Reduce complexity linear_discriminant_analysis. (#2452)

* Reduce complexity linear_discriminant_analysis.

* Fix whitespace

* Update machine_learning/linear_discriminant_analysis.py

Co-authored-by: Dhruv Manilawala <dhruvmanila@gmail.com>

* fixup! Format Python code with psf/black push

* Fix format to surpass pre-commit tests

* updating DIRECTORY.md

* Update machine_learning/linear_discriminant_analysis.py

Co-authored-by: Dhruv Manilawala <dhruvmanila@gmail.com>

* fixup! Format Python code with psf/black push

Co-authored-by: Dhruv Manilawala <dhruvmanila@gmail.com>
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

machine_learning/linear_discriminant_analysis.py

@@ -2,6 +2,7 @@
     Linear Discriminant Analysis
 
 
+
     Assumptions About Data :
         1. The input variables has a gaussian distribution.
         2. The variance calculated for each input variables by class grouping is the
@@ -44,6 +45,7 @@
 from math import log
 from os import name, system
 from random import gauss, seed
+from typing import Callable, TypeVar
 
 
 # Make a training dataset drawn from a gaussian distribution
@@ -245,6 +247,40 @@ 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"{user_input}: {err_msg}")
+                continue
+        except ValueError:
+            print(
+                f"{user_input}: Incorrect input type, expected {input_type.__name__!r}"
+            )
+
+
 # Main Function
 def main():
     """ This function starts execution phase """
@@ -254,48 +290,26 @@ 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
+        n_classes = valid_input(
-        while True:
+            input_type=int,
-            try:
+            condition=lambda x: x > 0,
-                user_input = int(
+            input_msg="Enter the number of classes (Data Groupings): ",
-                    input("Enter the number of classes (Data Groupings): ").strip()
+            err_msg="Number of classes should be positive!",
         )
-                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!")
 
         print("-" * 100)
 
-        std_dev = 1.0  # Default value for standard deviation of dataset
         # Trying to get the value of standard deviation
-        while True:
+        std_dev = valid_input(
-            try:
+            input_type=float,
-                user_sd = float(
+            condition=lambda x: x >= 0,
-                    input(
+            input_msg=(
                 "Enter the value of standard deviation"
                 "(Default value is 1.0 for all classes): "
-                    ).strip()
+            ),
-                    or "1.0"
+            err_msg="Standard deviation should not be negative!",
+            default="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!")
 
         print("-" * 100)
 
@@ -303,38 +317,24 @@ def main():
         # dataset
         counts = []  # An empty list to store instance counts of classes in dataset
         for i in range(n_classes):
-            while True:
+            user_count = valid_input(
-                try:
+                input_type=int,
-                    user_count = int(
+                condition=lambda x: x > 0,
-                        input(f"Enter The number of instances for class_{i+1}: ")
+                input_msg=(f"Enter The number of instances for class_{i+1}: "),
+                err_msg="Number of instances should be positive!",
             )
-                    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!")
         print("-" * 100)
 
         # An empty list to store values of user-entered means of classes
         user_means = []
         for a in range(n_classes):
-            while True:
+            user_mean = valid_input(
-                try:
+                input_type=float,
-                    user_mean = float(
+                input_msg=(f"Enter the value of mean for class_{a+1}: "),
-                        input(f"Enter the value of mean for class_{a+1}: ")
+                err_msg="This is an invalid value.",
             )
-                    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!")
         print("-" * 100)
 
         print("Standard deviation: ", std_dev)