msaf/pymf/svd.py
def frobenius_norm(self):
        """ Frobenius norm (||data - USV||) for a data matrix and a low rank
        approximation given by SVH using rank k for U and V
        
        Returns:
            frobenius norm: F = ||data - USV||
        """    
        if scipy.sparse.issparse(self.data):
            err = self.data - self.U*self.S*self.V    
            err = err.multiply(err)
            err = np.sqrt(err.sum())
        else:                
            err = self.data[:,:] - np.dot(np.dot(self.U, self.S), self.V)
            err = np.sqrt(np.sum(err**2))
                            
        return err
def frobenius_norm(self):
        """ Frobenius norm (||data - USV||) for a data matrix and a low rank
        approximation given by SVH using rank k for U and V
        
        Returns:
            frobenius norm: F = ||data - USV||
        """    
        if scipy.sparse.issparse(self.data):
            err = self.data - self.U*self.S*self.V    
            err = err.multiply(err)
            err = np.sqrt(err.sum())
        else:                
            err = self.data[:,:] - np.dot(np.dot(self.U, self.S), self.V)
            err = np.sqrt(np.sum(err**2))
                            
        return err
def frobenius_norm ( self ) : if scipy . sparse . issparse ( self . data ) : err = self . data - self . U * self . S * self . V err = err . multiply ( err ) err = np . sqrt ( err . sum ( ) ) else : err = self . data [ : , : ] - np . dot ( np . dot ( self . U , self . S ) , self . V ) err = np . sqrt ( np . sum ( err ** 2 ) ) return err
Frobenius norm (||data - USV||) for a data matrix and a low rank
        approximation given by SVH using rank k for U and V
        
        Returns:
            frobenius norm: F = ||data - USV||
Frobenius norm ( ||data - USV|| ) for a data matrix and a low rank approximation given by SVH using rank k for U and V Returns : frobenius norm : F = ||data - USV||