def __init__(self,ifeature=0):

    self.ifeature=ifeature       ## =0 explicit feature, =1 kernel 
    self.icategory=0      ## =0 vector =number of categories
    self.ncategory=0      ## idf icategory=1 it is the number of categories
    self.cat2vector=0     ## =0 indicator =1 mean 2 =median 3 =tetrahedron
    self.mdata=0
    self.itrain=None
    self.itest=None
    self.dataraw=None
    self.data=None       ## raw input
    self.XTrain=None      ## training features 
    self.XTrainNorm=None  ## normalized features
    self.XTest=None       ## test features 
    self.XTestNorm=None   ## normalized features
    self.Y0=None          ## set of distinc feature vectors     
    self.Y0Norm=None      ## set of distinc normalizedfeature vectors     

    self.K=None           ## external training kernel
    self.Kcross=None      ## externel test kernel
    self.Kraw=None
    self.Krawcross=None
    self.d1=None       ## norm of left factor of the kernel
    self.d2=None      ## norm of right factor of the kernel

    ## self.ilocal=2
    ## self.iscale=0

    self.norm=cls_norm()
    self.crossval=cls_crossval()
    self.kernel_params=cls_kernel_params()
    self.prekernel_params=cls_kernel_params()

    self.title=None
Exemple #2
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    def __init__(self, ifeature=1):

        self.ifeature = ifeature  ## =0 explicit feature, =1 kernel
        self.icategory = 0  ## =0 vector =number of categories
        self.ncategory = 0  ## idf icategory=1 it is the number of categories
        self.cat2vector = 0  ## =0 indicator =1 mean 2 =median 3 =tetrahedron
        self.mdata = 0
        self.itrain = None
        self.itest = None
        self.dataraw = None
        self.data = None  ## raw input
        self.XTrain = None  ## training features
        self.XTrainNorm = None  ## normalized features
        self.XTest = None  ## test features
        self.XTestNorm = None  ## normalized features

        self.K = None  ## external training kernel
        self.Kcross = None  ## externel test kernel
        self.d1 = None  ## norm of left factor of the kernel
        self.d2 = None  ## norm of right factor of the kernel

        self.norm = cls_norm()
        self.crossval = cls_crossval()
        self.kernel_params = cls_kernel_params()
        self.prekernel_params = cls_kernel_params()

        self.ioperator_valued = 0
        self.title = None
        self.kernel_computed = 0
Exemple #3
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    def __init__(self, ifeature=0):

        self.ifeature = ifeature  ## =0 explicit feature, =1 kernel
        self.icategory = 0  ## =0 vector =number of categories
        self.ncategory = 0  ## idf icategory=1 it is the number of categories
        self.cat2vector = 0  ## =0 indicator =1 mean 2 =median 3 =tetrahedron
        self.mdata = 0
        self.itrain = None
        self.itest = None
        self.dataraw = None
        self.data = None  ## raw input
        self.XTrain = None  ## training features
        self.XTrainNorm = None  ## normalized features
        self.XTest = None  ## test features
        self.XTestNorm = None  ## normalized features
        ## self.Y0=None          ## set of distinc feature vectors
        ## self.Y0Norm=None      ## set of distinc normalizedfeature vectors

        self.K = None  ## external training kernel
        self.Kcross = None  ## externel test kernel
        self.d1 = None  ## norm of left factor of the kernel
        self.d2 = None  ## norm of right factor of the kernel

        ## self.ilocal=2
        ## self.iscale=0

        self.norm = cls_norm()
        self.crossval = cls_crossval()
        self.kernel_params = cls_kernel_params()
        self.prekernel_params = None

        self.title = 'mvm_x'
        self.xbias = 0.0

        return
Exemple #4
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    def copy(self, data=None):

        new_obj = cls_feature(self.ifeature)
        new_obj.title = self.title
        new_obj.kernel_params = cls_kernel_params()
        new_obj.kernel_params.kernel_type = self.kernel_params.kernel_type
        new_obj.kernel_params.ipar1 = self.kernel_params.ipar1
        new_obj.kernel_params.ipar2 = self.kernel_params.ipar2
        if self.prekernel_params is not None:
            new_obj.prekernel_params = self.prekernel_params
        new_obj.crossval = self.crossval
        new_obj.norm = self.norm
        new_obj.xbias = self.xbias

        return (new_obj)
Exemple #5
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 def copy(self,data=None):
   
   new_obj=cls_feature(self.ifeature)
   new_obj.title=self.title
   new_obj.kernel_params=cls_kernel_params()
   new_obj.kernel_params.kernel_type=self.kernel_params.kernel_type
   new_obj.kernel_params.ipar1=self.kernel_params.ipar1
   new_obj.kernel_params.ipar2=self.kernel_params.ipar2
   if self.prekernel_params is not None:
     new_obj.prekernel_params=self.prekernel_params
   new_obj.crossval=self.crossval
   new_obj.norm=self.norm
   new_obj.xbias=self.xbias
   
   return(new_obj)
Exemple #6
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  def __init__(self,ifeature=0):

    self.ifeature=ifeature       ## =0 explicit feature, =1 kernel 
    self.icategory=0      ## =0 vector =number of categories
    self.ncategory=0      ## idf icategory=1 it is the number of categories
    self.cat2vector=0     ## =0 indicator =1 mean 2 =median 3 =tetrahedron
    self.mdata=0
    self.itrain=None
    self.itest=None
    self.dataraw=None
    self.data=None       ## raw input
    self.XTrain=None      ## training features 
    self.XTrainNorm=None  ## normalized features
    self.XTest=None       ## test features 
    self.XTestNorm=None   ## normalized features
    ## self.Y0=None          ## set of distinc feature vectors     
    ## self.Y0Norm=None      ## set of distinc normalizedfeature vectors     

    self.K=None           ## external training kernel
    self.Kcross=None      ## externel test kernel
    self.Kpre=None        ## prekernel can be used to build input and output
    self.d1=None       ## norm of left factor of the kernel
    self.d2=None      ## norm of right factor of the kernel

    ## self.ilocal=2
    ## self.iscale=0

    self.norm=cls_norm()
    self.crossval=cls_crossval()
    self.kernel_params=cls_kernel_params()
    self.prekernel_params=None

    self.title='mvm_y'

    self.ymax=10.0
    self.ymin=-10.0
    self.yrange=20
    self.ystep=(self.ymax-self.ymin)/self.yrange

    self.Y0Tetra=None

    self.ndim=4
    self.valrange=(0,1,2,3)
    self.classweight=np.ones((self.ndim,len(self.valrange)))
Exemple #7
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  def copy(self,data=None):
    
    new_obj=cls_feature(self.ifeature)
    new_obj.title=self.title
    new_obj.kernel_params=cls_kernel_params()
    new_obj.kernel_params.kernel_type=self.kernel_params.kernel_type
    new_obj.kernel_params.ipar1=self.kernel_params.ipar1
    new_obj.kernel_params.ipar2=self.kernel_params.ipar2
    if self.prekernel_params is not None:
      new_obj.prekernel_params=self.prekernel_params
    new_obj.crossval=self.crossval
    new_obj.norm=self.norm
    new_obj.ndim=self.ndim
    new_obj.valrange=self.valrange
    new_obj.classweight=self.classweight

    new_obj.ymax=self.ymax
    new_obj.ymin=self.ymin
    new_obj.ystep=self.ystep
    new_obj.yrange=self.yrange
    
    
    return(new_obj)