demo + utils venv

2019-02-03 13:40:10 +01:00
parent 5fa112490b
commit cfa9c8ea23
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+from __future__ import division, print_function, absolute_import
+
+from . import _nnls
+from numpy import asarray_chkfinite, zeros, double
+
+__all__ = ['nnls']
+
+
+def nnls(A, b, maxiter=None):
+    """
+    Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``. This is a wrapper
+    for a FORTRAN non-negative least squares solver.
+
+    Parameters
+    ----------
+    A : ndarray
+        Matrix ``A`` as shown above.
+    b : ndarray
+        Right-hand side vector.
+    maxiter: int, optional
+        Maximum number of iterations, optional.
+        Default is ``3 * A.shape[1]``.
+
+    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")
+
+    maxiter = -1 if maxiter is None else int(maxiter)
+
+    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, maxiter)
+    if mode != 1:
+        raise RuntimeError("too many iterations")
+
+    return x, rnorm