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Fix minor typing errors in maths/ (#8959)

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* updating DIRECTORY.md

* types(maths): Fix pylance issues in maths

* reset(vsc): Reset settings changes

* Update maths/jaccard_similarity.py

Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>

* revert(erosion_operation): Revert erosion_operation

* test(jaccard_similarity): Add doctest to test alternative_union

* types(newton_raphson): Add typehints to func bodies

---------

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>
This commit is contained in:
Caeden Perelli-Harris
2023-08-15 22:27:41 +01:00
committed by GitHub
parent 7618a92fee
commit 490e645ed3
10 changed files with 65 additions and 36 deletions
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33
maths/newton_raphson.py
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@ -1,16 +1,20 @@
"""
Author: P Shreyas Shetty
Implementation of Newton-Raphson method for solving equations of kind
f(x) = 0. It is an iterative method where solution is found by the expression
x[n+1] = x[n] + f(x[n])/f'(x[n])
If no solution exists, then either the solution will not be found when iteration
limit is reached or the gradient f'(x[n]) approaches zero. In both cases, exception
is raised. If iteration limit is reached, try increasing maxiter.
"""
Author: P Shreyas Shetty
Implementation of Newton-Raphson method for solving equations of kind
f(x) = 0. It is an iterative method where solution is found by the expression
x[n+1] = x[n] + f(x[n])/f'(x[n])
If no solution exists, then either the solution will not be found when iteration
limit is reached or the gradient f'(x[n]) approaches zero. In both cases, exception
is raised. If iteration limit is reached, try increasing maxiter.
"""
import math as m
from collections.abc import Callable
DerivativeFunc = Callable[[float], float]
def calc_derivative(f, a, h=0.001):
def calc_derivative(f: DerivativeFunc, a: float, h: float = 0.001) -> float:
"""
Calculates derivative at point a for function f using finite difference
method
@ -18,7 +22,14 @@ def calc_derivative(f, a, h=0.001):
return (f(a + h) - f(a - h)) / (2 * h)
def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6, logsteps=False):
def newton_raphson(
f: DerivativeFunc,
x0: float = 0,
maxiter: int = 100,
step: float = 0.0001,
maxerror: float = 1e-6,
logsteps: bool = False,
) -> tuple[float, float, list[float]]:
a = x0 # set the initial guess
steps = [a]
error = abs(f(a))
@ -36,7 +47,7 @@ def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6, logsteps=Fa
if logsteps:
# If logstep is true, then log intermediate steps
return a, error, steps
return a, error
return a, error, []
if __name__ == "__main__":