[Scipy-svn] r4627 - branches/stats_models
scipy-svn at scipy.org
scipy-svn at scipy.org
Fri Aug 8 16:06:42 EDT 2008
Author: chris.burns
Date: 2008-08-08 15:06:36 -0500 (Fri, 08 Aug 2008)
New Revision: 4627
Modified:
branches/stats_models/TODO.txt
branches/stats_models/smoothers.py
Log:
Fix invalid imports. Add note to TODO regarding code duplication.
Modified: branches/stats_models/TODO.txt
===================================================================
--- branches/stats_models/TODO.txt 2008-08-08 19:55:31 UTC (rev 4626)
+++ branches/stats_models/TODO.txt 2008-08-08 20:06:36 UTC (rev 4627)
@@ -32,3 +32,5 @@
at, most only checked attribute setting, not the results of applying
the function to data.
+* Remove code duplication. smoothers.py and bspline.py define
+ SmoothingSpline class.
Modified: branches/stats_models/smoothers.py
===================================================================
--- branches/stats_models/smoothers.py 2008-08-08 19:55:31 UTC (rev 4626)
+++ branches/stats_models/smoothers.py 2008-08-08 20:06:36 UTC (rev 4627)
@@ -9,10 +9,9 @@
from scipy.linalg import solveh_banded
from scipy.optimize import golden
-from scipy.stats.models import _bspline
-from scipy.stats.models.bspline import bspline, _band2array
+from scipy.stats.models import _hbspline
+from scipy.stats.models.bspline import BSpline, _band2array
-
class PolySmoother:
"""
Polynomial smoother up to a given order.
@@ -61,7 +60,7 @@
_y = y * _w
self.coef = N.dot(L.pinv(X).T, _y)
-class SmoothingSpline(bspline):
+class SmoothingSpline(BSpline):
penmax = 30.
@@ -153,7 +152,7 @@
"""
if self.pen > 0:
- _invband = _bspline.invband(self.chol.copy())
+ _invband = _hbspline.invband(self.chol.copy())
tr = _trace_symbanded(_invband, self.btb, lower=1)
return tr
else:
@@ -174,7 +173,7 @@
def __init__(self, knots, order=4, coef=None, M=None, target_df=None):
if target_df is not None:
self.target_df = target_df
- bspline.__init__(self, knots, order=order, coef=coef, M=M)
+ BSpline.__init__(self, knots, order=order, coef=coef, M=M)
self.target_reached = False
def fit(self, y, x=None, df=None, weights=None, tol=1.0e-03):
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