.1D'
>     >>> svmTest.inputs.out_file= 'pred2_model1'
>     >>> res = svmTest.run()

> in_filea pathlike object or string representing an existing file

>   A 3D or 3D+t AFNI brik dataset to be used for testing. Maps to a command-line argument: `-testvol %s`.

> modela unicode string

>   Modname is the basename for the brik containing the SVM model. Maps to a command-line argument: `-model %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> classouta boolean

>   Flag to specify that pname files should be integer-valued, corresponding to class category decisions. Maps to a command-line argument: `-classout`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> multiclassa boolean

>   Specifies multiclass algorithm for classification. Maps to a command-line argument: `-multiclass %s`.

> nodetrenda boolean

>   Flag to specify that pname files should not be linearly detrended. Maps to a command-line argument: `-nodetrend`.

> nopredcensorda boolean

>   Flag to prevent writing predicted values for censored time-points. Maps to a command-line argument: `-nopredcensord`.

> num_threadsan integer (int or long)

>   Set number of threads. (Nipype default value: `1`)

> optionsa unicode string

>   Additional options for SVM-light. Maps to a command-line argument: `%s`.

> out_filea pathlike object or string representing a file

>   Filename for .1D prediction file(s). Maps to a command-line argument: `-predictions %s`.

> outputtype‘NIFTI’ or ‘AFNI’ or ‘NIFTI_GZ’

>   AFNI output filetype.

> testlabelsa pathlike object or string representing an existing file

>   true class category .1D labels for the test dataset. It is used to calculate the prediction accuracy performance. Maps to a command-line argument: `-testlabels %s`.

> out_filea pathlike object or string representing an existing file

>   Output file.

## SVMTrain

Link to code

> Bases: AFNICommand

> Wrapped executable: `3dsvm`.

> Temporally predictive modeling with the support vector machine SVM Train Only For complete details, see the 3dsvm Documentation.

> Examples

>     >>> from nipype.interfaces import afni as afni
>     >>> svmTrain = afni.SVMTrain()
>     >>> svmTrain.inputs.in_file = 'run1+orig'
>     >>> svmTrain.inputs.trainlabels = 'run1_categories.1D'
>     >>> svmTrain.inputs.ttype = 'regression'
>     >>> svmTrain.inputs.mask = 'mask.nii'
>     >>> svmTrain.inputs.model = 'model_run1'
>     >>> svmTrain.inputs.alphas = 'alphas_run1'
>     >>> res = svmTrain.run()

> in_filea pathlike object or string representing an existing file

>   A 3D+t AFNI brik dataset to be used for training. Maps to a command-line argument: `-trainvol %s`.

> ttypea unicode string

>   Tname: classification or regression. Maps to a command-line argument: `-type %s`.

> alphasa pathlike object or string representing a file

>   Output alphas file name. Maps to a command-line argument: `-alpha %s`.

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> censora pathlike object or string representing an existing file

>   .1D censor file that allows the user to ignore certain samples in the training data. Maps to a command-line argument: `-censor %s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> kernela unicode string

>   String specifying type of kernel function:linear, polynomial, rbf, sigmoid. Maps to a command-line argument: `-kernel %s`.

> maska pathlike object or string representing an existing file

>   Byte-format brik file used to mask voxels in the analysis. Maps to a command-line argument: `-mask %s` (position: -1).

> max_iterationsan integer (int or long)

>   Specify the maximum number of iterations for the optimization. Maps to a command-line argument: `-max_iterations %d`.

> modela pathlike object or string representing a file

>   Basename for the brik containing the SVM model. Maps to a command-line argument: `-model %s`.

> nomodelmaska boolean

>   Flag to enable the omission of a mask file. Maps to a command-line argument: `-nomodelmask`.

> num_threadsan integer (int or long)

>   Set number of threads. (Nipype default value: `1`)

> optionsa unicode string

>   Additional options for SVM-light. Maps to a command-line argument: `%s`.

> out_filea pathlike object or string representing a file

>   Output sum of weighted linear support vectors file name. Maps to a command-line argument: `-bucket %s`.

> outputtype‘NIFTI’ or ‘AFNI’ or ‘NIFTI_GZ’

>   AFNI output filetype.

> trainlabelsa pathlike object or string representing an existing file

>   .1D labels corresponding to the stimulus paradigm for the training data. Maps to a command-line argument: `-trainlabels %s`.

> w_outa boolean

>   Output sum of weighted linear support vectors. Maps to a command-line argument: `-wout`.

> alphasa pathlike object or string representing a file

>   Output alphas file name.

> modela pathlike object or string representing a file

>   Brik containing the SVM model file name.

> out_filea pathlike object or string representing a file

>   Sum of weighted linear support vectors file name.