scalar ->sandbox, created _test_sparse

_test_sparse.py

+from sparse import *
+
+import unittest
+
+class _testCase_transpose(unittest.TestCase):
+    def setUp(self):
+        core.build_eval_mode()
+        numpy.random.seed(44)
+    def tearDown(self):
+        core.pop_mode()
+    def test_transpose(self):
+        a = SparseR(sparse.csr_matrix(sparse.speye(5,3)))
+        self.failUnless(a.data.shape == (5,3))
+        ta = transpose(a)
+        self.failUnless(ta.data.shape == (3,5))
+
+class _testCase_dot(unittest.TestCase):
+    def setUp(self):
+        core.build_eval_mode()
+        numpy.random.seed(44)
+    def tearDown(self):
+        core.pop_mode()
+    def test_basic0(self):
+        for mtype in [sparse.csc_matrix, sparse.csr_matrix]:
+            x = SparseR(mtype(sparse.speye(5,3)))
+            y = core.wrap(numpy.random.rand(3, 2))
+
+            z = dot(x,y)
+            self.failUnless(z.data.shape == (5,2))
+            self.failUnless(type(z.data) is mtype)
+    def test_basic1(self):
+        """dot: sparse left"""
+        a = numpy.asarray([[1, 0, 3, 0, 5], [0, 0, -2, 0, 0]],
+                dtype='float64')
+        b = numpy.random.rand(5, 3)
+        for mtype in [sparse.csr_matrix, sparse.csc_matrix, sparse.dok_matrix,
+                sparse.lil_matrix]:#, sparse.coo_matrix]:
+            #print type(a), mtype
+            m = mtype(a)
+            ab = m.dot(b)
+            try:
+                z = dot(SparseR(m),core.ResultBase(data=b))
+                self.failUnless(z.data.shape == ab.shape)
+                self.failUnless(type(z.data) == type(ab))
+            except Exception, e:
+                print 'cccc', mtype, e, str(e)
+                raise
+    def test_basic2(self):
+        """dot: sparse right"""
+        a = numpy.random.rand(2, 5)
+        b = numpy.asarray([[1, 0, 3, 0, 5], [0, 0, -2, 0, 0]],
+                dtype='float64').transpose()
+
+        for mtype in [sparse.csr_matrix, sparse.csc_matrix, sparse.dok_matrix,
+                sparse.lil_matrix]:#, sparse.coo_matrix]:
+            m = mtype(b)
+            ab = m.transpose().dot(a.transpose()).transpose()
+            z = dot(core.ResultBase(data=a),SparseR(mtype(b)))
+            self.failUnless(z.data.shape == ab.shape)
+            self.failUnless(type(z.data) == type(ab))
+    def test_graph_bprop0(self):
+        x = core.wrap(numpy.random.rand(10,2))
+        w = SparseR(sparse.csr_matrix(numpy.asarray([[1, 0, 3, 0, 5], [0, 0, -2, 0,
+            0]],dtype='float64')))
+
+        for epoch in xrange(50):
+            xw = sparse2dense(dot(x, w))
+            y = sparse2dense(dot(xw, transpose(w)))
+            loss = core.sum(core.sqr(x-y))
+            gy = y-x
+            g = grad.Grad({y:gy})
+            g.bprop()
+            lr = 0.002
+            g(w).data[1,0] = 0
+            g(w).data[1,4] = 0
+            w.data = -lr * g(w).data + w.data
+
+        self.failUnless('3.08560636025' == str(loss.data))
+
+    def test_graph_bprop1(self):
+        x = core.wrap(numpy.random.rand(10,2))
+        w = SparseR(sparse.csr_matrix(numpy.asarray([[1, 0, 3, 0, 5], [0, 0, -2, 0,
+            0]],dtype='float64')))
+
+        for epoch in xrange(50):
+            xw = sparse2dense(dot(x, w))
+            y = sparse2dense(dot(xw, transpose(w)))
+            loss = core.sum(core.sqr(x-y))
+            g = grad.grad(loss)
+            lr = 0.001
+
+            g(w).data[1,0] = 0
+            g(w).data[1,4] = 0
+            w.data = -lr * g(w).data + w.data
+
+        self.failUnless('3.08560636025' == str(loss.data))
+        
+if __name__ == '__main__':
+    unittest.main()
+

sparse.py

-import unittest
 
 import numpy
 from scipy import sparse
@@ -111,18 +110,6 @@ class transpose(op):
     def impl(x): return x.transpose() 
     def grad(self, x, gz): return transpose(gz)
 
-class _testCase_transpose(unittest.TestCase):
-    def setUp(self):
-        core.build_eval_mode()
-        numpy.random.seed(44)
-    def tearDown(self):
-        core.pop_mode()
-    def test_transpose(self):
-        a = SparseR(sparse.csr_matrix(sparse.speye(5,3)))
-        self.failUnless(a.data.shape == (5,3))
-        ta = transpose(a)
-        self.failUnless(ta.data.shape == (3,5))
-
 class dot(op):
     """
     Attributes:
@@ -155,87 +142,3 @@ class dot(op):
                     rval[i] = sparse2dense(rval[i])
         return rval
 
-class _testCase_dot(unittest.TestCase):
-    def setUp(self):
-        core.build_eval_mode()
-        numpy.random.seed(44)
-    def tearDown(self):
-        core.pop_mode()
-    def test_basic0(self):
-        for mtype in [sparse.csc_matrix, sparse.csr_matrix]:
-            x = SparseR(mtype(sparse.speye(5,3)))
-            y = core.wrap(numpy.random.rand(3, 2))
-
-            z = dot(x,y)
-            self.failUnless(z.data.shape == (5,2))
-            self.failUnless(type(z.data) is mtype)
-    def test_basic1(self):
-        """dot: sparse left"""
-        a = numpy.asarray([[1, 0, 3, 0, 5], [0, 0, -2, 0, 0]],
-                dtype='float64')
-        b = numpy.random.rand(5, 3)
-        for mtype in [sparse.csr_matrix, sparse.csc_matrix, sparse.dok_matrix,
-                sparse.lil_matrix]:#, sparse.coo_matrix]:
-            #print type(a), mtype
-            m = mtype(a)
-            ab = m.dot(b)
-            try:
-                z = dot(SparseR(m),core.ResultBase(data=b))
-                self.failUnless(z.data.shape == ab.shape)
-                self.failUnless(type(z.data) == type(ab))
-            except Exception, e:
-                print 'cccc', mtype, e, str(e)
-                raise
-    def test_basic2(self):
-        """dot: sparse right"""
-        a = numpy.random.rand(2, 5)
-        b = numpy.asarray([[1, 0, 3, 0, 5], [0, 0, -2, 0, 0]],
-                dtype='float64').transpose()
-
-        for mtype in [sparse.csr_matrix, sparse.csc_matrix, sparse.dok_matrix,
-                sparse.lil_matrix]:#, sparse.coo_matrix]:
-            m = mtype(b)
-            ab = m.transpose().dot(a.transpose()).transpose()
-            z = dot(core.ResultBase(data=a),SparseR(mtype(b)))
-            self.failUnless(z.data.shape == ab.shape)
-            self.failUnless(type(z.data) == type(ab))
-    def test_graph_bprop0(self):
-        x = core.wrap(numpy.random.rand(10,2))
-        w = SparseR(sparse.csr_matrix(numpy.asarray([[1, 0, 3, 0, 5], [0, 0, -2, 0,
-            0]],dtype='float64')))
-
-        for epoch in xrange(50):
-            xw = sparse2dense(dot(x, w))
-            y = sparse2dense(dot(xw, transpose(w)))
-            loss = core.sum(core.sqr(x-y))
-            gy = y-x
-            g = grad.Grad({y:gy})
-            g.bprop()
-            lr = 0.002
-            g(w).data[1,0] = 0
-            g(w).data[1,4] = 0
-            w.data = -lr * g(w).data + w.data
-
-        self.failUnless('3.08560636025' == str(loss.data))
-
-    def test_graph_bprop1(self):
-        x = core.wrap(numpy.random.rand(10,2))
-        w = SparseR(sparse.csr_matrix(numpy.asarray([[1, 0, 3, 0, 5], [0, 0, -2, 0,
-            0]],dtype='float64')))
-
-        for epoch in xrange(50):
-            xw = sparse2dense(dot(x, w))
-            y = sparse2dense(dot(xw, transpose(w)))
-            loss = core.sum(core.sqr(x-y))
-            g = grad.grad(loss)
-            lr = 0.001
-
-            g(w).data[1,0] = 0
-            g(w).data[1,4] = 0
-            w.data = -lr * g(w).data + w.data
-
-        self.failUnless('3.08560636025' == str(loss.data))
-        
-if __name__ == '__main__':
-    unittest.main()
-