[algorithm] added a minimal SVM version

bob/pad/base/algorithm/__init__.py

@@ -6,6 +6,9 @@ from .SVMCascadePCA import SVMCascadePCA
 from .MLP import MLP
 from .PadLDA import PadLDA
 from .OCSVM import OCSVM
+from .OCGMM import OCGMM
+from .SKLGMM import SKLGMM
+from .mySVM import mySVM
 
 def __appropriate__(*args):
     """Says object was actually declared here, and not in the import module.

bob/pad/base/algorithm/mySVM.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+
+import numpy
+from bob.pad.base.algorithm import Algorithm
+
+import bob.learn.libsvm
+import bob.io.base
+
+class mySVM(Algorithm):
+  """
+  This class interfaces a One Class SVM classifier used for PAD
+
+  """
+
+  def __init__(self, rescale=True, gamma=0.1, **kwargs):
+
+    Algorithm.__init__(self,  
+                       performs_projection=True,
+                       requires_projector_training=True, 
+                       **kwargs)
+
+    self.rescale = rescale 
+    self.gamma = gamma
+    self.machine = None
+    self.trainer = bob.learn.libsvm.Trainer(machine_type='C_SVC', kernel_type='RBF', probability=True)
+    setattr(self.trainer, 'gamma', self.gamma)
+
+
+  def train_projector(self, training_features, projector_file):
+    """
+    Trains a SVM
+
+    **Parameters**
+
+    training_features:
+    """
+    # training_features[0] - training features for the REAL class.
+    # training_features[1] - training features for the ATTACK class.
+    
+    # The data - "positive class only"
+    pos = numpy.array(training_features[0])
+    neg = numpy.array(training_features[1])
+
+    if self.rescale:
+      for i in range(pos.shape[0]):
+        min_value = numpy.min(pos[i])
+        max_value = numpy.max(pos[i])
+        pos[i] = ((2 * (pos[i] - min_value))/ (max_value - min_value)) - 1
+      for i in range(neg.shape[0]):
+        min_value = numpy.min(neg[i])
+        max_value = numpy.max(neg[i])
+        neg[i] = ((2 * (neg[i] - min_value))/ (max_value - min_value)) - 1
+    
+    data = [pos, neg]
+    
+    # train
+    self.machine = self.trainer.train(data)
+    f = bob.io.base.HDF5File(projector_file, 'w')
+    self.machine.save(f)
+    
+
+  def project(self, feature):
+    """
+    Project the given feature
+    """
+    if self.rescale:
+      min_value = numpy.min(feature)
+      max_value = numpy.max(feature)
+      feature = ((2 * (feature - min_value))/ (max_value - min_value)) - 1
+    
+    return self.machine(feature)
+
+  def score(self, toscore):
+    return [toscore[0]]