msaf/pymf/gmap.py
def factorize(self, show_progress=False, compute_w=True, compute_h=True,
                  compute_err=True, robust_cluster=3, niter=1, robust_nselect=-1):
        """ Factorize s.t. WH = data

            Parameters
            ----------
            show_progress : bool
                    print some extra information to stdout.
                    False, default
            compute_h : bool
                    iteratively update values for H.
                    True, default
            compute_w : bool
                    iteratively update values for W.
                    default, True
            compute_err : bool
                    compute Frobenius norm |data-WH| after each update and store
                    it to .ferr[k].
            robust_cluster : int, optional
                    set the number of clusters for robust map selection.
                    3, default
            robust_nselect : int, optional
                    set the number of samples to consider for robust map
                    selection.
                    -1, default (automatically determine suitable number)

            Updated Values
            --------------
            .W : updated values for W.
            .H : updated values for H.
            .ferr : Frobenius norm |data-WH|.
        """
        self._robust_cluster = robust_cluster
        self._robust_nselect = robust_nselect

        if self._robust_nselect == -1:
            self._robust_nselect = np.round(np.log(self.data.shape[1])*2)

        AA.factorize(self, niter=1, show_progress=show_progress,
                  compute_w=compute_w, compute_h=compute_h,
                  compute_err=compute_err)
def factorize(self, show_progress=False, compute_w=True, compute_h=True,
                  compute_err=True, robust_cluster=3, niter=1, robust_nselect=-1):
        """ Factorize s.t. WH = data

            Parameters
            ----------
            show_progress : bool
                    print some extra information to stdout.
                    False, default
            compute_h : bool
                    iteratively update values for H.
                    True, default
            compute_w : bool
                    iteratively update values for W.
                    default, True
            compute_err : bool
                    compute Frobenius norm |data-WH| after each update and store
                    it to .ferr[k].
            robust_cluster : int, optional
                    set the number of clusters for robust map selection.
                    3, default
            robust_nselect : int, optional
                    set the number of samples to consider for robust map
                    selection.
                    -1, default (automatically determine suitable number)

            Updated Values
            --------------
            .W : updated values for W.
            .H : updated values for H.
            .ferr : Frobenius norm |data-WH|.
        """
        self._robust_cluster = robust_cluster
        self._robust_nselect = robust_nselect

        if self._robust_nselect == -1:
            self._robust_nselect = np.round(np.log(self.data.shape[1])*2)

        AA.factorize(self, niter=1, show_progress=show_progress,
                  compute_w=compute_w, compute_h=compute_h,
                  compute_err=compute_err)
Factorize s.t. WH = data

            Parameters
            ----------
            show_progress : bool
                    print some extra information to stdout.
                    False, default
            compute_h : bool
                    iteratively update values for H.
                    True, default
            compute_w : bool
                    iteratively update values for W.
                    default, True
            compute_err : bool
                    compute Frobenius norm |data-WH| after each update and store
                    it to .ferr[k].
            robust_cluster : int, optional
                    set the number of clusters for robust map selection.
                    3, default
            robust_nselect : int, optional
                    set the number of samples to consider for robust map
                    selection.
                    -1, default (automatically determine suitable number)

            Updated Values
            --------------
            .W : updated values for W.
            .H : updated values for H.
            .ferr : Frobenius norm |data-WH|.