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-rw-r--r--numpy/polynomial/chebyshev.py76
-rw-r--r--numpy/polynomial/hermite.py76
-rw-r--r--numpy/polynomial/hermite_e.py76
-rw-r--r--numpy/polynomial/laguerre.py76
-rw-r--r--numpy/polynomial/legendre.py76
-rw-r--r--numpy/polynomial/polynomial.py76
-rw-r--r--numpy/polynomial/polyutils.py88
7 files changed, 94 insertions, 450 deletions
diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py
index 6fbdab065..5f69bf338 100644
--- a/numpy/polynomial/chebyshev.py
+++ b/numpy/polynomial/chebyshev.py
@@ -1652,81 +1652,7 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None):
--------
"""
- 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 = chebvander(x, lmax)
- else:
- deg = np.sort(deg)
- lmax = deg[-1]
- order = len(deg)
- van = chebvander(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(chebvander, x, y, deg, rcond, full, w)
def chebcompanion(c):
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):
diff --git a/numpy/polynomial/hermite_e.py b/numpy/polynomial/hermite_e.py
index af12ad909..834e26a60 100644
--- a/numpy/polynomial/hermite_e.py
+++ b/numpy/polynomial/hermite_e.py
@@ -1396,81 +1396,7 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None):
array([ 1.01690445, 1.99951418, 2.99948696]) # 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 = hermevander(x, lmax)
- else:
- deg = np.sort(deg)
- lmax = deg[-1]
- order = len(deg)
- van = hermevander(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(hermevander, x, y, deg, rcond, full, w)
def hermecompanion(c):
diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py
index 378dd4c46..5085ea908 100644
--- a/numpy/polynomial/laguerre.py
+++ b/numpy/polynomial/laguerre.py
@@ -1401,81 +1401,7 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None):
array([ 0.96971004, 2.00193749, 3.00288744]) # 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 = lagvander(x, lmax)
- else:
- deg = np.sort(deg)
- lmax = deg[-1]
- order = len(deg)
- van = lagvander(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(lagvander, x, y, deg, rcond, full, w)
def lagcompanion(c):
diff --git a/numpy/polynomial/legendre.py b/numpy/polynomial/legendre.py
index 29b5892a1..beccdfe5e 100644
--- a/numpy/polynomial/legendre.py
+++ b/numpy/polynomial/legendre.py
@@ -1435,81 +1435,7 @@ def legfit(x, y, deg, rcond=None, full=False, w=None):
--------
"""
- 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 = legvander(x, lmax)
- else:
- deg = np.sort(deg)
- lmax = deg[-1]
- order = len(deg)
- van = legvander(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(legvander, x, y, deg, rcond, full, w)
def legcompanion(c):
diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py
index 8a2cffc15..df92e92c2 100644
--- a/numpy/polynomial/polynomial.py
+++ b/numpy/polynomial/polynomial.py
@@ -1358,81 +1358,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None):
0.50443316, 0.28853036]), 1.1324274851176597e-014]
"""
- 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 = polyvander(x, lmax)
- else:
- deg = np.sort(deg)
- lmax = deg[-1]
- order = len(deg)
- van = polyvander(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 == 1:
- 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(polyvander, x, y, deg, rcond, full, w)
def polycompanion(c):
diff --git a/numpy/polynomial/polyutils.py b/numpy/polynomial/polyutils.py
index d8e922b0a..fb3ebe44a 100644
--- a/numpy/polynomial/polyutils.py
+++ b/numpy/polynomial/polyutils.py
@@ -600,3 +600,91 @@ def _sub(c1, c2):
c2[:c1.size] += c1
ret = c2
return trimseq(ret)
+
+
+def _fit(vander_f, x, y, deg, rcond=None, full=False, w=None):
+ """
+ Helper function used to implement the ``<type>fit`` functions.
+
+ Parameters
+ ----------
+ vander_f : function(array_like, int) -> ndarray
+ The 1d vander function, such as ``polyvander``
+ c1, c2 :
+ See the ``<type>fit`` functions for more detail
+ """
+ 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 = vander_f(x, lmax)
+ else:
+ deg = np.sort(deg)
+ lmax = deg[-1]
+ order = len(deg)
+ van = vander_f(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 = np.linalg.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, RankWarning, stacklevel=2)
+
+ if full:
+ return c, [resids, rank, s, rcond]
+ else:
+ return c
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