all generated tests work upon generator.py/tester.py now

shogun-toolbox · Aug 31, 2011 · 3deadd2 · 3deadd2
1 parent b17aeae
commit 3deadd2
Show file tree

Hide file tree

Showing 5 changed files with 29 additions and 23 deletions.
diff --git a/examples/undocumented/python_modular/distribution_ppwm_modular.py b/examples/undocumented/python_modular/distribution_ppwm_modular.py
@@ -54,7 +54,7 @@ def distribution_ppwm_modular (fm_dna=traindna, order=3):
 
 	#out_likelihood = histo.get_log_likelihood()
 	#out_sample = histo.get_log_likelihood_sample()
-	return ppwm,w,u
+	return w,u
 ###########################################################################
 # call functions
 ###########################################################################

diff --git a/examples/undocumented/python_modular/streaming_vw_createcache_modular.py b/examples/undocumented/python_modular/streaming_vw_createcache_modular.py
@@ -38,7 +38,7 @@ def streaming_vw_createcache_modular(fname):
 	features = StreamingVwFeatures(input_file, True, 1024);
 	vw = VowpalWabbit(features)
 	vw.train()
-	return vw
+	#return vw
 
 if __name__ == "__main__":
 	streaming_vw_createcache_modular(*parameter_list[0])
diff --git a/examples/undocumented/python_modular/streaming_vw_modular.py b/examples/undocumented/python_modular/streaming_vw_modular.py
@@ -6,25 +6,25 @@
 parameter_list=[[None]]
 
 def streaming_vw_modular(dummy):
-    """Runs the VW algorithm on a toy dataset in SVMLight format."""
+	"""Runs the VW algorithm on a toy dataset in SVMLight format."""
 
-    # Open the input file as a StreamingVwFile
-    input_file = StreamingVwFile("../data/fm_train_sparsereal.dat")
+	# Open the input file as a StreamingVwFile
+	input_file = StreamingVwFile("../data/fm_train_sparsereal.dat")
 
-    # Tell VW that the file is in SVMLight format
-    # Supported types are T_DENSE, T_SVMLIGHT and T_VW
-    input_file.set_parser_type(T_SVMLIGHT)
+	# Tell VW that the file is in SVMLight format
+	# Supported types are T_DENSE, T_SVMLIGHT and T_VW
+	input_file.set_parser_type(T_SVMLIGHT)
 
-    # Create a StreamingVwFeatures object, `True' indicating the examples are labelled
-    features = StreamingVwFeatures(input_file, True, 1024)
+	# Create a StreamingVwFeatures object, `True' indicating the examples are labelled
+	features = StreamingVwFeatures(input_file, True, 1024)
 
-    # Create a VW object from the features
-    vw = VowpalWabbit(features)
+	# Create a VW object from the features
+	vw = VowpalWabbit(features)
 
-    # Train
-    vw.train()
+	# Train
+	vw.train()
 
-    return vw
+	#return vw
 
 if __name__ == "__main__":
-    streaming_vw_modular(*parameter_list[0]))
+	streaming_vw_modular(*parameter_list[0])
diff --git a/src/shogun/kernel/SplineKernel.cpp b/src/shogun/kernel/SplineKernel.cpp
@@ -18,11 +18,11 @@
 
 using namespace shogun;
 
-CSplineKernel::CSplineKernel(void)
+CSplineKernel::CSplineKernel() : CDotKernel()
 {
 }
 
-CSplineKernel::CSplineKernel(CDotFeatures* l, CDotFeatures* r)
+CSplineKernel::CSplineKernel(CDotFeatures* l, CDotFeatures* r) : CDotKernel()
 {
 	init(l,r);
 }
@@ -34,6 +34,12 @@ CSplineKernel::~CSplineKernel()
 
 bool CSplineKernel::init(CFeatures* l, CFeatures* r)
 {
+	ASSERT(l->get_feature_type()==F_DREAL);
+	ASSERT(l->get_feature_type()==r->get_feature_type());
+
+	ASSERT(l->get_feature_class()==C_SIMPLE);
+	ASSERT(l->get_feature_class()==r->get_feature_class());
+
 	CDotKernel::init(l,r);
 	return init_normalizer();
 }
@@ -48,8 +54,8 @@ float64_t CSplineKernel::compute(int32_t idx_a, int32_t idx_b)
 	int32_t alen, blen;
 	bool afree, bfree;
 
-	float32_t* avec = ((CSimpleFeatures<float32_t>*) lhs)->get_feature_vector(idx_a, alen, afree);
-	float32_t* bvec = ((CSimpleFeatures<float32_t>*) rhs)->get_feature_vector(idx_b, blen, bfree);
+	float64_t* avec = ((CSimpleFeatures<float64_t>*) lhs)->get_feature_vector(idx_a, alen, afree);
+	float64_t* bvec = ((CSimpleFeatures<float64_t>*) rhs)->get_feature_vector(idx_b, blen, bfree);
 	ASSERT(alen == blen);
 
 	float64_t result = 0;
@@ -59,8 +65,8 @@ float64_t CSplineKernel::compute(int32_t idx_a, int32_t idx_b)
 		result += 1 + x*y + x*y*min - ((x+y)/2)*min*min + min*min*min/3;
 	}
 
-	((CSimpleFeatures<float32_t>*) lhs)->free_feature_vector(avec, idx_a, afree);
-	((CSimpleFeatures<float32_t>*) rhs)->free_feature_vector(bvec, idx_b, bfree);
+	((CSimpleFeatures<float64_t>*) lhs)->free_feature_vector(avec, idx_a, afree);
+	((CSimpleFeatures<float64_t>*) rhs)->free_feature_vector(bvec, idx_b, bfree);
 
 	return result;
 }
diff --git a/src/shogun/kernel/SplineKernel.h b/src/shogun/kernel/SplineKernel.h
@@ -70,7 +70,7 @@ class CSplineKernel: public CDotKernel
 		 *
 		 * @return name Spline
 		 */
-		virtual const char* get_name() const { return "Spline"; }
+		virtual const char* get_name() const { return "SplineKernel"; }
 
 	protected:
 		virtual float64_t compute(int32_t idx_a, int32_t idx_b);