interfaces.afni.svm

SVMTest

Link to code

Wraps command 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() # doctest: +SKIP

Inputs:

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

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
classout: (a boolean)
        Flag to specify that pname files should be integer-valued,
        corresponding to class category decisions.
        flag: -classout
environ: (a 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', nipype default value: {})
        Environment variables
multiclass: (a boolean)
        Specifies multiclass algorithm for classification
        flag: -multiclass %s
nodetrend: (a boolean)
        Flag to specify that pname files should not be linearly detrended
        flag: -nodetrend
nopredcensord: (a boolean)
        Flag to prevent writing predicted values for censored time-points
        flag: -nopredcensord
num_threads: (an integer (int or long), nipype default value: 1)
        set number of threads
options: (a unicode string)
        additional options for SVM-light
        flag: %s
out_file: (a file name)
        filename for .1D prediction file(s).
        flag: -predictions %s
outputtype: ('NIFTI' or 'AFNI' or 'NIFTI_GZ')
        AFNI output filetype
testlabels: (an existing file name)
        *true* class category .1D labels for the test dataset. It is used to
        calculate the prediction accuracy performance
        flag: -testlabels %s

Outputs:

out_file: (an existing file name)
        output file

References:: BibTeX(‘@article{Cox1996,author={R.W. Cox},title={AFNI: software for analysis and visualization of functional magnetic resonance neuroimages},journal={Computers and Biomedical research},volume={29},number={3},pages={162-173},year={1996},}’, key=’Cox1996’) BibTeX(‘@article{CoxHyde1997,author={R.W. Cox and J.S. Hyde},title={Software tools for analysis and visualization of fMRI data},journal={NMR in Biomedicine},volume={10},number={45},pages={171-178},year={1997},}’, key=’CoxHyde1997’)

SVMTrain

Link to code

Wraps command 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() # doctest: +SKIP

Inputs:

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

[Optional]
alphas: (a file name)
        output alphas file name
        flag: -alpha %s
args: (a unicode string)
        Additional parameters to the command
        flag: %s
censor: (an existing file name)
        .1D censor file that allows the user to ignore certain samples in
        the training data.
        flag: -censor %s
environ: (a 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', nipype default value: {})
        Environment variables
kernel: (a unicode string)
        string specifying type of kernel function:linear, polynomial, rbf,
        sigmoid
        flag: -kernel %s
mask: (an existing file name)
        byte-format brik file used to mask voxels in the analysis
        flag: -mask %s, position: -1
max_iterations: (an integer (int or long))
        Specify the maximum number of iterations for the optimization.
        flag: -max_iterations %d
model: (a file name)
        basename for the brik containing the SVM model
        flag: -model %s
nomodelmask: (a boolean)
        Flag to enable the omission of a mask file
        flag: -nomodelmask
num_threads: (an integer (int or long), nipype default value: 1)
        set number of threads
options: (a unicode string)
        additional options for SVM-light
        flag: %s
out_file: (a file name)
        output sum of weighted linear support vectors file name
        flag: -bucket %s
outputtype: ('NIFTI' or 'AFNI' or 'NIFTI_GZ')
        AFNI output filetype
trainlabels: (an existing file name)
        .1D labels corresponding to the stimulus paradigm for the training
        data.
        flag: -trainlabels %s
w_out: (a boolean)
        output sum of weighted linear support vectors
        flag: -wout

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:: BibTeX(‘@article{Cox1996,author={R.W. Cox},title={AFNI: software for analysis and visualization of functional magnetic resonance neuroimages},journal={Computers and Biomedical research},volume={29},number={3},pages={162-173},year={1996},}’, key=’Cox1996’) BibTeX(‘@article{CoxHyde1997,author={R.W. Cox and J.S. Hyde},title={Software tools for analysis and visualization of fMRI data},journal={NMR in Biomedicine},volume={10},number={45},pages={171-178},year={1997},}’, key=’CoxHyde1997’)