sketch.py 文件源码

def update_model(self, y):
        y = self.proj.reduce(np.array([y]).T)
        y = np.ravel(preprocessing.normalize(y, norm='l2', axis=0))

        if not hasattr(self, 'E'):
            self.E = np.zeros((self.k, self.ell))

        # combine current sketched matrix with input at time t
        zero_cols = np.nonzero([np.isclose(s_col, 0.0) for s_col in np.sum(self.E, axis=0)])[0]
        j = zero_cols[0] if zero_cols.size != 0 else self.ell - 1  # left-most all-zero column in B
        self.E[:, j] = y

        Gaussian = np.random.normal(0., 0.1, (self.k, 100 * self.ell))
        MM = np.dot(self.E, self.E.T)
        Q, R = ln.qr(np.dot(MM, Gaussian))

        # eig() returns eigen values/vectors with unsorted order
        s, A = ln.eig(np.dot(np.dot(Q.T, MM), Q))
        order = np.argsort(s)[::-1]
        s = s[order]
        A = A[:, order]

        U = np.dot(Q, A)

        # update the tracked orthonormal bases
        self.U_r = U[:, :self.r]

        # update ell orthogonal bases
        U_ell = U[:, :self.ell]
        s_ell = s[:self.ell]

        # shrink step in the Frequent Directions algorithm
        delta = s_ell[-1]
        s_ell = np.sqrt(s_ell - delta)

        self.E = np.dot(U_ell, np.diag(s_ell))