minor fix

activeLearning/activeLearningWlinGP1vs2.py

@@ -12,56 +12,19 @@ import activeLearningWlinGPprototype
 
 class Classifier(activeLearningWlinGPprototype.ClassifierPrototype):
 
-    def __init__(self,
-                 sigmaN = 0.00178,
-                 useDiversity = False,
-                 useDensity = False,
-                 useVariance = False,
-                 loNoise = True,
-                 configFile=None):
+
+    def __init__(self, sigmaN = 0.00178, loNoise = True, configFile=None):
 
         activeLearningWlinGPprototype.ClassifierPrototype.__init__(self, sigmaN=sigmaN, configFile=configFile)
-        self.useDiversity = helperFunctions.getConfig(configFile, 'activeLearning', 'useDiversity', useDiversity, 'bool', True)
-        self.useVariance = helperFunctions.getConfig(configFile, 'activeLearning', 'useVariance', useVariance, 'bool', True)
-        self.useDensity = helperFunctions.getConfig(configFile, 'activeLearning', 'useDensity', useDensity, 'bool', True)
         self.loNoise = helperFunctions.getConfig(configFile, 'activeLearning', 'loNoise', loNoise, 'bool', True)
 
 
-    def getDensity(self, sim):
-        return numpy.sum(sim, axis=1) / float(sim.shape[1])
-
-
-    def getDiversity(self, sim):
-        return 1.0 / numpy.max(sim, axis=1)
-
-
     # x.shape = (number of samples, feat dim)
     def calcAlScores(self, x):
 
         loNoise = (self.yUni == -1).any() and self.loNoise
         sortedScores = numpy.sort(self.infer(x, loNoise), axis=1)
-        alScores = numpy.absolute(sortedScores[:,-1] - sortedScores[:,-2])*(-1.0)
-        sim = None
-
-        if self.useDensity:
-
-            sim = numpy.dot(x, numpy.append(self.X, x, axis=0).T)
-            density = self.getDensity(sim)
-            alScores = numpy.multiply(alScores, density)
-
-        elif self.useDiversity:
-
-            if sim is None:
-                sim = numpy.dot(x, self.X.T)
-                diversity = self.getDiversity(sim)
-            else:
-                diversity = self.getDiversity(sim[:,:self.X.shape[0]])
-
-            alScores = numpy.multiply(alScores, diversity)
-
-        elif self.useVariance:
-
-            variance = self.calcSigmaF(x)
-            alScores = numpy.multiply(alScores, variance)
+        alScores = numpy.abs(sortedScores[:,-1] - sortedScores[:,-2])
 
-        return alScores
+        # since we actually want to select min(scores) instead of max(scores), we have to turn the scores around
+        return numpy.add(numpy.subtract(alScores, numpy.max(alScores))*(-1.0), numpy.finfo(numpy.float32).eps)