Esempio n. 1
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  def __init__(self, bands, border=2, baseline_interval=None, data_interval=None, Fs=1000.0, *args, **kwargs):
      """Initialize the FrequencyBandPowerMapper
 
        :Parameters:
          bands: list of 2-tuples
            list of (fl,fh), edge-frequencies for filtering
          border: int
            order of the filter to be used
          baseline_interval: 2-tuple of ints
            indices of the interval to be used for baseline correction
          data_interval: 2-tuple of ints
            indices of the interval to be used for cluster search
          args, kwargs: 
            additional arguments passed to parent class
      """
      Mapper.__init__(self,*args,**kwargs)
      self._bands = bands
      self._border = border
      self._baseline_interval = baseline_interval
      self._data_interval = data_interval
      self._Fs = Fs
      #TODO: Perform some checks, for valid intervals etc.
      #precalculate filter parameters (butter)
      self._bs = list(np.zeros((len(bands))))
      self._as = list(np.zeros((len(bands))))
      for i_b in range(len(bands)):
          [self._bs[i_b],self._as[i_b]]=butter(border,[bands[i_b][0]/(Fs/2),bands[i_b][1]/(Fs/2)], btype="band")
Esempio n. 2
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    def __init__(self, node, nodeargs=None, inspace=None):
        """
        Parameters
        ----------
        node : mdp.Node instance
          This node instance is taken as the pristine source of which a
          copy is made for actual processing upon each training attempt.
        nodeargs : dict
          Dictionary for additional arguments for all calls to the MDP
          node. The dictionary key's meaning is as follows:

          'train'
            Arguments for calls to `Node.train()`
          'stoptrain'
            Arguments for calls to `Node.stop_training()`
          'exec'
            Arguments for calls to `Node.execute()`
          'inv'
            Arguments for calls to `Node.inverse()`

          The value for each item is always a 2-tuple, consisting of a
          tuple (for the arguments), and a dictionary (for keyword
          arguments), i.e.  ((), {}). Both, tuple and dictionary have to be
          provided even if they are empty.
        inspace : see base class
        """
        # TODO: starting from MDP2.5 this check should become:
        # TODO:   if node.has_multiple_training_phases():      
        if not len(node._train_seq) == 1:
            raise ValueError("MDPNodeMapper does not support MDP nodes with "
                             "multiple training phases.")
        Mapper.__init__(self, inspace=inspace)
        self.__pristine_node = None
        self.node = node
        self.nodeargs = nodeargs
Esempio n. 3
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    def __init__(self, num, window=None, chunks_attr=None, position_attr=None,
                 attr_strategy='remove', inspace=None):
        """
        Parameters
        ----------
        num : int
          Number of output samples. If operating on chunks, this is the number
          of samples per chunk.
        window : str or float or tuple
          Passed to scipy.signal.resample
        chunks_attr : str or None
          If not None, this samples attribute defines chunks that will be
          resampled individually.
        position_attr : str
          A samples attribute with positional information that is passed
          to scipy.signal.resample. If not None, the output dataset will
          also contain a sample attribute of this name, with updated
          positional information (this is, however, only meaningful for
          equally spaced samples).
        attr_strategy : {'remove', 'sample', 'resample'}
          Strategy to process sample attributes during mapping. 'remove' will
          cause all sample attributes to be removed. 'sample' will pick orginal
          attribute values matching the new resampling frequency (e.g. every
          10th), and 'resample' will also apply the actual data resampling
          procedure to the attributes as well (which might not be possible, e.g.
          for literal attributes).
        """
        Mapper.__init__(self, inspace=inspace)

        self.__num = num
        self.__window_args = window
        self.__chunks_attr = chunks_attr
        self.__position_attr = position_attr
        self.__attr_strategy = attr_strategy
Esempio n. 4
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File: zscore.py Progetto: esc/PyMVPA
    def __init__(self, params=None, param_est=None, chunks_attr="chunks", dtype="float64", **kwargs):
        """
        Parameters
        ----------
        params : None or tuple(mean, std) or dict
          Fixed Z-Scoring parameters (mean, standard deviation). If provided,
          no parameters are estimated from the data. It is possible to specify
          individual parameters for each chunk by passing a dictionary with the
          chunk ids as keys and the parameter tuples as values. If None,
          parameters will be estimated from the training data.
        param_est : None or tuple(attrname, attrvalues)
          Limits the choice of samples used for automatic parameter estimation
          to a specific subset identified by a set of a given sample attribute
          values.  The tuple should have the name of that sample
          attribute as the first element, and a sequence of attribute values
          as the second element. If None, all samples will be used for parameter
          estimation.
        chunks_attr : str or None
          If provided, it specifies the name of a samples attribute in the
          training data, unique values of which will be used to identify chunks of
          samples, and to perform individual Z-scoring within them.
        dtype : Numpy dtype, optional
          Target dtype that is used for upcasting, in case integer data is to be
          Z-scored.
        """
        Mapper.__init__(self, **kwargs)

        self.__chunks_attr = chunks_attr
        self.__params = params
        self.__param_est = param_est
        self.__params_dict = None
        self.__dtype = dtype

        # secret switch to perform in-place z-scoring
        self._secret_inplace_zscore = False
Esempio n. 5
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    def __init__(self, selector=None, demean=True):
        """Initialize the ProjectionMapper

        Parameters
        ----------
        selector : None or list
          Which components (i.e. columns of the projection matrix)
          should be used for mapping. If `selector` is `None` all
          components are used. If a list is provided, all list
          elements are treated as component ids and the respective
          components are selected (all others are discarded).
        demean : bool
          Either data should be demeaned while computing
          projections and applied back while doing reverse()
        """
        Mapper.__init__(self)

        # by default we want to wipe the feature attributes out during mapping
        self._fa_filter = []

        self._selector = selector
        self._proj = None
        """Forward projection matrix."""
        self._recon = None
        """Reverse projection (reconstruction) matrix."""
        self._demean = demean
        """Flag whether to demean the to be projected data, prior to projection.
        """
        self._offset_in = None
        """Offset (most often just mean) in the input space"""
        self._offset_out = None
        """Offset (most often just mean) in the output space"""
Esempio n. 6
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    def __init__(self, dim=1, wavelet='sym4', mode='per', maxlevel=None):
        """Initialize _WaveletMapper mapper

        :Parameters:
          dim : int or tuple of int
            dimensions to work across (for now just scalar value, ie 1D
            transformation) is supported
          wavelet : basestring
            one from the families available withing pywt package
          mode : basestring
            periodization mode
          maxlevel : int or None
            number of levels to use. If None - automatically selected by pywt
        """
        Mapper.__init__(self)

        self._dim = dim
        """Dimension to work along"""

        self._maxlevel = maxlevel
        """Maximal level of decomposition. None for automatic"""

        if not wavelet in pywt.wavelist():
            raise ValueError, \
                  "Unknown family of wavelets '%s'. Please use one " \
                  "available from the list %s" % (wavelet, pywt.wavelist())
        self._wavelet = wavelet
        """Wavelet family to use"""

        if not mode in pywt.MODES.modes:
            raise ValueError, \
                  "Unknown periodization mode '%s'. Please use one " \
                  "available from the list %s" % (mode, pywt.MODES.modes)
        self._mode = mode
        """Periodization mode"""
Esempio n. 7
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    def __init__(self, selector=None, demean=True):
        """Initialize the ProjectionMapper

        Parameters
        ----------
        selector : None or list
          Which components (i.e. columns of the projection matrix)
          should be used for mapping. If `selector` is `None` all
          components are used. If a list is provided, all list
          elements are treated as component ids and the respective
          components are selected (all others are discarded).
        demean : bool
          Either data should be demeaned while computing
          projections and applied back while doing reverse()
        """
        Mapper.__init__(self)

        # by default we want to wipe the feature attributes out during mapping
        self._fa_filter = []

        self._selector = selector
        self._proj = None
        """Forward projection matrix."""
        self._recon = None
        """Reverse projection (reconstruction) matrix."""
        self._demean = demean
        """Flag whether to demean the to be projected data, prior to projection.
        """
        self._offset_in = None
        """Offset (most often just mean) in the input space"""
        self._offset_out = None
        """Offset (most often just mean) in the output space"""
Esempio n. 8
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File: fx.py Progetto: esc/PyMVPA
    def __init__(self, axis, fx, fxargs=None, uattrs=None,
                 attrfx='merge'):
        """
        Parameters
        ----------
        axis : {'samples', 'features'}
        fx : callable
        fxargs : tuple
        uattrs : list
          List of attribute names to consider. All possible combinations
          of unique elements of these attributes are used to determine the
          sample groups to operate on.
        attrfx : callable
          Functor that is called with each sample attribute elements matching
          the respective samples group. By default the unique value is
          determined. If the content of the attribute is not uniform for a
          samples group a unique string representation is created.
          If `None`, attributes are not altered.
        """
        Mapper.__init__(self)

        if not axis in ['samples', 'features']:
            raise ValueError("%s `axis` arguments can only be 'samples' or "
                             "'features' (got: '%s')." % repr(axis))
        self.__axis = axis
        self.__uattrs = uattrs
        self.__fx = fx
        if not fxargs is None:
            self.__fxargs = fxargs
        else:
            self.__fxargs = ()
        if attrfx == 'merge':
            self.__attrfx = _uniquemerge2literal
        else:
            self.__attrfx = attrfx
Esempio n. 9
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    def __init__(self, node, nodeargs=None, inspace=None):
        """
        Parameters
        ----------
        node : mdp.Node instance
          This node instance is taken as the pristine source of which a
          copy is made for actual processing upon each training attempt.
        nodeargs : dict
          Dictionary for additional arguments for all calls to the MDP
          node. The dictionary key's meaning is as follows:

          'train'
            Arguments for calls to `Node.train()`
          'stoptrain'
            Arguments for calls to `Node.stop_training()`
          'exec'
            Arguments for calls to `Node.execute()`
          'inv'
            Arguments for calls to `Node.inverse()`

          The value for each item is always a 2-tuple, consisting of a
          tuple (for the arguments), and a dictionary (for keyword
          arguments), i.e.  ((), {}). Both, tuple and dictionary have to be
          provided even if they are empty.
        inspace : see base class
        """
        # TODO: starting from MDP2.5 this check should become:
        # TODO:   if node.has_multiple_training_phases():
        if not len(node._train_seq) == 1:
            raise ValueError("MDPNodeMapper does not support MDP nodes with "
                             "multiple training phases.")
        Mapper.__init__(self, inspace=inspace)
        self.__pristine_node = None
        self.node = node
        self.nodeargs = nodeargs
Esempio n. 10
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    def __init__(self, axis, fx, fxargs=None, uattrs=None, attrfx='merge'):
        """
        Parameters
        ----------
        axis : {'samples', 'features'}
        fx : callable
        fxargs : tuple
        uattrs : list
          List of attribute names to consider. All possible combinations
          of unique elements of these attributes are used to determine the
          sample groups to operate on.
        attrfx : callable
          Functor that is called with each sample attribute elements matching
          the respective samples group. By default the unique value is
          determined. If the content of the attribute is not uniform for a
          samples group a unique string representation is created.
          If `None`, attributes are not altered.
        """
        Mapper.__init__(self)

        if not axis in ['samples', 'features']:
            raise ValueError("%s `axis` arguments can only be 'samples' or "
                             "'features' (got: '%s')." % repr(axis))
        self.__axis = axis
        self.__uattrs = uattrs
        self.__fx = fx
        if not fxargs is None:
            self.__fxargs = fxargs
        else:
            self.__fxargs = ()
        if attrfx == 'merge':
            self.__attrfx = _uniquemerge2literal
        else:
            self.__attrfx = attrfx
Esempio n. 11
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 def __init__(self,num_surrogates=10,blocksize=None,*args,**kwargs):
     Mapper.__init__(self,*args,**kwargs)
     self._num_surrogates = num_surrogates
     self._clusters = []  # Will become list of tuples (channel, band, (start,end), probability)
     self._is_trained = False
     #Shape for each sample of the data this mapper was trained on
     self._sample_shape = None
     self._blocksize = blocksize
Esempio n. 12
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 def __init__(self, num_surrogates=10, blocksize=None, *args, **kwargs):
     Mapper.__init__(self, *args, **kwargs)
     self._num_surrogates = num_surrogates
     self._clusters = [
     ]  # Will become list of tuples (channel, band, (start,end), probability)
     self._is_trained = False
     #Shape for each sample of the data this mapper was trained on
     self._sample_shape = None
     self._blocksize = blocksize
Esempio n. 13
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 def __init__(self, shape=None, **kwargs):
     """
     Parameters
     ----------
     shape : tuple
       The shape of a single sample. If this argument is given the mapper
       is going to be fully configured and no training is necessary anymore.
     """
     Mapper.__init__(self, **kwargs)
     self.__origshape = None
     self.__nfeatures = None
     if not shape is None:
         self._train_with_shape(shape)
Esempio n. 14
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 def __init__(self, shape=None, **kwargs):
     """
     Parameters
     ----------
     shape : tuple
       The shape of a single sample. If this argument is given the mapper
       is going to be fully configured and no training is necessary anymore.
     """
     Mapper.__init__(self, auto_train=True, **kwargs)
     self.__origshape = None
     self.__nfeatures = None
     if not shape is None:
         self._train_with_shape(shape)
Esempio n. 15
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    def __init__(self, mask, **kwargs):
        """Initialize MaskMapper

        :Parameters:
          mask : array
            an array in the original dataspace and its nonzero elements are
            used to define the features included in the dataset
        """
        Mapper.__init__(self, **kwargs)

        self.__mask = self.__maskdim = self.__masksize = \
                      self.__masknonzerosize = self.__forwardmap = \
                      self.__masknonzero = None # to make pylint happy
        self._initMask(mask)
Esempio n. 16
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    def __init__(self,
                 polyord=1,
                 chunks_attr=None,
                 opt_regs=None,
                 inspace=None):
        """
        Parameters
        ----------
        polyord : int or list, optional
          Order of the Legendre polynomial to remove from the data.  This
          will remove every polynomial up to and including the provided
          value.  For example, 3 will remove 0th, 1st, 2nd, and 3rd order
          polynomials from the data.  np.B.: The 0th polynomial is the
          baseline shift, the 1st is the linear trend.
          If you specify a single int and `chunks_attr` is not None, then this value
          is used for each chunk.  You can also specify a different polyord
          value for each chunk by providing a list or ndarray of polyord
          values the length of the number of chunks.
        chunks_attr : str or None
          If None, the whole dataset is detrended at once. Otherwise, the given
          samples attribute (given by its name) is used to define chunks of the
          dataset that are processed individually. In that case, all the samples
          within a chunk should be in contiguous order and the chunks should be
          sorted in order from low to high -- unless the dataset provides
          information about the coordinate of each sample in the space that
          should be spanned be the polynomials (see `inspace` argument).
        opt_regs : list or None
          Optional list of sample attribute names that should be used as
          additional regressors.  One example would be to regress out motion
          parameters.
        inspace : str or None
          If not None, a samples attribute of the same name is added to the
          mapped dataset that stores the coordinates of each sample in the
          space that is spanned by the polynomials. If an attribute of that
          name is already present in the input dataset its values are interpreted
          as sample coordinates in the space that should be spanned by the
          polynomials.
        """
        Mapper.__init__(self, inspace=inspace)

        self.__chunks_attr = chunks_attr
        self.__polyord = polyord
        self.__opt_reg = opt_regs

        # things that come from train()
        self._polycoords = None
        self._regs = None

        # secret switch to perform in-place detrending
        self._secret_inplace_detrend = False
Esempio n. 17
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    def __init__(self, polyord=1, chunks_attr=None, opt_regs=None, **kwargs):
        """
        Parameters
        ----------
        polyord : int or list, optional
          Order of the Legendre polynomial to remove from the data.  This
          will remove every polynomial up to and including the provided
          value.  For example, 3 will remove 0th, 1st, 2nd, and 3rd order
          polynomials from the data.  np.B.: The 0th polynomial is the
          baseline shift, the 1st is the linear trend.
          If you specify a single int and `chunks_attr` is not None, then this value
          is used for each chunk.  You can also specify a different polyord
          value for each chunk by providing a list or ndarray of polyord
          values the length of the number of chunks.
        chunks_attr : str or None
          If None, the whole dataset is detrended at once. Otherwise, the given
          samples attribute (given by its name) is used to define chunks of the
          dataset that are processed individually. In that case, all the samples
          within a chunk should be in contiguous order and the chunks should be
          sorted in order from low to high -- unless the dataset provides
          information about the coordinate of each sample in the space that
          should be spanned be the polynomials (see `inspace` argument).
        opt_regs : list or None
          Optional list of sample attribute names that should be used as
          additional regressors.  One example would be to regress out motion
          parameters.
        space : str or None
          If not None, a samples attribute of the same name is added to the
          mapped dataset that stores the coordinates of each sample in the
          space that is spanned by the polynomials. If an attribute of that
          name is already present in the input dataset its values are interpreted
          as sample coordinates in the space that should be spanned by the
          polynomials.
        """
        self.__chunks_attr = chunks_attr
        self.__polyord = polyord
        self.__opt_reg = opt_regs

        # things that come from train()
        self._polycoords = None
        self._regs = None

        # secret switch to perform in-place detrending
        self._secret_inplace_detrend = False

        # need to init last to prevent base class puking
        Mapper.__init__(self, **kwargs)
Esempio n. 18
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File: som.py Progetto: arokem/PyMVPA
 def __repr__(self):
     s = Mapper.__repr__(self).rstrip(' )')
     # beautify
     if not s[-1] == '(':
         s += ' '
     s += 'kshape=%s, niter=%i, learning_rate=%f, iradius=%f)' \
             % (str(tuple(self.kshape)), self.niter, self.lrate,
                self.radius)
     return s
Esempio n. 19
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 def __init__(self, shape=None, maxdims=None, **kwargs):
     """
     Parameters
     ----------
     shape : tuple
       The shape of a single sample. If this argument is given the mapper
       is going to be fully configured and no training is necessary anymore.
     maxdims : int or None
       The maximum number of dimensions to flatten (starting with the first).
       If None, all axes will be flattened.
     """
     # by default auto train
     kwargs['auto_train'] = kwargs.get('auto_train', True)
     Mapper.__init__(self, **kwargs)
     self.__origshape = None         # pylint pacifier
     self.__maxdims = maxdims
     if not shape is None:
         self._train_with_shape(shape)
Esempio n. 20
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 def __repr__(self):
     s = Mapper.__repr__(self).rstrip(' )')
     # beautify
     if not s[-1] == '(':
         s += ' '
     s += 'kshape=%s, niter=%i, learning_rate=%f, iradius=%f)' \
             % (str(tuple(self.kshape)), self.niter, self.lrate,
                self.radius)
     return s
Esempio n. 21
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File: som.py Progetto: arokem/PyMVPA
    def __init__(self, kshape, niter, learning_rate=0.005,
                 iradius=None):
        """
        Parameters
        ----------
        kshape : (int, int)
          Shape of the internal Kohonen layer. Currently, only 2D Kohonen
          layers are supported, although the length of an axis might be set
          to 1.
        niter : int
          Number of iteration during network training.
        learning_rate : float
          Initial learning rate, which will continuously decreased during
          network training.
        iradius : float or None
          Initial radius of the Gaussian neighborhood kernel radius, which
          will continuously decreased during network training. If `None`
          (default) the radius is set equal to the longest edge of the
          Kohonen layer.
        """
        # init base class
        Mapper.__init__(self)

        self.kshape = np.array(kshape, dtype='int')

        if iradius is None:
            self.radius = self.kshape.max()
        else:
            self.radius = iradius

        # learning rate
        self.lrate = learning_rate

        # number of training iterations
        self.niter = niter

        # precompute whatever can be done
        # scalar for decay of learning rate and radius across all iterations
        self.iter_scale = self.niter / np.log(self.radius)

        # the internal kohonen layer
        self._K = None
Esempio n. 22
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    def __init__(self, k, algorithm='lle', **kwargs):
        """
        :Parameters:
          k: int
            Number of nearest neighbor to be used by the algorithm.
          algorithm: 'lle' | 'hlle'
            Either use the standard LLE algorithm or Hessian Linear Local
            Embedding (HLLE).
          **kwargs:
            Additional arguments are passed to the underlying MDP node.
            Most importantly this is the `output_dim` argument, that determines
            the number of dimensions to mapper is using as output space.
        """
        # no meaningful metric
        Mapper.__init__(self, metric=None)

        self._algorithm = algorithm
        self._node_kwargs = kwargs
        self._k = k
        self._node = None
Esempio n. 23
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    def __init__(self, startpoints, boxlength, offset=0,
                 collision_resolution='mean'):
        """
        :Parameters:
          startpoints: sequence
            Index values along the first axis of 'data'.
          boxlength: int
            The number of elements after 'startpoint' along the first axis of
            'data' to be considered for the boxcar.
          offset: int
            The offset between the provided starting point and the actual start
            of the boxcar.
          collision_resolution : 'mean'
            if a sample belonged to multiple output samples, then on reverse,
            how to resolve the value
        """
        Mapper.__init__(self)

        startpoints = N.asanyarray(startpoints)
        if N.issubdtype(startpoints.dtype, 'i'):
            self.startpoints = startpoints
        else:
            if __debug__:
                debug('MAP', "Boxcar: obtained startpoints are not of int type."
                      " Rounding and changing dtype")
            self.startpoints = N.asanyarray(N.round(startpoints), dtype='i')

        # Sanity checks
        if boxlength < 1:
            raise ValueError, "Boxlength lower than 1 makes no sense."
        if boxlength - int(boxlength) != 0:
            raise ValueError, "boxlength must be an integer value."

        self.boxlength = int(boxlength)
        self.offset = offset
        self.__selectors = None

        if not collision_resolution in self._COLLISION_RESOLUTIONS:
            raise ValueError, "Unknown method to resolve the collision." \
                  " Valid are %s" % self._COLLISION_RESOLUTIONS
        self.__collision_resolution = collision_resolution
Esempio n. 24
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    def __init__(self, kshape, niter, learning_rate=0.005, iradius=None):
        """
        Parameters
        ----------
        kshape : (int, int)
          Shape of the internal Kohonen layer. Currently, only 2D Kohonen
          layers are supported, although the length of an axis might be set
          to 1.
        niter : int
          Number of iteration during network training.
        learning_rate : float
          Initial learning rate, which will continuously decreased during
          network training.
        iradius : float or None
          Initial radius of the Gaussian neighborhood kernel radius, which
          will continuously decreased during network training. If `None`
          (default) the radius is set equal to the longest edge of the
          Kohonen layer.
        """
        # init base class
        Mapper.__init__(self)

        self.kshape = np.array(kshape, dtype='int')

        if iradius is None:
            self.radius = self.kshape.max()
        else:
            self.radius = iradius

        # learning rate
        self.lrate = learning_rate

        # number of training iterations
        self.niter = niter

        # precompute whatever can be done
        # scalar for decay of learning rate and radius across all iterations
        self.iter_scale = self.niter / np.log(self.radius)

        # the internal kohonen layer
        self._K = None
Esempio n. 25
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    def __init__(self,
                 num,
                 window=None,
                 chunks_attr=None,
                 position_attr=None,
                 attr_strategy='remove',
                 inspace=None):
        """
        Parameters
        ----------
        num : int
          Number of output samples. If operating on chunks, this is the number
          of samples per chunk.
        window : str or float or tuple
          Passed to scipy.signal.resample
        chunks_attr : str or None
          If not None, this samples attribute defines chunks that will be
          resampled individually.
        position_attr : str
          A samples attribute with positional information that is passed
          to scipy.signal.resample. If not None, the output dataset will
          also contain a sample attribute of this name, with updated
          positional information (this is, however, only meaningful for
          equally spaced samples).
        attr_strategy : {'remove', 'sample', 'resample'}
          Strategy to process sample attributes during mapping. 'remove' will
          cause all sample attributes to be removed. 'sample' will pick orginal
          attribute values matching the new resampling frequency (e.g. every
          10th), and 'resample' will also apply the actual data resampling
          procedure to the attributes as well (which might not be possible, e.g.
          for literal attributes).
        """
        Mapper.__init__(self, inspace=inspace)

        self.__num = num
        self.__window_args = window
        self.__chunks_attr = chunks_attr
        self.__position_attr = position_attr
        self.__attr_strategy = attr_strategy
Esempio n. 26
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    def __init__(self, startpoints, boxlength, offset=0, **kwargs):
        """
        Parameters
        ----------
        startpoints : sequence
          Index values along the first axis of 'data'.
        boxlength : int
          The number of elements after 'startpoint' along the first axis of
          'data' to be considered for the boxcar.
        offset : int
          The offset between the provided starting point and the actual start
          of the boxcar.
        """
        Mapper.__init__(self, **kwargs)
        self._outshape = None

        startpoints = np.asanyarray(startpoints)
        if np.issubdtype(startpoints.dtype, 'i'):
            self.startpoints = startpoints
        else:
            if __debug__:
                debug('MAP', "Boxcar: obtained startpoints are not of int type."
                      " Rounding and changing dtype")
            self.startpoints = np.asanyarray(np.round(startpoints), dtype='i')

        # Sanity checks
        if boxlength < 1:
            raise ValueError, "Boxlength lower than 1 makes no sense."
        if boxlength - int(boxlength) != 0:
            raise ValueError, "boxlength must be an integer value."

        self.boxlength = int(boxlength)
        self.offset = offset
        self.__selectors = None

        # build a list of list where each sublist contains the indexes of to be
        # averaged data elements
        self.__selectors = [ slice(i + offset, i + offset + boxlength) \
                             for i in startpoints ]
Esempio n. 27
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    def __init__(self, fx=FirstAxisMean):
        """Initialize the PCAMapper

        Parameters:
          startpoints:    A sequence of index value along the first axis of
                          'data'.
          boxlength:      The number of elements after 'startpoint' along the
                          first axis of 'data' to be considered for averaging.
          offset:         The offset between the starting point and the
                          averaging window (boxcar).
          collision_resolution : string
            if a sample belonged to multiple output samples, then on reverse,
            how to resolve the value (choices: 'mean')
        """
        Mapper.__init__(self)

        self.__fx = fx
        self.__uniquechunks = None
        self.__uniquelabels = None
        self.__chunks = None
        self.__labels = None
        self.__datashape = None
Esempio n. 28
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    def __init__(self, startpoints, boxlength, offset=0, **kwargs):
        """
        Parameters
        ----------
        startpoints : sequence
          Index values along the first axis of 'data'.
        boxlength : int
          The number of elements after 'startpoint' along the first axis of
          'data' to be considered for the boxcar.
        offset : int
          The offset between the provided starting point and the actual start
          of the boxcar.
        """
        Mapper.__init__(self, **kwargs)
        self._outshape = None

        startpoints = np.asanyarray(startpoints)
        if np.issubdtype(startpoints.dtype, 'i'):
            self.startpoints = startpoints
        else:
            if __debug__:
                debug('MAP', "Boxcar: obtained startpoints are not of int type."
                      " Rounding and changing dtype")
            self.startpoints = np.asanyarray(np.round(startpoints), dtype='i')

        # Sanity checks
        if boxlength < 1:
            raise ValueError, "Boxlength lower than 1 makes no sense."
        if boxlength - int(boxlength) != 0:
            raise ValueError, "boxlength must be an integer value."

        self.boxlength = int(boxlength)
        self.offset = offset
        self.__selectors = None

        # build a list of list where each sublist contains the indexes of to be
        # averaged data elements
        self.__selectors = [ slice(i + offset, i + offset + boxlength) \
                             for i in startpoints ]
Esempio n. 29
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    def __init__(self, params=None, param_est=None, chunks_attr='chunks',
                 dtype='float64', inspace=None):
        """
        Parameters
        ----------
        params : None or tuple(mean, std) or dict
          Fixed Z-Scoring parameters (mean, standard deviation). If provided,
          no parameters are estimated from the data. It is possible to specify
          individual parameters for each chunk by passing a dictionary with the
          chunk ids as keys and the parameter tuples as values. If None,
          parameters will be estimated from the training data.
        param_est : None or tuple(attrname, attrvalues)
          Limits the choice of samples used for automatic parameter estimation
          to a specific subset identified by a set of a given sample attribute
          values.  The tuple should have the name of that sample
          attribute as the first element, and a sequence of attribute values
          as the second element. If None, all samples will be used for parameter
          estimation.
        chunks_attr : str or None
          If provided, it specifies the name of a samples attribute in the
          training data, unique values of which will be used to identify chunks of
          samples, and to perform individual Z-scoring within them.
        dtype : Numpy dtype, optional
          Target dtype that is used for upcasting, in case integer data is to be
          Z-scored.
        inspace : None
          Currently, this argument has no effect.
        """
        Mapper.__init__(self, inspace=inspace)

        self.__chunks_attr = chunks_attr
        self.__params = params
        self.__param_est = param_est
        self.__params_dict = None
        self.__dtype = dtype

        # secret switch to perform in-place z-scoring
        self._secret_inplace_zscore = False
Esempio n. 30
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 def __init__(self, flow, node_arguments=None, inspace=None):
     """
     Parameters
     ----------
     flow : mdp.Flow instance
       This flow instance is taken as the pristine source of which a
       copy is made for actual processing upon each training attempt.
     node_arguments : tuple, list
       A tuple or a list the same length as the flow. Each item is a
       list of arguments for the training of the corresponding node in
       the flow. If a node does not require additional arguments, None
       can be provided instead. Keyword arguments are currently not
       supported by mdp.Flow.
     inspace : see base class
     """
     if not node_arguments is None and len(node_arguments) != len(flow):
         raise ValueError("Length of node_arguments (%i) does not match the "
                          "number of nodes in the flow (%i)."
                          % (len(node_arguments), len(flow)))
     Mapper.__init__(self, inspace=inspace)
     self.__pristine_flow = None
     self.flow = flow
     self.node_arguments = node_arguments
Esempio n. 31
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  def __init__(self,
               bands,
               border=2,
               baseline_interval=None,
               data_interval=None,
               Fs=1000.0,
               *args,
               **kwargs):
      """Initialize the FrequencyBandPowerMapper
 
        :Parameters:
          bands: list of 2-tuples
            list of (fl,fh), edge-frequencies for filtering
          border: int
            order of the filter to be used
          baseline_interval: 2-tuple of ints
            indices of the interval to be used for baseline correction
          data_interval: 2-tuple of ints
            indices of the interval to be used for cluster search
          args, kwargs: 
            additional arguments passed to parent class
      """
      Mapper.__init__(self, *args, **kwargs)
      self._bands = bands
      self._border = border
      self._baseline_interval = baseline_interval
      self._data_interval = data_interval
      self._Fs = Fs
      #TODO: Perform some checks, for valid intervals etc.
      #precalculate filter parameters (butter)
      self._bs = list(np.zeros((len(bands))))
      self._as = list(np.zeros((len(bands))))
      for i_b in range(len(bands)):
          [self._bs[i_b], self._as[i_b]
           ] = butter(border,
                      [bands[i_b][0] / (Fs / 2), bands[i_b][1] / (Fs / 2)],
                      btype="band")
Esempio n. 32
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 def __init__(self, flow, node_arguments=None, inspace=None):
     """
     Parameters
     ----------
     flow : mdp.Flow instance
       This flow instance is taken as the pristine source of which a
       copy is made for actual processing upon each training attempt.
     node_arguments : tuple, list
       A tuple or a list the same length as the flow. Each item is a
       list of arguments for the training of the corresponding node in
       the flow. If a node does not require additional arguments, None
       can be provided instead. Keyword arguments are currently not
       supported by mdp.Flow.
     inspace : see base class
     """
     if not node_arguments is None and len(node_arguments) != len(flow):
         raise ValueError(
             "Length of node_arguments (%i) does not match the "
             "number of nodes in the flow (%i)." %
             (len(node_arguments), len(flow)))
     Mapper.__init__(self, inspace=inspace)
     self.__pristine_flow = None
     self.flow = flow
     self.node_arguments = node_arguments
Esempio n. 33
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    def __init__(self, dim=1, wavelet='sym4', mode='per', maxlevel=None):
        """Initialize _WaveletMapper mapper

        Parameters
        ----------
        dim : int or tuple of int
          dimensions to work across (for now just scalar value, ie 1D
          transformation) is supported
        wavelet : str
          one from the families available withing pywt package
        mode : str
          periodization mode
        maxlevel : int or None
          number of levels to use. If None - automatically selected by pywt
        """
        Mapper.__init__(self)

        self._dim = dim
        """Dimension to work along"""

        self._maxlevel = maxlevel
        """Maximal level of decomposition. None for automatic"""

        if not wavelet in pywt.wavelist():
            raise ValueError, \
                  "Unknown family of wavelets '%s'. Please use one " \
                  "available from the list %s" % (wavelet, pywt.wavelist())
        self._wavelet = wavelet
        """Wavelet family to use"""

        if not mode in pywt.MODES.modes:
            raise ValueError, \
                  "Unknown periodization mode '%s'. Please use one " \
                  "available from the list %s" % (mode, pywt.MODES.modes)
        self._mode = mode
        """Periodization mode"""
Esempio n. 34
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 def __repr__(self):
     s = Mapper.__repr__(self)
     m_repr = 'shape=%s' % repr(self.__origshape)
     return s.replace("(", "(%s, " % m_repr, 1)
Esempio n. 35
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 def __repr__(self):
     s = Mapper.__repr__(self)
     m_repr = 'shape=%s' % repr(self.__origshape)
     return s.replace("(", "(%s, " % m_repr, 1)
Esempio n. 36
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 def __init__(self, slicearg, **kwargs):
     Mapper.__init__(self, **kwargs)
     self._safe_assign_slicearg(slicearg)
Esempio n. 37
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    def __init__(self, chunks_attr=None, inspace=None):
        Mapper.__init__(self, inspace=inspace)

        self.__chunks_attr = chunks_attr