Improved readability (#1615)

* improved readability

* further readability improvements

* removed csv file and added f

machine_learning/knn_sklearn.py

@@ -7,8 +7,8 @@ iris = load_iris()
 iris.keys()
 
 
-print("Target names: \n {} ".format(iris.target_names))
-print("\n Features: \n {}".format(iris.feature_names))
+print(f"Target names: \n {iris.target_names} ")
+print(f"\n Features: \n {iris.feature_names}")
 
 # Train set e Test set
 X_train, X_test, y_train, y_test = train_test_split(

machine_learning/linear_discriminant_analysis.py

@@ -174,8 +174,8 @@ def accuracy(actual_y: list, predicted_y: list) -> float:
 def main():
     """ This function starts execution phase """
     while True:
-        print(" Linear Discriminant Analysis ".center(100, "*"))
-        print("*" * 100, "\n")
+        print(" Linear Discriminant Analysis ".center(50, "*"))
+        print("*" * 50, "\n")
         print("First of all we should specify the number of classes that")
         print("we want to generate as training dataset")
         # Trying to get number of classes
@@ -239,7 +239,7 @@ def main():
                     else:
                         print(
                             f"Your entered value is {user_count}, Number of "
-                            f"instances should be positive!"
+                            "instances should be positive!"
                         )
                         continue
                 except ValueError:
@@ -302,7 +302,7 @@ def main():
         # for loop iterates over number of elements in 'probabilities' list and print
         # out them in separated line
         for i, probability in enumerate(probabilities, 1):
-            print("Probability of class_{} is: {}".format(i, probability))
+            print(f"Probability of class_{i} is: {probability}")
         print("-" * 100)
 
         # Calculating the values of variance for each class

machine_learning/sequential_minimum_optimization.py

@@ -446,7 +446,7 @@ def count_time(func):
         start_time = time.time()
         func(*args, **kwargs)
         end_time = time.time()
-        print("smo algorithm cost {} seconds".format(end_time - start_time))
+        print(f"smo algorithm cost {end_time - start_time} seconds")
 
     return call_func
 
@@ -500,11 +500,9 @@ def test_cancel_data():
         if test_tags[i] == predict[i]:
             score += 1
     print(
-        "\r\nall: {}\r\nright: {}\r\nfalse: {}".format(
-            test_num, score, test_num - score
-        )
+        f"\r\nall: {test_num}\r\nright: {score}\r\nfalse: {test_num - score}"
     )
-    print("Rough Accuracy: {}".format(score / test_tags.shape[0]))
+    print(f"Rough Accuracy: {score / test_tags.shape[0]}")
 
 
 def test_demonstration():