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Reduce complexity linear_discriminant_analysis. (#2452)

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* 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>
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poloso 2020-11-10 21:35:11 -05:00 committed by GitHub
parent a6ad25c351
commit 4851942ec0
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1 changed files with 65 additions and 65 deletions
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130
machine_learning/linear_discriminant_analysis.py
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@ -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
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)
@ -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:
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)