diff --git a/volia/measures.py b/volia/measures.py
index 3aaebfa..6d5d721 100644
--- a/volia/measures.py
+++ b/volia/measures.py
@@ -90,7 +90,7 @@ def disequilibrium(matrix1, matrix2, isGlobal=False):
     )
 
 
-def compute_count_matrix(y_hat, y_truth):
+def compute_count_matrix(y_truth, y_hat):
     '''
         Check the size of the lists with assertion
     '''
@@ -121,7 +121,7 @@ def entropy_score(y_truth, y_hat):
         return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0)
 
     # Build count matrix
-    count_matrix = compute_count_matrix(y_hat, y_truth)
+    count_matrix = compute_count_matrix(y_truth, y_hat)
 
     # Build dividers vector
     dividers = count_matrix.sum(axis=1)
@@ -151,6 +151,42 @@ def entropy_score(y_truth, y_hat):
     return (result_matrix, result_vector, result)
 
 
+def purity_score(y_truth, y_hat):
+
+    def divide_line(a, divider):
+        '''
+        Sub function used for dividing matrix by a vector line by line.
+        '''
+        return np.divide(a, divider, out=np.zeros_like(a), where=divider!=0)
+
+    def compute_purity_score(count_matrix, axis=0):
+        count_per_row = count_matrix.sum(axis=axis)
+        dividers = np.square(count_per_row)
+        count_matrix_squared = np.square(count_matrix)
+        matrix_divided = np.apply_along_axis(divide_line, 0, np.asarray(count_matrix_squared, dtype=np.float), dividers)
+        vector_purity = np.sum(matrix_divided, axis=axis)
+
+        scalar_purity = np.average(vector_purity, weights=count_per_row)
+        return (vector_purity, scalar_purity)
+    
+
+    count_matrix = compute_count_matrix(y_truth, y_hat)
+    _, purity_cluster_score = compute_purity_score(count_matrix, 1)
+    _, purity_class_score = cluster_purity = compute_purity_score(count_matrix, 0)
+
+    K = np.sqrt(purity_cluster_score * purity_class_score)
+
+    for i in range(count_matrix.shape[0]):
+
+        for j in range(count_matrix.shape[1]):
+            count_matrix[i][j]
+        count_matrix[i]
+    return {
+        "purity_class_score": purity_class_score,
+        "purity_cluster_score": purity_cluster_score,
+        "K": K
+    }
+
 
 if __name__ == "__main__":
     # Hypothesis
@@ -158,8 +194,11 @@ if __name__ == "__main__":
     # Truth
     y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])
 
-    (result_matrix, result_vector, result) = entropy(y, y_hat)
+    (result_matrix, result_vector, result) = entropy_score(y, y_hat)
+
 
+    print(purity_score(y, y_hat))
+    exit(1)
     print("Result matrix: ")
     print(result_matrix)
     print("Result vector: ")