Exemplos de Kernel em Python

Exemplo n.º 1

Arquivo: libsvm.py Projeto: geeragh/PyMVPA

 def __init__(self, *args, **kwargs):
     Kernel.__init__(self, *args, **kwargs)
     self.compute()

Exemplo n.º 2

Arquivo: sg.py Projeto: arokem/PyMVPA

        return res


# Conversion methods
def _as_raw_sg(kernel):
    """Converts directly to a Shogun kernel"""
    return sgk.CustomKernel(kernel.as_raw_np())


def _as_sg(kernel):
    """Converts this kernel to a Shogun-based representation"""
    return PrecomputedSGKernel(matrix=kernel.as_raw_np())


Kernel.add_conversion("sg", _as_sg, _as_raw_sg)


class _BasicSGKernel(SGKernel):
    """Abstract class which can handle most shogun kernel types

    Subclasses can specify new kernels using the following declarations:

      - __kernel_cls__ = Shogun kernel class
      - __kp_order__ = Tuple which specifies the order of kernel params.
        If there is only one kernel param, this is not necessary
    """

    __TODO__ = """
    - Think either normalizer_* should not become proper Parameter.
    """

Exemplo n.º 3

Arquivo: libsvm.py Projeto: geeragh/PyMVPA

    
    def as_ls(self):
        return self
    
    def as_raw_np(self):
        raise ValueError, 'LibSVM calculates kernels internally; they ' +\
              'cannot be converted to Numpy'

# Conversion methods
def _as_ls(kernel):
    raise NotImplemented, 'LibSVM calculates kernels internally; they ' +\
          'cannot be converted from Numpy'
def _as_raw_ls(kernel):
    raise NotImplemented, 'LibSVM calculates kernels internally; they ' +\
          'cannot be converted from Numpy'
Kernel.add_conversion('ls', _as_ls, _as_raw_ls)

class LinearLSKernel(LSKernel):
    """A simple Linear kernel: K(a,b) = a*b.T"""
    __kernel_type__ = _svmc.LINEAR
    __kernel_name__ = 'linear'
    
class RbfLSKernel(LSKernel):
    """Radial Basis Function kernel (aka Gaussian):
    K(a,b) = exp(-gamma*||a-b||**2)
    """
    __kernel_type__ = _svmc.RBF
    __kernel_name__ = 'rbf'
    gamma = Parameter(1, doc='Gamma multiplying paramater for Rbf')
    
class PolyLSKernel(LSKernel):

Exemplo n.º 4

Arquivo: libsvm.py Projeto: heqing-psychology/PyMVPA

 def __init__(self, *args, **kwargs):
     """Base class for LIBSVM Kernels has no parameters
     """
     Kernel.__init__(self, *args, **kwargs)
     self.compute()

Exemplo n.º 5

Arquivo: libsvm.py Projeto: heqing-psychology/PyMVPA

        raise ValueError, 'LibSVM calculates kernels internally; they ' +\
              'cannot be converted to Numpy'


# Conversion methods
def _as_ls(kernel):
    raise NotImplemented, 'LibSVM calculates kernels internally; they ' +\
          'cannot be converted from Numpy'


def _as_raw_ls(kernel):
    raise NotImplemented, 'LibSVM calculates kernels internally; they ' +\
          'cannot be converted from Numpy'


Kernel.add_conversion('ls', _as_ls, _as_raw_ls)


class LinearLSKernel(LSKernel):
    """A simple Linear kernel: K(a,b) = a*b.T"""
    __kernel_type__ = _svmc.LINEAR
    __kernel_name__ = 'linear'


class RbfLSKernel(LSKernel):
    """Radial Basis Function kernel (aka Gaussian):
    K(a,b) = exp(-gamma*||a-b||**2)
    """
    __kernel_type__ = _svmc.RBF
    __kernel_name__ = 'rbf'
    gamma = Parameter(1, doc='Gamma multiplying paramater for Rbf')

Exemplo n.º 6

Arquivo: libsvm.py Projeto: B-Rich/PyMVPA

 def __init__(self, *args, **kwargs):
     """Base class for LIBSVM Kernels has no parameters
     """
     Kernel.__init__(self, *args, **kwargs)
     self.compute()

Exemplo n.º 7

Arquivo: sg.py Projeto: heqing-psychology/PyMVPA

        return res


# Conversion methods
def _as_raw_sg(kernel):
    """Converts directly to a Shogun kernel"""
    return sgk.CustomKernel(kernel.as_raw_np())


def _as_sg(kernel):
    """Converts this kernel to a Shogun-based representation"""
    return PrecomputedSGKernel(matrix=kernel.as_raw_np())


Kernel.add_conversion('sg', _as_sg, _as_raw_sg)


class _BasicSGKernel(SGKernel):
    """Abstract class which can handle most shogun kernel types

    Subclasses can specify new kernels using the following declarations:

      - __kernel_cls__ = Shogun kernel class
      - __kp_order__ = Tuple which specifies the order of kernel params.
        If there is only one kernel param, this is not necessary
    """

    __TODO__ = """
    - Think either normalizer_* should not become proper Parameter.
    """

Exemplo n.º 8

Arquivo: sg.py Projeto: geeragh/PyMVPA

    def __array__(self):
        return self._k.get_kernel_matrix()

    @staticmethod
    def _data2features(data):
        """Converts data to shogun features"""
        return RealFeatures(data.astype(float).T)

# Conversion methods
def _as_raw_sg(kernel):
    """Converts directly to a Shogun kernel"""
    return sgk.CustomKernel(kernel.as_raw_np())
def _as_sg(kernel):
    """Converts this kernel to a Shogun-based representation"""
    return PrecomputedSGKernel(matrix=kernel.as_raw_np())
Kernel.add_conversion('sg', _as_sg, _as_raw_sg)


class _BasicSGKernel(SGKernel):
    """Abstract class which can handle most shogun kernel types

    Subclasses can specify new kernels using the following declarations:

      - __kernel_cls__ = Shogun kernel class
      - __kp_order__ = Tuple which specifies the order of kernel params.
        If there is only one kernel param, this is not necessary
    """

    __TODO__ = """
    - Think either normalizer_* should not become proper Parameter.
    """