minor fix

activeLearning/activeLearningGPGenKemoc.py

@@ -67,7 +67,7 @@ class Classifier(activeLearningGPGenKprototype.ClassifierPrototype):
     return numpy.sum(sim, axis=1) / float(sim.shape[1])
 
 
-  # x.shape = (feat dim, number of samples)
+  # x.shape = (number of samples, feat dim)
   def calcAlScores(self, x):
 
     allX = numpy.append(self.X, x, axis=0)

activeLearning/activeLearningGPGenKprototype.py

@@ -256,13 +256,13 @@ class ClassifierPrototype:
         return probs/float(nmb)
 
 
-    # x.shape = (feat dim, number of samples)
+    # x.shape = (number of samples, feat dim)
     def calcAlScores(self, x):
 
         return None
 
 
-    # x.shape = (feat dim, number of samples)
+    # x.shape = (number of samples, feat dim)
     def getAlScores(self, x):
 
         alScores = self.calcAlScores(x)
@@ -276,7 +276,7 @@ class ClassifierPrototype:
         return alScores
 
 
-    # x.shape = (feat dim, number of samples)
+    # x.shape = (number of samples, feat dim)
     def chooseSample(self, x):
 
         return numpy.argmax(self.getAlScores(x), axis=0).item(0)

activeLearning/activeLearningGPLinKemoc.py

@@ -71,7 +71,7 @@ class Classifier(activeLearningGPLinKprototype.ClassifierPrototype):
     return 1.0 / numpy.max(sim, axis=1)
 
 
-  # x.shape = (feat dim, number of samples)
+  # x.shape = (number of samples, feat dim)
   def calcAlScores(self, x):
 
     allX = numpy.append(self.X, x, axis=0)

activeLearning/activeLearningGPLinKprototype.py

@@ -224,13 +224,13 @@ class ClassifierPrototype:
     return probs/float(nmb)
 
 
-  # x.shape = (feat dim, number of samples)
+  # x.shape = (number of samples, feat dim)
   def calcAlScores(self, x):
 
     return None
 
 
-  # x.shape = (feat dim, number of samples)
+  # x.shape = (number of samples, feat dim)
   def getAlScores(self, x):
 
     alScores = self.calcAlScores(x)
@@ -244,7 +244,7 @@ class ClassifierPrototype:
     return alScores
 
 
-  # x.shape = (feat dim, number of samples)
+  # x.shape = (number of samples, feat dim)
   def chooseSample(self, x):
 
     return numpy.argmax(self.getAlScores(x), axis=0).item(0)

activeLearning/activeLearningLinGPemoc.py

@@ -65,7 +65,7 @@ class Classifier(activeLearningLinGPprototype.ClassifierPrototype):
     return numpy.multiply(scores, density)
 
 
-   # x.shape = (feat dim, number of samples)
+   # x.shape = (number of samples, feat dim)
   def calcAlScores(self, x):
 
     if self.usePde:

activeLearning/activeLearningLinGPemocApprox.py

@@ -26,7 +26,7 @@ class Classifier(activeLearningLinGPprototype.ClassifierPrototype):
     self.cachedClusters = None
     self.cachedApprox = None
     self.cachedDensity = None
-    
+
 
   # x.shape = (number of samples, feat dim)
   def calcEMOC(self, x, allX=None, density=None):
@@ -73,7 +73,7 @@ class Classifier(activeLearningLinGPprototype.ClassifierPrototype):
     return numpy.multiply(scores, density)
 
 
-   # x.shape = (feat dim, number of samples)
+   # x.shape = (number of samples, feat dim)
   def calcAlScores(self, x):
 
     allX = numpy.append(self.X, x, axis=0)

activeLearning/activeLearningLinGPprototype.py

@@ -35,9 +35,6 @@ class ClassifierPrototype:
     if not numpy.all(numpy.isfinite(self.yUni)):
       raise Exception('not numpy.all(numpy.isfinite(self.yUni))')
 
-    if len(numpy.unique(self.y)) > 2:
-      raise Exception('len(numpy.unique(self.y)) > 2')
-
 
   def train2(self, X, y, sigmaN=None):
 
@@ -159,13 +156,13 @@ class ClassifierPrototype:
     return probs/float(nmb)
 
 
-  # x.shape = (feat dim, number of samples)
+  # x.shape = (number of samples, feat dim)
   def calcAlScores(self, x):
 
     return None
 
 
-  # x.shape = (feat dim, number of samples)
+  # x.shape = (number of samples, feat dim)
   def getAlScores(self, x):
 
     alScores = self.calcAlScores(x)
@@ -179,7 +176,7 @@ class ClassifierPrototype:
     return alScores
 
 
-  # x.shape = (feat dim, number of samples)
+  # x.shape = (number of samples, feat dim)
   def chooseSample(self, x):
 
     return numpy.argmax(self.getAlScores(x), axis=0).item(0)

activeLearning/activeLearningWlinGP1vs2.py

@@ -35,7 +35,7 @@ class Classifier(activeLearningWlinGPprototype.ClassifierPrototype):
         return 1.0 / numpy.max(sim, axis=1)
 
 
-    # x.shape = (feat dim, number of samples)
+    # x.shape = (number of samples, feat dim)
     def calcAlScores(self, x):
 
         loNoise = (self.yUni == -1).any() and self.loNoise

activeLearning/activeLearningWlinGPprototype.py

@@ -178,13 +178,13 @@ class ClassifierPrototype:
     return probs/float(nmb)
 
 
-  # x.shape = (feat dim, number of samples)
+  # x.shape = (number of samples, feat dim)
   def calcAlScores(self, x):
 
     return None
 
 
-  # x.shape = (feat dim, number of samples)
+  # x.shape = (number of samples, feat dim)
   def getAlScores(self, x):
 
     alScores = self.calcAlScores(x)
@@ -198,7 +198,7 @@ class ClassifierPrototype:
     return alScores
 
 
-  # x.shape = (feat dim, number of samples)
+  # x.shape = (number of samples, feat dim)
   def chooseSample(self, x):
 
     return numpy.argmax(self.getAlScores(x), axis=0).item(0)