Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/scipy/optimize/_nnls.py

@@ -0,0 +1,84 @@
+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``.
+
+    See Also
+    --------
+    lsq_linear : Linear least squares with bounds on the variables
+
+    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
+
+     Examples
+    --------
+    >>> from scipy.optimize import nnls
+    ...
+    >>> A = np.array([[1, 0], [1, 0], [0, 1]])
+    >>> b = np.array([2, 1, 1])
+    >>> nnls(A, b)
+    (array([1.5, 1. ]), 0.7071067811865475)
+
+    >>> b = np.array([-1, -1, -1])
+    >>> nnls(A, b)
+    (array([0., 0.]), 1.7320508075688772)
+
+    """
+
+    A, b = map(asarray_chkfinite, (A, b))
+
+    if len(A.shape) != 2:
+        raise ValueError("Expected a two-dimensional array (matrix)" +
+                         ", but the shape of A is %s" % (A.shape, ))
+    if len(b.shape) != 1:
+        raise ValueError("Expected a one-dimensional array (vector" +
+                         ", but the shape of b is %s" % (b.shape, ))
+
+    m, n = A.shape
+
+    if m != b.shape[0]:
+        raise ValueError(
+                "Incompatible dimensions. The first dimension of " +
+                "A is %s, while the shape of b is %s" % (m, (b.shape[0], )))
+
+    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