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Commit 895dffb

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pre-commit-ci[bot]github-actionstianyizheng02cclauss
authored
[pre-commit.ci] pre-commit autoupdate (#9543)
* [pre-commit.ci] pre-commit autoupdate updates: - [github.com/astral-sh/ruff-pre-commit: v0.0.291 → v0.0.292](astral-sh/ruff-pre-commit@v0.0.291...v0.0.292) - [github.com/codespell-project/codespell: v2.2.5 → v2.2.6](codespell-project/codespell@v2.2.5...v2.2.6) - [github.com/tox-dev/pyproject-fmt: 1.1.0 → 1.2.0](tox-dev/pyproject-fmt@1.1.0...1.2.0) * updating DIRECTORY.md * Fix typos in test_min_spanning_tree_prim.py * Fix typos * codespell --ignore-words-list=manuel --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com> Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com> Co-authored-by: Christian Clauss <cclauss@me.com>
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+97-118Lines changed: 97 additions & 118 deletions
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‎.pre-commit-config.yaml‎

Copy file name to clipboardExpand all lines: .pre-commit-config.yaml
+1-1Lines changed: 1 addition & 1 deletion
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@@ -26,7 +26,7 @@ repos:
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- id: black
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- repo: https://github.com/codespell-project/codespell
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rev: v2.2.5
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rev: v2.2.6
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hooks:
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- id: codespell
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additional_dependencies:
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‎computer_vision/cnn_classification.py.DISABLED.txt‎

Copy file name to clipboardExpand all lines: computer_vision/cnn_classification.py.DISABLED.txt
+2-2Lines changed: 2 additions & 2 deletions
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@@ -11,10 +11,10 @@ Download dataset from :
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https://lhncbc.nlm.nih.gov/LHC-publications/pubs/TuberculosisChestXrayImageDataSets.html
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1. Download the dataset folder and create two folder training set and test set
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in the parent dataste folder
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in the parent dataset folder
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2. Move 30-40 image from both TB positive and TB Negative folder
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in the test set folder
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3. The labels of the iamges will be extracted from the folder name
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3. The labels of the images will be extracted from the folder name
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the image is present in.
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2020
"""
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‎computer_vision/mosaic_augmentation.py‎

Copy file name to clipboardExpand all lines: computer_vision/mosaic_augmentation.py
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@@ -8,7 +8,7 @@
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import cv2
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import numpy as np
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# Parrameters
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# Parameters
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OUTPUT_SIZE = (720, 1280) # Height, Width
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SCALE_RANGE = (0.4, 0.6) # if height or width lower than this scale, drop it.
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FILTER_TINY_SCALE = 1 / 100
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‎dynamic_programming/min_distance_up_bottom.py‎

Copy file name to clipboardExpand all lines: dynamic_programming/min_distance_up_bottom.py
+4-7Lines changed: 4 additions & 7 deletions
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@@ -1,11 +1,8 @@
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"""
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Author : Alexander Pantyukhin
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Date : October 14, 2022
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This is implementation Dynamic Programming up bottom approach
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to find edit distance.
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The aim is to demonstate up bottom approach for solving the task.
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The implementation was tested on the
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leetcode: https://leetcode.com/problems/edit-distance/
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This is an implementation of the up-bottom approach to find edit distance.
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The implementation was tested on Leetcode: https://leetcode.com/problems/edit-distance/
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Levinstein distance
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Dynamic Programming: up -> down.
@@ -30,10 +27,10 @@ def min_distance_up_bottom(word1: str, word2: str) -> int:
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@functools.cache
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def min_distance(index1: int, index2: int) -> int:
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# if first word index is overflow - delete all from the second word
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# if first word index overflows - delete all from the second word
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if index1 >= len_word1:
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return len_word2 - index2
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# if second word index is overflow - delete all from the first word
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# if second word index overflows - delete all from the first word
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if index2 >= len_word2:
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return len_word1 - index1
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diff = int(word1[index1] != word2[index2]) # current letters not identical
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‎graphs/tests/test_min_spanning_tree_prim.py‎

Copy file name to clipboardExpand all lines: graphs/tests/test_min_spanning_tree_prim.py
+4-4Lines changed: 4 additions & 4 deletions
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@@ -22,12 +22,12 @@ def test_prim_successful_result():
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[1, 7, 11],
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]
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adjancency = defaultdict(list)
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adjacency = defaultdict(list)
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for node1, node2, cost in edges:
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adjancency[node1].append([node2, cost])
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adjancency[node2].append([node1, cost])
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adjacency[node1].append([node2, cost])
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adjacency[node2].append([node1, cost])
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result = mst(adjancency)
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result = mst(adjacency)
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expected = [
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[7, 6, 1],
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‎hashes/sha1.py‎

Copy file name to clipboardExpand all lines: hashes/sha1.py
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@@ -1,26 +1,28 @@
11
"""
2-
Demonstrates implementation of SHA1 Hash function in a Python class and gives utilities
3-
to find hash of string or hash of text from a file.
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Implementation of the SHA1 hash function and gives utilities to find hash of string or
3+
hash of text from a file. Also contains a Test class to verify that the generated hash
4+
matches what is returned by the hashlib library
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46
Usage: python sha1.py --string "Hello World!!"
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python sha1.py --file "hello_world.txt"
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When run without any arguments, it prints the hash of the string "Hello World!!
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Welcome to Cryptography"
8-
Also contains a Test class to verify that the generated Hash is same as that
9-
returned by the hashlib library
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11-
SHA1 hash or SHA1 sum of a string is a cryptographic function which means it is easy
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SHA1 hash or SHA1 sum of a string is a cryptographic function, which means it is easy
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to calculate forwards but extremely difficult to calculate backwards. What this means
13-
is, you can easily calculate the hash of a string, but it is extremely difficult to
14-
know the original string if you have its hash. This property is useful to communicate
15-
securely, send encrypted messages and is very useful in payment systems, blockchain
16-
and cryptocurrency etc.
17-
The Algorithm as described in the reference:
13+
is you can easily calculate the hash of a string, but it is extremely difficult to know
14+
the original string if you have its hash. This property is useful for communicating
15+
securely, send encrypted messages and is very useful in payment systems, blockchain and
16+
cryptocurrency etc.
17+
18+
The algorithm as described in the reference:
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First we start with a message. The message is padded and the length of the message
1920
is added to the end. It is then split into blocks of 512 bits or 64 bytes. The blocks
2021
are then processed one at a time. Each block must be expanded and compressed.
21-
The value after each compression is added to a 160bit buffer called the current hash
22-
state. After the last block is processed the current hash state is returned as
22+
The value after each compression is added to a 160-bit buffer called the current hash
23+
state. After the last block is processed, the current hash state is returned as
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the final hash.
25+
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Reference: https://deadhacker.com/2006/02/21/sha-1-illustrated/
2527
"""
2628
import argparse
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class SHA1Hash:
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"""
33-
Class to contain the entire pipeline for SHA1 Hashing Algorithm
35+
Class to contain the entire pipeline for SHA1 hashing algorithm
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>>> SHA1Hash(bytes('Allan', 'utf-8')).final_hash()
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'872af2d8ac3d8695387e7c804bf0e02c18df9e6e'
3638
"""
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def __init__(self, data):
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"""
40-
Inititates the variables data and h. h is a list of 5 8-digit Hexadecimal
42+
Initiates the variables data and h. h is a list of 5 8-digit hexadecimal
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numbers corresponding to
4244
(1732584193, 4023233417, 2562383102, 271733878, 3285377520)
4345
respectively. We will start with this as a message digest. 0x is how you write
44-
Hexadecimal numbers in Python
46+
hexadecimal numbers in Python
4547
"""
4648
self.data = data
4749
self.h = [0x67452301, 0xEFCDAB89, 0x98BADCFE, 0x10325476, 0xC3D2E1F0]
@@ -90,7 +92,7 @@ def final_hash(self):
9092
For each block, the variable h that was initialized is copied to a,b,c,d,e
9193
and these 5 variables a,b,c,d,e undergo several changes. After all the blocks
9294
are processed, these 5 variables are pairwise added to h ie a to h[0], b to h[1]
93-
and so on. This h becomes our final hash which is returned.
95+
and so on. This h becomes our final hash which is returned.
9496
"""
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self.padded_data = self.padding()
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self.blocks = self.split_blocks()
@@ -135,7 +137,7 @@ def test_sha1_hash():
135137
def main():
136138
"""
137139
Provides option 'string' or 'file' to take input and prints the calculated SHA1
138-
hash. unittest.main() has been commented because we probably don't want to run
140+
hash. unittest.main() has been commented out because we probably don't want to run
139141
the test each time.
140142
"""
141143
# unittest.main()
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‎maths/pi_generator.py‎

Copy file name to clipboardExpand all lines: maths/pi_generator.py
+12-19Lines changed: 12 additions & 19 deletions
Original file line numberDiff line numberDiff line change
@@ -3,60 +3,53 @@ def calculate_pi(limit: int) -> str:
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https://en.wikipedia.org/wiki/Leibniz_formula_for_%CF%80
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Leibniz Formula for Pi
55
6-
The Leibniz formula is the special case arctan 1 = 1/4 Pi .
6+
The Leibniz formula is the special case arctan(1) = pi / 4.
77
Leibniz's formula converges extremely slowly: it exhibits sublinear convergence.
88
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Convergence (https://en.wikipedia.org/wiki/Leibniz_formula_for_%CF%80#Convergence)
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We cannot try to prove against an interrupted, uncompleted generation.
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https://en.wikipedia.org/wiki/Leibniz_formula_for_%CF%80#Unusual_behaviour
13-
The errors can in fact be predicted;
14-
but those calculations also approach infinity for accuracy.
13+
The errors can in fact be predicted, but those calculations also approach infinity
14+
for accuracy.
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16-
Our output will always be a string since we can defintely store all digits in there.
17-
For simplicity' sake, let's just compare against known values and since our outpit
18-
is a string, we need to convert to float.
16+
Our output will be a string so that we can definitely store all digits.
1917
2018
>>> import math
2119
>>> float(calculate_pi(15)) == math.pi
2220
True
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24-
Since we cannot predict errors or interrupt any infinite alternating
25-
series generation since they approach infinity,
26-
or interrupt any alternating series, we are going to need math.isclose()
22+
Since we cannot predict errors or interrupt any infinite alternating series
23+
generation since they approach infinity, or interrupt any alternating series, we'll
24+
need math.isclose()
2725
2826
>>> math.isclose(float(calculate_pi(50)), math.pi)
2927
True
30-
3128
>>> math.isclose(float(calculate_pi(100)), math.pi)
3229
True
3330
34-
Since math.pi-constant contains only 16 digits, here some test with preknown values:
31+
Since math.pi contains only 16 digits, here are some tests with known values:
3532
3633
>>> calculate_pi(50)
3734
'3.14159265358979323846264338327950288419716939937510'
3835
>>> calculate_pi(80)
3936
'3.14159265358979323846264338327950288419716939937510582097494459230781640628620899'
40-
41-
To apply the Leibniz formula for calculating pi,
42-
the variables q, r, t, k, n, and l are used for the iteration process.
4337
"""
38+
# Variables used for the iteration process
4439
q = 1
4540
r = 0
4641
t = 1
4742
k = 1
4843
n = 3
4944
l = 3
45+
5046
decimal = limit
5147
counter = 0
5248

5349
result = ""
5450

55-
"""
56-
We will avoid using yield since we otherwise get a Generator-Object,
57-
which we can't just compare against anything. We would have to make a list out of it
58-
after the generation, so we will just stick to plain return logic:
59-
"""
51+
# We can't compare against anything if we make a generator,
52+
# so we'll stick with plain return logic
6053
while counter != decimal + 1:
6154
if 4 * q + r - t < n * t:
6255
result += str(n)
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‎maths/radians.py‎

Copy file name to clipboardExpand all lines: maths/radians.py
+2-2Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,7 @@
33

44
def radians(degree: float) -> float:
55
"""
6-
Coverts the given angle from degrees to radians
6+
Converts the given angle from degrees to radians
77
https://en.wikipedia.org/wiki/Radian
88
99
>>> radians(180)
@@ -16,7 +16,7 @@ def radians(degree: float) -> float:
1616
1.9167205845401725
1717
1818
>>> from math import radians as math_radians
19-
>>> all(abs(radians(i)-math_radians(i)) <= 0.00000001 for i in range(-2, 361))
19+
>>> all(abs(radians(i) - math_radians(i)) <= 1e-8 for i in range(-2, 361))
2020
True
2121
"""
2222

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‎maths/square_root.py‎

Copy file name to clipboardExpand all lines: maths/square_root.py
+3-4Lines changed: 3 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -19,14 +19,13 @@ def get_initial_point(a: float) -> float:
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2020

2121
def square_root_iterative(
22-
a: float, max_iter: int = 9999, tolerance: float = 0.00000000000001
22+
a: float, max_iter: int = 9999, tolerance: float = 1e-14
2323
) -> float:
2424
"""
25-
Square root is aproximated using Newtons method.
25+
Square root approximated using Newton's method.
2626
https://en.wikipedia.org/wiki/Newton%27s_method
2727
28-
>>> all(abs(square_root_iterative(i)-math.sqrt(i)) <= .00000000000001
29-
... for i in range(500))
28+
>>> all(abs(square_root_iterative(i) - math.sqrt(i)) <= 1e-14 for i in range(500))
3029
True
3130
3231
>>> square_root_iterative(-1)
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‎neural_network/convolution_neural_network.py‎

Copy file name to clipboardExpand all lines: neural_network/convolution_neural_network.py
+4-4Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -2,7 +2,7 @@
22
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
33
Name - - CNN - Convolution Neural Network For Photo Recognizing
44
Goal - - Recognize Handing Writing Word Photo
5-
Detail：Total 5 layers neural network
5+
Detail: Total 5 layers neural network
66
* Convolution layer
77
* Pooling layer
88
* Input layer layer of BP
@@ -24,7 +24,7 @@ def __init__(
2424
self, conv1_get, size_p1, bp_num1, bp_num2, bp_num3, rate_w=0.2, rate_t=0.2
2525
):
2626
"""
27-
:param conv1_get: [a,c,d]，size, number, step of convolution kernel
27+
:param conv1_get: [a,c,d], size, number, step of convolution kernel
2828
:param size_p1: pooling size
2929
:param bp_num1: units number of flatten layer
3030
:param bp_num2: units number of hidden layer
@@ -71,7 +71,7 @@ def save_model(self, save_path):
7171
with open(save_path, "wb") as f:
7272
pickle.dump(model_dic, f)
7373

74-
print(f"Model saved： {save_path}")
74+
print(f"Model saved: {save_path}")
7575

7676
@classmethod
7777
def read_model(cls, model_path):
@@ -210,7 +210,7 @@ def _calculate_gradient_from_pool(
210210
def train(
211211
self, patterns, datas_train, datas_teach, n_repeat, error_accuracy, draw_e=bool
212212
):
213-
# model traning
213+
# model training
214214
print("----------------------Start Training-------------------------")
215215
print((" - - Shape: Train_Data ", np.shape(datas_train)))
216216
print((" - - Shape: Teach_Data ", np.shape(datas_teach)))

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