Merge pull request #13130 from eric-wieser/unify-polyfit

MAINT: Unify polynomial fitting functions

1 files changed, 1 insertions, 75 deletions

diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py
index 626f85ccb..1ee5a6a27 100644
--- a/numpy/polynomial/hermite.py
+++ b/numpy/polynomial/hermite.py
@@ -1402,81 +1402,7 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None):
     array([1.0218, 1.9986, 2.9999]) # may vary
 
     """
-    x = np.asarray(x) + 0.0
-    y = np.asarray(y) + 0.0
-    deg = np.asarray(deg)
-
-    # check arguments.
-    if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0:
-        raise TypeError("deg must be an int or non-empty 1-D array of int")
-    if deg.min() < 0:
-        raise ValueError("expected deg >= 0")
-    if x.ndim != 1:
-        raise TypeError("expected 1D vector for x")
-    if x.size == 0:
-        raise TypeError("expected non-empty vector for x")
-    if y.ndim < 1 or y.ndim > 2:
-        raise TypeError("expected 1D or 2D array for y")
-    if len(x) != len(y):
-        raise TypeError("expected x and y to have same length")
-
-    if deg.ndim == 0:
-        lmax = deg
-        order = lmax + 1
-        van = hermvander(x, lmax)
-    else:
-        deg = np.sort(deg)
-        lmax = deg[-1]
-        order = len(deg)
-        van = hermvander(x, lmax)[:, deg]
-
-    # set up the least squares matrices in transposed form
-    lhs = van.T
-    rhs = y.T
-    if w is not None:
-        w = np.asarray(w) + 0.0
-        if w.ndim != 1:
-            raise TypeError("expected 1D vector for w")
-        if len(x) != len(w):
-            raise TypeError("expected x and w to have same length")
-        # apply weights. Don't use inplace operations as they
-        # can cause problems with NA.
-        lhs = lhs * w
-        rhs = rhs * w
-
-    # set rcond
-    if rcond is None:
-        rcond = len(x)*np.finfo(x.dtype).eps
-
-    # Determine the norms of the design matrix columns.
-    if issubclass(lhs.dtype.type, np.complexfloating):
-        scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1))
-    else:
-        scl = np.sqrt(np.square(lhs).sum(1))
-    scl[scl == 0] = 1
-
-    # Solve the least squares problem.
-    c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond)
-    c = (c.T/scl).T
-
-    # Expand c to include non-fitted coefficients which are set to zero
-    if deg.ndim > 0:
-        if c.ndim == 2:
-            cc = np.zeros((lmax+1, c.shape[1]), dtype=c.dtype)
-        else:
-            cc = np.zeros(lmax+1, dtype=c.dtype)
-        cc[deg] = c
-        c = cc
-
-    # warn on rank reduction
-    if rank != order and not full:
-        msg = "The fit may be poorly conditioned"
-        warnings.warn(msg, pu.RankWarning, stacklevel=2)
-
-    if full:
-        return c, [resids, rank, s, rcond]
-    else:
-        return c
+    return pu._fit(hermvander, x, y, deg, rcond, full, w)
 
 
 def hermcompanion(c):