Comments added to features

xbob/boosting/core/gaussiantrainer.py

-
-class GaussianMachine():
-
-    def __init__(self, num_classes):
-        self.means = numpy.zeros(num_classes)
-        self.variance = numpy.zeros(num_classes)
-        self.selected_index = 0
-
-
-    def get_weak_scores(self, features):
-        num_classes = self.means.shape[0]
-        
-
-        for i in range(num_classes):
-            mean_i = means[i]
-            variance_i = variance[i]
-            feature_i = features[:,i]
-            demon = numpy.sqrt(2*numpy.pi*variance_i)
-            scores[i] = numpy.exp(-(((feature_i - mean_i)**2)/2*variance_i))
-
-        return scores
-             
-class GaussianTrainer():
-
-    def __init__(self, num_classes):
-        self.num_classes = num_classes
-
-
-    def compute_weak_trainer(self, features, loss_grad):
-
-        num_features = features.shape[1]
-        means = numpy.zeros([num_features,self.num_classes])
-        variances = numpy.zeros([num_features,self.num_classes])
-        summed_loss = numpy.zeros(num_features)
-        gauss_machine = GaussianMachine()
-
-        for feature_index in range(num_features)
-            single_feature = features[:,feature_index]
-            means[i,;], variances[i,:], loss[i] = compute_current_loss(single_feature, classes, loss_grad)
-
-        gauss_machine.selected_index = numpy.argmin(loss)
-        gauss_machine.means = means[optimum_index,:]
-        gauss_machine.variance = variances[optimum_index,:]
-        
-        
-
-    def compute_current_loss(feature, classes, loss_grad):
-        num_samples = feature.shape[0]
-        scores = numpy.zeros([num_samples, self.num_classes])
-
-        for class_index in range(self.num_classes):
-            samples_i = feature[loss_grad[:,class_index] < 0]
-            mean[i] = numpy.mean(samples_i)
-            variance[i] = numpy.std(samples_i)**2
-            scores[:,class_index] = numpy.exp(-(((feature_i - mean_i)**2)/2*variance_i))
-            
-
-        scores_sum = numpy.sum(scores)
-
-        return mean, variance, scores_sum
-

xbob/boosting/features/local_feature.py

@@ -37,7 +37,7 @@ class lbp_feature():
         img: Input images
 
         return:
-        int_img: The integral image of the input image."""
+        integral_xy: The integral image of the input image."""
 
         integral_y = numpy.cumsum(img,0)
         integral_xy = numpy.cumsum(integral_y,1)
@@ -100,14 +100,12 @@ class lbp_feature():
 
 
     def lbp(self, block_sum):
-        """Function to compute the LBP for a image at single scale. 
+        """Function to compute the LBP(3x3) for a image at single scale. 
 
         The LBP features of the given image is computed and the feature map is returned
 
         Inputs:
-        coord: The coordinates specify the neighbour to be considered.
-        feature_map_dimx: feature map's dimension along the columns.
-        feature_map_dimy: Feature maps dimension along the rows.
+        block_sum: The image with each pixel representing the sum of a block.
 
         Return:
         feature_map: The lbp feature map
@@ -129,9 +127,7 @@ class lbp_feature():
         The tLBP features of the given image is computed and the feature map is returned
 
         Inputs:
-        coord: The coordinates specify the neighbour to be considered.
-        feature_map_dimx: feature map's dimension along the columns.
-        feature_map_dimy: Feature maps dimension along the rows.
+        block_sum: The image with each pixel representing the sum of a block.
 
         Return:
         feature_map: The lbp feature map
@@ -158,9 +154,7 @@ class lbp_feature():
         The dLBP features of the given image is computed and the feature map is returned
 
         Inputs:
-        coord: The coordinates specify the neighbour to be considered.
-        feature_map_dimx: feature map's dimension along the columns.
-        feature_map_dimy: Feature maps dimension along the rows.
+        block_sum: The image with each pixel representing the sum of a block.
 
         Return:
         feature_map: The lbp feature map
@@ -189,7 +183,7 @@ class lbp_feature():
         The mLBP features of the given image is computed and the feature map is returned. 
 
         Inputs:
-        block_sum: The image that to apply mlbp operator on.
+        block_sum: The image with each pixel representing the sum of a block.
 
         Return:
         feature_map: The lbp feature map
@@ -234,6 +228,17 @@ class lbp_feature():
         return feature_vector.shape[0]
 
     def get_map_dimension(self, block_sum):
+        """ The function computes the dimension of the LBP (R = 1, P = 8) type feature image
+
+        The feature image returned by LBP with radius R is less than the image size by 2*R -1, for 
+        R = 1, the feature map is smaller than the image by 2 pixel in both direction.
+
+        Input: 
+
+        block_sum: The image with each pixel representing the sum of a block.
+
+        Return:
+        feature_map_dimx, feature_map_dimy : The dimensions of the feature map."""
 
         # Initialize the size of the final feature map that will be obtained
         feature_map_dimy = block_sum.shape[0] -2