Reduce complexity linear_discriminant_analysis.

Author: Pablo Osorio Lopez

machine_learning/linear_discriminant_analysis.py

@@ -44,6 +44,7 @@
 from math import log
 from os import name, system
 from random import gauss, seed
+from typing import TypeVar, Callable
 
 
 # Make a training dataset drawn from a gaussian distribution
@@ -245,6 +246,38 @@ def accuracy(actual_y: list, predicted_y: list) -> float:
     return (correct / len(actual_y)) * 100
 
 
+num = TypeVar("num")
+
+
+def valid_input(
+    input_type: Callable[[object], num], # Usually float or int
+    input_msg: str,
+    err_msg: str,
+    condition: Callable[[num], bool] = lambda x: True,
+    default: str = None,
+) -> num:
+    """
+    Ask for user value and validate that it fulfill a condition.
+
+    :input_type: user input expected type of value
+    :input_msg: message to show user in the screen
+    :err_msg: message to show in the screen in case of error
+    :condition: function that represents the condition that user input is valid.
+    :default: Default value in case the user does not type anything
+    :return: user's input
+    """
+    while True:
+        try:
+            user_input = input_type(input(input_msg).strip() or default)
+            if condition(user_input):
+                return user_input
+            else:
+                print(f"Your entered value is {user_input}. {err_msg}")
+                continue
+        except ValueError:
+            print("Your entered value is not numerical!")
+
+
 # Main Function
 def main():
     """ This function starts execution phase """
@@ -254,87 +287,51 @@ def main():
         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
-        n_classes = 0
-        while True:
-            try:
-                user_input = int(
-                    input("Enter the number of classes (Data Groupings): ").strip()
-                )
-                if user_input > 0:
-                    n_classes = user_input
-                    break
-                else:
-                    print(
-                        f"Your entered value is {user_input} , Number of classes "
-                        f"should be positive!"
-                    )
-                    continue
-            except ValueError:
-                print("Your entered value is not numerical!")
+        n_classes = valid_input(
+            input_type=int,
+            condition=lambda x: x > 0,
+            input_msg="Enter the number of classes (Data Groupings): ",
+            err_msg="Number of classes should be positive!",
+        )
 
         print("-" * 100)
 
-        std_dev = 1.0  # Default value for standard deviation of dataset
         # Trying to get the value of standard deviation
-        while True:
-            try:
-                user_sd = float(
-                    input(
-                        "Enter the value of standard deviation"
-                        "(Default value is 1.0 for all classes): "
-                    ).strip()
-                    or "1.0"
-                )
-                if user_sd >= 0.0:
-                    std_dev = user_sd
-                    break
-                else:
-                    print(
-                        f"Your entered value is {user_sd}, Standard deviation should "
-                        f"not be negative!"
-                    )
-                    continue
-            except ValueError:
-                print("Your entered value is not numerical!")
+        std_dev = valid_input(
+            input_type=float,
+            condition=lambda x: x >= 0,
+            input_msg=(
+                "Enter the value of standard deviation"
+                "(Default value is 1.0 for all classes): "
+            ),
+            err_msg="Standard deviation should not be negative!",
+            default="1.0",
+        )
 
         print("-" * 100)
 
         # Trying to get number of instances in classes and theirs means to generate
         # dataset
         counts = []  # An empty list to store instance counts of classes in dataset
         for i in range(n_classes):
-            while True:
-                try:
-                    user_count = int(
-                        input(f"Enter The number of instances for class_{i+1}: ")
-                    )
-                    if user_count > 0:
-                        counts.append(user_count)
-                        break
-                    else:
-                        print(
-                            f"Your entered value is {user_count}, Number of "
-                            "instances should be positive!"
-                        )
-                        continue
-                except ValueError:
-                    print("Your entered value is not numerical!")
+            user_count = valid_input(
+                input_type=int,
+                condition=lambda x: x > 0,
+                input_msg=(f"Enter The number of instances for class_{i+1}: "),
+                err_msg="Number of instances should be positive!",
+            )
+            counts.append(user_count)
         print("-" * 100)
 
         # An empty list to store values of user-entered means of classes
         user_means = []
         for a in range(n_classes):
-            while True:
-                try:
-                    user_mean = float(
-                        input(f"Enter the value of mean for class_{a+1}: ")
-                    )
-                    if isinstance(user_mean, float):
-                        user_means.append(user_mean)
-                        break
-                    print(f"You entered an invalid value: {user_mean}")
-                except ValueError:
-                    print("Your entered value is not numerical!")
+            user_mean = valid_input(
+                input_type=float,
+                input_msg=(f"Enter the value of mean for class_{a+1}: "),
+                err_msg="This is an invalid value.",
+            )
+            user_means.append(user_mean)
         print("-" * 100)
 
         print("Standard deviation: ", std_dev)