diff --git a/AIclassifier.py b/AIclassifier.py
index 39acbab..1d00ad5 100644
--- a/AIclassifier.py
+++ b/AIclassifier.py
@@ -57,7 +57,7 @@ def format_sentVector_to_SparseMatrix(vectors_list, dictionary):
         counts = Counter(sent_vector)
         for index, freq in counts.items():
             if len(counts.items()) > 0:
-                sparse_vector[index] = 1 #freq/len(sent_vector) # DIFFERENT CONFIGURATION POSSIBILITIES # 1
+                sparse_vector[index] = freq/len(sent_vector) # 1 DIFFERENT CONFIGURATION POSSIBILITIES # 1
         if (i == 0): # TO DO: OPTIMIZE, NO NEED TO CHECK THIS EVERY TURN
             matrix_array = [sparse_vector]
         else:
diff --git a/output/AI Classifier/1Label_confusion_matrix_NonNorm.png b/output/AI Classifier/1Label_confusion_matrix_NonNorm.png
index 641d6f4..6136d03 100644
Binary files a/output/AI Classifier/1Label_confusion_matrix_NonNorm.png and b/output/AI Classifier/1Label_confusion_matrix_NonNorm.png differ
diff --git a/output/AI Classifier/1Label_confusion_matrix_NormTrue.png b/output/AI Classifier/1Label_confusion_matrix_NormTrue.png
index c90cdcc..a0305bb 100644
Binary files a/output/AI Classifier/1Label_confusion_matrix_NormTrue.png and b/output/AI Classifier/1Label_confusion_matrix_NormTrue.png differ
diff --git a/output/AI Classifier/1labelPredictionsStatsTest.txt b/output/AI Classifier/1labelPredictionsStatsTest.txt
index e6800f9..605787c 100644
--- a/output/AI Classifier/1labelPredictionsStatsTest.txt	
+++ b/output/AI Classifier/1labelPredictionsStatsTest.txt	
@@ -2,13 +2,13 @@ Performance measures - Unigram Dictionary - Adaboost
 
 Test set:
 
-Precision macro: 0.368
-Precision Individually: [0.556 0.    0.551 0.4   0.333]
-Recall macro: 0.302
-Recall Individually: [0.357 0.    0.871 0.182 0.1  ]
-F1 Score micro: 0.522
-F1 Score macro: 0.303
-F1 Score weighted: 0.467
-F1 Score Individually: [0.435 0.    0.675 0.25  0.154]
+Precision macro: 0.254
+Precision Individually: [0.357 0.    0.59  0.125 0.2  ]
+Recall macro: 0.258
+Recall Individually: [0.357 0.    0.742 0.091 0.1  ]
+F1 Score micro: 0.448
+F1 Score macro: 0.251
+F1 Score weighted: 0.416
+F1 Score Individually: [0.357 0.    0.657 0.105 0.133]