[Scipy-svn] r4137 - in trunk/scipy/optimize: . tests

[scipy-svn at scipy.org]

Author: stefan
Date: 2008-04-13 13:38:35 -0500 (Sun, 13 Apr 2008)
New Revision: 4137

Modified:
   trunk/scipy/optimize/minpack.py
   trunk/scipy/optimize/tests/test_optimize.py
Log:
Add tests for leastsq [provided by Andrew Straw].  Fix: input parameters are no longer
modified.  Closes #637.


Modified: trunk/scipy/optimize/minpack.py
===================================================================
--- trunk/scipy/optimize/minpack.py	2008-04-11 22:43:35 UTC (rev 4136)
+++ trunk/scipy/optimize/minpack.py	2008-04-13 18:38:35 UTC (rev 4137)
@@ -1,7 +1,7 @@
 import _minpack
 
 from numpy import atleast_1d, dot, take, triu, shape, eye, \
-                  transpose, zeros, product, greater
+                  transpose, zeros, product, greater, array
 
 error = _minpack.error
 
@@ -108,7 +108,7 @@
       fixed_point -- scalar fixed-point finder
 
     """
-    x0 = atleast_1d(x0)
+    x0 = array(x0,ndmin=1)
     n = len(x0)
     if type(args) != type(()): args = (args,)
     check_func(func,x0,args,n,(n,))
@@ -262,7 +262,7 @@
       fixed_point -- scalar fixed-point finder
 
     """
-    x0 = atleast_1d(x0)
+    x0 = array(x0,ndmin=1)
     n = len(x0)
     if type(args) != type(()): args = (args,)
     m = check_func(func,x0,args,n)[0]

Modified: trunk/scipy/optimize/tests/test_optimize.py
===================================================================
--- trunk/scipy/optimize/tests/test_optimize.py	2008-04-11 22:43:35 UTC (rev 4136)
+++ trunk/scipy/optimize/tests/test_optimize.py	2008-04-13 18:38:35 UTC (rev 4137)
@@ -1,17 +1,25 @@
 """ Unit tests for optimization routines
-Author: Ed Schofield
-Nov 2005
+Authors:
+ Ed Schofield, Nov 2005
+ Andrew Straw, April 2008
+
+To run it in its simplest form::
+  nosetests test_optimize.py
+
+
 """
 
 from scipy.testing import *
 
 from scipy import optimize
-from numpy import array, zeros, float64, dot, log, exp, inf
+from scipy.optimize import leastsq
+from numpy import array, zeros, float64, dot, log, exp, inf, \
+     pi, sin, cos
+import numpy as np
 from scipy.optimize.tnc import RCSTRINGS, MSG_NONE
+import numpy.random
+from math import pow
 
-
-from math import sin, cos, pow
-
 class TestOptimize(TestCase):
     """ Test case for a simple constrained entropy maximization problem
     (the machine translation example of Berger et al in
@@ -192,11 +200,11 @@
         def test5fg(x):
             f = sin(x[0]+x[1])+pow(x[0]-x[1],2)-1.5*x[0]+2.5*x[1]+1.0
             dif = [0,0]
-            v1 = cos(x[0]+x[1]);
-            v2 = 2.0*(x[0]-x[1]);
+            v1 = cos(x[0]+x[1])
+            v2 = 2.0*(x[0]-x[1])
 
-            dif[0] = v1+v2-1.5;
-            dif[1] = v1-v2+2.5;
+            dif[0] = v1+v2-1.5
+            dif[1] = v1-v2+2.5
             return f, dif
         self.tests.append((test5fg, [0,0], [(-1.5, 4),(-3,3)],
                            [-0.54719755119659763, -1.5471975511965976]))
@@ -241,6 +249,44 @@
         if ef > 1e-8:
             raise err
 
+class TestLeastSq(TestCase):
+    def setUp(self):
+        x = np.linspace(0, 10, 40)
+        a,b,c = 3.1, 42, -304.2
+        self.x = x
+        self.abc = a,b,c
+        y_true = a*x**2 + b*x + c
+        self.y_meas = y_true + 0.01*np.random.standard_normal( y_true.shape )
 
+    def residuals(self, p, y, x):
+        a,b,c = p
+        err = y-(a*x**2 + b*x + c)
+        return err
+
+    def test_basic(self):
+        p0 = numpy.array([0,0,0])
+        params_fit, ier = leastsq(self.residuals, p0,
+                                  args=(self.y_meas, self.x))
+        assert ier in (1,2,3,4), 'solution not found (ier=%d)'%ier
+        assert_array_almost_equal( params_fit, self.abc, decimal=2) # low precision due to random
+
+    def test_full_output(self):
+        p0 = numpy.array([0,0,0])
+        full_output = leastsq(self.residuals, p0,
+                              args=(self.y_meas, self.x),
+                              full_output=True)
+        params_fit, cov_x, infodict, mesg, ier = full_output
+        assert ier in (1,2,3,4), 'solution not found: %s'%mesg
+
+    def test_input_untouched(self):
+        p0 = numpy.array([0,0,0],dtype=numpy.float64)
+        p0_copy = numpy.array(p0, copy=True)
+        full_output = leastsq(self.residuals, p0,
+                              args=(self.y_meas, self.x),
+                              full_output=True)
+        params_fit, cov_x, infodict, mesg, ier = full_output
+        assert ier in (1,2,3,4), 'solution not found: %s'%mesg
+        assert_array_equal(p0, p0_copy)
+
 if __name__ == "__main__":
     nose.run(argv=['', __file__])