Cleaned up python svm struct code a little.

python_examples/svm_struct.py

 #!/usr/bin/python
 # The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
 #
-# 
+# This is an example illustrating the use of the structural SVM solver from the dlib C++
+# Library.  This example will briefly introduce it and then walk through an example showing
+# how to use it to create a simple multi-class classifier.  
+#
 #
 # COMPILING THE DLIB PYTHON INTERFACE
 #   Dlib comes with a compiled python interface for python 2.7 on MS Windows.  If
@@ -15,6 +18,7 @@
 import dlib
 
 def dot(a, b):
+    "Compute the dot product between the two vectors a and b."
     return sum(i*j for i,j in zip(a,b))
 
 
@@ -23,30 +27,35 @@ class three_class_classifier_problem:
     be_verbose = True
     epsilon = 0.0001 
 
+
     def __init__(self, samples, labels):
         self.num_samples = len(samples)
         self.num_dimensions = len(samples[0])*3
         self.samples = samples
         self.labels = labels
 
-    def make_psi(self, psi, vector, label):
+
+    def make_psi(self, vector, label):
+        psi = dlib.vector()
         psi.resize(self.num_dimensions)
         dims = len(vector)
-        if (label == 1):
+        if (label == 0):
             for i in range(0,dims):
                 psi[i] = vector[i]
-        elif (label == 2):
+        elif (label == 1):
             for i in range(dims,2*dims):
                 psi[i] = vector[i-dims]
-        else:
+        else: # the label must be 2
             for i in range(2*dims,3*dims):
                 psi[i] = vector[i-2*dims]
+        return psi
+
 
+    def get_truth_joint_feature_vector(self, idx):
+        return self.make_psi(self.samples[idx], self.labels[idx])
 
-    def get_truth_joint_feature_vector(self, idx, psi):
-        self.make_psi(psi, self.samples[idx], self.labels[idx])
 
-    def separation_oracle(self, idx, current_solution, psi):
+    def separation_oracle(self, idx, current_solution):
         samp = samples[idx]
         dims = len(samp)
         scores = [0,0,0]
@@ -56,29 +65,28 @@ class three_class_classifier_problem:
         scores[2] = dot(current_solution[2*dims:3*dims], samp)
 
         # Add in the loss-augmentation
-        if (labels[idx] != 1): 
+        if (labels[idx] != 0): 
             scores[0] += 1
-        if (labels[idx] != 2): 
+        if (labels[idx] != 1): 
             scores[1] += 1
-        if (labels[idx] != 3): 
+        if (labels[idx] != 2): 
             scores[2] += 1
 
-
         # Now figure out which classifier has the largest loss-augmented score.
-        max_scoring_label = scores.index(max(scores))+1
+        max_scoring_label = scores.index(max(scores))
         if (max_scoring_label == labels[idx]):
             loss = 0
         else:
             loss = 1
 
-        self.make_psi(psi, samp, max_scoring_label)
+        psi = self.make_psi(samp, max_scoring_label)
 
-        return loss
+        return loss,psi
 
 
 
-samples = [ [0,0,1], [0,1,0], [1,0,0]];
-labels = [1, 2, 3]
+samples = [[0,0,1], [0,1,0], [1,0,0]];
+labels =  [0,1,2]
 
 problem = three_class_classifier_problem(samples, labels)
 weights = dlib.solve_structural_svm_problem(problem)

tools/python/src/svm_struct.cpp

@@ -37,7 +37,7 @@ public:
         feature_vector_type& psi 
     ) const 
     {
-        problem.attr("get_truth_joint_feature_vector")(idx,boost::ref(psi));
+        psi = extract<feature_vector_type&>(problem.attr("get_truth_joint_feature_vector")(idx));
     }
 
     virtual void separation_oracle (
@@ -47,7 +47,19 @@ public:
         feature_vector_type& psi
     ) const 
     {
-        loss = extract<double>(problem.attr("separation_oracle")(idx,boost::ref(current_solution),boost::ref(psi)));
+        object res = problem.attr("separation_oracle")(idx,boost::ref(current_solution));
+        pyassert(len(res) == 2, "separation_oracle() must return two objects, the loss and the psi vector");
+        // let the user supply the output arguments in any order.
+        if (extract<double>(res[0]).check())
+        {
+            loss = extract<double>(res[0]);
+            psi = extract<feature_vector_type&>(res[1]);
+        }
+        else
+        {
+            psi = extract<feature_vector_type&>(res[0]);
+            loss = extract<double>(res[1]);
+        }
     }
 
 private: