msaf/pymf/chnmf.py
def _map_w_to_data(self):
        """ Return data points that are most similar to basis vectors W
        """

        # assign W to the next best data sample
        self._Wmapped_index = vq(self.data, self.W)
        self.Wmapped = np.zeros(self.W.shape)

        # do not directly assign, i.e. Wdist = self.data[:,sel]
        # as self might be unsorted (in non ascending order)
        # -> sorting sel would screw the matching to W if
        # self.data is stored as a hdf5 table (see h5py)
        for i, s in enumerate(self._Wmapped_index):
            self.Wmapped[:,i] = self.data[:,s]
def _map_w_to_data(self):
        # assign W to the next best data sample
        self._Wmapped_index = vq(self.data, self.W)
        self.Wmapped = np.zeros(self.W.shape)

        # do not directly assign, i.e. Wdist = self.data[:,sel]
        # as self might be unsorted (in non ascending order)
        # -> sorting sel would screw the matching to W if
        # self.data is stored as a hdf5 table (see h5py)
        for i, s in enumerate(self._Wmapped_index):
            self.Wmapped[:,i] = self.data[:,s]
def _map_w_to_data ( self ) : # assign W to the next best data sample self . _Wmapped_index = vq ( self . data , self . W ) self . Wmapped = np . zeros ( self . W . shape ) # do not directly assign, i.e. Wdist = self.data[:,sel] # as self might be unsorted (in non ascending order) # -> sorting sel would screw the matching to W if # self.data is stored as a hdf5 table (see h5py) for i , s in enumerate ( self . _Wmapped_index ) : self . Wmapped [ : , i ] = self . data [ : , s ]
Return data points that are most similar to basis vectors W
Return data points that are most similar to basis vectors W