[svm] added some debug information

bob/pad/base/algorithm/SVM.py

@@ -230,6 +230,8 @@ class SVM(Algorithm):
             A trained SVM machine.
         """
 
+        kernel_type = 'LINEAR'
+
         one_class_flag = (
             machine_type == 'ONE_CLASS')  # True if one-class SVM is used
 
@@ -237,11 +239,21 @@ class SVM(Algorithm):
         real_train, real_cv, attack_train, attack_cv = \
             prepare_data_for_hyper_param_grid_search(training_features, n_samples)
 
+        # at this point the first half for every class is for training, the 2nd half for CV
+        
+        #for f in range(real_train.shape[0]):
+        #  print("real feature {} = {}".format(f, real_train[f]))
+        
         if mean_std_norm_flag:
             # normalize the data:
             real_train, real_cv, attack_train, attack_cv = norm_train_cv_data(
                 real_train, real_cv, attack_train, attack_cv, one_class_flag)
 
+        # at this point, features have been normalized using the mean and std across all
+        # training features (i.e. real and attack examples ...)
+
+
+
         precisions_cv = [
         ]  # for saving the precision on the cross-validation set
 
@@ -253,6 +265,7 @@ class SVM(Algorithm):
 
         for trainer_grid_search_param in trainer_grid_search_params_list:
 
+            
             # initialize the SVM trainer:
             trainer = bob.learn.libsvm.Trainer(
                 machine_type=machine_type,
@@ -275,7 +288,32 @@ class SVM(Algorithm):
                 data = [np.copy(real_train)
                         ]  # only real class is used for training
 
+          
+            print(data[0].shape)
+            print(data[1].shape)
+            stacked_data = np.vstack((data[0], data[1]))
+            print(stacked_data.shape)
+            col_sum = np.sum(stacked_data, axis=0)
+            print(col_sum)
+            import sys
+            sys.exit()
+
+            #print("=== TRAINING DATA ===")
+            #print("data for training (len) -> {}".format(len(data)))
+            #print("real data for training (shape) -> {}".format(data[0].shape))
+            #print("attack data for training (shape) -> {}".format(data[1].shape))
+            ##for i in range(data[0].shape[0]):
+            #  print("real example {} -> shape = {}".format(i, data[0][i].shape))
+            #for i in range(data[1].shape[0]):
+            #  print("attack example {} -> shape = {}".format(i, data[1][i].shape))
+            
+            #print("trained machine shape (before training) -> {}".format(machine.shape))
             machine = trainer.train(data)  # train the machine
+            #print("trained machine shape -> {}".format(machine.shape))
+
+            #print("=== CV DATA ===")
+            #print("real examples CV shape -> {}".format(np.copy(real_cv).shape))
+            #print("attack examples CV -> {}".format(np.copy(attack_cv).shape))
 
             precision_cv = self.comp_prediction_precision(
                 machine, np.copy(real_cv), np.copy(attack_cv))