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
