mirror of
https://github.com/davisking/dlib.git
synced 2024-11-01 10:14:53 +08:00
87 lines
2.6 KiB
Python
Executable File
87 lines
2.6 KiB
Python
Executable File
#!/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
|
|
|