msaf/pymf/nmf.py
def frobenius_norm(self):
        """ Frobenius norm (||data - WH||) of a data matrix and a low rank
        approximation given by WH

        Returns:
            frobenius norm: F = ||data - WH||
        """

        # check if W and H exist
        if hasattr(self,'H') and hasattr(self,'W') and not scipy.sparse.issparse(self.data):
            err = np.sqrt( np.sum((self.data[:,:] - np.dot(self.W, self.H))**2 ))
        else:
            err = -123456

        return err
def frobenius_norm(self):
        """ Frobenius norm (||data - WH||) of a data matrix and a low rank
        approximation given by WH

        Returns:
            frobenius norm: F = ||data - WH||
        """

        # check if W and H exist
        if hasattr(self,'H') and hasattr(self,'W') and not scipy.sparse.issparse(self.data):
            err = np.sqrt( np.sum((self.data[:,:] - np.dot(self.W, self.H))**2 ))
        else:
            err = -123456

        return err
def frobenius_norm ( self ) : # check if W and H exist if hasattr ( self , 'H' ) and hasattr ( self , 'W' ) and not scipy . sparse . issparse ( self . data ) : err = np . sqrt ( np . sum ( ( self . data [ : , : ] - np . dot ( self . W , self . H ) ) ** 2 ) ) else : err = - 123456 return err
Frobenius norm (||data - WH||) of a data matrix and a low rank
        approximation given by WH

        Returns:
            frobenius norm: F = ||data - WH||
Frobenius norm ( ||data - WH|| ) of a data matrix and a low rank approximation given by WH