def nnls(A, b): A, b = map(np.asarray_chkfinite, (A, b)) #if len(A.shape) != 2: # raise ValueError("expected matrix") #if len(b.shape) != 1: # raise ValueError("expected vector") m, n = A.shape #if m != b.shape[0]: # raise ValueError("incompatible dimensions") #maxiter = -1 if maxiter is None else int(maxiter) maxiter = -1 #maxiter = int(5*n) w = np.zeros((n, ), dtype=np.double) zz = np.zeros((m, ), dtype=np.double) index = np.zeros((n, ), dtype=int) x, rnorm, mode = _nnls.nnls(A, m, n, b, w, zz, index, maxiter) #if mode != 1: # raise RuntimeError("too many iterations") return x, rnorm
def nnls(A, b): """ Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``. This is a wrapper for a FORTAN non-negative least squares solver. Parameters ---------- A : ndarray Matrix ``A`` as shown above. b : ndarray Right-hand side vector. Returns ------- x : ndarray Solution vector. rnorm : float The residual, ``|| Ax-b ||_2``. Notes ----- The FORTRAN code was published in the book below. The algorithm is an active set method. It solves the KKT (Karush-Kuhn-Tucker) conditions for the non-negative least squares problem. References ---------- Lawson C., Hanson R.J., (1987) Solving Least Squares Problems, SIAM """ A, b = map(asarray_chkfinite, (A, b)) if len(A.shape) != 2: raise ValueError("expected matrix") if len(b.shape) != 1: raise ValueError("expected vector") m, n = A.shape if m != b.shape[0]: raise ValueError("incompatible dimensions") w = zeros((n, ), dtype=double) zz = zeros((m, ), dtype=double) index = zeros((n, ), dtype=int) x, rnorm, mode = _nnls.nnls(A, m, n, b, w, zz, index) if mode != 1: raise RuntimeError("too many iterations") return x, rnorm
def nnls(A,b): """ Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``. This is a wrapper for a FORTAN non-negative least squares solver. Parameters ---------- A : ndarray Matrix ``A`` as shown above. b : ndarray Right-hand side vector. Returns ------- x : ndarray Solution vector. rnorm : float The residual, ``|| Ax-b ||_2``. Notes ----- The FORTRAN code was published in the book below. The algorithm is an active set method. It solves the KKT (Karush-Kuhn-Tucker) conditions for the non-negative least squares problem. References ---------- Lawson C., Hanson R.J., (1987) Solving Least Squares Problems, SIAM """ A,b = map(asarray_chkfinite, (A,b)) if len(A.shape)!=2: raise ValueError("expected matrix") if len(b.shape)!=1: raise ValueError("expected vector") m,n = A.shape if m != b.shape[0]: raise ValueError("incompatible dimensions") w = zeros((n,), dtype=double) zz = zeros((m,), dtype=double) index=zeros((n,), dtype=int) x,rnorm,mode = _nnls.nnls(A,m,n,b,w,zz,index) if mode != 1: raise RuntimeError("too many iterations") return x, rnorm
def nnls(A, b): """ Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``. Parameters ---------- A : ndarray Matrix ``A`` as shown above. b : ndarray Right-hand side vector. Returns ------- x : ndarray Solution vector. rnorm : float The residual, ``|| Ax-b ||_2``. Notes ----- This is a wrapper for ``NNLS.F``. """ A, b = map(asarray_chkfinite, (A, b)) if len(A.shape) != 2: raise ValueError("expected matrix") if len(b.shape) != 1: raise ValueError("expected vector") m, n = A.shape if m != b.shape[0]: raise ValueError("incompatible dimensions") w = zeros((n, ), dtype=double) zz = zeros((m, ), dtype=double) index = zeros((n, ), dtype=int) x, rnorm, mode = _nnls.nnls(A, m, n, b, w, zz, index) if mode != 1: raise RuntimeError("too many iterations") return x, rnorm
def nnls(A,b): """ Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``. Parameters ---------- A : ndarray Matrix ``A`` as shown above. b : ndarray Right-hand side vector. Returns ------- x : ndarray Solution vector. rnorm : float The residual, ``|| Ax-b ||_2``. Notes ----- This is a wrapper for ``NNLS.F``. """ A,b = map(asarray_chkfinite, (A,b)) if len(A.shape)!=2: raise ValueError("expected matrix") if len(b.shape)!=1: raise ValueError("expected vector") m,n = A.shape if m != b.shape[0]: raise ValueError("incompatible dimensions") w = zeros((n,), dtype=double) zz = zeros((m,), dtype=double) index=zeros((n,), dtype=int) x,rnorm,mode = _nnls.nnls(A,m,n,b,w,zz,index) if mode != 1: raise RuntimeError("too many iterations") return x, rnorm
def nnls(A, b): """ Solve || Ax - b ||_2 -> min with x>=0 Inputs: A -- matrix as above b -- vector as above Outputs: x -- solution vector rnorm -- residual || Ax-b ||_2 wrapper around NNLS.F code below nnls/ directory """ A, b = map(asarray_chkfinite, (A, b)) if len(A.shape) != 2: raise ValueError, "expected matrix" if len(b.shape) != 1: raise ValueError, "expected vector" m, n = A.shape if m != b.shape[0]: raise ValueError, "incompatible dimensions" w = zeros((n, ), dtype=double) zz = zeros((m, ), dtype=double) index = zeros((n, ), dtype=int) x, rnorm, mode = _nnls.nnls(A, m, n, b, w, zz, index) if mode != 1: raise RuntimeError, "too many iterations" return x, rnorm
def nnls(A,b): """ Solve || Ax - b ||_2 -> min with x>=0 Inputs: A -- matrix as above b -- vector as above Outputs: x -- solution vector rnorm -- residual || Ax-b ||_2 wrapper around NNLS.F code below nnls/ directory """ A,b = map(asarray_chkfinite, (A,b)) if len(A.shape)!=2: raise ValueError, "expected matrix" if len(b.shape)!=1: raise ValueError, "expected vector" m,n = A.shape if m != b.shape[0]: raise ValueError, "incompatible dimensions" w = zeros((n,), dtype=double) zz = zeros((m,), dtype=double) index=zeros((n,), dtype=int) x,rnorm,mode = _nnls.nnls(A,m,n,b,w,zz,index) if mode != 1: raise RuntimeError, "too many iterations" return x, rnorm