Changeset 185

Timestamp:

05/19/08 15:06:13 (7 months ago)

Author:

edsuom

Message:

Unit testing support C code for model

Files:

• projects/AsynCluster/trunk/svpmc/model.c (modified) (3 diffs)
• projects/AsynCluster/trunk/svpmc/test/test_model.py (modified) (4 diffs)

projects/AsynCluster/trunk/svpmc/model.c

@@ -156 +156 @@
-double normLogDensity(double x)
-{
-3.1415926535897931
+M_PI
-  return -0.5*x*x + logC;
-}
@@ -251 +251 @@
-  int k;
-  double L;
-1E20
-1E20
+HUGE_VAL
+HUGE_VAL
-  for(k=0; k<n; k++) {
-    L = sp[k][1]->Lx;
@@ -271 +271 @@
-// the log-probability of the proposal's having been selected.
-struct selection \
-double Dp[], 
-
-0
+
+
+
-  double val;
-  double pSum = 0;
-
+
+
-  struct selection result;
-
-  // Compute linear probability density for each proposal
-0=0; k0<n; k0
-0][1]->Lx + Lz[k0
-0
+=0; k<n; k
+][1]->Lx + Lz[k
+
-    pSum += val;
-    if(val > pMax) {
-      pMax = val;
-0
+
-    }
-  }

projects/AsynCluster/trunk/svpmc/test/test_model.py

@@ -376 +376 @@
-        H2(k1, k0) = PA1(S.h, k1);
-    }
+    free(S.z);
+    free(S.h);
+    free(S.x0);
+    free(S.x1);
-    """
-    
@@ -455 +459 @@
-
-class Test_Model_logLikelihood(BaseCase):
-    N = 100
-    
-    code = """
-    int k0, k1;
@@ -470 +472 @@
-    S.x0 = (double *) malloc(sizeof(double) * Nx[0]);
-    S.x1 = (double *) malloc(sizeof(double) * Nx[0]);
+
+    for(k1=0; k1<Nx[0]; k1++) {
+        PA1(S.z, k1) = Z1(k1);
+        PA1(S.h, k1) = H1(k1);
+    }
-    
-    for(k0=0; k0<Nx[1]; k0++) {
- {
+
-        xk[k1] = X2(k1, k0);
-        PA1(S.z, k1) = Z2(k1, k0);
-        PA1(S.h, k1) = H2(k1, k0);
-      }
-      // This is what's being tested
-      logLikelihood(&S, &P, xk);
-    }
+    free(S.z);
+    free(S.h);
+    free(S.x0);
+    free(S.x1);
-    return_val = S.Lx;
-    """
@@ -501 +509 @@
-
-    def test_Lx_accumulates(self):
-
-
+
-        Lx = self._logLikelihood(s.ones((1,Ns)), 0)
-        distObj = stats.norm(0, 1)
-s.log(Ns*
+Ns*s.log(
-            
+    def test_Lx_multivariate(self):
+        Nt = 8
+        Ns = 100
+        Lx = self._logLikelihood(s.ones((Nt,Ns)), 0)
+        distObj = stats.norm(0, 1)
+        self.failUnlessAlmostEqual(Lx, Nt*Ns*s.log(distObj.pdf(1)))
+
+
+class Test_Model_spArray(BaseCase):
+    code = """
+    int k;
+    const int n = NL[0];
+    struct selection spSelection;
+    struct sample sp[n][2];
+    double Lz[n];
+    py::tuple result(2);
+    
+    for(k=0; k<n; k++) {
+      Lz[k] = Z1(k);
+      sp[k][1].Lx = L1(k);
+    }
+    
+    return_val = S.Lx;
+    """
+
-
-class Test_Model_likelihood(BaseCase):