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* updating DIRECTORY.md * isort --profile black . * Black after * updating DIRECTORY.md Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
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.github/workflows/autoblack.yml
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if: failure()
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if: failure()
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run: |
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run: |
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black .
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black .
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isort --profile black --recursive .
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isort --profile black .
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git config --global user.name github-actions
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git config --global user.name github-actions
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git config --global user.email '${GITHUB_ACTOR}@users.noreply.github.com'
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git config --global user.email '${GITHUB_ACTOR}@users.noreply.github.com'
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git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/$GITHUB_REPOSITORY
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git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/$GITHUB_REPOSITORY
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@ -253,6 +253,7 @@
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* [Finding Bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py)
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* [Finding Bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py)
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* [Frequent Pattern Graph Miner](https://github.com/TheAlgorithms/Python/blob/master/graphs/frequent_pattern_graph_miner.py)
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* [Frequent Pattern Graph Miner](https://github.com/TheAlgorithms/Python/blob/master/graphs/frequent_pattern_graph_miner.py)
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* [G Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/graphs/g_topological_sort.py)
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* [G Topological Sort](https://github.com/TheAlgorithms/Python/blob/master/graphs/g_topological_sort.py)
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* [Gale Shapley Bigraph](https://github.com/TheAlgorithms/Python/blob/master/graphs/gale_shapley_bigraph.py)
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* [Graph List](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py)
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* [Graph List](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py)
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* [Graph Matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py)
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* [Graph Matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py)
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* [Graphs Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py)
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* [Graphs Floyd Warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py)
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@ -596,6 +597,7 @@
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## Searches
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## Searches
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* [Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py)
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* [Binary Search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py)
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* [Double Linear Search](https://github.com/TheAlgorithms/Python/blob/master/searches/double_linear_search.py)
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* [Fibonacci Search](https://github.com/TheAlgorithms/Python/blob/master/searches/fibonacci_search.py)
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* [Fibonacci Search](https://github.com/TheAlgorithms/Python/blob/master/searches/fibonacci_search.py)
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* [Hill Climbing](https://github.com/TheAlgorithms/Python/blob/master/searches/hill_climbing.py)
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* [Hill Climbing](https://github.com/TheAlgorithms/Python/blob/master/searches/hill_climbing.py)
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* [Interpolation Search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py)
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* [Interpolation Search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py)
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@ -6,9 +6,10 @@ flake8 : passed
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mypy : passed
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mypy : passed
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"""
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"""
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from numpy import array, cos, sin, radians, cross # type: ignore
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from typing import List
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from typing import List
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from numpy import array, cos, cross, radians, sin # type: ignore
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def polar_force(
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def polar_force(
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magnitude: float, angle: float, radian_mode: bool = False
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magnitude: float, angle: float, radian_mode: bool = False
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@ -2,9 +2,9 @@
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# Author: Syed Haseeb Shah (github.com/QuantumNovice)
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# Author: Syed Haseeb Shah (github.com/QuantumNovice)
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# The Newton-Raphson method (also known as Newton's method) is a way to
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# The Newton-Raphson method (also known as Newton's method) is a way to
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# quickly find a good approximation for the root of a real-valued function
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# quickly find a good approximation for the root of a real-valued function
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from decimal import Decimal
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from decimal import Decimal
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from math import * # noqa: F401, F403
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from math import * # noqa: F401, F403
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from sympy import diff
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from sympy import diff
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@ -35,8 +35,8 @@ https://www.youtube.com/watch?v=kfmNeskzs2o
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https://www.youtube.com/watch?v=4RhLNDqcjpA
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https://www.youtube.com/watch?v=4RhLNDqcjpA
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"""
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"""
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import string
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import string
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import numpy
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import numpy
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@ -1,5 +1,5 @@
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import string
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import itertools
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import itertools
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import string
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def chunker(seq, size):
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def chunker(seq, size):
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@ -10,7 +10,7 @@ without needing to store any additional data except the position of the first
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original character. The BWT is thus a "free" method of improving the efficiency
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original character. The BWT is thus a "free" method of improving the efficiency
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of text compression algorithms, costing only some extra computation.
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of text compression algorithms, costing only some extra computation.
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"""
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"""
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from typing import List, Dict
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from typing import Dict, List
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def all_rotations(s: str) -> List[str]:
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def all_rotations(s: str) -> List[str]:
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@ -1,5 +1,5 @@
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import numpy as np
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import cv2
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import cv2
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import numpy as np
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"""
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"""
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Harris Corner Detector
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Harris Corner Detector
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@ -35,7 +35,7 @@ https://www.geeksforgeeks.org/segment-tree-efficient-implementation/
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>>> st.query(0, 2)
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>>> st.query(0, 2)
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[1, 2, 3]
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[1, 2, 3]
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"""
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"""
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from typing import List, Callable, TypeVar
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from typing import Callable, List, TypeVar
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T = TypeVar("T")
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T = TypeVar("T")
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@ -3,9 +3,8 @@ Segment_tree creates a segment tree with a given array and function,
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allowing queries to be done later in log(N) time
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allowing queries to be done later in log(N) time
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function takes 2 values and returns a same type value
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function takes 2 values and returns a same type value
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"""
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"""
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from queue import Queue
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from collections.abc import Sequence
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from collections.abc import Sequence
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from queue import Queue
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class SegmentTreeNode(object):
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class SegmentTreeNode(object):
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#!/usr/bin/env python3
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#!/usr/bin/env python3
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from hash_table import HashTable
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from hash_table import HashTable
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from number_theory.prime_numbers import next_prime, check_prime
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from number_theory.prime_numbers import check_prime, next_prime
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class DoubleHash(HashTable):
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class DoubleHash(HashTable):
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from hash_table import HashTable
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from collections import deque
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from collections import deque
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from hash_table import HashTable
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class HashTableWithLinkedList(HashTable):
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class HashTableWithLinkedList(HashTable):
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def __init__(self, *args, **kwargs):
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def __init__(self, *args, **kwargs):
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@ -1,8 +1,7 @@
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"""
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"""
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Implemented an algorithm using opencv to convert a colored image into its negative
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Implemented an algorithm using opencv to convert a colored image into its negative
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"""
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"""
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from cv2 import destroyAllWindows, imread, imshow, waitKey
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from cv2 import imread, imshow, waitKey, destroyAllWindows
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def convert_to_negative(img):
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def convert_to_negative(img):
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@ -1,8 +1,8 @@
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"""
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"""
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Implementation Burke's algorithm (dithering)
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Implementation Burke's algorithm (dithering)
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"""
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"""
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from cv2 import destroyAllWindows, imread, imshow, waitKey
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import numpy as np
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import numpy as np
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from cv2 import destroyAllWindows, imread, imshow, waitKey
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class Burkes:
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class Burkes:
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@ -1,5 +1,6 @@
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import cv2
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import cv2
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import numpy as np
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import numpy as np
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from digital_image_processing.filters.convolve import img_convolve
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from digital_image_processing.filters.convolve import img_convolve
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from digital_image_processing.filters.sobel_filter import sobel_filter
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from digital_image_processing.filters.sobel_filter import sobel_filter
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@ -9,11 +9,11 @@ Inputs:
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Output:
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Output:
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img:A 2d zero padded image with values in between 0 and 1
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img:A 2d zero padded image with values in between 0 and 1
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"""
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"""
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import math
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import sys
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import cv2
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import cv2
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import numpy as np
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import numpy as np
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import math
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import sys
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def vec_gaussian(img: np.ndarray, variance: float) -> np.ndarray:
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def vec_gaussian(img: np.ndarray, variance: float) -> np.ndarray:
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@ -1,8 +1,8 @@
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# @Author : lightXu
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# @Author : lightXu
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# @File : convolve.py
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# @File : convolve.py
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# @Time : 2019/7/8 0008 下午 16:13
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# @Time : 2019/7/8 0008 下午 16:13
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from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
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from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
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from numpy import array, zeros, ravel, pad, dot, uint8
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from numpy import array, dot, pad, ravel, uint8, zeros
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def im2col(image, block_size):
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def im2col(image, block_size):
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@ -1,10 +1,11 @@
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"""
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"""
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Implementation of gaussian filter algorithm
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Implementation of gaussian filter algorithm
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"""
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"""
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from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
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from numpy import pi, mgrid, exp, square, zeros, ravel, dot, uint8
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from itertools import product
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from itertools import product
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from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
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from numpy import dot, exp, mgrid, pi, ravel, square, uint8, zeros
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def gen_gaussian_kernel(k_size, sigma):
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def gen_gaussian_kernel(k_size, sigma):
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center = k_size // 2
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center = k_size // 2
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@ -1,9 +1,8 @@
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"""
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"""
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Implementation of median filter algorithm
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Implementation of median filter algorithm
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"""
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"""
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from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
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from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
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from numpy import divide, int8, multiply, ravel, sort, zeros_like
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from numpy import zeros_like, ravel, sort, multiply, divide, int8
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def median_filter(gray_img, mask=3):
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def median_filter(gray_img, mask=3):
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@ -2,7 +2,8 @@
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# @File : sobel_filter.py
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# @File : sobel_filter.py
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# @Time : 2019/7/8 0008 下午 16:26
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# @Time : 2019/7/8 0008 下午 16:26
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import numpy as np
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import numpy as np
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from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey
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from cv2 import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
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from digital_image_processing.filters.convolve import img_convolve
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from digital_image_processing.filters.convolve import img_convolve
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@ -6,10 +6,9 @@ Created on Fri Sep 28 15:22:29 2018
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import copy
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import copy
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import os
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import os
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|
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import numpy as np
|
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|
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import cv2
|
import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib import pyplot as plt
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class contrastStretch:
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class contrastStretch:
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|
|
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@ -1,6 +1,6 @@
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""" Multiple image resizing techniques """
|
""" Multiple image resizing techniques """
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import numpy as np
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import numpy as np
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from cv2 import imread, imshow, waitKey, destroyAllWindows
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from cv2 import destroyAllWindows, imread, imshow, waitKey
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|
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|
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class NearestNeighbour:
|
class NearestNeighbour:
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|
|
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@ -1,6 +1,6 @@
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from matplotlib import pyplot as plt
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|
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import numpy as np
|
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import cv2
|
import cv2
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|
import numpy as np
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|
from matplotlib import pyplot as plt
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|
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|
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def get_rotation(
|
def get_rotation(
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|
|
|
@ -1,8 +1,7 @@
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"""
|
"""
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Implemented an algorithm using opencv to tone an image with sepia technique
|
Implemented an algorithm using opencv to tone an image with sepia technique
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"""
|
"""
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|
from cv2 import destroyAllWindows, imread, imshow, waitKey
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from cv2 import imread, imshow, waitKey, destroyAllWindows
|
|
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|
|
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|
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def make_sepia(img, factor: int):
|
def make_sepia(img, factor: int):
|
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|
|
|
@ -1,21 +1,21 @@
|
||||||
"""
|
"""
|
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PyTest's for Digital Image Processing
|
PyTest's for Digital Image Processing
|
||||||
"""
|
"""
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|
from cv2 import COLOR_BGR2GRAY, cvtColor, imread
|
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import digital_image_processing.edge_detection.canny as canny
|
|
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import digital_image_processing.filters.gaussian_filter as gg
|
|
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import digital_image_processing.filters.median_filter as med
|
|
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import digital_image_processing.filters.sobel_filter as sob
|
|
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import digital_image_processing.filters.convolve as conv
|
|
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import digital_image_processing.change_contrast as cc
|
|
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import digital_image_processing.convert_to_negative as cn
|
|
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import digital_image_processing.sepia as sp
|
|
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import digital_image_processing.dithering.burkes as bs
|
|
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import digital_image_processing.resize.resize as rs
|
|
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from cv2 import imread, cvtColor, COLOR_BGR2GRAY
|
|
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from numpy import array, uint8
|
from numpy import array, uint8
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from PIL import Image
|
from PIL import Image
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|
|
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|
from digital_image_processing import change_contrast as cc
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|
from digital_image_processing import convert_to_negative as cn
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|
from digital_image_processing import sepia as sp
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|
from digital_image_processing.dithering import burkes as bs
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|
from digital_image_processing.edge_detection import canny as canny
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|
from digital_image_processing.filters import convolve as conv
|
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|
from digital_image_processing.filters import gaussian_filter as gg
|
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|
from digital_image_processing.filters import median_filter as med
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|
from digital_image_processing.filters import sobel_filter as sob
|
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|
from digital_image_processing.resize import resize as rs
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|
|
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img = imread(r"digital_image_processing/image_data/lena_small.jpg")
|
img = imread(r"digital_image_processing/image_data/lena_small.jpg")
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gray = cvtColor(img, COLOR_BGR2GRAY)
|
gray = cvtColor(img, COLOR_BGR2GRAY)
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|
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|
|
|
@ -1,5 +1,5 @@
|
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from itertools import accumulate
|
|
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from bisect import bisect
|
from bisect import bisect
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|
from itertools import accumulate
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|
|
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|
|
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def fracKnapsack(vl, wt, W, n):
|
def fracKnapsack(vl, wt, W, n):
|
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|
|
|
@ -73,9 +73,10 @@ if __name__ == "__main__":
|
||||||
A random simulation of this algorithm.
|
A random simulation of this algorithm.
|
||||||
"""
|
"""
|
||||||
import time
|
import time
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
from random import randint
|
from random import randint
|
||||||
|
|
||||||
|
from matplotlib import pyplot as plt
|
||||||
|
|
||||||
inputs = [10, 100, 1000, 10000, 50000, 100000, 200000, 300000, 400000, 500000]
|
inputs = [10, 100, 1000, 10000, 50000, 100000, 200000, 300000, 400000, 500000]
|
||||||
tim = []
|
tim = []
|
||||||
for i in inputs:
|
for i in inputs:
|
||||||
|
|
|
@ -16,9 +16,7 @@
|
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# frequencies will be placed near the root of the tree while the nodes
|
# frequencies will be placed near the root of the tree while the nodes
|
||||||
# with low frequencies will be placed near the leaves of the tree thus
|
# with low frequencies will be placed near the leaves of the tree thus
|
||||||
# reducing search time in the most frequent instances.
|
# reducing search time in the most frequent instances.
|
||||||
|
|
||||||
import sys
|
import sys
|
||||||
|
|
||||||
from random import randint
|
from random import randint
|
||||||
|
|
||||||
|
|
||||||
|
|
|
@ -9,7 +9,6 @@ Python:
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import skfuzzy as fuzz
|
import skfuzzy as fuzz
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
# Create universe of discourse in Python using linspace ()
|
# Create universe of discourse in Python using linspace ()
|
||||||
X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
|
X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
|
||||||
|
@ -45,7 +44,7 @@ if __name__ == "__main__":
|
||||||
# max-product composition
|
# max-product composition
|
||||||
|
|
||||||
# Plot each set A, set B and each operation result using plot() and subplot().
|
# Plot each set A, set B and each operation result using plot() and subplot().
|
||||||
import matplotlib.pyplot as plt
|
from matplotlib import pyplot as plt
|
||||||
|
|
||||||
plt.figure()
|
plt.figure()
|
||||||
|
|
||||||
|
|
|
@ -1,4 +1,5 @@
|
||||||
from math import atan, cos, radians, sin, tan
|
from math import atan, cos, radians, sin, tan
|
||||||
|
|
||||||
from haversine_distance import haversine_distance
|
from haversine_distance import haversine_distance
|
||||||
|
|
||||||
|
|
||||||
|
|
|
@ -1,7 +1,7 @@
|
||||||
# https://en.wikipedia.org/wiki/B%C3%A9zier_curve
|
# https://en.wikipedia.org/wiki/B%C3%A9zier_curve
|
||||||
# https://www.tutorialspoint.com/computer_graphics/computer_graphics_curves.htm
|
# https://www.tutorialspoint.com/computer_graphics/computer_graphics_curves.htm
|
||||||
|
|
||||||
from typing import List, Tuple
|
from typing import List, Tuple
|
||||||
|
|
||||||
from scipy.special import comb
|
from scipy.special import comb
|
||||||
|
|
||||||
|
|
||||||
|
@ -78,7 +78,7 @@ class BezierCurve:
|
||||||
step_size: defines the step(s) at which to evaluate the Bezier curve.
|
step_size: defines the step(s) at which to evaluate the Bezier curve.
|
||||||
The smaller the step size, the finer the curve produced.
|
The smaller the step size, the finer the curve produced.
|
||||||
"""
|
"""
|
||||||
import matplotlib.pyplot as plt
|
from matplotlib import pyplot as plt
|
||||||
|
|
||||||
to_plot_x: List[float] = [] # x coordinates of points to plot
|
to_plot_x: List[float] = [] # x coordinates of points to plot
|
||||||
to_plot_y: List[float] = [] # y coordinates of points to plot
|
to_plot_y: List[float] = [] # y coordinates of points to plot
|
||||||
|
|
|
@ -1,6 +1,5 @@
|
||||||
from collections import deque
|
from collections import deque
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
# Accept No. of Nodes and edges
|
# Accept No. of Nodes and edges
|
||||||
n, m = map(int, input().split(" "))
|
n, m = map(int, input().split(" "))
|
||||||
|
|
|
@ -12,8 +12,7 @@ while Q is non-empty:
|
||||||
mark w as explored
|
mark w as explored
|
||||||
add w to Q (at the end)
|
add w to Q (at the end)
|
||||||
"""
|
"""
|
||||||
|
from typing import Dict, Set
|
||||||
from typing import Set, Dict
|
|
||||||
|
|
||||||
G = {
|
G = {
|
||||||
"A": ["B", "C"],
|
"A": ["B", "C"],
|
||||||
|
|
|
@ -11,8 +11,7 @@ Pseudocode:
|
||||||
if v unexplored:
|
if v unexplored:
|
||||||
DFS(G, v)
|
DFS(G, v)
|
||||||
"""
|
"""
|
||||||
|
from typing import Dict, Set
|
||||||
from typing import Set, Dict
|
|
||||||
|
|
||||||
|
|
||||||
def depth_first_search(graph: Dict, start: str) -> Set[int]:
|
def depth_first_search(graph: Dict, start: str) -> Set[int]:
|
||||||
|
|
|
@ -1,7 +1,7 @@
|
||||||
from collections import deque
|
|
||||||
import random as rand
|
|
||||||
import math as math
|
import math as math
|
||||||
|
import random as rand
|
||||||
import time
|
import time
|
||||||
|
from collections import deque
|
||||||
|
|
||||||
# the default weight is 1 if not assigned but all the implementation is weighted
|
# the default weight is 1 if not assigned but all the implementation is weighted
|
||||||
|
|
||||||
|
|
|
@ -1,4 +1,5 @@
|
||||||
import heapq
|
import heapq
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
|
|
@ -1,4 +1,5 @@
|
||||||
import unittest
|
import unittest
|
||||||
|
|
||||||
import greedy_knapsack as kp
|
import greedy_knapsack as kp
|
||||||
|
|
||||||
|
|
||||||
|
|
|
@ -23,10 +23,9 @@ state. After the last block is processed the current hash state is returned as
|
||||||
the final hash.
|
the final hash.
|
||||||
Reference: https://deadhacker.com/2006/02/21/sha-1-illustrated/
|
Reference: https://deadhacker.com/2006/02/21/sha-1-illustrated/
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import argparse
|
import argparse
|
||||||
import struct
|
|
||||||
import hashlib # hashlib is only used inside the Test class
|
import hashlib # hashlib is only used inside the Test class
|
||||||
|
import struct
|
||||||
import unittest
|
import unittest
|
||||||
|
|
||||||
|
|
||||||
|
|
|
@ -6,9 +6,16 @@ Created on Mon Feb 26 15:40:07 2018
|
||||||
|
|
||||||
This file contains the test-suite for the linear algebra library.
|
This file contains the test-suite for the linear algebra library.
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import unittest
|
import unittest
|
||||||
from lib import Matrix, Vector, axpy, squareZeroMatrix, unitBasisVector, zeroVector
|
|
||||||
|
from lib import (
|
||||||
|
Matrix,
|
||||||
|
Vector,
|
||||||
|
axpy,
|
||||||
|
squareZeroMatrix,
|
||||||
|
unitBasisVector,
|
||||||
|
zeroVector,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
class Test(unittest.TestCase):
|
class Test(unittest.TestCase):
|
||||||
|
|
|
@ -1,10 +1,9 @@
|
||||||
# Gaussian Naive Bayes Example
|
# Gaussian Naive Bayes Example
|
||||||
|
from matplotlib import pyplot as plt
|
||||||
from sklearn.naive_bayes import GaussianNB
|
|
||||||
from sklearn.metrics import plot_confusion_matrix
|
|
||||||
from sklearn.datasets import load_iris
|
from sklearn.datasets import load_iris
|
||||||
|
from sklearn.metrics import plot_confusion_matrix
|
||||||
from sklearn.model_selection import train_test_split
|
from sklearn.model_selection import train_test_split
|
||||||
import matplotlib.pyplot as plt
|
from sklearn.naive_bayes import GaussianNB
|
||||||
|
|
||||||
|
|
||||||
def main():
|
def main():
|
||||||
|
|
|
@ -52,11 +52,12 @@ Usage:
|
||||||
|
|
||||||
|
|
||||||
"""
|
"""
|
||||||
|
import warnings
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
from matplotlib import pyplot as plt
|
from matplotlib import pyplot as plt
|
||||||
from sklearn.metrics import pairwise_distances
|
from sklearn.metrics import pairwise_distances
|
||||||
import warnings
|
|
||||||
|
|
||||||
warnings.filterwarnings("ignore")
|
warnings.filterwarnings("ignore")
|
||||||
|
|
||||||
|
@ -193,7 +194,7 @@ def kmeans(
|
||||||
|
|
||||||
# Mock test below
|
# Mock test below
|
||||||
if False: # change to true to run this test case.
|
if False: # change to true to run this test case.
|
||||||
import sklearn.datasets as ds
|
from sklearn import datasets as ds
|
||||||
|
|
||||||
dataset = ds.load_iris()
|
dataset = ds.load_iris()
|
||||||
k = 3
|
k = 3
|
||||||
|
|
|
@ -1,5 +1,6 @@
|
||||||
import numpy as np
|
|
||||||
from collections import Counter
|
from collections import Counter
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
from sklearn import datasets
|
from sklearn import datasets
|
||||||
from sklearn.model_selection import train_test_split
|
from sklearn.model_selection import train_test_split
|
||||||
|
|
||||||
|
|
|
@ -1,5 +1,5 @@
|
||||||
from sklearn.model_selection import train_test_split
|
|
||||||
from sklearn.datasets import load_iris
|
from sklearn.datasets import load_iris
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
from sklearn.neighbors import KNeighborsClassifier
|
from sklearn.neighbors import KNeighborsClassifier
|
||||||
|
|
||||||
# Load iris file
|
# Load iris file
|
||||||
|
|
|
@ -41,11 +41,9 @@
|
||||||
|
|
||||||
Author: @EverLookNeverSee
|
Author: @EverLookNeverSee
|
||||||
"""
|
"""
|
||||||
|
|
||||||
from math import log
|
from math import log
|
||||||
from os import name, system
|
from os import name, system
|
||||||
from random import gauss
|
from random import gauss, seed
|
||||||
from random import seed
|
|
||||||
|
|
||||||
|
|
||||||
# Make a training dataset drawn from a gaussian distribution
|
# Make a training dataset drawn from a gaussian distribution
|
||||||
|
|
|
@ -7,8 +7,8 @@ We try to set the weight of these features, over many iterations, so that they b
|
||||||
fit our dataset. In this particular code, I had used a CSGO dataset (ADR vs
|
fit our dataset. In this particular code, I had used a CSGO dataset (ADR vs
|
||||||
Rating). We try to best fit a line through dataset and estimate the parameters.
|
Rating). We try to best fit a line through dataset and estimate the parameters.
|
||||||
"""
|
"""
|
||||||
import requests
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
import requests
|
||||||
|
|
||||||
|
|
||||||
def collect_dataset():
|
def collect_dataset():
|
||||||
|
|
|
@ -14,14 +14,12 @@ Helpful resources:
|
||||||
Coursera ML course
|
Coursera ML course
|
||||||
https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
|
https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import matplotlib.pyplot as plt
|
from matplotlib import pyplot as plt
|
||||||
|
from sklearn import datasets
|
||||||
|
|
||||||
# get_ipython().run_line_magic('matplotlib', 'inline')
|
# get_ipython().run_line_magic('matplotlib', 'inline')
|
||||||
|
|
||||||
from sklearn import datasets
|
|
||||||
|
|
||||||
|
|
||||||
# In[67]:
|
# In[67]:
|
||||||
|
|
||||||
|
|
|
@ -4,14 +4,12 @@
|
||||||
* http://colah.github.io/posts/2015-08-Understanding-LSTMs
|
* http://colah.github.io/posts/2015-08-Understanding-LSTMs
|
||||||
* https://en.wikipedia.org/wiki/Long_short-term_memory
|
* https://en.wikipedia.org/wiki/Long_short-term_memory
|
||||||
"""
|
"""
|
||||||
|
|
||||||
from keras.layers import Dense, LSTM
|
|
||||||
from keras.models import Sequential
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
from keras.layers import LSTM, Dense
|
||||||
|
from keras.models import Sequential
|
||||||
from sklearn.preprocessing import MinMaxScaler
|
from sklearn.preprocessing import MinMaxScaler
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
"""
|
"""
|
||||||
First part of building a model is to get the data and prepare
|
First part of building a model is to get the data and prepare
|
||||||
|
|
|
@ -1,6 +1,5 @@
|
||||||
from sklearn.neural_network import MLPClassifier
|
from sklearn.neural_network import MLPClassifier
|
||||||
|
|
||||||
|
|
||||||
X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
|
X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
|
||||||
y = [0, 1, 0, 0]
|
y = [0, 1, 0, 0]
|
||||||
|
|
||||||
|
|
|
@ -1,5 +1,5 @@
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
from matplotlib import pyplot as plt
|
||||||
from sklearn.linear_model import LinearRegression
|
from sklearn.linear_model import LinearRegression
|
||||||
|
|
||||||
# Splitting the dataset into the Training set and Test set
|
# Splitting the dataset into the Training set and Test set
|
||||||
|
|
|
@ -1,10 +1,9 @@
|
||||||
# Random Forest Classifier Example
|
# Random Forest Classifier Example
|
||||||
|
from matplotlib import pyplot as plt
|
||||||
from sklearn.datasets import load_iris
|
from sklearn.datasets import load_iris
|
||||||
from sklearn.model_selection import train_test_split
|
|
||||||
from sklearn.ensemble import RandomForestClassifier
|
from sklearn.ensemble import RandomForestClassifier
|
||||||
from sklearn.metrics import plot_confusion_matrix
|
from sklearn.metrics import plot_confusion_matrix
|
||||||
import matplotlib.pyplot as plt
|
from sklearn.model_selection import train_test_split
|
||||||
|
|
||||||
|
|
||||||
def main():
|
def main():
|
||||||
|
|
|
@ -1,10 +1,8 @@
|
||||||
# Random Forest Regressor Example
|
# Random Forest Regressor Example
|
||||||
|
|
||||||
from sklearn.datasets import load_boston
|
from sklearn.datasets import load_boston
|
||||||
from sklearn.model_selection import train_test_split
|
|
||||||
from sklearn.ensemble import RandomForestRegressor
|
from sklearn.ensemble import RandomForestRegressor
|
||||||
from sklearn.metrics import mean_absolute_error
|
from sklearn.metrics import mean_absolute_error, mean_squared_error
|
||||||
from sklearn.metrics import mean_squared_error
|
from sklearn.model_selection import train_test_split
|
||||||
|
|
||||||
|
|
||||||
def main():
|
def main():
|
||||||
|
|
|
@ -36,9 +36,9 @@ import os
|
||||||
import sys
|
import sys
|
||||||
import urllib.request
|
import urllib.request
|
||||||
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
from matplotlib import pyplot as plt
|
||||||
from sklearn.datasets import make_blobs, make_circles
|
from sklearn.datasets import make_blobs, make_circles
|
||||||
from sklearn.preprocessing import StandardScaler
|
from sklearn.preprocessing import StandardScaler
|
||||||
|
|
||||||
|
|
|
@ -1,5 +1,5 @@
|
||||||
from sklearn.datasets import load_iris
|
|
||||||
from sklearn import svm
|
from sklearn import svm
|
||||||
|
from sklearn.datasets import load_iris
|
||||||
from sklearn.model_selection import train_test_split
|
from sklearn.model_selection import train_test_split
|
||||||
|
|
||||||
|
|
||||||
|
|
|
@ -1,4 +1,4 @@
|
||||||
from typing import Tuple, List
|
from typing import List, Tuple
|
||||||
|
|
||||||
|
|
||||||
def n31(a: int) -> Tuple[List[int], int]:
|
def n31(a: int) -> Tuple[List[int], int]:
|
||||||
|
|
|
@ -7,10 +7,10 @@
|
||||||
reference-->Su, Francis E., et al. "Fibonacci Number Formula." Math Fun Facts.
|
reference-->Su, Francis E., et al. "Fibonacci Number Formula." Math Fun Facts.
|
||||||
<http://www.math.hmc.edu/funfacts>
|
<http://www.math.hmc.edu/funfacts>
|
||||||
"""
|
"""
|
||||||
import math
|
|
||||||
import functools
|
import functools
|
||||||
|
import math
|
||||||
import time
|
import time
|
||||||
from decimal import getcontext, Decimal
|
from decimal import Decimal, getcontext
|
||||||
|
|
||||||
getcontext().prec = 100
|
getcontext().prec = 100
|
||||||
|
|
||||||
|
|
|
@ -1,6 +1,7 @@
|
||||||
import math
|
import math
|
||||||
from scipy.integrate import quad
|
|
||||||
from numpy import inf
|
from numpy import inf
|
||||||
|
from scipy.integrate import quad
|
||||||
|
|
||||||
|
|
||||||
def gamma(num: float) -> float:
|
def gamma(num: float) -> float:
|
||||||
|
|
|
@ -5,7 +5,7 @@ python/black : True
|
||||||
python : 3.7.3
|
python : 3.7.3
|
||||||
|
|
||||||
"""
|
"""
|
||||||
from numpy import pi, sqrt, exp
|
from numpy import exp, pi, sqrt
|
||||||
|
|
||||||
|
|
||||||
def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
|
def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
|
||||||
|
|
|
@ -1,5 +1,5 @@
|
||||||
from typing import Callable, Union
|
|
||||||
import math as m
|
import math as m
|
||||||
|
from typing import Callable, Union
|
||||||
|
|
||||||
|
|
||||||
def line_length(
|
def line_length(
|
||||||
|
|
|
@ -5,8 +5,8 @@ python/black : True
|
||||||
flake8 : True
|
flake8 : True
|
||||||
"""
|
"""
|
||||||
|
|
||||||
from maths.prime_factors import prime_factors
|
|
||||||
from maths.is_square_free import is_square_free
|
from maths.is_square_free import is_square_free
|
||||||
|
from maths.prime_factors import prime_factors
|
||||||
|
|
||||||
|
|
||||||
def mobius(n: int) -> int:
|
def mobius(n: int) -> int:
|
||||||
|
|
|
@ -7,7 +7,6 @@
|
||||||
limit is reached or the gradient f'(x[n]) approaches zero. In both cases, exception
|
limit is reached or the gradient f'(x[n]) approaches zero. In both cases, exception
|
||||||
is raised. If iteration limit is reached, try increasing maxiter.
|
is raised. If iteration limit is reached, try increasing maxiter.
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import math as m
|
import math as m
|
||||||
|
|
||||||
|
|
||||||
|
@ -42,7 +41,7 @@ def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6, logsteps=Fa
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
import matplotlib.pyplot as plt
|
from matplotlib import pyplot as plt
|
||||||
|
|
||||||
f = lambda x: m.tanh(x) ** 2 - m.exp(3 * x) # noqa: E731
|
f = lambda x: m.tanh(x) ** 2 - m.exp(3 * x) # noqa: E731
|
||||||
solution, error, steps = newton_raphson(
|
solution, error, steps = newton_raphson(
|
||||||
|
|
|
@ -1,5 +1,5 @@
|
||||||
from typing import Generator
|
|
||||||
import math
|
import math
|
||||||
|
from typing import Generator
|
||||||
|
|
||||||
|
|
||||||
def slow_primes(max: int) -> Generator[int, None, None]:
|
def slow_primes(max: int) -> Generator[int, None, None]:
|
||||||
|
|
|
@ -9,9 +9,9 @@ After through ReLU, the element of the vector always 0 or real number.
|
||||||
Script inspired from its corresponding Wikipedia article
|
Script inspired from its corresponding Wikipedia article
|
||||||
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
|
https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
|
||||||
"""
|
"""
|
||||||
|
from typing import List
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from typing import List
|
|
||||||
|
|
||||||
|
|
||||||
def relu(vector: List[float]):
|
def relu(vector: List[float]):
|
||||||
|
|
|
@ -3,8 +3,8 @@ Find Volumes of Various Shapes.
|
||||||
|
|
||||||
Wikipedia reference: https://en.wikipedia.org/wiki/Volume
|
Wikipedia reference: https://en.wikipedia.org/wiki/Volume
|
||||||
"""
|
"""
|
||||||
from typing import Union
|
|
||||||
from math import pi, pow
|
from math import pi, pow
|
||||||
|
from typing import Union
|
||||||
|
|
||||||
|
|
||||||
def vol_cube(side_length: Union[int, float]) -> float:
|
def vol_cube(side_length: Union[int, float]) -> float:
|
||||||
|
|
|
@ -1,5 +1,5 @@
|
||||||
import datetime
|
|
||||||
import argparse
|
import argparse
|
||||||
|
import datetime
|
||||||
|
|
||||||
|
|
||||||
def zeller(date_input: str) -> str:
|
def zeller(date_input: str) -> str:
|
||||||
|
|
|
@ -6,11 +6,13 @@ the pytest run configuration
|
||||||
-vv -m mat_ops -p no:cacheprovider
|
-vv -m mat_ops -p no:cacheprovider
|
||||||
"""
|
"""
|
||||||
|
|
||||||
|
import logging
|
||||||
|
|
||||||
# standard libraries
|
# standard libraries
|
||||||
import sys
|
import sys
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import pytest
|
import pytest
|
||||||
import logging
|
|
||||||
|
|
||||||
# Custom/local libraries
|
# Custom/local libraries
|
||||||
from matrix import matrix_operation as matop
|
from matrix import matrix_operation as matop
|
||||||
|
|
|
@ -17,9 +17,8 @@ Github : https://github.com/RiptideBo
|
||||||
Date: 2017.11.23
|
Date: 2017.11.23
|
||||||
|
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import matplotlib.pyplot as plt
|
from matplotlib import pyplot as plt
|
||||||
|
|
||||||
|
|
||||||
def sigmoid(x):
|
def sigmoid(x):
|
||||||
|
|
|
@ -14,8 +14,9 @@
|
||||||
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
|
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
|
||||||
"""
|
"""
|
||||||
import pickle
|
import pickle
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import matplotlib.pyplot as plt
|
from matplotlib import pyplot as plt
|
||||||
|
|
||||||
|
|
||||||
class CNN:
|
class CNN:
|
||||||
|
|
|
@ -1,5 +1,5 @@
|
||||||
from abc import abstractmethod
|
|
||||||
import sys
|
import sys
|
||||||
|
from abc import abstractmethod
|
||||||
from collections import deque
|
from collections import deque
|
||||||
|
|
||||||
|
|
||||||
|
|
|
@ -27,8 +27,8 @@ Credits: This code was written by editing the code from
|
||||||
http://www.riannetrujillo.com/blog/python-fractal/
|
http://www.riannetrujillo.com/blog/python-fractal/
|
||||||
|
|
||||||
"""
|
"""
|
||||||
import turtle
|
|
||||||
import sys
|
import sys
|
||||||
|
import turtle
|
||||||
|
|
||||||
PROGNAME = "Sierpinski Triangle"
|
PROGNAME = "Sierpinski Triangle"
|
||||||
|
|
||||||
|
|
|
@ -5,8 +5,8 @@ By listing the first six prime numbers:
|
||||||
|
|
||||||
We can see that the 6th prime is 13. What is the Nth prime number?
|
We can see that the 6th prime is 13. What is the Nth prime number?
|
||||||
"""
|
"""
|
||||||
import math
|
|
||||||
import itertools
|
import itertools
|
||||||
|
import math
|
||||||
|
|
||||||
|
|
||||||
def primeCheck(number):
|
def primeCheck(number):
|
||||||
|
|
|
@ -15,7 +15,6 @@ words?
|
||||||
"""
|
"""
|
||||||
import os
|
import os
|
||||||
|
|
||||||
|
|
||||||
# Precomputes a list of the 100 first triangular numbers
|
# Precomputes a list of the 100 first triangular numbers
|
||||||
TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
|
TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
|
||||||
|
|
||||||
|
|
|
@ -3,9 +3,9 @@ Shortest job remaining first
|
||||||
Please note arrival time and burst
|
Please note arrival time and burst
|
||||||
Please use spaces to separate times entered.
|
Please use spaces to separate times entered.
|
||||||
"""
|
"""
|
||||||
|
from typing import List
|
||||||
|
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
from typing import List
|
|
||||||
|
|
||||||
|
|
||||||
def calculate_waitingtime(
|
def calculate_waitingtime(
|
||||||
|
|
|
@ -1,6 +1,6 @@
|
||||||
#!/usr/bin/env python3
|
#!/usr/bin/env python3
|
||||||
|
|
||||||
import os
|
import os
|
||||||
|
|
||||||
from build_directory_md import good_file_paths
|
from build_directory_md import good_file_paths
|
||||||
|
|
||||||
filepaths = list(good_file_paths())
|
filepaths = list(good_file_paths())
|
||||||
|
|
|
@ -150,7 +150,7 @@ def hill_climbing(
|
||||||
solution_found = True
|
solution_found = True
|
||||||
|
|
||||||
if visualization:
|
if visualization:
|
||||||
import matplotlib.pyplot as plt
|
from matplotlib import pyplot as plt
|
||||||
|
|
||||||
plt.plot(range(iterations), scores)
|
plt.plot(range(iterations), scores)
|
||||||
plt.xlabel("Iterations")
|
plt.xlabel("Iterations")
|
||||||
|
|
|
@ -84,7 +84,7 @@ def simulated_annealing(
|
||||||
current_state = next_state
|
current_state = next_state
|
||||||
|
|
||||||
if visualization:
|
if visualization:
|
||||||
import matplotlib.pyplot as plt
|
from matplotlib import pyplot as plt
|
||||||
|
|
||||||
plt.plot(range(iterations), scores)
|
plt.plot(range(iterations), scores)
|
||||||
plt.xlabel("Iterations")
|
plt.xlabel("Iterations")
|
||||||
|
|
|
@ -24,9 +24,8 @@ python tabu_search.py -f your_file_name.txt -number_of_iterations_of_tabu_search
|
||||||
-s size_of_tabu_search
|
-s size_of_tabu_search
|
||||||
e.g. python tabu_search.py -f tabudata2.txt -i 4 -s 3
|
e.g. python tabu_search.py -f tabudata2.txt -i 4 -s 3
|
||||||
"""
|
"""
|
||||||
|
|
||||||
import copy
|
|
||||||
import argparse
|
import argparse
|
||||||
|
import copy
|
||||||
|
|
||||||
|
|
||||||
def generate_neighbours(path):
|
def generate_neighbours(path):
|
||||||
|
|
|
@ -3,8 +3,8 @@
|
||||||
#
|
#
|
||||||
# Sort large text files in a minimum amount of memory
|
# Sort large text files in a minimum amount of memory
|
||||||
#
|
#
|
||||||
import os
|
|
||||||
import argparse
|
import argparse
|
||||||
|
import os
|
||||||
|
|
||||||
|
|
||||||
class FileSplitter:
|
class FileSplitter:
|
||||||
|
|
|
@ -1,5 +1,6 @@
|
||||||
from random import randint
|
from random import randint
|
||||||
from tempfile import TemporaryFile
|
from tempfile import TemporaryFile
|
||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
|
|
@ -1,10 +1,9 @@
|
||||||
import sys
|
import sys
|
||||||
import webbrowser
|
import webbrowser
|
||||||
|
|
||||||
|
import requests
|
||||||
from bs4 import BeautifulSoup
|
from bs4 import BeautifulSoup
|
||||||
from fake_useragent import UserAgent
|
from fake_useragent import UserAgent
|
||||||
import requests
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
print("Googling.....")
|
print("Googling.....")
|
||||||
|
|
|
@ -1,5 +1,5 @@
|
||||||
from bs4 import BeautifulSoup
|
|
||||||
import requests
|
import requests
|
||||||
|
from bs4 import BeautifulSoup
|
||||||
|
|
||||||
|
|
||||||
def imdb_top(imdb_top_n):
|
def imdb_top(imdb_top_n):
|
||||||
|
|
Loading…
Reference in New Issue
Block a user