Skip to content

Reduce complexity linear_discriminant_analysis. #2452

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 8 commits into from
Nov 11, 2020
Merged

Reduce complexity linear_discriminant_analysis. #2452

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
8 commits
Select commit Hold shift + click to select a range
978375f
Reduce complexity linear_discriminant_analysis.
Sep 19, 2020
8429db6
Fix whitespace
Sep 19, 2020
f772d45
Update machine_learning/linear_discriminant_analysis.py
NumberPiOso Nov 10, 2020
06a38a6
fixup! Format Python code with psf/black push
Nov 10, 2020
b69d426
Fix format to surpass pre-commit tests
Nov 10, 2020
ddf4c58
updating DIRECTORY.md
Nov 10, 2020
cd532f1
Update machine_learning/linear_discriminant_analysis.py
NumberPiOso Nov 10, 2020
db82688
fixup! Format Python code with psf/black push
Nov 10, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 2 additions & 0 deletions DIRECTORY.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -86,6 +86,7 @@

## Conversions
* [Binary To Decimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_decimal.py)
* [Binary To Octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/binary_to_octal.py)
* [Decimal To Any](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_any.py)
* [Decimal To Binary](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary.py)
* [Decimal To Hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_hexadecimal.py)
Expand Down Expand Up @@ -709,6 +710,7 @@
## Strings
* [Aho Corasick](https://github.com/TheAlgorithms/Python/blob/master/strings/aho_corasick.py)
* [Boyer Moore Search](https://github.com/TheAlgorithms/Python/blob/master/strings/boyer_moore_search.py)
* [Can String Be Rearranged As Palindrome](https://github.com/TheAlgorithms/Python/blob/master/strings/can_string_be_rearranged_as_palindrome.py)
* [Capitalize](https://github.com/TheAlgorithms/Python/blob/master/strings/capitalize.py)
* [Check Anagrams](https://github.com/TheAlgorithms/Python/blob/master/strings/check_anagrams.py)
* [Check Pangram](https://github.com/TheAlgorithms/Python/blob/master/strings/check_pangram.py)
Expand Down
130 changes: 65 additions & 65 deletions machine_learning/linear_discriminant_analysis.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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 """
Expand All @@ -254,87 +290,51 @@ 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)
Expand Down
4 changes: 3 additions & 1 deletion strings/can_string_be_rearranged_as_palindrome.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,9 @@
# Counter is faster for long strings and non-Counter is faster for short strings.


def can_string_be_rearranged_as_palindrome_counter(input_str: str = "",) -> bool:
def can_string_be_rearranged_as_palindrome_counter(
input_str: str = "",
) -> bool:
"""
A Palindrome is a String that reads the same forward as it does backwards.
Examples of Palindromes mom, dad, malayalam
Expand Down