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#!/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
w1 = weights[0:3]
w2 = weights[3:6]
w3 = weights[6:9]
print "scores for class 1 sample: ", dot(w1, samples[0]), dot(w2,samples[0]), dot(w3, samples[0])
print "scores for class 2 sample: ", dot(w1, samples[1]), dot(w2,samples[1]), dot(w3, samples[1])
print "scores for class 3 sample: ", dot(w1, samples[2]), dot(w2,samples[2]), dot(w3, samples[2])