Added initial version of structural svm python example program.

python_examples/svm_struct.py

@@ -0,0 +1,86 @@
+#!/usr/bin/python
+# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+#
+# 
+#
+# COMPILING THE DLIB PYTHON INTERFACE
+#   Dlib comes with a compiled python interface for python 2.7 on MS Windows.  If
+#   you are using another python version or operating system then you need to
+#   compile the dlib python interface before you can use this file.  To do this,
+#   run compile_dlib_python_module.bat.  This should work on any operating system
+#   so long as you have CMake and boost-python installed.  On Ubuntu, this can be
+#   done easily by running the command:  sudo apt-get install libboost-python-dev cmake
+
+
+import dlib
+
+def dot(a, b):
+    return sum(i*j for i,j in zip(a,b))
+
+
+class three_class_classifier_problem:
+    C = 10
+    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):
+        psi.resize(self.num_dimensions)
+        dims = len(vector)
+        if (label == 1):
+            for i in range(0,dims):
+                psi[i] = vector[i]
+        elif (label == 2):
+            for i in range(dims,2*dims):
+                psi[i] = vector[i-dims]
+        else:
+            for i in range(2*dims,3*dims):
+                psi[i] = vector[i-2*dims]
+
+
+    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):
+        samp = samples[idx]
+        dims = len(samp)
+        scores = [0,0,0]
+        # compute scores for each of the three classifiers
+        scores[0] = dot(current_solution[0:dims], samp)
+        scores[1] = dot(current_solution[dims:2*dims], samp)
+        scores[2] = dot(current_solution[2*dims:3*dims], samp)
+
+        # Add in the loss-augmentation
+        if (labels[idx] != 1): 
+            scores[0] += 1
+        if (labels[idx] != 2): 
+            scores[1] += 1
+        if (labels[idx] != 3): 
+            scores[2] += 1
+
+
+        # Now figure out which classifier has the largest loss-augmented score.
+        max_scoring_label = scores.index(max(scores))+1
+        if (max_scoring_label == labels[idx]):
+            loss = 0
+        else:
+            loss = 1
+
+        self.make_psi(psi, samp, max_scoring_label)
+
+        return loss
+
+
+
+samples = [ [0,0,1], [0,1,0], [1,0,0]];
+labels = [1, 2, 3]
+
+problem = three_class_classifier_problem(samples, labels)
+weights = dlib.solve_structural_svm_problem(problem)
+print weights
+