[Scipy-svn] r2863 - in trunk/Lib/sandbox/pyloess: . src tests
scipy-svn@scip...
scipy-svn@scip...
Wed Mar 21 17:54:13 CDT 2007
Author: pierregm
Date: 2007-03-21 17:53:54 -0500 (Wed, 21 Mar 2007)
New Revision: 2863
Added:
trunk/Lib/sandbox/pyloess/LICENSE.txt
trunk/Lib/sandbox/pyloess/src/S.h
trunk/Lib/sandbox/pyloess/src/_loess.c
trunk/Lib/sandbox/pyloess/src/_loess.pyx
trunk/Lib/sandbox/pyloess/src/c_loess.pxd
trunk/Lib/sandbox/pyloess/src/c_numpy.pxd
trunk/Lib/sandbox/pyloess/src/c_python.pxd
trunk/Lib/sandbox/pyloess/src/cloess.h
trunk/Lib/sandbox/pyloess/src/linpack_lite.f
trunk/Lib/sandbox/pyloess/src/loess.c
trunk/Lib/sandbox/pyloess/src/loess.h
trunk/Lib/sandbox/pyloess/src/loessc.c
trunk/Lib/sandbox/pyloess/src/loessf.f
trunk/Lib/sandbox/pyloess/src/misc.c
trunk/Lib/sandbox/pyloess/src/predict.c
trunk/Lib/sandbox/pyloess/tests/gas_data
trunk/Lib/sandbox/pyloess/tests/gas_result
trunk/Lib/sandbox/pyloess/tests/madeup_data
trunk/Lib/sandbox/pyloess/tests/madeup_result
Modified:
trunk/Lib/sandbox/pyloess/README
trunk/Lib/sandbox/pyloess/__init__.py
trunk/Lib/sandbox/pyloess/pyloess.py
trunk/Lib/sandbox/pyloess/setup.py
trunk/Lib/sandbox/pyloess/tests/test_pyloess.py
Log:
moved loess from the sandbox to the base package
deleted the sandbox directory
added tests for loess (2D and 1D)
Added: trunk/Lib/sandbox/pyloess/LICENSE.txt
===================================================================
--- trunk/Lib/sandbox/pyloess/LICENSE.txt 2007-03-21 21:28:18 UTC (rev 2862)
+++ trunk/Lib/sandbox/pyloess/LICENSE.txt 2007-03-21 22:53:54 UTC (rev 2863)
@@ -0,0 +1,16 @@
+From the original C package:
+
+/*
+ * The authors of this software are Cleveland, Grosse, and Shyu.
+ * Copyright (c) 1989, 1992 by AT&T.
+ * Permission to use, copy, modify, and distribute this software for any
+ * purpose without fee is hereby granted, provided that this entire notice
+ * is included in all copies of any software which is or includes a copy
+ * or modification of this software and in all copies of the supporting
+ * documentation for such software.
+ * THIS SOFTWARE IS BEING PROVIDED "AS IS", WITHOUT ANY EXPRESS OR IMPLIED
+ * WARRANTY. IN PARTICULAR, NEITHER THE AUTHORS NOR AT&T MAKE ANY
+ * REPRESENTATION OR WARRANTY OF ANY KIND CONCERNING THE MERCHANTABILITY
+ * OF THIS SOFTWARE OR ITS FITNESS FOR ANY PARTICULAR PURPOSE.
+ */
+
Modified: trunk/Lib/sandbox/pyloess/README
===================================================================
--- trunk/Lib/sandbox/pyloess/README 2007-03-21 21:28:18 UTC (rev 2862)
+++ trunk/Lib/sandbox/pyloess/README 2007-03-21 22:53:54 UTC (rev 2863)
@@ -14,3 +14,8 @@
The initial file is available at:
http://netlib.bell-labs.com/netlib/a/stl.gz
Simple to double precision conversion by Pierre GERARD-MARCHANT, 2007/03.
+
+
+Note :
+The sandbox subfolder contains a pre-alpha version of the loess routines. These
+routines are not fully tested, but they run.
Modified: trunk/Lib/sandbox/pyloess/__init__.py
===================================================================
--- trunk/Lib/sandbox/pyloess/__init__.py 2007-03-21 21:28:18 UTC (rev 2862)
+++ trunk/Lib/sandbox/pyloess/__init__.py 2007-03-21 22:53:54 UTC (rev 2863)
@@ -1,10 +1,16 @@
"""
-Numpy wrappers for lowess and stl.
+Numpy wrappers for lowess, loess and stl.
+
+
+:author: Pierre GF Gerard-Marchant
+:contact: pierregm_at_uga_edu
+:date: $Date$
+:version: $Id$
"""
__author__ = "Pierre GF Gerard-Marchant"
__version__ = '1.0'
-__revision__ = "$Revision: 150 $"
-__date__ = '$Date: 2007-02-28 23:42:16 -0500 (Wed, 28 Feb 2007) $'
+__revision__ = "$Revision$"
+__date__ = '$Date$'
import pyloess
-from pyloess import lowess, stl
\ No newline at end of file
+from pyloess import lowess, stl, loess, loess_anova
\ No newline at end of file
Property changes on: trunk/Lib/sandbox/pyloess/__init__.py
___________________________________________________________________
Name: svn:keywords
+ Date
Author
Revision
Id
Modified: trunk/Lib/sandbox/pyloess/pyloess.py
===================================================================
--- trunk/Lib/sandbox/pyloess/pyloess.py 2007-03-21 21:28:18 UTC (rev 2862)
+++ trunk/Lib/sandbox/pyloess/pyloess.py 2007-03-21 22:53:54 UTC (rev 2863)
@@ -8,35 +8,43 @@
initial author: W. S. Cleveland, 1979.
Simple to double precision conversion of the Fortran code by Pierre
-GERARD-MARCHANT, 2007/03.
+Gerard-Marchant, 2007/03.
STL:
Initial Fortran code available at:
http://netlib.bell-labs.com/netlib/a/stl.gz
Initial Authors: R. B. Cleveland, W. S. Cleveland, J. E. McRae, and
I. Terpenning, 1990.
-Simple-to-double precision conversion of the Fortran code by Pierre
-GERARD-MARCHANT, 2007/03.
+Simple-to-double precision conversion of the Fortran code by Pierre
+Gerard-Marchant, 2007/03.
+LOESS:
+Initial C/Fortran package avialable at
+http://netlib.bell-labs.com/netlib/a/dloess.gz
+Initial authors: W. S. Cleveland, E. Grosse and Shyu
+Adaptation to Pyrex/Python by Pierre Gerard-Marchant, 2007/03
:author: Pierre GF Gerard-Marchant
:contact: pierregm_at_uga_edu
-:date: $Date: 2007-02-28 02:23:25 -0500 (Wed, 28 Feb 2007) $
-:version: $Id: generic.py 145 2007-02-28 07:23:25Z backtopop $
+:date: $Date$
+:version: $Id$
"""
-__author__ = "Pierre GF Gerard-Marchant ($Author: backtopop $)"
+__author__ = "Pierre GF Gerard-Marchant ($Author$)"
__version__ = '1.0'
-__revision__ = "$Revision: 145 $"
-__date__ = '$Date: 2007-02-28 02:23:25 -0500 (Wed, 28 Feb 2007) $'
+__revision__ = "$Revision$"
+__date__ = '$Date$'
import numpy
from numpy import bool_, complex_, float_, int_, str_, object_
import numpy.core.numeric as numeric
from numpy.core.records import recarray
-import _lowess, _stl
+import _lowess, _stl, _loess
+#####---------------------------------------------------------------------------
+#--- --- STL ---
+#####---------------------------------------------------------------------------
def lowess(x,y,f=0.5,nsteps=2,delta=0):
"""Performs a robust locally weighted regression (lowess).
@@ -141,9 +149,9 @@
return numeric.fromiter(zip(*_lowess.lowess(x,y,f,nsteps,delta,)),
dtype=dtyp).view(recarray)
-#--------------------------------------------------------------------------
+#####---------------------------------------------------------------------------
#--- --- STL ---
-#####----------------------------------------------------------------------
+#####---------------------------------------------------------------------------
def stl(y, np=12, ns=7, nt=None, nl=13, isdeg=1, itdeg=1, ildeg=1,
nsjump=None,ntjump=None,nljump=None, robust=True, ni=None,no=None):
"""Decomposes a time series into seasonal and trend components.
@@ -264,5 +272,124 @@
result = numeric.fromiter(zip(trn,szn,y-trn-szn,rw), dtype=dtyp)
return result.view(recarray)
-################################################################################
+#####---------------------------------------------------------------------------
+#--- --- Loess ---
+#####---------------------------------------------------------------------------
+loess = _loess.loess
+"""
+loess : locally weighted estimates. Multi-variate version
+:Keywords:
+ x : ndarray
+ A (n,p) ndarray of independent variables, with n the number of observations
+ and p the number of variables.
+ y : ndarray
+ A (n,) ndarray of observations
+ weights : ndarray
+ A (n,) ndarray of weights to be given to individual observations in the
+ sum of squared residuals that forms the local fitting criterion. If not
+ None, the weights should be non negative. If the different observations
+ have non-equal variances, the weights should be inversely proportional
+ to the variances.
+ By default, an unweighted fit is carried out (all the weights are one).
+ surface : string ["interpolate"]
+ Determines whether the fitted surface is computed directly at all points
+ ("direct") or whether an interpolation method is used ("interpolate").
+ The default ("interpolate") is what most users should use unless special
+ circumstances warrant.
+ statistics : string ["approximate"]
+ Determines whether the statistical quantities are computed exactly
+ ("exact") or approximately ("approximate"). "exact" should only be used
+ for testing the approximation in statistical development and is not meant
+ for routine usage because computation time can be horrendous.
+ trace_hat : string ["wait.to.decide"]
+ Determines how the trace of the hat matrix should be computed. The hat
+ matrix is used in the computation of the statistical quantities.
+ If "exact", an exact computation is done; this could be slow when the
+ number of observations n becomes large. If "wait.to.decide" is selected,
+ then a default is "exact" for n < 500 and "approximate" otherwise.
+ This option is only useful when the fitted surface is interpolated. If
+ surface is "exact", an exact computation is always done for the trace.
+ Setting trace_hat to "approximate" for large dataset will substantially
+ reduce the computation time.
+ iterations : integer
+ Number of iterations of the robust fitting method. If the family is
+ "gaussian", the number of iterations is set to 0.
+ cell : integer
+ Maximum cell size of the kd-tree. Suppose k = floor(n*cell*span),
+ where n is the number of observations, and span the smoothing parameter.
+ Then, a cell is further divided if the number of observations within it
+ is greater than or equal to k. This option is only used if the surface
+ is interpolated.
+ span : float [0.75]
+ Smoothing factor, as a fraction of the number of points to take into
+ account.
+ degree : integer [2]
+ Overall degree of locally-fitted polynomial. 1 is locally-linear
+ fitting and 2 is locally-quadratic fitting. Degree should be 2 at most.
+ normalize : boolean [True]
+ Determines whether the independent variables should be normalized.
+ If True, the normalization is performed by setting the 10% trimmed
+ standard deviation to one. If False, no normalization is carried out.
+ This option is only useful for more than one variable. For spatial
+ coordinates predictors or variables with a common scale, it should be
+ set to False.
+ family : string ["gaussian"]
+ Determines the assumed distribution of the errors. The values are
+ "gaussian" or "symmetric". If "gaussian" is selected, the fit is
+ performed with least-squares. If "symmetric" is selected, the fit
+ is performed robustly by redescending M-estimators.
+ parametric_flags : sequence [ [False]*p ]
+ Indicates which independent variables should be conditionally-parametric
+ (if there are two or more independent variables). The argument should
+ be a sequence of booleans, with the same size as the number of independent
+ variables, specified in the order of the predictor group ordered in x.
+ drop_square : sequence [ [False]* p]
+ When there are two or more independent variables and when a 2nd order
+ polynomial is used, "drop_square_flags" specifies those numeric predictors
+ whose squares should be dropped from the set of fitting variables.
+ The method of specification is the same as for parametric.
+
+:Outputs:
+ fitted_values : ndarray
+ The (n,) ndarray of fitted values.
+ fitted_residuals : ndarray
+ The (n,) ndarray of fitted residuals (observations - fitted values).
+ enp : float
+ Equivalent number of parameters.
+ s : float
+ Estimate of the scale of residuals.
+ one_delta: float
+ Statistical parameter used in the computation of standard errors.
+ two_delta : float
+ Statistical parameter used in the computation of standard errors.
+ pseudovalues : ndarray
+ The (n,) ndarray of adjusted values of the response when robust estimation
+ is used.
+ trace_hat : float
+ Trace of the operator hat matrix.
+ diagonal :
+ Diagonal of the operator hat matrix.
+ robust : ndarray
+ The (n,) ndarray of robustness weights for robust fitting.
+ divisor : ndarray
+ The (p,) array of normalization divisors for numeric predictors.
+
+
+ newdata : ndarray
+ The (m,p) array of independent variables where the surface must be estimated.
+ values : ndarray
+ The (m,) ndarray of loess values evaluated at newdata
+ stderr : ndarray
+ The (m,) ndarray of the estimates of the standard error on the estimated
+ values.
+ residual_scale : float
+ Estimate of the scale of the residuals
+ df : integer
+ Degrees of freedom of the t-distribution used to compute pointwise
+ confidence intervals for the evaluated surface.
+ nest : integer
+ Number of new observations.
+"""
+
+loess_anova = _loess.anova
Property changes on: trunk/Lib/sandbox/pyloess/pyloess.py
___________________________________________________________________
Name: svn:keywords
+ Date
Author
Revision
Id
Modified: trunk/Lib/sandbox/pyloess/setup.py
===================================================================
--- trunk/Lib/sandbox/pyloess/setup.py 2007-03-21 21:28:18 UTC (rev 2862)
+++ trunk/Lib/sandbox/pyloess/setup.py 2007-03-21 22:53:54 UTC (rev 2863)
@@ -1,22 +1,43 @@
#!/usr/bin/env python
+__author__ = "Pierre GF Gerard-Marchant ($Author$)"
__version__ = '1.0'
-__revision__ = "$Revision: 2811 $"
-__date__ = '$Date: 2007-03-02 06:30:02 -0500 (Fri, 02 Mar 2007) $'
+__revision__ = "$Revision$"
+__date__ = '$Date$'
import os
from os.path import join
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import Configuration
+ from numpy.distutils.system_info import get_info, dict_append
confgr = Configuration('pyloess',parent_package,top_path)
+ # Configuration of LOWESS
confgr.add_extension('_lowess',
sources=[join('src', 'f_lowess.pyf'),
join('src', 'lowess.f'),]
)
+ # Configuration of STL
confgr.add_extension('_stl',
sources=[join('src', 'f_stl.pyf'),
join('src', 'stl.f')],
)
+ # Configuration of LOESS
+ f_sources = ('loessf.f', 'linpack_lite.f')
+ confgr.add_library('floess',
+ sources = [join('src',x) for x in f_sources])
+ blas_info = get_info('blas_opt')
+ build_info = {}
+ dict_append(build_info, **blas_info)
+ dict_append(build_info, libraries=['floess'])
+ c_sources = ['_loess.c', 'loess.c', 'loessc.c', 'misc.c', 'predict.c',]
+ confgr.add_extension('_loess',
+ sources=[join('src', x) for x in c_sources],
+ depends = [join('src','*.h'),
+ join('src','*.pyx'),
+ join('src','*.pxd')
+ ],
+ **build_info
+ )
confgr.add_data_dir('tests')
return confgr
Property changes on: trunk/Lib/sandbox/pyloess/setup.py
___________________________________________________________________
Name: svn:keywords
+ Date
Author
Revision
Id
Added: trunk/Lib/sandbox/pyloess/src/S.h
===================================================================
--- trunk/Lib/sandbox/pyloess/src/S.h 2007-03-21 21:28:18 UTC (rev 2862)
+++ trunk/Lib/sandbox/pyloess/src/S.h 2007-03-21 22:53:54 UTC (rev 2863)
@@ -0,0 +1,32 @@
+#include <stdio.h>
+#include <math.h>
+
+#define Calloc(n,t) (t *)calloc((unsigned)(n),sizeof(t))
+#define Free(p) free((char *)(p))
+
+/* the mapping from f77 to C intermediate code -- may be machine dependent
+ * the first definition satisfies lint's narrowminded preprocessing & should
+ * stay the same for all implementations. The __STDC__ definition is for
+ * ANSI standard conforming C compilers. The #else definition should
+ * generate the version of the fortran subroutine & common block names x
+ * handed to the local loader; e.g., "x_" in system V, Berkeley & 9th edition
+ */
+
+#ifdef lint
+#define F77_SUB(x) x
+#define F77_COM(x) x
+#else
+#ifdef __STDC__
+#define F77_SUB(x) x##_
+#define F77_COM(x) x##_
+#else
+#define F77_SUB(x) x/**/_
+#define F77_COM(x) x/**/_
+#endif
+#endif
+
+#define NULL_ENTRY ((int *)NULL)
+
+
+
+
Added: trunk/Lib/sandbox/pyloess/src/_loess.c
===================================================================
--- trunk/Lib/sandbox/pyloess/src/_loess.c 2007-03-21 21:28:18 UTC (rev 2862)
+++ trunk/Lib/sandbox/pyloess/src/_loess.c 2007-03-21 22:53:54 UTC (rev 2863)
@@ -0,0 +1,6287 @@
+/* Generated by Pyrex 0.9.5.1a on Wed Mar 21 17:37:22 2007 */
+
+#include "Python.h"
+#include "structmember.h"
+#ifndef PY_LONG_LONG
+ #define PY_LONG_LONG LONG_LONG
+#endif
+#ifdef __cplusplus
+#define __PYX_EXTERN_C extern "C"
+#else
+#define __PYX_EXTERN_C extern
+#endif
+__PYX_EXTERN_C double pow(double, double);
+#include "stdlib.h"
+#include "numpy/arrayobject.h"
+#include "loess.h"
+#include "cloess.h"
+
+
+typedef struct {PyObject **p; char *s;} __Pyx_InternTabEntry; /*proto*/
+typedef struct {PyObject **p; char *s; long n;} __Pyx_StringTabEntry; /*proto*/
+
+static PyObject *__pyx_m;
+static PyObject *__pyx_b;
+static int __pyx_lineno;
+static char *__pyx_filename;
+static char **__pyx_f;
+
+static int __Pyx_GetStarArgs(PyObject **args, PyObject **kwds, char *kwd_list[], int nargs, PyObject **args2, PyObject **kwds2); /*proto*/
+
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/
+
+static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/
+
+static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
+
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
+
+static PyObject *__Pyx_UnpackItem(PyObject *); /*proto*/
+static int __Pyx_EndUnpack(PyObject *); /*proto*/
+
+static int __Pyx_PrintItem(PyObject *); /*proto*/
+static int __Pyx_PrintNewline(void); /*proto*/
+
+static int __Pyx_InternStrings(__Pyx_InternTabEntry *t); /*proto*/
+
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
+
+static int __Pyx_SetVtable(PyObject *dict, void *vtable); /*proto*/
+
+static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, long size); /*proto*/
+
+static void __Pyx_AddTraceback(char *funcname); /*proto*/
+
+/* Declarations from c_python */
+
+
+/* Declarations from c_numpy */
+
+static PyTypeObject *__pyx_ptype_7c_numpy_dtype = 0;
+static PyTypeObject *__pyx_ptype_7c_numpy_ndarray = 0;
+static PyTypeObject *__pyx_ptype_7c_numpy_flatiter = 0;
+static PyTypeObject *__pyx_ptype_7c_numpy_broadcast = 0;
+
+/* Declarations from c_loess */
+
+
+/* Declarations from _loess */
+
+
+struct __pyx_obj_6_loess_loess_inputs {
+ PyObject_HEAD
+ loess_inputs (*_base);
+};
+
+
+struct __pyx_obj_6_loess_loess_control {
+ PyObject_HEAD
+ loess_control (*_base);
+};
+
+
+struct __pyx_obj_6_loess_loess_kd_tree {
+ PyObject_HEAD
+ loess_kd_tree (*_base);
+};
+
+
+struct __pyx_obj_6_loess_loess_model {
+ PyObject_HEAD
+ struct __pyx_vtabstruct_6_loess_loess_model *__pyx_vtab;
+ loess_model (*_base);
+ long npar;
+};
+
+struct __pyx_vtabstruct_6_loess_loess_model {
+ PyObject *((*setup)(struct __pyx_obj_6_loess_loess_model *,loess_model (*),long ));
+};
+static struct __pyx_vtabstruct_6_loess_loess_model *__pyx_vtabptr_6_loess_loess_model;
+
+
+struct __pyx_obj_6_loess_loess_outputs {
+ PyObject_HEAD
+ loess_outputs (*_base);
+ long nobs;
+ long npar;
+ int activated;
+};
+
+
+struct __pyx_obj_6_loess_conf_intervals {
+ PyObject_HEAD
+ struct __pyx_vtabstruct_6_loess_conf_intervals *__pyx_vtab;
+ conf_inv _base;
+ PyArrayObject *lower;
+ PyArrayObject *fit;
+ PyArrayObject *upper;
+};
+
+struct __pyx_vtabstruct_6_loess_conf_intervals {
+ PyObject *((*setup)(struct __pyx_obj_6_loess_conf_intervals *,conf_inv ,long ));
+};
+static struct __pyx_vtabstruct_6_loess_conf_intervals *__pyx_vtabptr_6_loess_conf_intervals;
+
+
+struct __pyx_obj_6_loess_loess_predicted {
+ PyObject_HEAD
+ struct __pyx_vtabstruct_6_loess_loess_predicted *__pyx_vtab;
+ prediction _base;
+ long nest;
+ struct __pyx_obj_6_loess_conf_intervals *confidence_intervals;
+};
+
+struct __pyx_vtabstruct_6_loess_loess_predicted {
+ PyObject *((*setup)(struct __pyx_obj_6_loess_loess_predicted *,prediction ,long ));
+};
+static struct __pyx_vtabstruct_6_loess_loess_predicted *__pyx_vtabptr_6_loess_loess_predicted;
+
+
+struct __pyx_obj_6_loess_loess {
+ PyObject_HEAD
+ loess _base;
+ struct __pyx_obj_6_loess_loess_inputs *inputs;
+ struct __pyx_obj_6_loess_loess_model *model;
+ struct __pyx_obj_6_loess_loess_control *control;
+ struct __pyx_obj_6_loess_loess_kd_tree *kd_tree;
+ struct __pyx_obj_6_loess_loess_outputs *outputs;
+ struct __pyx_obj_6_loess_loess_predicted *predicted;
+ long nobs;
+ long npar;
+};
+
+
+struct __pyx_obj_6_loess_anova {
+ PyObject_HEAD
+ double dfn;
+ double dfd;
+ double F_value;
+ double Pr_F;
+};
+
+static PyTypeObject *__pyx_ptype_6_loess_loess_inputs = 0;
+static PyTypeObject *__pyx_ptype_6_loess_loess_control = 0;
+static PyTypeObject *__pyx_ptype_6_loess_loess_kd_tree = 0;
+static PyTypeObject *__pyx_ptype_6_loess_loess_model = 0;
+static PyTypeObject *__pyx_ptype_6_loess_loess_outputs = 0;
+static PyTypeObject *__pyx_ptype_6_loess_conf_intervals = 0;
+static PyTypeObject *__pyx_ptype_6_loess_loess_predicted = 0;
+static PyTypeObject *__pyx_ptype_6_loess_loess = 0;
+static PyTypeObject *__pyx_ptype_6_loess_anova = 0;
+static PyObject *__pyx_k32;
+static PyObject *__pyx_k33;
+static PyObject *__pyx_k34;
+static PyObject *(__pyx_f_6_loess_floatarray_from_data(int ,int ,double (*))); /*proto*/
+static PyObject *(__pyx_f_6_loess_boolarray_from_data(int ,int ,int (*))); /*proto*/
+
+
+/* Implementation of _loess */
+
+static char (__pyx_k1[]) = "A (n,) ndarray of weights to be given to individual observations in the \n sum of squared residuals that forms the local fitting criterion. If not\n None, the weights should be non negative. If the different observations\n have non-equal variances, the weights should be inversely proportional \n to the variances.\n By default, an unweighted fit is carried out (all the weights are one).\n ";
+static char (__pyx_k2[]) = "Number of observations.";
+static char (__pyx_k3[]) = "Number of independent variables.";
+static char (__pyx_k4[]) = "\n surface : string [\"interpolate\"]\n Determines whether the fitted surface is computed directly at all points\n (\"direct\") or whether an interpolation method is used (\"interpolate\").\n The default (\"interpolate\") is what most users should use unless special \n circumstances warrant.\n ";
+static char (__pyx_k5[]) = "\n statistics : string [\"approximate\"]\n Determines whether the statistical quantities are computed exactly \n (\"exact\") or approximately (\"approximate\"). \"exact\" should only be used \n for testing the approximation in statistical development and is not meant \n for routine usage because computation time can be horrendous.\n ";
+static char (__pyx_k6[]) = "\n trace_hat : string [\"wait.to.decide\"]\n Determines how the trace of the hat matrix should be computed. The hat\n matrix is used in the computation of the statistical quantities. \n If \"exact\", an exact computation is done; this could be slow when the\n number of observations n becomes large. If \"wait.to.decide\" is selected, \n then a default is \"exact\" for n < 500 and \"approximate\" otherwise. \n This option is only useful when the fitted surface is interpolated. If \n surface is \"exact\", an exact computation is always done for the trace. \n Setting trace_hat to \"approximate\" for large dataset will substantially \n reduce the computation time.\n ";
+static char (__pyx_k7[]) = "\n iterations : integer\n Number of iterations of the robust fitting method. If the family is \n \"gaussian\", the number of iterations is set to 0.\n ";
+static char (__pyx_k8[]) = "\n cell : integer\n Maximum cell size of the kd-tree. Suppose k = floor(n*cell*span),\n where n is the number of observations, and span the smoothing parameter.\n Then, a cell is further divided if the number of observations within it \n is greater than or equal to k. This option is only used if the surface \n is interpolated.\n ";
+static char (__pyx_k9[]) = "\n normalize : boolean [True]\n Determines whether the independent variables should be normalized. \n If True, the normalization is performed by setting the 10% trimmed \n standard deviation to one. If False, no normalization is carried out. \n This option is only useful for more than one variable. For spatial\n coordinates predictors or variables with a common scale, it should be \n set to False.\n ";
+static char (__pyx_k10[]) = "Smoothing factor, as a fraction of the number of points to take into\n account. By default, span=0.75.";
+static char (__pyx_k11[]) = "\n degree : integer [2]\n Overall degree of locally-fitted polynomial. 1 is locally-linear \n fitting and 2 is locally-quadratic fitting. Degree should be 2 at most.\n ";
+static char (__pyx_k12[]) = "\n family : string [\"gaussian\"]\n Determines the assumed distribution of the errors. The values are \n \"gaussian\" or \"symmetric\". If \"gaussian\" is selected, the fit is \n performed with least-squares. If \"symmetric\" is selected, the fit\n is performed robustly by redescending M-estimators.\n ";
+static char (__pyx_k13[]) = "\n parametric_flags : sequence [ [False]*p ]\n Indicates which independent variables should be conditionally-parametric\n (if there are two or more independent variables). The argument should \n be a sequence of booleans, with the same size as the number of independent \n variables, specified in the order of the predictor group ordered in x. \n ";
+static char (__pyx_k14[]) = "\n drop_square : sequence [ [False]* p]\n When there are two or more independent variables and when a 2nd order\n polynomial is used, \"drop_square_flags\" specifies those numeric predictors \n whose squares should be dropped from the set of fitting variables. \n The method of specification is the same as for parametric. \n ";
+static char (__pyx_k15[]) = "\n fitted_values : ndarray\n The (n,) ndarray of fitted values.\n ";
+static char (__pyx_k16[]) = "\n fitted_residuals : ndarray\n The (n,) ndarray of fitted residuals (observations - fitted values).\n ";
+static char (__pyx_k17[]) = "\n pseudovalues : ndarray\n The (n,) ndarray of adjusted values of the response when robust estimation \n is used.\n ";
+static char (__pyx_k18[]) = "\n diagonal :\n Diagonal of the operator hat matrix.\n ";
+static char (__pyx_k19[]) = "\n robust : ndarray\n The (n,) ndarray of robustness weights for robust fitting.\n ";
+static char (__pyx_k20[]) = "Equivalent number of parameters.";
+static char (__pyx_k21[]) = "\n enp : float\n Equivalent number of parameters.\n ";
+static char (__pyx_k22[]) = "\n s : float\n Estimate of the scale of residuals.\n ";
+static char (__pyx_k23[]) = "\n one_delta: float\n Statistical parameter used in the computation of standard errors.\n ";
+static char (__pyx_k24[]) = "\n two_delta : float\n Statistical parameter used in the computation of standard errors.\n ";
+static char (__pyx_k25[]) = "\n trace_hat : float \n Trace of the operator hat matrix.\n ";
+static char (__pyx_k26[]) = "\n values : ndarray\n The (m,) ndarray of loess values evaluated at newdata\n ";
+static char (__pyx_k27[]) = "\n stderr : ndarray\n The (m,) ndarray of the estimates of the standard error on the estimated\n values.\n ";
+static char (__pyx_k28[]) = "\n residual_scale : float\n Estimate of the scale of the residuals\n ";
+static char (__pyx_k29[]) = "\n df : integer\n Degrees of freedom of the t-distribution used to compute pointwise \n confidence intervals for the evaluated surface.\n ";
+static char (__pyx_k31[]) = "\n:Keywords:\n x : ndarray\n A (n,p) ndarray of independent variables, with n the number of observations\n and p the number of variables.\n y : ndarray\n A (n,) ndarray of observations\n weights : ndarray\n A (n,) ndarray of weights to be given to individual observations in the \n sum of squared residuals that forms the local fitting criterion. If not\n None, the weights should be non negative. If the different observations\n have non-equal variances, the weights should be inversely proportional \n to the variances.\n By default, an unweighted fit is carried out (all the weights are one).\n surface : string [\"interpolate\"]\n Determines whether the fitted surface is computed directly at all points\n (\"direct\") or whether an interpolation method is used (\"interpolate\").\n The default (\"interpolate\") is what most users should use unless special \n circumstances warrant.\n statistics : string [\"approximate\"]\n Determines whether the statistical quantities are computed exactly \n (\"exact\") or approximately (\"approximate\"). \"exact\" should only be used \n for testing the approximation in statistical development and is not meant \n for routine usage because computation time can be horrendous.\n trace_hat : string [\"wait.to.decide\"]\n Determines how the trace of the hat matrix should be computed. The hat\n matrix is used in the computation of the statistical quantities. \n If \"exact\", an exact computation is done; this could be slow when the\n number of observations n becomes large. If \"wait.to.decide\" is selected, \n then a default is \"exact\" for n < 500 and \"approximate\" otherwise. \n This option is only useful when the fitted surface is interpolated. If \n surface is \"exact\", an exact computation is always done for the trace. \n Setting trace_hat to \"approximate\" for large dataset will substantially \n reduce the computation time.\n iterations : integer\n Number of iterations of the robust fitting method. If the family is \n \"gaussian\", the number of iterations is set to 0.\n cell : integer\n Maximum cell size of the kd-tree. Suppose k = floor(n*cell*span),\n where n is the number of observations, and span the smoothing parameter.\n Then, a cell is further divided if the number of observations within it \n is greater than or equal to k. This option is only used if the surface \n is interpolated.\n span : float [0.75]\n Smoothing factor, as a fraction of the number of points to take into\n account. \n degree : integer [2]\n Overall degree of locally-fitted polynomial. 1 is locally-linear \n fitting and 2 is locally-quadratic fitting. Degree should be 2 at most.\n normalize : boolean [True]\n Determines whether the independent variables should be normalized. \n If True, the normalization is performed by setting the 10% trimmed \n standard deviation to one. If False, no normalization is carried out. \n This option is only useful for more than one variable. For spatial\n coordinates predictors or variables with a common scale, it should be \n set to False.\n family : string [\"gaussian\"]\n Determines the assumed distribution of the errors. The values are \n \"gaussian\" or \"symmetric\". If \"gaussian\" is selected, the fit is \n performed with least-squares. If \"symmetric\" is selected, the fit\n is performed robustly by redescending M-estimators.\n parametric_flags : sequence [ [False]*p ]\n Indicates which independent variables should be conditionally-parametric\n (if there are two or more independent variables). The argument should \n be a sequence of booleans, with the same size as the number of independent \n variables, specified in the order of the predictor group ordered in x. \n drop_square : sequence [ [False]* p]\n When there are two or more independent variables and when a 2nd order\n polynomial is used, \"drop_square_flags\" specifies those numeric predictors \n whose squares should be dropped from the set of fitting variables. \n The method of specification is the same as for parametric. \n \n:Outputs:\n fitted_values : ndarray\n The (n,) ndarray of fitted values.\n fitted_residuals : ndarray\n The (n,) ndarray of fitted residuals (observations - fitted values).\n enp : float\n Equivalent number of parameters.\n s : float\n Estimate of the scale of residuals.\n one_delta: float\n Statistical parameter used in the computation of standard errors.\n two_delta : float\n Statistical parameter used in the computation of standard errors.\n pseudovalues : ndarray\n The (n,) ndarray of adjusted values of the response when robust estimation \n is used.\n trace_hat : float \n Trace of the operator hat matrix.\n diagonal :\n Diagonal of the operator hat matrix.\n robust : ndarray\n The (n,) ndarray of robustness weights for robust fitting.\n divisor : ndarray\n The (p,) array of normalization divisors for numeric predictors.\n \n\n newdata : ndarray\n The (m,p) array of independent variables where the surface must be estimated.\n values : ndarray\n The (m,) ndarray of loess values evaluated at newdata\n stderr : ndarray\n The (m,) ndarray of the estimates of the standard error on the estimated\n values.\n residual_scale : float\n Estimate of the scale of the residuals\n df : integer\n Degrees of freedom of the t-distribution used to compute pointwise \n confidence intervals for the evaluated surface.\n nest : integer\n Number of new observations.\n \n \n";
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+static PyObject *__pyx_f_6_loess_13loess_outputs_12pseudovalues___get__(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_f_6_loess_13loess_outputs_12pseudovalues___get__(PyObject *__pyx_v_self) {
+ PyObject *__pyx_r;
+ PyObject *__pyx_1 = 0;
+ Py_INCREF(__pyx_v_self);
+
+ /* "/home/backtopop/workspace/pyloess/src/_loess.pyx":547 */
+ __pyx_1 = __pyx_f_6_loess_floatarray_from_data(((struct __pyx_obj_6_loess_loess_outputs *)__pyx_v_self)->nobs,1,((struct __pyx_obj_6_loess_loess_outputs *)__pyx_v_self)->_base->pseudovalues); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 547; goto __pyx_L1;}
+ __pyx_r = __pyx_1;
+ __pyx_1 = 0;
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+
+ __pyx_r = Py_None; Py_INCREF(Py_None);
+ goto __pyx_L0;
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+ Py_DECREF(__pyx_v_self);
+ return __pyx_r;
+}
+
+static PyObject *__pyx_f_6_loess_13loess_outputs_8diagonal___get__(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_f_6_loess_13loess_outputs_8diagonal___get__(PyObject *__pyx_v_self) {
+ PyObject *__pyx_r;
+ PyObject *__pyx_1 = 0;
+ Py_INCREF(__pyx_v_self);
+
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+ __pyx_1 = __pyx_f_6_loess_floatarray_from_data(((struct __pyx_obj_6_loess_loess_outputs *)__pyx_v_self)->nobs,1,((struct __pyx_obj_6_loess_loess_outputs *)__pyx_v_self)->_base->diagonal); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 555; goto __pyx_L1;}
+ __pyx_r = __pyx_1;
+ __pyx_1 = 0;
+ goto __pyx_L0;
+
+ __pyx_r = Py_None; Py_INCREF(Py_None);
+ goto __pyx_L0;
+ __pyx_L1:;
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+ __pyx_r = 0;
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+}
+
+static PyObject *__pyx_f_6_loess_13loess_outputs_6robust___get__(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_f_6_loess_13loess_outputs_6robust___get__(PyObject *__pyx_v_self) {
+ PyObject *__pyx_r;
+ PyObject *__pyx_1 = 0;
+ Py_INCREF(__pyx_v_self);
+
+ /* "/home/backt