msaf/pymf/sub.py
def factorize(self):
        """Do factorization s.t. data = dot(dot(data,beta),H), under the convexity constraint
            beta >=0, sum(beta)=1, H >=0, sum(H)=1
        """
        # compute new coefficients for reconstructing data points
        self.update_w()

        # for CHNMF it is sometimes useful to only compute
        # the basis vectors
        if self._compute_h:
            self.update_h()

        self.W = self.mdl.W
        self.H = self.mdl.H

        self.ferr = np.zeros(1)
        self.ferr[0] = self.mdl.frobenius_norm()
        self._print_cur_status(' Fro:' + str(self.ferr[0]))
def factorize(self):
        """Do factorization s.t. data = dot(dot(data,beta),H), under the convexity constraint
            beta >=0, sum(beta)=1, H >=0, sum(H)=1
        """
        # compute new coefficients for reconstructing data points
        self.update_w()

        # for CHNMF it is sometimes useful to only compute
        # the basis vectors
        if self._compute_h:
            self.update_h()

        self.W = self.mdl.W
        self.H = self.mdl.H

        self.ferr = np.zeros(1)
        self.ferr[0] = self.mdl.frobenius_norm()
        self._print_cur_status(' Fro:' + str(self.ferr[0]))
def factorize ( self ) : # compute new coefficients for reconstructing data points self . update_w ( ) # for CHNMF it is sometimes useful to only compute # the basis vectors if self . _compute_h : self . update_h ( ) self . W = self . mdl . W self . H = self . mdl . H self . ferr = np . zeros ( 1 ) self . ferr [ 0 ] = self . mdl . frobenius_norm ( ) self . _print_cur_status ( ' Fro:' + str ( self . ferr [ 0 ] ) )
Do factorization s.t. data = dot(dot(data,beta),H), under the convexity constraint
            beta >=0, sum(beta)=1, H >=0, sum(H)=1
Do factorization s . t . data = dot ( dot ( data beta ) H ) under the convexity constraint beta > = 0 sum ( beta ) = 1 H > = 0 sum ( H ) = 1