[Scipy-svn] r4782 - branches/spatial/scipy/spatial/tests
scipy-svn at scipy.org
scipy-svn at scipy.org
Sun Oct 5 10:11:41 EDT 2008
Author: peridot
Date: 2008-10-05 09:11:35 -0500 (Sun, 05 Oct 2008)
New Revision: 4782
Modified:
branches/spatial/scipy/spatial/tests/test_kdtree.py
Log:
Cut down size of test cases (run time 30s -> 3s)
Modified: branches/spatial/scipy/spatial/tests/test_kdtree.py
===================================================================
--- branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-05 09:36:46 UTC (rev 4781)
+++ branches/spatial/scipy/spatial/tests/test_kdtree.py 2008-10-05 14:11:35 UTC (rev 4782)
@@ -75,10 +75,10 @@
class test_random(ConsistencyTests):
def setUp(self):
- self.n = 1000
+ self.n = 100
self.k = 4
self.data = np.random.randn(self.n, self.k)
- self.kdtree = KDTree(self.data)
+ self.kdtree = KDTree(self.data,leafsize=2)
self.x = np.random.randn(self.k)
self.d = 0.2
self.m = 10
@@ -192,10 +192,10 @@
class test_random_ball(ball_consistency):
def setUp(self):
- n = 1000
+ n = 100
k = 4
self.data = np.random.randn(n,k)
- self.T = KDTree(self.data)
+ self.T = KDTree(self.data,leafsize=2)
self.x = np.random.randn(k)
self.p = 2.
self.eps = 0
@@ -252,7 +252,7 @@
class test_two_random_trees(two_trees_consistency):
def setUp(self):
- n = 100
+ n = 50
k = 4
self.data1 = np.random.randn(n,k)
self.T1 = KDTree(self.data1,leafsize=2)
@@ -315,8 +315,8 @@
class test_count_neighbors:
def setUp(self):
- n = 100
- k = 4
+ n = 50
+ k = 2
self.T1 = KDTree(np.random.randn(n,k),leafsize=2)
self.T2 = KDTree(np.random.randn(n,k),leafsize=2)
@@ -331,7 +331,7 @@
np.sum([len(l) for l in self.T1.query_ball_tree(self.T2,r)]))
def test_multiple_radius(self):
- rs = np.exp(np.linspace(np.log(0.01),np.log(10),10))
+ rs = np.exp(np.linspace(np.log(0.01),np.log(10),3))
results = self.T1.count_neighbors(self.T2, rs)
assert np.all(np.diff(results)>=0)
for r,result in zip(rs, results):
@@ -339,7 +339,7 @@
class test_sparse_distance_matrix:
def setUp(self):
- n = 100
+ n = 50
k = 4
self.T1 = KDTree(np.random.randn(n,k),leafsize=2)
self.T2 = KDTree(np.random.randn(n,k),leafsize=2)
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