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Fixes LGTM issues (#1745)

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* Fixes redefinition of a variable

* Fixes implementing __eq__

* Updates docstring
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onlinejudge95 2020-02-11 02:53:19 +05:30 committed by GitHub
parent 80718bd880
commit 6fdd53c676
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2 changed files with 21 additions and 11 deletions
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2
dynamic_programming/max_sum_contiguous_subsequence.py
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@ -6,7 +6,7 @@ def max_subarray_sum(nums: list) -> int:
if not nums:
return 0
n = len(nums)
s = [0] * n
res, s, s_pre = nums[0], nums[0], nums[0]
for i in range(1, n):
s = max(nums[i], s_pre + nums[i])

30
searches/hill_climbing.py
View File

@ -4,17 +4,18 @@ import math
class SearchProblem:
"""
A interface to define search problems. The interface will be illustrated using
the example of mathematical function.
An interface to define search problems.
The interface will be illustrated using the example of mathematical function.
"""
def __init__(self, x: int, y: int, step_size: int, function_to_optimize):
"""
The constructor of the search problem.
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
x: the x coordinate of the current search state.
y: the y coordinate of the current search state.
step_size: size of the step to take when looking for neighbors.
function_to_optimize: a function to optimize having the signature f(x, y).
"""
self.x = x
self.y = y
@ -63,6 +64,14 @@ class SearchProblem:
"""
return hash(str(self))
def __eq__(self, obj):
"""
Check if the 2 objects are equal.
"""
if isinstance(obj, SearchProblem):
return hash(str(self)) == hash(str(obj))
return False
def __str__(self):
"""
string representation of the current search state.
@ -85,10 +94,11 @@ def hill_climbing(
max_iter: int = 10000,
) -> SearchProblem:
"""
implementation of the hill climbling algorithm. We start with a given state, find
all its neighbors, move towards the neighbor which provides the maximum (or
minimum) change. We keep doing this until we are at a state where we do not
have any neighbors which can improve the solution.
Implementation of the hill climbling algorithm.
We start with a given state, find all its neighbors,
move towards the neighbor which provides the maximum (or minimum) change.
We keep doing this until we are at a state where we do not have any
neighbors which can improve the solution.
Args:
search_prob: The search state at the start.
find_max: If True, the algorithm should find the maximum else the minimum.