.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.afni.svm
===================


.. _nipype.interfaces.afni.svm.SVMTest:


.. index:: SVMTest

SVMTest
-------

`Link to code <file:///build/nipype-1.1.8/nipype/interfaces/afni/svm.py#L148>`__

Wraps the executable command ``3dsvm``.

Temporally predictive modeling with the support vector machine
SVM Test Only
For complete details, see the `3dsvm Documentation.
<https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dsvm.html>`_

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() # doctest: +SKIP

Inputs::

        [Mandatory]
        model: (a unicode string)
                modname is the basename for the brik containing the SVM model
                argument: ``-model %s``
        in_file: (an existing file name)
                A 3D or 3D+t AFNI brik dataset to be used for testing.
                argument: ``-testvol %s``

        [Optional]
        nodetrend: (a boolean)
                Flag to specify that pname files should not be linearly detrended
                argument: ``-nodetrend``
        num_threads: (an integer (int or long), nipype default value: 1)
                set number of threads
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        out_file: (a file name)
                filename for .1D prediction file(s).
                argument: ``-predictions %s``
        testlabels: (an existing file name)
                *true* class category .1D labels for the test dataset. It is used to
                calculate the prediction accuracy performance
                argument: ``-testlabels %s``
        outputtype: (u'NIFTI_GZ' or u'AFNI' or u'NIFTI')
                AFNI output filetype
        multiclass: (a boolean)
                Specifies multiclass algorithm for classification
                argument: ``-multiclass %s``
        nopredcensord: (a boolean)
                Flag to prevent writing predicted values for censored time-points
                argument: ``-nopredcensord``
        environ: (a dictionary with keys which are a newbytes or None or a
                  newstr or None and with values which are a newbytes or None or a
                  newstr or None, nipype default value: {})
                Environment variables
        classout: (a boolean)
                Flag to specify that pname files should be integer-valued,
                corresponding to class category decisions.
                argument: ``-classout``
        options: (a unicode string)
                additional options for SVM-light
                argument: ``%s``

Outputs::

        out_file: (an existing file name)
                output file

References:
~~~~~~~~~~~
None
None

.. _nipype.interfaces.afni.svm.SVMTrain:


.. index:: SVMTrain

SVMTrain
--------

`Link to code <file:///build/nipype-1.1.8/nipype/interfaces/afni/svm.py#L80>`__

Wraps the executable command ``3dsvm``.

Temporally predictive modeling with the support vector machine
SVM Train Only
For complete details, see the `3dsvm Documentation.
<https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dsvm.html>`_

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() # doctest: +SKIP

Inputs::

        [Mandatory]
        ttype: (a unicode string)
                tname: classification or regression
                argument: ``-type %s``
        in_file: (an existing file name)
                A 3D+t AFNI brik dataset to be used for training.
                argument: ``-trainvol %s``

        [Optional]
        trainlabels: (an existing file name)
                .1D labels corresponding to the stimulus paradigm for the training
                data.
                argument: ``-trainlabels %s``
        kernel: (a unicode string)
                string specifying type of kernel function:linear, polynomial, rbf,
                sigmoid
                argument: ``-kernel %s``
        out_file: (a file name)
                output sum of weighted linear support vectors file name
                argument: ``-bucket %s``
        num_threads: (an integer (int or long), nipype default value: 1)
                set number of threads
        mask: (an existing file name)
                byte-format brik file used to mask voxels in the analysis
                argument: ``-mask %s``, position: -1
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        censor: (an existing file name)
                .1D censor file that allows the user to ignore certain samples in
                the training data.
                argument: ``-censor %s``
        outputtype: (u'NIFTI_GZ' or u'AFNI' or u'NIFTI')
                AFNI output filetype
        nomodelmask: (a boolean)
                Flag to enable the omission of a mask file
                argument: ``-nomodelmask``
        w_out: (a boolean)
                output sum of weighted linear support vectors
                argument: ``-wout``
        environ: (a dictionary with keys which are a newbytes or None or a
                  newstr or None and with values which are a newbytes or None or a
                  newstr or None, nipype default value: {})
                Environment variables
        model: (a file name)
                basename for the brik containing the SVM model
                argument: ``-model %s``
        alphas: (a file name)
                output alphas file name
                argument: ``-alpha %s``
        options: (a unicode string)
                additional options for SVM-light
                argument: ``%s``
        max_iterations: (an integer (int or long))
                Specify the maximum number of iterations for the optimization.
                argument: ``-max_iterations %d``

Outputs::

        alphas: (a file name)
                output alphas file name
        model: (a file name)
                brik containing the SVM model file name
        out_file: (a file name)
                sum of weighted linear support vectors file name

References:
~~~~~~~~~~~
None
None
