python类lstsq()的实例源码

def get_device_location(gpsPoints):
    """
        params: takes an array of gps positions and signal stregnths of the form [{"x": 1, "y": 4, "z": 1.6, "signal": 1.3}]
        returns: Position and output power of emitter of the form {"x": 1, "y": 4, "z": 1.6, "sigPower": 1.3}
    """
    aRows = []
    bRows = []
    last = gpsPoints.pop()
    for gpsPoint in gpsPoints:
        aRow = [2.0 * (last["x"] - gpsPoint["x"]),
                2.0 * (last["y"] - gpsPoint["y"]),
                2.0 * (last["z"] - gpsPoint["z"]),
                (-1 * gpsPoint["signal"]**2 + last["signal"]**2)]
        bRow = [-(gpsPoint["x"]**2 - last["x"]**2)  - (gpsPoint["y"]**2 - last["y"]**2) - (gpsPoint["z"]**2 - last["z"]**2)]
        aRows.append(aRow)
        bRows.append(bRow)
    aMatrix = matrix(aRows)
    bMatrix = matrix(bRows)
    solution = lstsq(aMatrix, bMatrix)[0]
    return {"x": solution.item(0), "y": solution.item(1), "z": solution.item(2), "sigPower": solution.item(3)**0.5}

def do(self, a, b):
        arr = np.asarray(a)
        m, n = arr.shape
        u, s, vt = linalg.svd(a, 0)
        x, residuals, rank, sv = linalg.lstsq(a, b)
        if m <= n:
            assert_almost_equal(b, dot(a, x))
            assert_equal(rank, m)
        else:
            assert_equal(rank, n)
        assert_almost_equal(sv, sv.__array_wrap__(s))
        if rank == n and m > n:
            expect_resids = (
                np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
            expect_resids = np.asarray(expect_resids)
            if len(np.asarray(b).shape) == 1:
                expect_resids.shape = (1,)
                assert_equal(residuals.shape, expect_resids.shape)
        else:
            expect_resids = np.array([]).view(type(x))
        assert_almost_equal(residuals, expect_resids)
        assert_(np.issubdtype(residuals.dtype, np.floating))
        assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
        assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix)))

def test_ridge():
    """Test Ridge regression for different values of mu."""
    # A simple sum function (with intercept)
    X = [[1, 2], [3, 4], [5, 6]]
    y = [sum(x)+1 for x in X]
    T = [[7, 8], [9, 10], [2, 1]]

    model = RidgeRegression(mu=0.0).fit(X, y)  # OLS
    assert_array_almost_equal([1, 1], model.coef_)
    assert_array_almost_equal([16, 20, 4], model.predict(T))
    assert_almost_equal(1.0, model.intercept_)

    # Equivalence with standard numpy least squares
    Xc = X - np.mean(X, axis=0)
    assert_almost_equal(la.lstsq(Xc, y)[0], model.coef_)

    model = RidgeRegression(mu=0.5).fit(X, y)
    assert_array_almost_equal([0.91428571, 0.91428571], model.coef_)
    assert_array_almost_equal([15.31428571, 18.97142857, 4.34285714],
                              model.predict(T))

    model = RidgeRegression(mu=1.0).fit(X, y)
    assert_array_almost_equal([0.84210526, 0.84210526], model.coef_)
    assert_array_almost_equal([14.73684211, 18.10526316, 4.63157895],
                              model.predict(T))

def do(self, a, b):
        arr = np.asarray(a)
        m, n = arr.shape
        u, s, vt = linalg.svd(a, 0)
        x, residuals, rank, sv = linalg.lstsq(a, b)
        if m <= n:
            assert_almost_equal(b, dot(a, x))
            assert_equal(rank, m)
        else:
            assert_equal(rank, n)
        assert_almost_equal(sv, sv.__array_wrap__(s))
        if rank == n and m > n:
            expect_resids = (
                np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
            expect_resids = np.asarray(expect_resids)
            if len(np.asarray(b).shape) == 1:
                expect_resids.shape = (1,)
                assert_equal(residuals.shape, expect_resids.shape)
        else:
            expect_resids = np.array([]).view(type(x))
        assert_almost_equal(residuals, expect_resids)
        assert_(np.issubdtype(residuals.dtype, np.floating))
        assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
        assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix)))

def do(self, a, b):
        arr = np.asarray(a)
        m, n = arr.shape
        u, s, vt = linalg.svd(a, 0)
        x, residuals, rank, sv = linalg.lstsq(a, b)
        if m <= n:
            assert_almost_equal(b, dot(a, x))
            assert_equal(rank, m)
        else:
            assert_equal(rank, n)
        assert_almost_equal(sv, sv.__array_wrap__(s))
        if rank == n and m > n:
            expect_resids = (
                np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
            expect_resids = np.asarray(expect_resids)
            if len(np.asarray(b).shape) == 1:
                expect_resids.shape = (1,)
                assert_equal(residuals.shape, expect_resids.shape)
        else:
            expect_resids = np.array([]).view(type(x))
        assert_almost_equal(residuals, expect_resids)
        assert_(np.issubdtype(residuals.dtype, np.floating))
        assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
        assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix)))

def do(self, a, b):
        arr = np.asarray(a)
        m, n = arr.shape
        u, s, vt = linalg.svd(a, 0)
        x, residuals, rank, sv = linalg.lstsq(a, b)
        if m <= n:
            assert_almost_equal(b, dot(a, x))
            assert_equal(rank, m)
        else:
            assert_equal(rank, n)
        assert_almost_equal(sv, sv.__array_wrap__(s))
        if rank == n and m > n:
            expect_resids = (
                np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
            expect_resids = np.asarray(expect_resids)
            if len(np.asarray(b).shape) == 1:
                expect_resids.shape = (1,)
                assert_equal(residuals.shape, expect_resids.shape)
        else:
            expect_resids = np.array([]).view(type(x))
        assert_almost_equal(residuals, expect_resids)
        assert_(np.issubdtype(residuals.dtype, np.floating))
        assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
        assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix)))

def vector_to_star_basis(self, standard_vec):
        '''
        convert a vector in the standard basis to a point in the star's basis.

        This solves basis_matrix * rv = input, which is essentially computing the
        inverse of basis_matrix, which can become ill-conditioned.
        '''

        Timers.tic("vector_to_star_basis()")

        rv = np.linalg.solve(self.basis_matrix.T, standard_vec)

        #rv = lstsq(self.basis_matrix.T, np.array(standard_vec, dtype=float))[0]

        # double-check that we've found the solution within some tolerance
        if not np.allclose(np.dot(self.basis_matrix.T, rv), standard_vec):
            raise RuntimeError("basis matrix was ill-conditioned, vector_to_star_basis() failed")

        Timers.toc("vector_to_star_basis()")

        assert isinstance(rv, np.ndarray)

        return rv

def do(self, a, b):
        arr = np.asarray(a)
        m, n = arr.shape
        u, s, vt = linalg.svd(a, 0)
        x, residuals, rank, sv = linalg.lstsq(a, b)
        if m <= n:
            assert_almost_equal(b, dot(a, x))
            assert_equal(rank, m)
        else:
            assert_equal(rank, n)
        assert_almost_equal(sv, sv.__array_wrap__(s))
        if rank == n and m > n:
            expect_resids = (
                np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
            expect_resids = np.asarray(expect_resids)
            if len(np.asarray(b).shape) == 1:
                expect_resids.shape = (1,)
                assert_equal(residuals.shape, expect_resids.shape)
        else:
            expect_resids = np.array([]).view(type(x))
        assert_almost_equal(residuals, expect_resids)
        assert_(np.issubdtype(residuals.dtype, np.floating))
        assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
        assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix)))

def do(self, a, b):
        arr = np.asarray(a)
        m, n = arr.shape
        u, s, vt = linalg.svd(a, 0)
        x, residuals, rank, sv = linalg.lstsq(a, b)
        if m <= n:
            assert_almost_equal(b, dot(a, x))
            assert_equal(rank, m)
        else:
            assert_equal(rank, n)
        assert_almost_equal(sv, sv.__array_wrap__(s))
        if rank == n and m > n:
            expect_resids = (
                np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
            expect_resids = np.asarray(expect_resids)
            if len(np.asarray(b).shape) == 1:
                expect_resids.shape = (1,)
                assert_equal(residuals.shape, expect_resids.shape)
        else:
            expect_resids = np.array([]).view(type(x))
        assert_almost_equal(residuals, expect_resids)
        assert_(np.issubdtype(residuals.dtype, np.floating))
        assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
        assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix)))

def do(self, a, b):
        arr = np.asarray(a)
        m, n = arr.shape
        u, s, vt = linalg.svd(a, 0)
        x, residuals, rank, sv = linalg.lstsq(a, b)
        if m <= n:
            assert_almost_equal(b, dot(a, x))
            assert_equal(rank, m)
        else:
            assert_equal(rank, n)
        assert_almost_equal(sv, sv.__array_wrap__(s))
        if rank == n and m > n:
            expect_resids = (
                np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
            expect_resids = np.asarray(expect_resids)
            if len(np.asarray(b).shape) == 1:
                expect_resids.shape = (1,)
                assert_equal(residuals.shape, expect_resids.shape)
        else:
            expect_resids = np.array([]).view(type(x))
        assert_almost_equal(residuals, expect_resids)
        assert_(np.issubdtype(residuals.dtype, np.floating))
        assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
        assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix)))

def left_least_squares(x,y,rcond=-1,fast=False):
    'find the A that best fits y-A*x'
    if fast:
        return la.lstsq( np.matmul(x,x.T) ,np.matmul(x,y.T) ,rcond=rcond )[0].T  # faster, but less stable
    else:
        return la.lstsq( x.T,y.T,rcond=rcond)[0].T

def solve(A, y, delta, method):
  if method == 'ridge_reg_chol':
    R = cholesky(dot(A.T, A) + delta*np.identity(A.shape[1]))
    z = lstsq(R.T, dot(A.T, y))[0]
    x = lstsq(R, z)[0]
  elif method == 'ridge_reg_inv':
    x = dot(dot(inv(dot(A.T, A) + delta*np.identity(A.shape[1])), A.T), y)
  elif method == 'ls_mldivide':
    if delta > 0:
      print('ignoring lambda; no regularization used')
    x = lstsq(A, y)[0]
  loss = 0.5 * (dot(A, x) - y) **2
  return x.reshape(-1, 1)

def trainClassifer(self,labels,vectors,ilog=None):
        '''
        Train the polynomial.  Do not call this function
        manually, instead call the train function on the super
        class.
        '''
        #build matrix
        matrix = []
        for each in vectors:
            if len(each) != 2:
                raise ValueError("ERROR: Vector length=%d.  Polynomial2D only predicts for vectors of length 2."%len(each))
            x,y = each
            matrix.append(self.buildRow(x,y))

        matrix = array(matrix)
        labels = array(labels)

        x,resids,rank,s = lstsq(matrix,labels)

        self.x = x
        self.resids = resids
        self.rank = rank
        self.s = s

        if rank != matrix.shape[1]:
            print "WARNING: Polynomial is not fully constrained."

def _fit(self, X, y):
        self.coef_ = la.lstsq(X, y)[0]

def solve(self):
        self._check_nodes()
        # Solve LS
        self.VU = [node[key] for node in self.U.values() for key in ("ux","uy")]
        self.VF = [node[key] for node in self.F.values() for key in ("fx","fy")]
        knw = [pos for pos,value in enumerate(self.VU) if not value is np.nan]
        unknw = [pos for pos,value in enumerate(self.VU) if value is np.nan]
        self.K2S = np.delete(np.delete(self.KG,knw,0),knw,1)
        self.F2S = np.delete(self.VF,knw,0)

        # For displacements
        try:
            self.solved_u = la.solve(self.K2S,self.F2S)
        except:
            print("Solved using LSTSQ")
            self.solved_u = la.lstsq(self.K2S, self.F2S)[0]

        for k,ic in enumerate(unknw):
            nd, var = self.index2key(ic)
            self.U[nd][var] = self.solved_u[k]

        # Updating nodes displacements
        for nd in self.nodes.values():
            if np.isnan(nd.ux):
                nd.ux = self.U[nd.label]["ux"]
            if np.isnan(nd.uy):
                nd.uy = self.U[nd.label]["uy"]

        # For nodal forces/reactions
        self.NF = self.F.copy()
        self.VU = [node[key] for node in self.U.values() for key in ("ux","uy")]
        nf_calc = np.dot(self.KG, self.VU)
        for k in range(2*self.get_number_of_nodes()):
            nd, var = self.index2key(k, ("fx","fy"))
            self.NF[nd][var] = nf_calc[k]
            cnlab = np.floor(k/float(self.dof))
            if var=="fx": 
                self.nodes[cnlab].fx = nf_calc[k]
            elif var=="fy": 
                self.nodes[cnlab].fy = nf_calc[k]

def _fit(self):
        """Compute a least squares fit."""
        A = self._plegendre(self._normalize(self.get_x()))
        self.beta = la.lstsq(A, self.get_fx())[0]

def mils(A, B, y, p=1):
    # x_hat,z_hat = mils(A,B,y,p) produces p pairs of optimal solutions to
    #               the mixed integer least squares problem min_{x,z}||y-Ax-Bz||, 
    #               where x and z are real and integer vectors, respectively.
    #
    # Input arguments:
    #    A - m by k real matrix
    #    B - m by n real matrix
    #          [A,B] has full column rank
    #    y - m-dimensional real vector
    #    p - the number of optimal solutions
    #
    # Output arguments:
    #    x_hat - k by p real matrix
    #    z_hat - n by p integer matrix (in double precision). 
    #           The pair {x_hat(:,j),z_hat(:,j)} is the j-th optimal solution
    #           i.e., its residual is the j-th smallest, so
    #           ||y-A*x_hat(:,1)-B*z_hat(:,1)||<=...<=||y-A*x_hat(:,p)-B*z_hat(:,p)||

    m, k = A.shape
    m2, n = B.shape
    if m != m2 or m != len(y) or len(y[1]) != 1:
        raise ValueError("Input arguments have a matrix dimension error!")

    if rank(A) + rank(B) < k + n:
        raise ValueError("hmmm...")

    Q, R = qr(A, mode='complete')
    Q_A = Q[:, :k]
    Q_Abar = Q[:, k:]
    R_A = R[:k, :]

    # Compute the p optimal integer least squares solutions
    z_hat = ils(dot(Q_Abar.T, B), dot(Q_Abar.T, y), p)

    # Compute the corresponding real least squares solutions
    x_hat = lstsq(R_A, dot(Q_A.T, (dot(y, ones((1, p))) - dot(B, z_hat))))

    return x_hat, z_hat

def bundletoa(D, I, J, xt, yt, debug=1, opts=[]):
    res = None
    jac = None

    for kkk in range(0, 10):

        res, jac = calcresandjac(D, I, J, xt, yt, opts)

        dz = linalg.lstsq(-((jac.conj().T) * jac + 0.1 *
                            np.eye(jac.shape[1])), (jac.conj().T) * res)

        xtn, ytn = updatexy(xt, yt, dz)
        res2, jac2 = calcresandjac(D, I, J, xt, yt, opts)

        cc = np.linalg.norm(jac * dz) / np.linalg.norm(res)

        if np.linalg.norm(res) < np.linalg.norm(res2):

            if cc > 1e-4:

                kkkk = 1
                while (kkkk < 50) and (
                        np.linalg.norm(res) < np.linalg.norm(res2)):
                    dz = dz / 2
                    xtn, ytn = updatexy(xt, yt, dz)
                    res2, jac2 = calcresandjac(D, I, J, xtn, ytn, opts)
                    kkkk = kkkk + 1

        if debug:
            aa_1 = np.linalg.norm(res)
            aa_2 = np.linalg.norm(res + jac * dz)
            aa_3 = np.linalg.norm(res2)
            aa = np.concatenate((aa_1, aa_2, aa_3), 1)

            bb = aa
            bb = bb - bb[1]
            bb = bb / bb[0]

            cc = np.linalg.norm(jac * dz) / np.linalg.norm(res)

            print(aa, bb, cc)

        if np.linalg.norm(res2) < np.linalg.norm(res):

            xt = xtn
            yt = ytn
        else:

            if debug:
                print(kkk, ' stalled')

    xopt = xt
    yopt = yt

    return xopt, yopt, res, jac

def AffineFromPointsLS(src,dst,new_size,filter=BILINEAR, normalize=True):  
    '''
     An affine transform that will rotate, translate, and scale to map one 
     set of points to the other. For example, to align eye coordinates in face images.

     Find a transform (a,b,tx,ty) such that it maps the source points to the 
     destination points::

         a*x1-b*y1+tx = x2
         b*x1+a*y1+ty = y2

     This method minimizes the squared error to find an optimal fit between the 
     points.

     @param src: a list of link.Points in the source image.
     @param dst: a list of link.Points in the destination image.
     @param new_size: new size for the image.
     @param filter: PIL filter to use.
    '''  
    if normalize:
        # Normalize Points
        src_norm = AffineNormalizePoints(src)
        src = src_norm.transformPoints(src)
        dst_norm = AffineNormalizePoints(dst)
        dst = dst_norm.transformPoints(dst)

    # Compute the transformation parameters
    A = []
    b = []
    for i in range(len(src)):
        A.append([src[i].X(),-src[i].Y(),1,0])
        A.append([src[i].Y(), src[i].X(),0,1])
        b.append(dst[i].X())
        b.append(dst[i].Y())

    A = array(A)
    b = array(b)

    result,resids,rank,s = lstsq(A,b)

    a,b,tx,ty = result    
    # Create the transform matrix
    matrix = array([[a,-b,tx],[b,a,ty],[0,0,1]],'d')

    if normalize:
        matrix = dot(dst_norm.inverse,dot(matrix,src_norm.matrix))

    return AffineTransform(matrix,new_size,filter)

def _split_state (a_old, o_old, state, q0l, q1l):
# use least-squares to find new parameters
# add new state to end
    ns = a_old.shape[0]
    no = o_old.shape[1]
    A = numpy.zeros((ns+1,ns+1))
    O = numpy.zeros((ns+1,no))
    A[0:ns,0:ns] = a_old
    O[0:ns,:] = o_old
    # fill in p ( * | snew2)
    A[ns,:] = A[state,:]
    # fill in p (q | snew2)
    O[ns,:] = O[state,:]  # order of these three lines matters
    O[ns,q0l] = 0
    w2 = numpy.sum (O[ns,:])
    O[ns,:] = O[ns,:] / w2
    # fill in p (q | snew1)
    O[state,q1l] = 0
    w1 = numpy.sum(O[state,:])
    O[state,:] = O[state,:] / w1
    # finally, fill in p( snew1 | *)  p ( snew2 | *)
    A[:,ns] = A[:,state]
    A[:,state] = w1 / (w1+w2) * A[:,state]
    A[:,ns] = w2 / (w1++w2) * A[:,ns]
    # done
    return A,O,ns

    # tricky part is the incoming state reallocate by solving linear system
    #  (we'll call the coefficients M)
#     M = numpy.zeros(( ns + no*ns,  2*(ns+1) ))
#     b = numpy.zeros( M.shape[0] )
#     ri = 0
#     def idx (s0, s1): return s0 if s1 == state else ns + s0
#     for si in range(ns):
#         M[ri,idx(si,state)] = M[ri,idx(si,ns)] = 1
#         b[ri] = a_old[si,state]
#         ri += 1
#     # q1 obs constraint
#     print M.shape
#     for si in range(ns):
#         for qi in range(no):
#             print "O[state,qi]", O[state,qi]
#             M[ri,idx(si,state)] = O[state,qi]
#             M[ri,idx(si,ns)] = O[si,qi]
#             print ri, "o_old", state, qi, o_old[state,qi]
#             print ri, b[ri]
#             b[ri] = o_old[state,qi]
#             ri += 1
#     # solve        
#     x,resids,rank,s = linalg.lstsq(M,b)
#     print "X", x
#     print "RESIDS", resids
#     A[:,state] = x[0:ns+1]
#     A[:,state] = A[:,state] / numpy.sum(A[:,state])
#     A[:,ns] = x[ns+1:]
#     A[:,ns] = A[:,ns]  / numpy.sum(A[:,ns])
#     # finally
#     return A,O,ns

def optimize_model_coefficients(self, inliers, model_coefficients):
        '''
        Recompute the model coefficients using the given inlier set and return them to the user.

        These are the coefficients of the model after refinement
        (e.g., after SVD)

        # Parameters
        inliers : list of int
            The data inliers supporting the model
        model_coefficients : coefficients array
            The initial guess for the model coefficients

        # Returns
        optimized_coefficients : coefficients array
            The resultant recomputed coefficients after non-linear optimization
        '''
        logger = logging.getLogger('pcl.sac.SampleConsensusModel.optimize_model_coefficients')
        if len(model_coefficients) != self.model_size:
            logger.error('Invalid number of model coefficients given (%lu).',
                         len(model_coefficients))
            return model_coefficients
        if len(inliers) <= self.sample_size:
            logger.error('Not enough inliers found to optimize model coefficients (%lu)!',
                         len(model_coefficients))
            return model_coefficients

        ###### Followings are implemetation of original PCL using PCA ######

        # covariance_matrix, xyz_centroid = compute_mean_and_covariance_matrix(self._input, inliers)
        # eigen_value, eigen_vector = np.linalg.eigh(covariance_matrix)
        # smallest = np.argmin(eigen_value)
        #
        # optimized_coefficients = eigen_vector[:, smallest].tolist()
        # optimized_coefficients.append(-np.dot(optimized_coefficients + [0], xyz_centroid))

        cloud = self._input.xyz
        if inliers is not None:
            cloud = cloud[inliers]

        # Use Least-Squares to fit the plane through all the given sample points
        # and find out its coefficients
        constant = -np.ones(len(cloud))
        optimized_coefficients, *_ = lstsq(cloud, constant)
        optimized_coefficients = optimized_coefficients.tolist()
        optimized_coefficients.append(1)

        if not self._is_model_valid(optimized_coefficients):
            logger.warning('Optimized coefficients invalid, returning original one')
            return model_coefficients

        return optimized_coefficients

def _slow_path(self):
        """Frisch-Waugh-Lovell implementation, works for all scenarios"""
        has_effect = self.entity_effects or self.time_effects or self.other_effects
        w = self.weights.values2d
        root_w = np.sqrt(w)

        y = root_w * self.dependent.values2d
        x = root_w * self.exog.values2d
        if not has_effect:
            ybar = root_w @ lstsq(root_w, y)[0]
            return y, x, ybar, 0, 0

        drop_first = self._constant
        d = []
        if self.entity_effects:
            d.append(self.dependent.dummies('entity', drop_first=drop_first).values)
            drop_first = True
        if self.time_effects:
            d.append(self.dependent.dummies('time', drop_first=drop_first).values)
            drop_first = True
        if self.other_effects:
            oe = self._other_effect_cats.dataframe
            for c in oe:
                dummies = pd.get_dummies(oe[c], drop_first=drop_first).astype(np.float64)
                d.append(dummies.values)
                drop_first = True

        d = np.column_stack(d)
        wd = root_w * d
        if self.has_constant:
            wd -= root_w * (w.T @ d / w.sum())
            z = np.ones_like(root_w)
            d -= z * (z.T @ d / z.sum())

        x_mean = np.linalg.lstsq(wd, x)[0]
        y_mean = np.linalg.lstsq(wd, y)[0]

        # Save fitted unweighted effects to use in eps calculation
        x_effects = d @ x_mean
        y_effects = d @ y_mean

        # Purge fitted, weighted values
        x = x - wd @ x_mean
        y = y - wd @ y_mean

        ybar = root_w @ lstsq(root_w, y)[0]
        return y, x, ybar, y_effects, x_effects