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Tighten up psf/black and flake8 (#2024)

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* Tighten up psf/black and flake8

* Fix some tests

* Fix some E741

* Fix some E741

* updating DIRECTORY.md

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
This commit is contained in:
Christian Clauss 2020-05-22 08:10:11 +02:00 committed by GitHub
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commit 1f8a21d727
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124 changed files with 583 additions and 495 deletions
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10
.travis.yml
View File

@ -4,13 +4,13 @@ language: python
python: 3.8
cache: pip
before_install: pip install --upgrade pip setuptools six
install: pip install -r requirements.txt
install: pip install black flake8
before_script:
- black --check . || true
- IGNORE=E123,E203,E265,E266,E302,E401,E402,E712,E731,E741,E743,F811,F841,W291,W293,W503
- flake8 . --count --ignore=$IGNORE --max-complexity=25 --max-line-length=127 --show-source --statistics
script:
- black --check .
- flake8 --ignore=E203,W503 --max-complexity=25 --max-line-length=120 --statistics --count .
- scripts/validate_filenames.py # no uppercase, no spaces, in a directory
- pip install -r requirements.txt # fast fail on black, flake8, validate_filenames
script:
- mypy --ignore-missing-imports .
- pytest --doctest-modules --cov-report=term-missing:skip-covered --cov=. .
after_success:

3
DIRECTORY.md
View File

@ -222,6 +222,7 @@
* [Bellman Ford](https://github.com/TheAlgorithms/Python/blob/master/graphs/bellman_ford.py)
* [Bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs.py)
* [Bfs Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_shortest_path.py)
* [Bidirectional A Star](https://github.com/TheAlgorithms/Python/blob/master/graphs/bidirectional_a_star.py)
* [Breadth First Search](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search.py)
* [Breadth First Search Shortest Path](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search_shortest_path.py)
* [Check Bipartite Graph Bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_bfs.py)
@ -242,6 +243,7 @@
* [Graph List](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py)
* [Graph Matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py)
* [Graphs Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py)
* [Greedy Best First](https://github.com/TheAlgorithms/Python/blob/master/graphs/greedy_best_first.py)
* [Kahns Algorithm Long](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_long.py)
* [Kahns Algorithm Topo](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_topo.py)
* [Minimum Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py)
@ -409,6 +411,7 @@
* [Fischer Yates Shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py)
* [Frequency Finder](https://github.com/TheAlgorithms/Python/blob/master/other/frequency_finder.py)
* [Game Of Life](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life.py)
* [Gauss Easter](https://github.com/TheAlgorithms/Python/blob/master/other/gauss_easter.py)
* [Greedy](https://github.com/TheAlgorithms/Python/blob/master/other/greedy.py)
* [Integeration By Simpson Approx](https://github.com/TheAlgorithms/Python/blob/master/other/integeration_by_simpson_approx.py)
* [Largest Subarray Sum](https://github.com/TheAlgorithms/Python/blob/master/other/largest_subarray_sum.py)

1
arithmetic_analysis/newton_forward_interpolation.py
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@ -2,6 +2,7 @@
import math
# for calculating u value
def ucal(u, p):
"""

12
backtracking/coloring.py
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@ -18,7 +18,7 @@ def valid_coloring(
>>> neighbours = [0,1,0,1,0]
>>> colored_vertices = [0, 2, 1, 2, 0]
>>> color = 1
>>> valid_coloring(neighbours, colored_vertices, color)
True
@ -37,11 +37,11 @@ def valid_coloring(
def util_color(
graph: List[List[int]], max_colors: int, colored_vertices: List[int], index: int
) -> bool:
"""
"""
Pseudo-Code
Base Case:
1. Check if coloring is complete
1. Check if coloring is complete
1.1 If complete return True (meaning that we successfully colored graph)
Recursive Step:
@ -60,7 +60,7 @@ def util_color(
>>> max_colors = 3
>>> colored_vertices = [0, 1, 0, 0, 0]
>>> index = 3
>>> util_color(graph, max_colors, colored_vertices, index)
True
@ -87,11 +87,11 @@ def util_color(
def color(graph: List[List[int]], max_colors: int) -> List[int]:
"""
"""
Wrapper function to call subroutine called util_color
which will either return True or False.
If True is returned colored_vertices list is filled with correct colorings
>>> graph = [[0, 1, 0, 0, 0],
... [1, 0, 1, 0, 1],
... [0, 1, 0, 1, 0],

30
backtracking/hamiltonian_cycle.py
View File

@ -1,9 +1,9 @@
"""
A Hamiltonian cycle (Hamiltonian circuit) is a graph cycle
A Hamiltonian cycle (Hamiltonian circuit) is a graph cycle
through a graph that visits each node exactly once.
Determining whether such paths and cycles exist in graphs
Determining whether such paths and cycles exist in graphs
is the 'Hamiltonian path problem', which is NP-complete.
Wikipedia: https://en.wikipedia.org/wiki/Hamiltonian_path
"""
from typing import List
@ -18,7 +18,7 @@ def valid_connection(
2. Next vertex should not be in path
If both validations succeeds we return true saying that it is possible to connect this vertices
either we return false
Case 1:Use exact graph as in main function, with initialized values
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
@ -56,11 +56,11 @@ def util_hamilton_cycle(graph: List[List[int]], path: List[int], curr_ind: int)
Recursive Step:
2. Iterate over each vertex
Check if next vertex is valid for transiting from current vertex
2.1 Remember next vertex as next transition
2.1 Remember next vertex as next transition
2.2 Do recursive call and check if going to this vertex solves problem
2.3 if next vertex leads to solution return True
2.4 else backtrack, delete remembered vertex
Case 1: Use exact graph as in main function, with initialized values
>>> graph = [[0, 1, 0, 1, 0],
... [1, 0, 1, 1, 1],
@ -111,12 +111,12 @@ def hamilton_cycle(graph: List[List[int]], start_index: int = 0) -> List[int]:
Wrapper function to call subroutine called util_hamilton_cycle,
which will either return array of vertices indicating hamiltonian cycle
or an empty list indicating that hamiltonian cycle was not found.
Case 1:
Following graph consists of 5 edges.
Case 1:
Following graph consists of 5 edges.
If we look closely, we can see that there are multiple Hamiltonian cycles.
For example one result is when we iterate like:
For example one result is when we iterate like:
(0)->(1)->(2)->(4)->(3)->(0)
(0)---(1)---(2)
| / \ |
| / \ |
@ -130,10 +130,10 @@ def hamilton_cycle(graph: List[List[int]], start_index: int = 0) -> List[int]:
... [0, 1, 1, 1, 0]]
>>> hamilton_cycle(graph)
[0, 1, 2, 4, 3, 0]
Case 2:
Case 2:
Same Graph as it was in Case 1, changed starting index from default to 3
(0)---(1)---(2)
| / \ |
| / \ |
@ -147,11 +147,11 @@ def hamilton_cycle(graph: List[List[int]], start_index: int = 0) -> List[int]:
... [0, 1, 1, 1, 0]]
>>> hamilton_cycle(graph, 3)
[3, 0, 1, 2, 4, 3]
Case 3:
Following Graph is exactly what it was before, but edge 3-4 is removed.
Result is that there is no Hamiltonian Cycle anymore.
(0)---(1)---(2)
| / \ |
| / \ |

4
backtracking/minimax.py
View File

@ -1,10 +1,10 @@
import math
""" Minimax helps to achieve maximum score in a game by checking all possible moves
depth is current depth in game tree.
depth is current depth in game tree.
nodeIndex is index of current node in scores[].
if move is of maximizer return true else false
leaves of game tree is stored in scores[]
leaves of game tree is stored in scores[]
height is maximum height of Game tree
"""

16
backtracking/n_queens.py
View File

@ -1,9 +1,9 @@
"""
The nqueens problem is of placing N queens on a N * N
The nqueens problem is of placing N queens on a N * N
chess board such that no queen can attack any other queens placed
on that chess board.
This means that one queen cannot have any other queen on its horizontal, vertical and
This means that one queen cannot have any other queen on its horizontal, vertical and
diagonal lines.
"""
@ -12,7 +12,7 @@ solution = []
def isSafe(board, row, column):
"""
This function returns a boolean value True if it is safe to place a queen there considering
This function returns a boolean value True if it is safe to place a queen there considering
the current state of the board.
Parameters :
@ -40,13 +40,13 @@ def isSafe(board, row, column):
def solve(board, row):
"""
It creates a state space tree and calls the safe function until it receives a
False Boolean and terminates that branch and backtracks to the next
It creates a state space tree and calls the safe function until it receives a
False Boolean and terminates that branch and backtracks to the next
possible solution branch.
"""
if row >= len(board):
"""
If the row number exceeds N we have board with a successful combination
If the row number exceeds N we have board with a successful combination
and that combination is appended to the solution list and the board is printed.
"""
@ -56,9 +56,9 @@ def solve(board, row):
return
for i in range(len(board)):
"""
For every row it iterates through each column to check if it is feasible to place a
For every row it iterates through each column to check if it is feasible to place a
queen there.
If all the combinations for that particular branch are successful the board is
If all the combinations for that particular branch are successful the board is
reinitialized for the next possible combination.
"""
if isSafe(board, row, i):

26
ciphers/decrypt_caesar_with_chi_squared.py
View File

@ -1,9 +1,12 @@
#!/usr/bin/env python3
def decrypt_caesar_with_chi_squared(
ciphertext: str,
cipher_alphabet=None,
frequencies_dict=None,
case_sensetive: bool = False,
) -> list:
) -> tuple:
"""
Basic Usage
===========
@ -96,15 +99,19 @@ def decrypt_caesar_with_chi_squared(
Further Reading
================
* http://practicalcryptography.com/cryptanalysis/text-characterisation/chi-squared-statistic/
* http://practicalcryptography.com/cryptanalysis/text-characterisation/chi-squared-
statistic/
* https://en.wikipedia.org/wiki/Letter_frequency
* https://en.wikipedia.org/wiki/Chi-squared_test
* https://en.m.wikipedia.org/wiki/Caesar_cipher
Doctests
========
>>> decrypt_caesar_with_chi_squared('dof pz aol jhlzhy jpwoly zv wvwbshy? pa pz avv lhzf av jyhjr!')
(7, 3129.228005747531, 'why is the caesar cipher so popular? it is too easy to crack!')
>>> decrypt_caesar_with_chi_squared(
... 'dof pz aol jhlzhy jpwoly zv wvwbshy? pa pz avv lhzf av jyhjr!'
... ) # doctest: +NORMALIZE_WHITESPACE
(7, 3129.228005747531,
'why is the caesar cipher so popular? it is too easy to crack!')
>>> decrypt_caesar_with_chi_squared('crybd cdbsxq')
(10, 233.35343938980898, 'short string')
@ -172,7 +179,7 @@ def decrypt_caesar_with_chi_squared(
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
chi_squared_statistic = 0
chi_squared_statistic = 0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
@ -181,7 +188,8 @@ def decrypt_caesar_with_chi_squared(
# Get the amount of times the letter occurs in the message
occurrences = decrypted_with_shift.count(letter)
# Get the excepcted amount of times the letter should appear based on letter frequencies
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
expected = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
@ -194,7 +202,8 @@ def decrypt_caesar_with_chi_squared(
# Get the amount of times the letter occurs in the message
occurrences = decrypted_with_shift.count(letter)
# Get the excepcted amount of times the letter should appear based on letter frequencies
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
expected = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
@ -209,7 +218,8 @@ def decrypt_caesar_with_chi_squared(
decrypted_with_shift,
]
# Get the most likely cipher by finding the cipher with the smallest chi squared statistic
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
most_likely_cipher = min(
chi_squared_statistic_values, key=chi_squared_statistic_values.get
)

4
ciphers/elgamal_key_generator.py
View File

@ -1,7 +1,9 @@
import os
import random
import sys
import rabin_miller as rabinMiller, cryptomath_module as cryptoMath
import cryptomath_module as cryptoMath
import rabin_miller as rabinMiller
min_primitive_root = 3

8
ciphers/mixed_keyword_cypher.py
View File

@ -25,7 +25,7 @@ def mixed_keyword(key="college", pt="UNIVERSITY"):
for i in key:
if i not in temp:
temp.append(i)
l = len(temp)
len_temp = len(temp)
# print(temp)
alpha = []
modalpha = []
@ -40,17 +40,17 @@ def mixed_keyword(key="college", pt="UNIVERSITY"):
k = 0
for i in range(r):
t = []
for j in range(l):
for j in range(len_temp):
t.append(temp[k])
if not (k < 25):
break
k += 1
modalpha.append(t)
# print(modalpha)
d = dict()
d = {}
j = 0
k = 0
for j in range(l):
for j in range(len_temp):
for i in modalpha:
if not (len(i) - 1 >= j):
break

3
ciphers/simple_substitution_cipher.py
View File

@ -1,4 +1,5 @@
import sys, random
import random
import sys
LETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"

5
ciphers/transposition_cipher_encrypt_decrypt_file.py
View File

@ -1,4 +1,7 @@
import time, os, sys
import os
import sys
import time
import transposition_cipher as transCipher

2
conversions/decimal_to_octal.py
View File

@ -8,7 +8,7 @@ import math
def decimal_to_octal(num: int) -> str:
"""Convert a Decimal Number to an Octal Number.
>>> all(decimal_to_octal(i) == oct(i) for i in (0, 2, 8, 64, 65, 216, 255, 256, 512))
True
"""

18
data_structures/binary_tree/avl_tree.py
View File

@ -89,8 +89,8 @@ def leftrotation(node):
Bl Br UB Br C
/
UB
UB = unbalanced node
UB = unbalanced node
"""
print("left rotation node:", node.getdata())
ret = node.getleft()
@ -120,11 +120,11 @@ def rightrotation(node):
def rlrotation(node):
r"""
A A Br
A A Br
/ \ / \ / \
B C RR Br C LR B A
/ \ --> / \ --> / / \
Bl Br B UB Bl UB C
Bl Br B UB Bl UB C
\ /
UB Bl
RR = rightrotation LR = leftrotation
@ -276,13 +276,13 @@ class AVLtree:
if __name__ == "__main__":
t = AVLtree()
t.traversale()
l = list(range(10))
random.shuffle(l)
for i in l:
lst = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
t.traversale()
random.shuffle(l)
for i in l:
random.shuffle(lst)
for i in lst:
t.del_node(i)
t.traversale()

7
data_structures/binary_tree/basic_binary_tree.py
View File

@ -1,4 +1,9 @@
class Node: # This is the Class Node with a constructor that contains data variable to type data and left, right pointers.
class Node:
"""
This is the Class Node with a constructor that contains data variable to type data
and left, right pointers.
"""
def __init__(self, data):
self.data = data
self.left = None

33
data_structures/binary_tree/lazy_segment_tree.py
View File

@ -16,8 +16,8 @@ class SegmentTree:
def right(self, idx):
return idx * 2 + 1
def build(self, idx, l, r, A):
if l == r:
def build(self, idx, l, r, A): # noqa: E741
if l == r: # noqa: E741
self.st[idx] = A[l - 1]
else:
mid = (l + r) // 2
@ -25,14 +25,16 @@ class SegmentTree:
self.build(self.right(idx), mid + 1, r, A)
self.st[idx] = max(self.st[self.left(idx)], self.st[self.right(idx)])
# update with O(lg N) (Normal segment tree without lazy update will take O(Nlg N) for each update)
def update(
self, idx, l, r, a, b, val
): # update(1, 1, N, a, b, v) for update val v to [a,b]
if self.flag[idx] == True:
# update with O(lg N) (Normal segment tree without lazy update will take O(Nlg N)
# for each update)
def update(self, idx, l, r, a, b, val): # noqa: E741
"""
update(1, 1, N, a, b, v) for update val v to [a,b]
"""
if self.flag[idx] is True:
self.st[idx] = self.lazy[idx]
self.flag[idx] = False
if l != r:
if l != r: # noqa: E741
self.lazy[self.left(idx)] = self.lazy[idx]
self.lazy[self.right(idx)] = self.lazy[idx]
self.flag[self.left(idx)] = True
@ -40,9 +42,9 @@ class SegmentTree:
if r < a or l > b:
return True
if l >= a and r <= b:
if l >= a and r <= b: # noqa: E741
self.st[idx] = val
if l != r:
if l != r: # noqa: E741
self.lazy[self.left(idx)] = val
self.lazy[self.right(idx)] = val
self.flag[self.left(idx)] = True
@ -55,18 +57,21 @@ class SegmentTree:
return True
# query with O(lg N)
def query(self, idx, l, r, a, b): # query(1, 1, N, a, b) for query max of [a,b]
if self.flag[idx] == True:
def query(self, idx, l, r, a, b): # noqa: E741
"""
query(1, 1, N, a, b) for query max of [a,b]
"""
if self.flag[idx] is True:
self.st[idx] = self.lazy[idx]
self.flag[idx] = False
if l != r:
if l != r: # noqa: E741
self.lazy[self.left(idx)] = self.lazy[idx]
self.lazy[self.right(idx)] = self.lazy[idx]
self.flag[self.left(idx)] = True
self.flag[self.right(idx)] = True
if r < a or l > b:
return -math.inf
if l >= a and r <= b:
if l >= a and r <= b: # noqa: E741
return self.st[idx]
mid = (l + r) // 2
q1 = self.query(self.left(idx), l, mid, a, b)

15
data_structures/binary_tree/non_recursive_segment_tree.py
View File

@ -1,6 +1,7 @@
"""
A non-recursive Segment Tree implementation with range query and single element update,
works virtually with any list of the same type of elements with a "commutative" combiner.
works virtually with any list of the same type of elements with a "commutative"
combiner.
Explanation:
https://www.geeksforgeeks.org/iterative-segment-tree-range-minimum-query/
@ -22,7 +23,8 @@ https://www.geeksforgeeks.org/segment-tree-efficient-implementation/
>>> st.update(4, 1)
>>> st.query(3, 4)
0
>>> st = SegmentTree([[1, 2, 3], [3, 2, 1], [1, 1, 1]], lambda a, b: [a[i] + b[i] for i in range(len(a))])
>>> st = SegmentTree([[1, 2, 3], [3, 2, 1], [1, 1, 1]], lambda a, b: [a[i] + b[i] for i
... in range(len(a))])
>>> st.query(0, 1)
[4, 4, 4]
>>> st.query(1, 2)
@ -47,7 +49,8 @@ class SegmentTree:
>>> SegmentTree(['a', 'b', 'c'], lambda a, b: '{}{}'.format(a, b)).query(0, 2)
'abc'
>>> SegmentTree([(1, 2), (2, 3), (3, 4)], lambda a, b: (a[0] + b[0], a[1] + b[1])).query(0, 2)
>>> SegmentTree([(1, 2), (2, 3), (3, 4)],
... lambda a, b: (a[0] + b[0], a[1] + b[1])).query(0, 2)
(6, 9)
"""
self.N = len(arr)
@ -78,7 +81,7 @@ class SegmentTree:
p = p // 2
self.st[p] = self.fn(self.st[p * 2], self.st[p * 2 + 1])
def query(self, l: int, r: int) -> T:
def query(self, l: int, r: int) -> T: # noqa: E741
"""
Get range query value in log(N) time
:param l: left element index
@ -95,9 +98,9 @@ class SegmentTree:
>>> st.query(2, 3)
7
"""
l, r = l + self.N, r + self.N
l, r = l + self.N, r + self.N # noqa: E741
res = None
while l <= r:
while l <= r: # noqa: E741
if l % 2 == 1:
res = self.st[l] if res is None else self.fn(res, self.st[l])
if r % 2 == 0:

22
data_structures/binary_tree/segment_tree.py
View File

@ -15,8 +15,8 @@ class SegmentTree:
def right(self, idx):
return idx * 2 + 1
def build(self, idx, l, r):
if l == r:
def build(self, idx, l, r): # noqa: E741
if l == r: # noqa: E741
self.st[idx] = A[l]
else:
mid = (l + r) // 2
@ -27,12 +27,13 @@ class SegmentTree:
def update(self, a, b, val):
return self.update_recursive(1, 0, self.N - 1, a - 1, b - 1, val)
def update_recursive(
self, idx, l, r, a, b, val
): # update(1, 1, N, a, b, v) for update val v to [a,b]
def update_recursive(self, idx, l, r, a, b, val): # noqa: E741
"""
update(1, 1, N, a, b, v) for update val v to [a,b]
"""
if r < a or l > b:
return True
if l == r:
if l == r: # noqa: E741
self.st[idx] = val
return True
mid = (l + r) // 2
@ -44,12 +45,13 @@ class SegmentTree:
def query(self, a, b):
return self.query_recursive(1, 0, self.N - 1, a - 1, b - 1)
def query_recursive(
self, idx, l, r, a, b
): # query(1, 1, N, a, b) for query max of [a,b]
def query_recursive(self, idx, l, r, a, b): # noqa: E741
"""
query(1, 1, N, a, b) for query max of [a,b]
"""
if r < a or l > b:
return -math.inf
if l >= a and r <= b:
if l >= a and r <= b: # noqa: E741
return self.st[idx]
mid = (l + r) // 2
q1 = self.query_recursive(self.left(idx), l, mid, a, b)

5
data_structures/binary_tree/treap.py
View File

@ -1,3 +1,5 @@
# flake8: noqa
from random import random
from typing import Tuple
@ -161,7 +163,8 @@ def main():
"""After each command, program prints treap"""
root = None
print(
"enter numbers to create a tree, + value to add value into treap, - value to erase all nodes with value. 'q' to quit. "
"enter numbers to create a tree, + value to add value into treap, "
"- value to erase all nodes with value. 'q' to quit. "
)
args = input()

2
data_structures/hashing/quadratic_probing.py
View File

@ -5,7 +5,7 @@ from hash_table import HashTable
class QuadraticProbing(HashTable):
"""
Basic Hash Table example with open addressing using Quadratic Probing
Basic Hash Table example with open addressing using Quadratic Probing
"""
def __init__(self, *args, **kwargs):

2
data_structures/heap/binomial_heap.py
View File

@ -1,3 +1,5 @@
# flake8: noqa
"""
Binomial Heap
Reference: Advanced Data Structures, Peter Brass

4
data_structures/heap/min_heap.py
View File

@ -66,7 +66,7 @@ class MinHeap:
# this is min-heapify method
def sift_down(self, idx, array):
while True:
l = self.get_left_child_idx(idx)
l = self.get_left_child_idx(idx) # noqa: E741
r = self.get_right_child_idx(idx)
smallest = idx
@ -132,7 +132,7 @@ class MinHeap:
self.sift_up(self.idx_of_element[node])
## USAGE
# USAGE
r = Node("R", -1)
b = Node("B", 6)

8
data_structures/linked_list/deque_doubly.py
View File

@ -1,5 +1,5 @@
"""
Implementing Deque using DoublyLinkedList ...
Implementing Deque using DoublyLinkedList ...
Operations:
1. insertion in the front -> O(1)
2. insertion in the end -> O(1)
@ -61,7 +61,7 @@ class _DoublyLinkedBase:
class LinkedDeque(_DoublyLinkedBase):
def first(self):
""" return first element
""" return first element
>>> d = LinkedDeque()
>>> d.add_first('A').first()
'A'
@ -84,7 +84,7 @@ class LinkedDeque(_DoublyLinkedBase):
raise Exception("List is empty")
return self._trailer._prev._data
### DEque Insert Operations (At the front, At the end) ###
# DEque Insert Operations (At the front, At the end)
def add_first(self, element):
""" insertion in the front
@ -100,7 +100,7 @@ class LinkedDeque(_DoublyLinkedBase):
"""
return self._insert(self._trailer._prev, element, self._trailer)
### DEqueu Remove Operations (At the front, At the end) ###
# DEqueu Remove Operations (At the front, At the end)
def remove_first(self):
""" removal from the front

2
data_structures/linked_list/middle_element_of_linked_list.py
View File

@ -43,7 +43,7 @@ class LinkedList:
-20
>>> link.middle_element()
12
>>>
>>>
"""
slow_pointer = self.head
fast_pointer = self.head

20
data_structures/stacks/postfix_evaluation.py
View File

@ -22,7 +22,7 @@ import operator as op
def Solve(Postfix):
Stack = []
Div = lambda x, y: int(x / y) # integer division operation
Div = lambda x, y: int(x / y) # noqa: E731 integer division operation
Opr = {
"^": op.pow,
"*": op.mul,
@ -38,29 +38,27 @@ def Solve(Postfix):
for x in Postfix:
if x.isdigit(): # if x in digit
Stack.append(x) # append x to stack
print(
x.rjust(8), ("push(" + x + ")").ljust(12), ",".join(Stack), sep=" | "
) # output in tabular format
# output in tabular format
print(x.rjust(8), ("push(" + x + ")").ljust(12), ",".join(Stack), sep=" | ")
else:
B = Stack.pop() # pop stack
print(
"".rjust(8), ("pop(" + B + ")").ljust(12), ",".join(Stack), sep=" | "
) # output in tabular format
# output in tabular format
print("".rjust(8), ("pop(" + B + ")").ljust(12), ",".join(Stack), sep=" | ")
A = Stack.pop() # pop stack
print(
"".rjust(8), ("pop(" + A + ")").ljust(12), ",".join(Stack), sep=" | "
) # output in tabular format
# output in tabular format
print("".rjust(8), ("pop(" + A + ")").ljust(12), ",".join(Stack), sep=" | ")
Stack.append(
str(Opr[x](int(A), int(B)))
) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8),
("push(" + A + x + B + ")").ljust(12),
",".join(Stack),
sep=" | ",
) # output in tabular format
)
return int(Stack[0])

2
data_structures/trie/trie.py
View File

@ -1,7 +1,7 @@
"""
A Trie/Prefix Tree is a kind of search tree used to provide quick lookup
of words/patterns in a set of words. A basic Trie however has O(n^2) space complexity
making it impractical in practice. It however provides O(max(search_string, length of longest word))
making it impractical in practice. It however provides O(max(search_string, length of longest word))
lookup time making it an optimal approach when space is not an issue.
"""

6
digital_image_processing/dithering/burkes.py
View File

@ -54,9 +54,9 @@ class Burkes:
current_error = greyscale + self.error_table[x][y] - 255
"""
Burkes error propagation (`*` is current pixel):
* 8/32 4/32
2/32 4/32 8/32 4/32 2/32
* 8/32 4/32
2/32 4/32 8/32 4/32 2/32
"""
self.error_table[y][x + 1] += int(8 / 32 * current_error)
self.error_table[y][x + 2] += int(4 / 32 * current_error)

4
digital_image_processing/edge_detection/canny.py
View File

@ -29,8 +29,8 @@ def canny(image, threshold_low=15, threshold_high=30, weak=128, strong=255):
dst = np.zeros((image_row, image_col))
"""
Non-maximum suppression. If the edge strength of the current pixel is the largest compared to the other pixels
in the mask with the same direction, the value will be preserved. Otherwise, the value will be suppressed.
Non-maximum suppression. If the edge strength of the current pixel is the largest compared to the other pixels
in the mask with the same direction, the value will be preserved. Otherwise, the value will be suppressed.
"""
for row in range(1, image_row - 1):
for col in range(1, image_col - 1):

72
digital_image_processing/index_calculation.py
View File

@ -6,26 +6,28 @@
# Imports
import numpy as np
# Class implemented to calculus the index
class IndexCalculation:
"""
# Class Summary
This algorithm consists in calculating vegetation indices, these indices
can be used for precision agriculture for example (or remote sensing). There are
functions to define the data and to calculate the implemented indices.
This algorithm consists in calculating vegetation indices, these
indices can be used for precision agriculture for example (or remote
sensing). There are functions to define the data and to calculate the
implemented indices.
# Vegetation index
https://en.wikipedia.org/wiki/Vegetation_Index
A Vegetation Index (VI) is a spectral transformation of two or more bands designed
to enhance the contribution of vegetation properties and allow reliable spatial and
temporal inter-comparisons of terrestrial photosynthetic activity and canopy
structural variations
A Vegetation Index (VI) is a spectral transformation of two or more bands
designed to enhance the contribution of vegetation properties and allow
reliable spatial and temporal inter-comparisons of terrestrial
photosynthetic activity and canopy structural variations
# Information about channels (Wavelength range for each)
* nir - near-infrared
https://www.malvernpanalytical.com/br/products/technology/near-infrared-spectroscopy
Wavelength Range 700 nm to 2500 nm
* Red Edge
* Red Edge
https://en.wikipedia.org/wiki/Red_edge
Wavelength Range 680 nm to 730 nm
* red
@ -38,7 +40,7 @@ class IndexCalculation:
https://en.wikipedia.org/wiki/Color
Wavelength Range 520 nm to 560 nm
# Implemented index list
#"abbreviationOfIndexName" -- list of channels used
@ -84,17 +86,19 @@ class IndexCalculation:
#"NDRE" -- redEdge, nir
#list of all index implemented
#allIndex = ["ARVI2", "CCCI", "CVI", "GLI", "NDVI", "BNDVI", "redEdgeNDVI", "GNDVI",
"GBNDVI", "GRNDVI", "RBNDVI", "PNDVI", "ATSAVI", "BWDRVI", "CIgreen",
"CIrededge", "CI", "CTVI", "GDVI", "EVI", "GEMI", "GOSAVI", "GSAVI",
"Hue", "IVI", "IPVI", "I", "RVI", "MRVI", "MSAVI", "NormG", "NormNIR",
"NormR", "NGRDI", "RI", "S", "IF", "DVI", "TVI", "NDRE"]
#allIndex = ["ARVI2", "CCCI", "CVI", "GLI", "NDVI", "BNDVI", "redEdgeNDVI",
"GNDVI", "GBNDVI", "GRNDVI", "RBNDVI", "PNDVI", "ATSAVI",
"BWDRVI", "CIgreen", "CIrededge", "CI", "CTVI", "GDVI", "EVI",
"GEMI", "GOSAVI", "GSAVI", "Hue", "IVI", "IPVI", "I", "RVI",
"MRVI", "MSAVI", "NormG", "NormNIR", "NormR", "NGRDI", "RI",
"S", "IF", "DVI", "TVI", "NDRE"]
#list of index with not blue channel
#notBlueIndex = ["ARVI2", "CCCI", "CVI", "NDVI", "redEdgeNDVI", "GNDVI", "GRNDVI",
"ATSAVI", "CIgreen", "CIrededge", "CTVI", "GDVI", "GEMI", "GOSAVI",
"GSAVI", "IVI", "IPVI", "RVI", "MRVI", "MSAVI", "NormG", "NormNIR",
"NormR", "NGRDI", "RI", "DVI", "TVI", "NDRE"]
#notBlueIndex = ["ARVI2", "CCCI", "CVI", "NDVI", "redEdgeNDVI", "GNDVI",
"GRNDVI", "ATSAVI", "CIgreen", "CIrededge", "CTVI", "GDVI",
"GEMI", "GOSAVI", "GSAVI", "IVI", "IPVI", "RVI", "MRVI",
"MSAVI", "NormG", "NormNIR", "NormR", "NGRDI", "RI", "DVI",
"TVI", "NDRE"]
#list of index just with RGB channels
#RGBIndex = ["GLI", "CI", "Hue", "I", "NGRDI", "RI", "S", "IF"]
@ -121,8 +125,8 @@ class IndexCalculation:
self, index="", red=None, green=None, blue=None, redEdge=None, nir=None
):
"""
performs the calculation of the index with the values instantiated in the class
:str index: abbreviation of index name to perform
performs the calculation of the index with the values instantiated in the class
:str index: abbreviation of index name to perform
"""
self.setMatrices(red=red, green=green, blue=blue, redEdge=redEdge, nir=nir)
funcs = {
@ -213,8 +217,8 @@ class IndexCalculation:
def NDVI(self):
"""
Normalized Difference self.nir/self.red Normalized Difference Vegetation Index,
Calibrated NDVI - CDVI
Normalized Difference self.nir/self.red Normalized Difference Vegetation
Index, Calibrated NDVI - CDVI
https://www.indexdatabase.de/db/i-single.php?id=58
:return: index
"""
@ -222,7 +226,7 @@ class IndexCalculation:
def BNDVI(self):
"""
Normalized Difference self.nir/self.blue self.blue-normalized difference
Normalized Difference self.nir/self.blue self.blue-normalized difference
vegetation index
https://www.indexdatabase.de/db/i-single.php?id=135
:return: index
@ -410,7 +414,7 @@ class IndexCalculation:
"""
return (self.nir / ((self.nir + self.red) / 2)) * (self.NDVI() + 1)
def I(self):
def I(self): # noqa: E741,E743
"""
Intensity
https://www.indexdatabase.de/db/i-single.php?id=36
@ -471,8 +475,9 @@ class IndexCalculation:
def NGRDI(self):
"""
Normalized Difference self.green/self.red Normalized self.green self.red
difference index, Visible Atmospherically Resistant Indices self.green (VIself.green)
Normalized Difference self.green/self.red Normalized self.green self.red
difference index, Visible Atmospherically Resistant Indices self.green
(VIself.green)
https://www.indexdatabase.de/db/i-single.php?id=390
:return: index
"""
@ -506,7 +511,7 @@ class IndexCalculation:
def DVI(self):
"""
Simple Ratio self.nir/self.red Difference Vegetation Index, Vegetation Index
Simple Ratio self.nir/self.red Difference Vegetation Index, Vegetation Index
Number (VIN)
https://www.indexdatabase.de/db/i-single.php?id=12
:return: index
@ -535,7 +540,7 @@ nir = np.ones((1000,1000, 1),dtype="float64") * 52200
# Examples of how to use the class
# instantiating the class
# instantiating the class
cl = IndexCalculation()
# instantiating the class with the values
@ -556,9 +561,12 @@ indexValue_form2 = cl.CCCI()
indexValue_form3 = cl.calculation("CCCI", red=red, green=green, blue=blue,
redEdge=redEdge, nir=nir).astype(np.float64)
print("Form 1: "+np.array2string(indexValue_form1, precision=20, separator=', ', floatmode='maxprec_equal'))
print("Form 2: "+np.array2string(indexValue_form2, precision=20, separator=', ', floatmode='maxprec_equal'))
print("Form 3: "+np.array2string(indexValue_form3, precision=20, separator=', ', floatmode='maxprec_equal'))
print("Form 1: "+np.array2string(indexValue_form1, precision=20, separator=', ',
floatmode='maxprec_equal'))
print("Form 2: "+np.array2string(indexValue_form2, precision=20, separator=', ',
floatmode='maxprec_equal'))
print("Form 3: "+np.array2string(indexValue_form3, precision=20, separator=', ',
floatmode='maxprec_equal'))
# A list of examples results for different type of data at NDVI
# float16 -> 0.31567383 #NDVI (red = 50, nir = 100)

28
divide_and_conquer/max_subarray_sum.py
View File

@ -1,10 +1,10 @@
"""
Given a array of length n, max_subarray_sum() finds
"""
Given a array of length n, max_subarray_sum() finds
the maximum of sum of contiguous sub-array using divide and conquer method.
Time complexity : O(n log n)
Ref : INTRODUCTION TO ALGORITHMS THIRD EDITION
Ref : INTRODUCTION TO ALGORITHMS THIRD EDITION
(section : 4, sub-section : 4.1, page : 70)
"""
@ -13,10 +13,10 @@ Ref : INTRODUCTION TO ALGORITHMS THIRD EDITION
def max_sum_from_start(array):
""" This function finds the maximum contiguous sum of array from 0 index
Parameters :
Parameters :
array (list[int]) : given array
Returns :
Returns :
max_sum (int) : maximum contiguous sum of array from 0 index
"""
@ -32,10 +32,10 @@ def max_sum_from_start(array):
def max_cross_array_sum(array, left, mid, right):
""" This function finds the maximum contiguous sum of left and right arrays
Parameters :
array, left, mid, right (list[int], int, int, int)
Returns :
Parameters :
array, left, mid, right (list[int], int, int, int)
Returns :
(int) : maximum of sum of contiguous sum of left and right arrays
"""
@ -48,11 +48,11 @@ def max_cross_array_sum(array, left, mid, right):
def max_subarray_sum(array, left, right):
""" Maximum contiguous sub-array sum, using divide and conquer method
Parameters :
array, left, right (list[int], int, int) :
Parameters :
array, left, right (list[int], int, int) :
given array, current left index and current right index
Returns :
Returns :
int : maximum of sum of contiguous sub-array
"""

2
divide_and_conquer/mergesort.py
View File

@ -1,5 +1,5 @@
def merge(a, b, m, e):
l = a[b : m + 1]
l = a[b : m + 1] # noqa: E741
r = a[m + 1 : e + 1]
k = b
i = 0

6
dynamic_programming/factorial.py
View File

@ -26,9 +26,9 @@ def factorial(num):
# factorial of num
# uncomment the following to see how recalculations are avoided
##result=[-1]*10
##result[0]=result[1]=1
##print(factorial(5))
# result=[-1]*10
# result[0]=result[1]=1
# print(factorial(5))
# print(factorial(3))
# print(factorial(7))

4
dynamic_programming/iterating_through_submasks.py
View File

@ -1,5 +1,5 @@
"""
Author : Syed Faizan (3rd Year Student IIIT Pune)
Author : Syed Faizan (3rd Year Student IIIT Pune)
github : faizan2700
You are given a bitmask m and you want to efficiently iterate through all of
its submasks. The mask s is submask of m if only bits that were included in
@ -33,7 +33,7 @@ def list_of_submasks(mask: int) -> List[int]:
Traceback (most recent call last):
...
AssertionError: mask needs to be positive integer, your input 0
"""
fmt = "mask needs to be positive integer, your input {}"

2
dynamic_programming/longest_common_subsequence.py
View File

@ -76,7 +76,7 @@ if __name__ == "__main__":
expected_subseq = "GTAB"
ln, subseq = longest_common_subsequence(a, b)
## print("len =", ln, ", sub-sequence =", subseq)
print("len =", ln, ", sub-sequence =", subseq)
import doctest
doctest.testmod()

17
dynamic_programming/longest_increasing_subsequence.py
View File

@ -1,11 +1,14 @@
"""
Author : Mehdi ALAOUI
This is a pure Python implementation of Dynamic Programming solution to the longest increasing subsequence of a given sequence.
This is a pure Python implementation of Dynamic Programming solution to the longest
increasing subsequence of a given sequence.
The problem is :
Given an array, to find the longest and increasing sub-array in that given array and return it.
Example: [10, 22, 9, 33, 21, 50, 41, 60, 80] as input will return [10, 22, 33, 41, 60, 80] as output
Given an array, to find the longest and increasing sub-array in that given array and
return it.
Example: [10, 22, 9, 33, 21, 50, 41, 60, 80] as input will return
[10, 22, 33, 41, 60, 80] as output
"""
from typing import List
@ -21,11 +24,13 @@ def longest_subsequence(array: List[int]) -> List[int]: # This function is recu
[8]
>>> longest_subsequence([1, 1, 1])
[1, 1, 1]
>>> longest_subsequence([])
[]
"""
array_length = len(array)
if (
array_length <= 1
): # If the array contains only one element, we return it (it's the stop condition of recursion)
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
pivot = array[0]

11
dynamic_programming/longest_increasing_subsequence_o(nlogn).py
View File

@ -1,19 +1,19 @@
#############################
# Author: Aravind Kashyap
# File: lis.py
# comments: This programme outputs the Longest Strictly Increasing Subsequence in O(NLogN)
# Where N is the Number of elements in the list
# comments: This programme outputs the Longest Strictly Increasing Subsequence in
# O(NLogN) Where N is the Number of elements in the list
#############################
from typing import List
def CeilIndex(v, l, r, key):
def CeilIndex(v, l, r, key): # noqa: E741
while r - l > 1:
m = (l + r) // 2
if v[m] >= key:
r = m
else:
l = m
l = m # noqa: E741
return r
@ -23,7 +23,8 @@ def LongestIncreasingSubsequenceLength(v: List[int]) -> int:
6
>>> LongestIncreasingSubsequenceLength([])
0
>>> LongestIncreasingSubsequenceLength([0, 8, 4, 12, 2, 10, 6, 14, 1, 9, 5, 13, 3, 11, 7, 15])
>>> LongestIncreasingSubsequenceLength([0, 8, 4, 12, 2, 10, 6, 14, 1, 9, 5, 13, 3,
... 11, 7, 15])
6
>>> LongestIncreasingSubsequenceLength([5, 4, 3, 2, 1])
1

6
dynamic_programming/max_sub_array.py
View File

@ -44,12 +44,12 @@ def max_sub_array(nums: List[int]) -> int:
>>> max_sub_array([-2, 1, -3, 4, -1, 2, 1, -5, 4])
6
An empty (sub)array has sum 0.
>>> max_sub_array([])
0
If all elements are negative, the largest subarray would be the empty array,
If all elements are negative, the largest subarray would be the empty array,
having the sum 0.
>>> max_sub_array([-1, -2, -3])
0

2
dynamic_programming/minimum_partition.py
View File

@ -23,7 +23,7 @@ def findMin(arr):
dp[i][j] = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2), -1, -1):
if dp[n][j] == True:
if dp[n][j] is True:
diff = s - 2 * j
break

13
dynamic_programming/optimal_binary_search_tree.py
View File

@ -40,7 +40,7 @@ class Node:
def print_binary_search_tree(root, key, i, j, parent, is_left):
"""
Recursive function to print a BST from a root table.
>>> key = [3, 8, 9, 10, 17, 21]
>>> root = [[0, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 3], [0, 0, 2, 3, 3, 3], \
[0, 0, 0, 3, 3, 3], [0, 0, 0, 0, 4, 5], [0, 0, 0, 0, 0, 5]]
@ -73,7 +73,7 @@ def find_optimal_binary_search_tree(nodes):
The dynamic programming algorithm below runs in O(n^2) time.
Implemented from CLRS (Introduction to Algorithms) book.
https://en.wikipedia.org/wiki/Introduction_to_Algorithms
>>> find_optimal_binary_search_tree([Node(12, 8), Node(10, 34), Node(20, 50), \
Node(42, 3), Node(25, 40), Node(37, 30)])
Binary search tree nodes:
@ -104,14 +104,15 @@ def find_optimal_binary_search_tree(nodes):
# This 2D array stores the overall tree cost (which's as minimized as possible);
# for a single key, cost is equal to frequency of the key.
dp = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes array
# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
# array
sum = [[freqs[i] if i == j else 0 for j in range(n)] for i in range(n)]
# stores tree roots that will be used later for constructing binary search tree
root = [[i if i == j else 0 for j in range(n)] for i in range(n)]
for l in range(2, n + 1): # l is an interval length
for i in range(n - l + 1):
j = i + l - 1
for interval_length in range(2, n + 1):
for i in range(n - interval_length + 1):
j = i + interval_length - 1
dp[i][j] = sys.maxsize # set the value to "infinity"
sum[i][j] = sum[i][j - 1] + freqs[j]

17
dynamic_programming/subset_generation.py
View File

@ -1,12 +1,15 @@
# Python program to print all subset combinations of n element in given set of r element.
# arr[] ---> Input Array
# data[] ---> Temporary array to store current combination
# start & end ---> Staring and Ending indexes in arr[]
# index ---> Current index in data[]
# r ---> Size of a combination to be printed
def combination_util(arr, n, r, index, data, i):
# Current combination is ready to be printed,
# print it
"""
Current combination is ready to be printed, print it
arr[] ---> Input Array
data[] ---> Temporary array to store current combination
start & end ---> Staring and Ending indexes in arr[]
index ---> Current index in data[]
r ---> Size of a combination to be printed
"""
if index == r:
for j in range(r):
print(data[j], end=" ")

4
geodesy/lamberts_ellipsoidal_distance.py
View File

@ -15,8 +15,8 @@ def lamberts_ellipsoidal_distance(
Representing the earth as an ellipsoid allows us to approximate distances between points
on the surface much better than a sphere. Ellipsoidal formulas treat the Earth as an
oblate ellipsoid which means accounting for the flattening that happens at the North
and South poles. Lambert's formulae provide accuracy on the order of 10 meteres over
oblate ellipsoid which means accounting for the flattening that happens at the North
and South poles. Lambert's formulae provide accuracy on the order of 10 meteres over
thousands of kilometeres. Other methods can provide millimeter-level accuracy but this
is a simpler method to calculate long range distances without increasing computational
intensity.

8
graphs/articulation_points.py
View File

@ -1,5 +1,5 @@
# Finding Articulation Points in Undirected Graph
def computeAP(l):
def computeAP(l): # noqa: E741
n = len(l)
outEdgeCount = 0
low = [0] * n
@ -36,12 +36,12 @@ def computeAP(l):
isArt[i] = outEdgeCount > 1
for x in range(len(isArt)):
if isArt[x] == True:
if isArt[x] is True:
print(x)
# Adjacency list of graph
l = {
data = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
@ -52,4 +52,4 @@ l = {
7: [6, 8],
8: [5, 7],
}
computeAP(l)
computeAP(data)

11
graphs/basic_graphs.py
View File

@ -1,3 +1,6 @@
from collections import deque
if __name__ == "__main__":
# Accept No. of Nodes and edges
n, m = map(int, input().split(" "))
@ -72,7 +75,6 @@ def dfs(G, s):
Q - Traversal Stack
--------------------------------------------------------------------------------
"""
from collections import deque
def bfs(G, s):
@ -125,7 +127,6 @@ def dijk(G, s):
Topological Sort
--------------------------------------------------------------------------------
"""
from collections import deque
def topo(G, ind=None, Q=None):
@ -235,10 +236,10 @@ def prim(G, s):
def edglist():
n, m = map(int, input().split(" "))
l = []
edges = []
for i in range(m):
l.append(map(int, input().split(" ")))
return l, n
edges.append(map(int, input().split(" ")))
return edges, n
"""

2
graphs/bellman_ford.py
View File

@ -9,7 +9,7 @@ def printDist(dist, V):
def BellmanFord(graph: List[Dict[str, int]], V: int, E: int, src: int) -> int:
"""
Returns shortest paths from a vertex src to all
Returns shortest paths from a vertex src to all
other vertices.
"""
mdist = [float("inf") for i in range(V)]

11
graphs/breadth_first_search_shortest_path.py
View File

@ -1,6 +1,8 @@
"""Breath First Search (BFS) can be used when finding the shortest path
"""Breath First Search (BFS) can be used when finding the shortest path
from a given source node to a target node in an unweighted graph.
"""
from typing import Dict
graph = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
@ -11,8 +13,6 @@ graph = {
"G": ["C"],
}
from typing import Dict
class Graph:
def __init__(self, graph: Dict[str, str], source_vertex: str) -> None:
@ -46,8 +46,9 @@ class Graph:
def shortest_path(self, target_vertex: str) -> str:
"""This shortest path function returns a string, describing the result:
1.) No path is found. The string is a human readable message to indicate this.
2.) The shortest path is found. The string is in the form `v1(->v2->v3->...->vn)`,
where v1 is the source vertex and vn is the target vertex, if it exists separately.
2.) The shortest path is found. The string is in the form
`v1(->v2->v3->...->vn)`, where v1 is the source vertex and vn is the target
vertex, if it exists separately.
>>> g = Graph(graph, "G")
>>> g.breath_first_search()

19
graphs/check_bipartite_graph_bfs.py
View File

@ -1,21 +1,22 @@
# Check whether Graph is Bipartite or Not using BFS
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def checkBipartite(l):
def checkBipartite(graph):
queue = []
visited = [False] * len(l)
color = [-1] * len(l)
visited = [False] * len(graph)
color = [-1] * len(graph)
def bfs():
while queue:
u = queue.pop(0)
visited[u] = True
for neighbour in l[u]:
for neighbour in graph[u]:
if neighbour == u:
return False
@ -29,16 +30,16 @@ def checkBipartite(l):
return True
for i in range(len(l)):
for i in range(len(graph)):
if not visited[i]:
queue.append(i)
color[i] = 0
if bfs() == False:
if bfs() is False:
return False
return True
# Adjacency List of graph
l = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}
print(checkBipartite(l))
if __name__ == "__main__":
# Adjacency List of graph
print(checkBipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))

19
graphs/check_bipartite_graph_dfs.py
View File

@ -1,27 +1,28 @@
# Check whether Graph is Bipartite or Not using DFS
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def check_bipartite_dfs(l):
visited = [False] * len(l)
color = [-1] * len(l)
def check_bipartite_dfs(graph):
visited = [False] * len(graph)
color = [-1] * len(graph)
def dfs(v, c):
visited[v] = True
color[v] = c
for u in l[v]:
for u in graph[v]:
if not visited[u]:
dfs(u, 1 - c)
for i in range(len(l)):
for i in range(len(graph)):
if not visited[i]:
dfs(i, 0)
for i in range(len(l)):
for j in l[i]:
for i in range(len(graph)):
for j in graph[i]:
if color[i] == color[j]:
return False
@ -29,5 +30,5 @@ def check_bipartite_dfs(l):
# Adjacency list of graph
l = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(l))
graph = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))

8
graphs/depth_first_search.py
View File

@ -1,6 +1,6 @@
"""The DFS function simply calls itself recursively for every unvisited child of
its argument. We can emulate that behaviour precisely using a stack of iterators.
Instead of recursively calling with a node, we'll push an iterator to the node's
"""The DFS function simply calls itself recursively for every unvisited child of
its argument. We can emulate that behaviour precisely using a stack of iterators.
Instead of recursively calling with a node, we'll push an iterator to the node's
children onto the iterator stack. When the iterator at the top of the stack
terminates, we'll pop it off the stack.
@ -21,7 +21,7 @@ def depth_first_search(graph: Dict, start: str) -> Set[int]:
:param graph: directed graph in dictionary format
:param vertex: starting vectex as a string
:returns: the trace of the search
>>> G = { "A": ["B", "C", "D"], "B": ["A", "D", "E"],
>>> G = { "A": ["B", "C", "D"], "B": ["A", "D", "E"],
... "C": ["A", "F"], "D": ["B", "D"], "E": ["B", "F"],
... "F": ["C", "E", "G"], "G": ["F"] }
>>> start = "A"

4
graphs/depth_first_search_2.py
View File

@ -28,7 +28,7 @@ class Graph:
# call the recursive helper function
for i in range(len(self.vertex)):
if visited[i] == False:
if visited[i] is False:
self.DFSRec(i, visited)
def DFSRec(self, startVertex, visited):
@ -39,7 +39,7 @@ class Graph:
# Recur for all the vertices that are adjacent to this node
for i in self.vertex.keys():
if visited[i] == False:
if visited[i] is False:
self.DFSRec(i, visited)

5
graphs/dijkstra.py
View File

@ -1,6 +1,6 @@
"""pseudo-code"""
"""
pseudo-code
DIJKSTRA(graph G, start vertex s, destination vertex d):
//all nodes initially unexplored
@ -30,7 +30,6 @@ only the distance between previous vertex and current vertex but the entire
distance between each vertex that makes up the path from start vertex to target
vertex.
"""
import heapq

2
graphs/dinic.py
View File

@ -37,7 +37,7 @@ class Dinic:
# Here we calculate the flow that reaches the sink
def max_flow(self, source, sink):
flow, self.q[0] = 0, source
for l in range(31): # l = 30 maybe faster for random data
for l in range(31): # noqa: E741 l = 30 maybe faster for random data
while True:
self.lvl, self.ptr = [0] * len(self.q), [0] * len(self.q)
qi, qe, self.lvl[source] = 0, 1, 1

48
graphs/directed_and_undirected_(weighted)_graph.py
View File

@ -71,8 +71,8 @@ class DirectedGraph:
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the function the count
# will be random from 10 to 10000
# c is the count of nodes you want and if you leave it or pass -1 to the function
# the count will be random from 10 to 10000
def fill_graph_randomly(self, c=-1):
if c == -1:
c = (math.floor(rand.random() * 10000)) + 10
@ -168,14 +168,14 @@ class DirectedGraph:
and indirect_parents.count(__[1]) > 0
and not on_the_way_back
):
l = len(stack) - 1
while True and l >= 0:
if stack[l] == __[1]:
len_stack = len(stack) - 1
while True and len_stack >= 0:
if stack[len_stack] == __[1]:
anticipating_nodes.add(__[1])
break
else:
anticipating_nodes.add(stack[l])
l -= 1
anticipating_nodes.add(stack[len_stack])
len_stack -= 1
if visited.count(__[1]) < 1:
stack.append(__[1])
visited.append(__[1])
@ -221,15 +221,15 @@ class DirectedGraph:
and indirect_parents.count(__[1]) > 0
and not on_the_way_back
):
l = len(stack) - 1
while True and l >= 0:
if stack[l] == __[1]:
len_stack_minus_one = len(stack) - 1
while True and len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == __[1]:
anticipating_nodes.add(__[1])
break
else:
return True
anticipating_nodes.add(stack[l])
l -= 1
anticipating_nodes.add(stack[len_stack_minus_one])
len_stack_minus_one -= 1
if visited.count(__[1]) < 1:
stack.append(__[1])
visited.append(__[1])
@ -341,8 +341,8 @@ class Graph:
if len(stack) == 0:
return visited
# c is the count of nodes you want and if you leave it or pass -1 to the function the count
# will be random from 10 to 10000
# c is the count of nodes you want and if you leave it or pass -1 to the function
# the count will be random from 10 to 10000
def fill_graph_randomly(self, c=-1):
if c == -1:
c = (math.floor(rand.random() * 10000)) + 10
@ -397,14 +397,14 @@ class Graph:
and indirect_parents.count(__[1]) > 0
and not on_the_way_back
):
l = len(stack) - 1
while True and l >= 0:
if stack[l] == __[1]:
len_stack = len(stack) - 1
while True and len_stack >= 0:
if stack[len_stack] == __[1]:
anticipating_nodes.add(__[1])
break
else:
anticipating_nodes.add(stack[l])
l -= 1
anticipating_nodes.add(stack[len_stack])
len_stack -= 1
if visited.count(__[1]) < 1:
stack.append(__[1])
visited.append(__[1])
@ -450,15 +450,15 @@ class Graph:
and indirect_parents.count(__[1]) > 0
and not on_the_way_back
):
l = len(stack) - 1
while True and l >= 0:
if stack[l] == __[1]:
len_stack_minus_one = len(stack) - 1
while True and len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == __[1]:
anticipating_nodes.add(__[1])
break
else:
return True
anticipating_nodes.add(stack[l])
l -= 1
anticipating_nodes.add(stack[len_stack_minus_one])
len_stack_minus_one -= 1
if visited.count(__[1]) < 1:
stack.append(__[1])
visited.append(__[1])

2
graphs/eulerian_path_and_circuit_for_undirected_graph.py
View File

@ -9,7 +9,7 @@
def dfs(u, graph, visited_edge, path=[]):
path = path + [u]
for v in graph[u]:
if visited_edge[u][v] == False:
if visited_edge[u][v] is False:
visited_edge[u][v], visited_edge[v][u] = True, True
path = dfs(v, graph, visited_edge, path)
return path

10
graphs/finding_bridges.py
View File

@ -1,7 +1,7 @@
# Finding Bridges in Undirected Graph
def computeBridges(l):
def computeBridges(graph):
id = 0
n = len(l) # No of vertices in graph
n = len(graph) # No of vertices in graph
low = [0] * n
visited = [False] * n
@ -9,7 +9,7 @@ def computeBridges(l):
visited[at] = True
low[at] = id
id += 1
for to in l[at]:
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
@ -28,7 +28,7 @@ def computeBridges(l):
print(bridges)
l = {
graph = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
@ -39,4 +39,4 @@ l = {
7: [6, 8],
8: [5, 7],
}
computeBridges(l)
computeBridges(graph)

2
graphs/frequent_pattern_graph_miner.py
View File

@ -19,7 +19,7 @@ edge_array = [
['ab-e1', 'ac-e3', 'bc-e4', 'bd-e2', 'bh-e12', 'cd-e2', 'df-e8', 'dh-e10'],
['ab-e1', 'ac-e3', 'ad-e5', 'bc-e4', 'bd-e2', 'cd-e2', 'ce-e4', 'de-e1', 'df-e8',
'dg-e5', 'ef-e3', 'eg-e2', 'fg-e6']
]
]
# fmt: on

14
graphs/kahns_algorithm_long.py
View File

@ -1,10 +1,10 @@
# Finding longest distance in Directed Acyclic Graph using KahnsAlgorithm
def longestDistance(l):
indegree = [0] * len(l)
def longestDistance(graph):
indegree = [0] * len(graph)
queue = []
longDist = [1] * len(l)
longDist = [1] * len(graph)
for key, values in l.items():
for key, values in graph.items():
for i in values:
indegree[i] += 1
@ -14,7 +14,7 @@ def longestDistance(l):
while queue:
vertex = queue.pop(0)
for x in l[vertex]:
for x in graph[vertex]:
indegree[x] -= 1
if longDist[vertex] + 1 > longDist[x]:
@ -27,5 +27,5 @@ def longestDistance(l):
# Adjacency list of Graph
l = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longestDistance(l)
graph = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longestDistance(graph)

19
graphs/kahns_algorithm_topo.py
View File

@ -1,11 +1,14 @@
# Kahn's Algorithm is used to find Topological ordering of Directed Acyclic Graph using BFS
def topologicalSort(l):
indegree = [0] * len(l)
def topologicalSort(graph):
"""
Kahn's Algorithm is used to find Topological ordering of Directed Acyclic Graph
using BFS
"""
indegree = [0] * len(graph)
queue = []
topo = []
cnt = 0
for key, values in l.items():
for key, values in graph.items():
for i in values:
indegree[i] += 1
@ -17,17 +20,17 @@ def topologicalSort(l):
vertex = queue.pop(0)
cnt += 1
topo.append(vertex)
for x in l[vertex]:
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(x)
if cnt != len(l):
if cnt != len(graph):
print("Cycle exists")
else:
print(topo)
# Adjacency List of Graph
l = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topologicalSort(l)
graph = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topologicalSort(graph)

4
graphs/minimum_spanning_tree_prims.py
View File

@ -2,7 +2,7 @@ import sys
from collections import defaultdict
def PrimsAlgorithm(l):
def PrimsAlgorithm(l): # noqa: E741
nodePosition = []
@ -109,7 +109,7 @@ if __name__ == "__main__":
e = int(input("Enter number of edges: ").strip())
adjlist = defaultdict(list)
for x in range(e):
l = [int(x) for x in input().strip().split()]
l = [int(x) for x in input().strip().split()] # noqa: E741
adjlist[l[0]].append([l[1], l[2]])
adjlist[l[1]].append([l[0], l[2]])
print(PrimsAlgorithm(adjlist))

33
hashes/chaos_machine.py
View File

@ -79,24 +79,23 @@ def reset():
machine_time = 0
#######################################
if __name__ == "__main__":
# Initialization
reset()
# Initialization
reset()
# Pushing Data (Input)
import random
# Pushing Data (Input)
import random
message = random.sample(range(0xFFFFFFFF), 100)
for chunk in message:
push(chunk)
message = random.sample(range(0xFFFFFFFF), 100)
for chunk in message:
push(chunk)
# for controlling
inp = ""
# for controlling
inp = ""
# Pulling Data (Output)
while inp in ("e", "E"):
print("%s" % format(pull(), "#04x"))
print(buffer_space)
print(params_space)
inp = input("(e)exit? ").strip()
# Pulling Data (Output)
while inp in ("e", "E"):
print("%s" % format(pull(), "#04x"))
print(buffer_space)
print(params_space)
inp = input("(e)exit? ").strip()

1
hashes/hamming_code.py
View File

@ -47,6 +47,7 @@
# Imports
import numpy as np
# Functions of binary conversion--------------------------------------
def text_to_bits(text, encoding="utf-8", errors="surrogatepass"):
"""

18
linear_algebra/src/lib.py
View File

@ -27,10 +27,10 @@ import random
class Vector:
"""
This class represents a vector of arbitrary size.
You need to give the vector components.
You need to give the vector components.
Overview about the methods:
constructor(components : list) : init the vector
set(components : list) : changes the vector components.
__str__() : toString method
@ -124,7 +124,7 @@ class Vector:
def __mul__(self, other):
"""
mul implements the scalar multiplication
mul implements the scalar multiplication
and the dot-product
"""
if isinstance(other, float) or isinstance(other, int):
@ -167,7 +167,7 @@ def zeroVector(dimension):
def unitBasisVector(dimension, pos):
"""
returns a unit basis vector with a One
returns a unit basis vector with a One
at index 'pos' (indexing at 0)
"""
# precondition
@ -196,7 +196,7 @@ def randomVector(N, a, b):
"""
input: size (N) of the vector.
random range (a,b)
output: returns a random vector of size N, with
output: returns a random vector of size N, with
random integer components between 'a' and 'b'.
"""
random.seed(None)
@ -208,10 +208,10 @@ class Matrix:
"""
class: Matrix
This class represents a arbitrary matrix.
Overview about the methods:
__str__() : returns a string representation
__str__() : returns a string representation
operator * : implements the matrix vector multiplication
implements the matrix-scalar multiplication.
changeComponent(x,y,value) : changes the specified component.

2
linear_algebra/src/test_linear_algebra.py
View File

@ -19,7 +19,7 @@ class Test(unittest.TestCase):
x = Vector([1, 2, 3])
self.assertEqual(x.component(0), 1)
self.assertEqual(x.component(2), 3)
y = Vector()
_ = Vector()
def test_str(self):
"""

27
machine_learning/k_means_clust.py
View File

@ -11,9 +11,11 @@ Python:
Inputs:
- X , a 2D numpy array of features.
- k , number of clusters to create.
- initial_centroids , initial centroid values generated by utility function(mentioned in usage).
- initial_centroids , initial centroid values generated by utility function(mentioned
in usage).
- maxiter , maximum number of iterations to process.
- heterogeneity , empty list that will be filled with hetrogeneity values if passed to kmeans func.
- heterogeneity , empty list that will be filled with hetrogeneity values if passed
to kmeans func.
Usage:
1. define 'k' value, 'X' features array and 'hetrogeneity' empty list
@ -22,7 +24,8 @@ Usage:
initial_centroids = get_initial_centroids(
X,
k,
seed=0 # seed value for initial centroid generation, None for randomness(default=None)
seed=0 # seed value for initial centroid generation,
# None for randomness(default=None)
)
3. find centroids and clusters using kmeans function.
@ -37,7 +40,8 @@ Usage:
)
4. Plot the loss function, hetrogeneity values for every iteration saved in hetrogeneity list.
4. Plot the loss function, hetrogeneity values for every iteration saved in
hetrogeneity list.
plot_heterogeneity(
heterogeneity,
k
@ -46,8 +50,9 @@ Usage:
5. Have fun..
"""
from sklearn.metrics import pairwise_distances
import numpy as np
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances
TAG = "K-MEANS-CLUST/ "
@ -118,9 +123,6 @@ def compute_heterogeneity(data, k, centroids, cluster_assignment):
return heterogeneity
from matplotlib import pyplot as plt
def plot_heterogeneity(heterogeneity, k):
plt.figure(figsize=(7, 4))
plt.plot(heterogeneity, linewidth=4)
@ -136,9 +138,11 @@ def kmeans(
):
"""This function runs k-means on given data and initial set of centroids.
maxiter: maximum number of iterations to run.(default=500)
record_heterogeneity: (optional) a list, to store the history of heterogeneity as function of iterations
record_heterogeneity: (optional) a list, to store the history of heterogeneity
as function of iterations
if None, do not store the history.
verbose: if True, print how many data points changed their cluster labels in each iteration"""
verbose: if True, print how many data points changed their cluster labels in
each iteration"""
centroids = initial_centroids[:]
prev_cluster_assignment = None
@ -149,7 +153,8 @@ def kmeans(
# 1. Make cluster assignments using nearest centroids
cluster_assignment = assign_clusters(data, centroids)
# 2. Compute a new centroid for each of the k clusters, averaging all data points assigned to that cluster.
# 2. Compute a new centroid for each of the k clusters, averaging all data
# points assigned to that cluster.
centroids = revise_centroids(data, k, cluster_assignment)
# Check for convergence: if none of the assignments changed, stop

5
machine_learning/linear_discriminant_analysis.py
View File

@ -186,7 +186,8 @@ def predict_y_values(
>>> means = [5.011267842911003, 10.011267842911003, 15.011267842911002]
>>> variance = 0.9618530973487494
>>> probabilities = [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]
>>> predict_y_values(x_items, means, variance, probabilities) # doctest: +NORMALIZE_WHITESPACE
>>> predict_y_values(x_items, means, variance,
... probabilities) # doctest: +NORMALIZE_WHITESPACE
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2]
@ -211,7 +212,7 @@ def predict_y_values(
# appending discriminant values of each item to 'results' list
results.append(temp)
return [l.index(max(l)) for l in results]
return [result.index(max(result)) for result in results]
# Calculating Accuracy

14
machine_learning/polymonial_regression.py
View File

@ -1,5 +1,12 @@
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
dataset = pd.read_csv(
@ -9,16 +16,9 @@ X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
poly_reg = PolynomialFeatures(degree=4)
X_poly = poly_reg.fit_transform(X)
pol_reg = LinearRegression()

1
machine_learning/scoring_functions.py
View File

@ -14,6 +14,7 @@ import numpy as np
and types of data
"""
# Mean Absolute Error
def mae(predict, actual):
"""

2
maths/aliquot_sum.py
View File

@ -9,7 +9,7 @@ def aliquot_sum(input_num: int) -> int:
@return: the aliquot sum of input_num, if input_num is positive.
Otherwise, raise a ValueError
Wikipedia Explanation: https://en.wikipedia.org/wiki/Aliquot_sum
>>> aliquot_sum(15)
9
>>> aliquot_sum(6)

4
maths/allocation_number.py
View File

@ -4,8 +4,8 @@ from typing import List
def allocation_num(number_of_bytes: int, partitions: int) -> List[str]:
"""
Divide a number of bytes into x partitions.
In a multi-threaded download, this algorithm could be used to provide
In a multi-threaded download, this algorithm could be used to provide
each worker thread with a block of non-overlapping bytes to download.
For example:
for i in allocation_list:

10
maths/bailey_borwein_plouffe.py
View File

@ -1,16 +1,16 @@
def bailey_borwein_plouffe(digit_position: int, precision: int = 1000) -> str:
"""
Implement a popular pi-digit-extraction algorithm known as the
Implement a popular pi-digit-extraction algorithm known as the
Bailey-Borwein-Plouffe (BBP) formula to calculate the nth hex digit of pi.
Wikipedia page:
https://en.wikipedia.org/wiki/Bailey%E2%80%93Borwein%E2%80%93Plouffe_formula
@param digit_position: a positive integer representing the position of the digit to extract.
@param digit_position: a positive integer representing the position of the digit to extract.
The digit immediately after the decimal point is located at position 1.
@param precision: number of terms in the second summation to calculate.
A higher number reduces the chance of an error but increases the runtime.
@return: a hexadecimal digit representing the digit at the nth position
in pi's decimal expansion.
>>> "".join(bailey_borwein_plouffe(i) for i in range(1, 11))
'243f6a8885'
>>> bailey_borwein_plouffe(5, 10000)
@ -59,11 +59,11 @@ def _subsum(
# only care about first digit of fractional part; don't need decimal
"""
Private helper function to implement the summation
functionality.
functionality.
@param digit_pos_to_extract: digit position to extract
@param denominator_addend: added to denominator of fractions in the formula
@param precision: same as precision in main function
@return: floating-point number whose integer part is not important
@return: floating-point number whose integer part is not important
"""
sum = 0.0
for sum_index in range(digit_pos_to_extract + precision):

5
maths/collatz_sequence.py
View File

@ -18,8 +18,9 @@ def collatz_sequence(n: int) -> List[int]:
Traceback (most recent call last):
...
Exception: Sequence only defined for natural numbers
>>> collatz_sequence(43)
[43, 130, 65, 196, 98, 49, 148, 74, 37, 112, 56, 28, 14, 7, 22, 11, 34, 17, 52, 26, 13, 40, 20, 10, 5, 16, 8, 4, 2, 1]
>>> collatz_sequence(43) # doctest: +NORMALIZE_WHITESPACE
[43, 130, 65, 196, 98, 49, 148, 74, 37, 112, 56, 28, 14, 7,
22, 11, 34, 17, 52, 26, 13, 40, 20, 10, 5, 16, 8, 4, 2, 1]
"""
if not isinstance(n, int) or n < 1:

2
maths/find_max_recursion.py
View File

@ -6,7 +6,7 @@ def find_max(nums, left, right):
:param left: index of first element
:param right: index of last element
:return: max in nums
>>> nums = [1, 3, 5, 7, 9, 2, 4, 6, 8, 10]
>>> find_max(nums, 0, len(nums) - 1) == max(nums)
True

12
maths/gamma.py
View File

@ -6,8 +6,8 @@ from numpy import inf
def gamma(num: float) -> float:
"""
https://en.wikipedia.org/wiki/Gamma_function
In mathematics, the gamma function is one commonly
used extension of the factorial function to complex numbers.
In mathematics, the gamma function is one commonly
used extension of the factorial function to complex numbers.
The gamma function is defined for all complex numbers except the non-positive integers
@ -16,7 +16,7 @@ def gamma(num: float) -> float:
...
ValueError: math domain error
>>> gamma(0)
Traceback (most recent call last):
@ -27,12 +27,12 @@ def gamma(num: float) -> float:
>>> gamma(9)
40320.0
>>> from math import gamma as math_gamma
>>> from math import gamma as math_gamma
>>> all(gamma(i)/math_gamma(i) <= 1.000000001 and abs(gamma(i)/math_gamma(i)) > .99999999 for i in range(1, 50))
True
>>> from math import gamma as math_gamma
>>> from math import gamma as math_gamma
>>> gamma(-1)/math_gamma(-1) <= 1.000000001
Traceback (most recent call last):
...
@ -40,7 +40,7 @@ def gamma(num: float) -> float:
>>> from math import gamma as math_gamma
>>> gamma(3.3) - math_gamma(3.3) <= 0.00000001
>>> gamma(3.3) - math_gamma(3.3) <= 0.00000001
True
"""

4
maths/gaussian.py
View File

@ -12,7 +12,7 @@ def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
"""
>>> gaussian(1)
0.24197072451914337
>>> gaussian(24)
3.342714441794458e-126
@ -25,7 +25,7 @@ def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12,
5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27,
2.14638374e-32, 7.99882776e-38, 1.09660656e-43])
>>> gaussian(15)
5.530709549844416e-50

4
maths/is_square_free.py
View File

@ -13,12 +13,12 @@ def is_square_free(factors: List[int]) -> bool:
returns True if the factors are square free.
>>> is_square_free([1, 1, 2, 3, 4])
False
These are wrong but should return some value
it simply checks for repition in the numbers.
>>> is_square_free([1, 3, 4, 'sd', 0.0])
True
>>> is_square_free([1, 0.5, 2, 0.0])
True
>>> is_square_free([1, 2, 2, 5])

8
maths/kth_lexicographic_permutation.py
View File

@ -1,15 +1,15 @@
def kthPermutation(k, n):
"""
Finds k'th lexicographic permutation (in increasing order) of
Finds k'th lexicographic permutation (in increasing order) of
0,1,2,...n-1 in O(n^2) time.
Examples:
First permutation is always 0,1,2,...n
>>> kthPermutation(0,5)
[0, 1, 2, 3, 4]
The order of permutation of 0,1,2,3 is [0,1,2,3], [0,1,3,2], [0,2,1,3],
[0,2,3,1], [0,3,1,2], [0,3,2,1], [1,0,2,3], [1,0,3,2], [1,2,0,3],
[0,2,3,1], [0,3,1,2], [0,3,2,1], [1,0,2,3], [1,0,3,2], [1,2,0,3],
[1,2,3,0], [1,3,0,2]
>>> kthPermutation(10,4)
[1, 3, 0, 2]

10
maths/lucas_lehmer_primality_test.py
View File

@ -1,12 +1,12 @@
"""
In mathematics, the Lucas–Lehmer test (LLT) is a primality test for Mersenne numbers.
https://en.wikipedia.org/wiki/Lucas%E2%80%93Lehmer_primality_test
A Mersenne number is a number that is one less than a power of two.
That is M_p = 2^p - 1
https://en.wikipedia.org/wiki/Mersenne_prime
The Lucas–Lehmer test is the primality test used by the
The Lucas–Lehmer test is the primality test used by the
Great Internet Mersenne Prime Search (GIMPS) to locate large primes.
"""
@ -17,10 +17,10 @@ def lucas_lehmer_test(p: int) -> bool:
"""
>>> lucas_lehmer_test(p=7)
True
>>> lucas_lehmer_test(p=11)
False
# M_11 = 2^11 - 1 = 2047 = 23 * 89
"""

2
maths/matrix_exponentiation.py
View File

@ -4,7 +4,7 @@ import timeit
"""
Matrix Exponentiation is a technique to solve linear recurrences in logarithmic time.
You read more about it here:
You read more about it here:
http://zobayer.blogspot.com/2010/11/matrix-exponentiation.html
https://www.hackerearth.com/practice/notes/matrix-exponentiation-1/
"""

2
maths/modular_exponential.py
View File

@ -1,6 +1,6 @@
"""
Modular Exponential.
Modular exponentiation is a type of exponentiation performed over a modulus.
Modular exponentiation is a type of exponentiation performed over a modulus.
For more explanation, please check https://en.wikipedia.org/wiki/Modular_exponentiation
"""

10
maths/monte_carlo.py
View File

@ -45,13 +45,13 @@ def area_under_curve_estimator(
) -> float:
"""
An implementation of the Monte Carlo method to find area under
a single variable non-negative real-valued continuous function,
say f(x), where x lies within a continuous bounded interval,
say [min_value, max_value], where min_value and max_value are
a single variable non-negative real-valued continuous function,
say f(x), where x lies within a continuous bounded interval,
say [min_value, max_value], where min_value and max_value are
finite numbers
1. Let x be a uniformly distributed random variable between min_value to
1. Let x be a uniformly distributed random variable between min_value to
max_value
2. Expected value of f(x) =
2. Expected value of f(x) =
(integrate f(x) from min_value to max_value)/(max_value - min_value)
3. Finding expected value of f(x):
a. Repeatedly draw x from uniform distribution

4
maths/newton_raphson.py
View File

@ -24,7 +24,7 @@ def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6, logsteps=Fa
a = x0 # set the initial guess
steps = [a]
error = abs(f(a))
f1 = lambda x: calc_derivative(f, x, h=step) # Derivative of f(x)
f1 = lambda x: calc_derivative(f, x, h=step) # noqa: E731 Derivative of f(x)
for _ in range(maxiter):
if f1(a) == 0:
raise ValueError("No converging solution found")
@ -44,7 +44,7 @@ def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6, logsteps=Fa
if __name__ == "__main__":
import matplotlib.pyplot as plt
f = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)
f = lambda x: m.tanh(x) ** 2 - m.exp(3 * x) # noqa: E731
solution, error, steps = newton_raphson(
f, x0=10, maxiter=1000, step=1e-6, logsteps=True
)

2
maths/prime_factors.py
View File

@ -7,7 +7,7 @@ from typing import List
def prime_factors(n: int) -> List[int]:
"""
Returns prime factors of n as a list.
>>> prime_factors(0)
[]
>>> prime_factors(100)

6
maths/prime_sieve_eratosthenes.py
View File

@ -1,11 +1,13 @@
# flake8: noqa
"""
Sieve of Eratosthenes
Input : n =10
Output : 2 3 5 7
Output: 2 3 5 7
Input : n = 20
Output: 2 3 5 7 11 13 17 19
Output: 2 3 5 7 11 13 17 19
you can read in detail about this at
https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes

4
maths/radians.py
View File

@ -14,8 +14,8 @@ def radians(degree: float) -> float:
4.782202150464463
>>> radians(109.82)
1.9167205845401725
>>> from math import radians as math_radians
>>> from math import radians as math_radians
>>> all(abs(radians(i)-math_radians(i)) <= 0.00000001 for i in range(-2, 361))
True
"""

30
maths/radix2_fft.py
View File

@ -12,36 +12,36 @@ class FFT:
Reference:
https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm#The_radix-2_DIT_case
For polynomials of degree m and n the algorithms has complexity
For polynomials of degree m and n the algorithms has complexity
O(n*logn + m*logm)
The main part of the algorithm is split in two parts:
1) __DFT: We compute the discrete fourier transform (DFT) of A and B using a
bottom-up dynamic approach -
1) __DFT: We compute the discrete fourier transform (DFT) of A and B using a
bottom-up dynamic approach -
2) __multiply: Once we obtain the DFT of A*B, we can similarly
invert it to obtain A*B
The class FFT takes two polynomials A and B with complex coefficients as arguments;
The class FFT takes two polynomials A and B with complex coefficients as arguments;
The two polynomials should be represented as a sequence of coefficients starting
from the free term. Thus, for instance x + 2*x^3 could be represented as
[0,1,0,2] or (0,1,0,2). The constructor adds some zeros at the end so that the
polynomials have the same length which is a power of 2 at least the length of
their product.
from the free term. Thus, for instance x + 2*x^3 could be represented as
[0,1,0,2] or (0,1,0,2). The constructor adds some zeros at the end so that the
polynomials have the same length which is a power of 2 at least the length of
their product.
Example:
Create two polynomials as sequences
>>> A = [0, 1, 0, 2] # x+2x^3
>>> B = (2, 3, 4, 0) # 2+3x+4x^2
Create an FFT object with them
>>> x = FFT(A, B)
Print product
>>> print(x.product) # 2x + 3x^2 + 8x^3 + 4x^4 + 6x^5
[(-0+0j), (2+0j), (3+0j), (8+0j), (6+0j), (8+0j)]
__str__ test
>>> print(x)
A = 0*x^0 + 1*x^1 + 2*x^0 + 3*x^2

4
maths/sieve_of_eratosthenes.py
View File

@ -16,7 +16,7 @@ import math
def sieve(n):
"""
Returns a list with all prime numbers up to n.
>>> sieve(50)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
>>> sieve(25)
@ -31,7 +31,7 @@ def sieve(n):
[]
"""
l = [True] * (n + 1)
l = [True] * (n + 1) # noqa: E741
prime = []
start = 2
end = int(math.sqrt(n))

4
maths/square_root.py
View File

@ -24,10 +24,10 @@ def square_root_iterative(
"""
Square root is aproximated using Newtons method.
https://en.wikipedia.org/wiki/Newton%27s_method
>>> all(abs(square_root_iterative(i)-math.sqrt(i)) <= .00000000000001 for i in range(0, 500))
True
>>> square_root_iterative(-1)
Traceback (most recent call last):
...

12
matrix/sherman_morrison.py
View File

@ -11,7 +11,7 @@ class Matrix:
Example:
>>> a = Matrix(2, 3, 1)
>>> a
>>> a
Matrix consist of 2 rows and 3 columns
[1, 1, 1]
[1, 1, 1]
@ -186,10 +186,10 @@ class Matrix:
Example:
>>> a = Matrix(2, 3)
>>> for r in range(2):
>>> for r in range(2):
... for c in range(3):
... a[r,c] = r*c
...
...
>>> a.transpose()
Matrix consist of 3 rows and 2 columns
[0, 0]
@ -209,14 +209,14 @@ class Matrix:
Apply Sherman-Morrison formula in O(n^2).
To learn this formula, please look this: https://en.wikipedia.org/wiki/Sherman%E2%80%93Morrison_formula
This method returns (A + uv^T)^(-1) where A^(-1) is self. Returns None if it's impossible to calculate.
Warning: This method doesn't check if self is invertible.
Warning: This method doesn't check if self is invertible.
Make sure self is invertible before execute this method.
Example:
>>> ainv = Matrix(3, 3, 0)
>>> for i in range(3): ainv[i,i] = 1
...
>>> u = Matrix(3, 1, 0)
...
>>> u = Matrix(3, 1, 0)
>>> u[0,0], u[1,0], u[2,0] = 1, 2, -3
>>> v = Matrix(3, 1, 0)
>>> v[0,0], v[1,0], v[2,0] = 4, -2, 5

2
networking_flow/ford_fulkerson.py
View File

@ -16,7 +16,7 @@ def BFS(graph, s, t, parent):
while queue:
u = queue.pop(0)
for ind in range(len(graph[u])):
if visited[ind] == False and graph[u][ind] > 0:
if visited[ind] is False and graph[u][ind] > 0:
queue.append(ind)
visited[ind] = True
parent[ind] = u

2
networking_flow/minimum_cut.py
View File

@ -19,7 +19,7 @@ def BFS(graph, s, t, parent):
while queue:
u = queue.pop(0)
for ind in range(len(graph[u])):
if visited[ind] == False and graph[u][ind] > 0:
if visited[ind] is False and graph[u][ind] > 0:
queue.append(ind)
visited[ind] = True
parent[ind] = u

10
other/activity_selection.py
View File

@ -1,7 +1,9 @@
"""The following implementation assumes that the activities
# flake8: noqa
"""The following implementation assumes that the activities
are already sorted according to their finish time"""
"""Prints a maximum set of activities that can be done by a
"""Prints a maximum set of activities that can be done by a
single person, one at a time"""
# n --> Total number of activities
# start[]--> An array that contains start time of all activities
@ -10,8 +12,8 @@ single person, one at a time"""
def printMaxActivities(start, finish):
"""
>>> start = [1, 3, 0, 5, 8, 5]
>>> finish = [2, 4, 6, 7, 9, 9]
>>> start = [1, 3, 0, 5, 8, 5]
>>> finish = [2, 4, 6, 7, 9, 9]
>>> printMaxActivities(start, finish)
The following activities are selected:
0 1 3 4

5
other/anagrams.py
View File

@ -1,4 +1,7 @@
import collections, pprint, time, os
import collections
import os
import pprint
import time
start_time = time.time()
print("creating word list...")

5
other/detecting_english_programmatically.py
View File

@ -55,6 +55,7 @@ def isEnglish(message, wordPercentage=20, letterPercentage=85):
return wordsMatch and lettersMatch
import doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
doctest.testmod()

2
other/dijkstra_bankers_algorithm.py
View File

@ -145,7 +145,7 @@ class BankersAlgorithm:
Process 5 is executing.
Updated available resource stack for processes: 8 5 9 7
The process is in a safe state.
<BLANKLINE>
<BLANKLINE>
"""
need_list = self.__need()
alloc_resources_table = self.__allocated_resources_table

8
other/game_of_life.py
View File

@ -1,4 +1,4 @@
"""Conway's Game Of Life, Author Anurag Kumar(mailto:anuragkumarak95@gmail.com)
"""Conway's Game Of Life, Author Anurag Kumar(mailto:anuragkumarak95@gmail.com)
Requirements:
- numpy
@ -13,7 +13,7 @@ Usage:
- $python3 game_o_life <canvas_size:int>
Game-Of-Life Rules:
1.
Any live cell with fewer than two live neighbours
dies, as if caused by under-population.
@ -27,8 +27,10 @@ Game-Of-Life Rules:
Any dead cell with exactly three live neighbours be-
comes a live cell, as if by reproduction.
"""
import random
import sys
import numpy as np
import random, sys
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

8
other/integeration_by_simpson_approx.py
View File

@ -43,14 +43,14 @@ def simpson_integration(function, a: float, b: float, precision: int = 4) -> flo
Returns:
result : the value of the approximated integration of function in range a to b
Raises:
AssertionError: function is not callable
AssertionError: a is not float or integer
AssertionError: function should return float or integer
AssertionError: b is not float or integer
AssertionError: precision is not positive integer
>>> simpson_integration(lambda x : x*x,1,2,3)
2.333
@ -72,7 +72,7 @@ def simpson_integration(function, a: float, b: float, precision: int = 4) -> flo
Traceback (most recent call last):
...
AssertionError: the function(object) passed should be callable your input : wrong_input
>>> simpson_integration(lambda x : x*x,3.45,3.2,1)
-2.8
@ -85,7 +85,7 @@ def simpson_integration(function, a: float, b: float, precision: int = 4) -> flo
Traceback (most recent call last):
...
AssertionError: precision should be positive integer your input : -1
"""
assert callable(
function

1
other/magicdiamondpattern.py
View File

@ -1,5 +1,6 @@
# Python program for generating diamond pattern in Python 3.7+
# Function to print upper half of diamond (pyramid)
def floyd(n):
"""

4
other/sdes.py
View File

@ -44,9 +44,9 @@ def function(expansion, s0, s1, key, message):
right = message[4:]
temp = apply_table(right, expansion)
temp = XOR(temp, key)
l = apply_sbox(s0, temp[:4])
l = apply_sbox(s0, temp[:4]) # noqa: E741
r = apply_sbox(s1, temp[4:])
l = "0" * (2 - len(l)) + l
l = "0" * (2 - len(l)) + l # noqa: E741
r = "0" * (2 - len(r)) + r
temp = apply_table(l + r, p4_table)
temp = XOR(left, temp)

2
project_euler/problem_02/sol4.py
View File

@ -48,7 +48,7 @@ def solution(n):
"""
try:
n = int(n)
except (TypeError, ValueError) as e:
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or passive of cast to int.")
if n <= 0:
raise ValueError("Parameter n must be greater or equal to one.")

2
project_euler/problem_03/sol1.py
View File

@ -50,7 +50,7 @@ def solution(n):
"""
try:
n = int(n)
except (TypeError, ValueError) as e:
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or passive of cast to int.")
if n <= 0:
raise ValueError("Parameter n must be greater or equal to one.")

2
project_euler/problem_03/sol2.py
View File

@ -37,7 +37,7 @@ def solution(n):
"""
try:
n = int(n)
except (TypeError, ValueError) as e:
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or passive of cast to int.")
if n <= 0:
raise ValueError("Parameter n must be greater or equal to one.")

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