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Added Whitespace and Docstring (#924)
* Added Whitespace and Docstring I modified the file to make Pylint happier and make the code more readable. * Beautified Code and Added Docstring I modified the file to make Pylint happier and make the code more readable. * Added DOCSTRINGS, Wikipedia link, and whitespace I added DOCSTRINGS and whitespace to make the code more readable and understandable. * Improved Formatting * Wrapped comments * Fixed spelling error for `movement` variable * Added DOCSTRINGs * Improved Formatting * Corrected whitespace to improve readability. * Added docstrings. * Made comments fit inside an 80 column layout.
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@ -1,32 +1,36 @@
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"""Lower-Upper (LU) Decomposition."""
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# lower–upper (LU) decomposition - https://en.wikipedia.org/wiki/LU_decomposition
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import numpy
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def LUDecompose (table):
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def LUDecompose(table):
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# Table that contains our data
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# Table has to be a square array so we need to check first
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rows,columns=numpy.shape(table)
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L=numpy.zeros((rows,columns))
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U=numpy.zeros((rows,columns))
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if rows!=columns:
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rows, columns = numpy.shape(table)
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L = numpy.zeros((rows, columns))
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U = numpy.zeros((rows, columns))
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if rows != columns:
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return []
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for i in range (columns):
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for j in range(i-1):
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sum=0
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for k in range (j-1):
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sum+=L[i][k]*U[k][j]
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L[i][j]=(table[i][j]-sum)/U[j][j]
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L[i][i]=1
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for j in range(i-1,columns):
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sum1=0
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for k in range(i-1):
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sum1+=L[i][k]*U[k][j]
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U[i][j]=table[i][j]-sum1
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return L,U
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for i in range(columns):
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for j in range(i - 1):
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sum = 0
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for k in range(j - 1):
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sum += L[i][k] * U[k][j]
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L[i][j] = (table[i][j] - sum) / U[j][j]
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L[i][i] = 1
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for j in range(i - 1, columns):
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sum1 = 0
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for k in range(i - 1):
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sum1 += L[i][k] * U[k][j]
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U[i][j] = table[i][j] - sum1
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return L, U
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if __name__ == "__main__":
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matrix =numpy.array([[2,-2,1],
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[0,1,2],
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[5,3,1]])
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L,U = LUDecompose(matrix)
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matrix = numpy.array([[2, -2, 1],
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[0, 1, 2],
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[5, 3, 1]])
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L, U = LUDecompose(matrix)
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print(L)
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print(U)
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@ -1,18 +1,25 @@
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"""Newton's Method."""
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# Newton's Method - https://en.wikipedia.org/wiki/Newton%27s_method
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def newton(function,function1,startingInt): #function is the f(x) and function1 is the f'(x)
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x_n=startingInt
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# function is the f(x) and function1 is the f'(x)
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def newton(function, function1, startingInt):
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x_n = startingInt
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while True:
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x_n1=x_n-function(x_n)/function1(x_n)
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if abs(x_n-x_n1) < 10**-5:
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x_n1 = x_n - function(x_n) / function1(x_n)
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if abs(x_n - x_n1) < 10**-5:
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return x_n1
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x_n=x_n1
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x_n = x_n1
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def f(x):
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return (x**3) - (2 * x) -5
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return (x**3) - (2 * x) - 5
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def f1(x):
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return 3 * (x**2) -2
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return 3 * (x**2) - 2
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if __name__ == "__main__":
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print(newton(f,f1,3))
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print(newton(f, f1, 3))
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"""Tower of Hanoi."""
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# @author willx75
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# Tower of Hanoi recursion game algorithm is a game, it consists of three rods and a number of disks of different sizes, which can slide onto any rod
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# Tower of Hanoi recursion game algorithm is a game, it consists of three rods
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# and a number of disks of different sizes, which can slide onto any rod
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import logging
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logging.basicConfig(level=logging.DEBUG)
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def Tower_Of_Hanoi(n, source, dest, by, mouvement):
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def Tower_Of_Hanoi(n, source, dest, by, movement):
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"""Tower of Hanoi - Move plates to different rods."""
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if n == 0:
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return n
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elif n == 1:
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mouvement += 1
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# no print statement (you could make it an optional flag for printing logs)
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movement += 1
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# no print statement
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# (you could make it an optional flag for printing logs)
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logging.debug('Move the plate from', source, 'to', dest)
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return mouvement
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return movement
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else:
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mouvement = mouvement + Tower_Of_Hanoi(n-1, source, by, dest, 0)
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movement = movement + Tower_Of_Hanoi(n - 1, source, by, dest, 0)
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logging.debug('Move the plate from', source, 'to', dest)
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mouvement = mouvement + 1 + Tower_Of_Hanoi(n-1, by, dest, source, 0)
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return mouvement
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movement = movement + 1 + Tower_Of_Hanoi(n - 1, by, dest, source, 0)
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return movement
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"""Absolute Value."""
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def absVal(num):
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"""
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Function to fins absolute value of numbers.
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Find the absolute value of a number.
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>>absVal(-5)
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5
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>>absVal(0)
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else:
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return num
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def main():
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"""Print absolute value of -34."""
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print(absVal(-34)) # = 34
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if __name__ == '__main__':
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main()
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"""Find mean of a list of numbers."""
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def average(nums):
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"""Find mean of a list of numbers."""
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sum = 0
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for x in nums:
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sum += x
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print(avg)
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return avg
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def main():
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"""Call average module to find mean of a specific list of numbers."""
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average([2, 4, 6, 8, 20, 50, 70])
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if __name__ == '__main__':
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main()
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"""Find Least Common Multiple."""
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# https://en.wikipedia.org/wiki/Least_common_multiple
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def find_lcm(num_1, num_2):
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"""Find the LCM of two numbers."""
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max = num_1 if num_1 > num_2 else num_2
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lcm = max
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while (True):
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def main():
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"""Use test numbers to run the find_lcm algorithm."""
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num_1 = 12
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num_2 = 76
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print(find_lcm(num_1, num_2))
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#!/usr/bin/env python
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"""Illustrate how to implement bucket sort algorithm."""
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# Author: OMKAR PATHAK
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# This program will illustrate how to implement bucket sort algorithm
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# Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works by distributing the
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# elements of an array into a number of buckets. Each bucket is then sorted individually, either using
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# a different sorting algorithm, or by recursively applying the bucket sorting algorithm. It is a
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# distribution sort, and is a cousin of radix sort in the most to least significant digit flavour.
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# Bucket sort is a generalization of pigeonhole sort. Bucket sort can be implemented with comparisons
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# and therefore can also be considered a comparison sort algorithm. The computational complexity estimates
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# involve the number of buckets.
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# Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works
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# by distributing the elements of an array into a number of buckets.
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# Each bucket is then sorted individually, either using a different sorting
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# algorithm, or by recursively applying the bucket sorting algorithm. It is a
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# distribution sort, and is a cousin of radix sort in the most to least
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# significant digit flavour.
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# Bucket sort is a generalization of pigeonhole sort. Bucket sort can be
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# implemented with comparisons and therefore can also be considered a
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# comparison sort algorithm. The computational complexity estimates involve the
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# number of buckets.
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# Time Complexity of Solution:
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# Best Case O(n); Average Case O(n); Worst Case O(n)
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DEFAULT_BUCKET_SIZE=5
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DEFAULT_BUCKET_SIZE = 5
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def bucket_sort(my_list, bucket_size=DEFAULT_BUCKET_SIZE):
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if len(my_list) == 0:
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buckets = [[] for _ in range(int(bucket_count))]
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for i in range(len(my_list)):
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buckets[int((my_list[i] - min_value) // bucket_size)].append(my_list[i])
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buckets[int((my_list[i] - min_value) // bucket_size)
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].append(my_list[i])
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return sorted([buckets[i][j] for i in range(len(buckets))
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for j in range(len(buckets[i]))])
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if __name__ == "__main__":
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user_input = input('Enter numbers separated by a comma:').strip()
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unsorted = [float(n) for n in user_input.split(',') if len(user_input) > 0]
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"""Gnome Sort Algorithm."""
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from __future__ import print_function
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def gnome_sort(unsorted):
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"""
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Pure implementation of the gnome sort algorithm in Python.
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"""
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"""Pure implementation of the gnome sort algorithm in Python."""
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if len(unsorted) <= 1:
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return unsorted
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i = 1
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while i < len(unsorted):
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if unsorted[i-1] <= unsorted[i]:
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if unsorted[i - 1] <= unsorted[i]:
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i += 1
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else:
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unsorted[i-1], unsorted[i] = unsorted[i], unsorted[i-1]
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unsorted[i - 1], unsorted[i] = unsorted[i], unsorted[i - 1]
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i -= 1
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if (i == 0):
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i = 1
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if __name__ == '__main__':
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try:
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raw_input # Python 2
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"""Test Sort Algorithms for Errors."""
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from bogo_sort import bogo_sort
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from bubble_sort import bubble_sort
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from bucket_sort import bucket_sort
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TODO:
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- Fix some broken tests in particular cases (as [] for example),
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- Unify the input format: should always be function(input_collection) (no additional args)
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- Unify the output format: should always be a collection instead of updating input elements
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and returning None
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- Unify the output format: should always be a collection instead of
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updating input elements and returning None
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- Rewrite some algorithms in function format (in case there is no function definition)
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'''
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"""Topological Sort."""
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from __future__ import print_function
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# a
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# / \
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# return sort
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return sort
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if __name__ == '__main__':
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sort = topological_sort('a', [], [])
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print(sort)
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# Tree_sort algorithm
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# Build a BST and in order traverse.
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"""
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Tree_sort algorithm.
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Build a BST and in order traverse.
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"""
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class node():
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# BST data structure
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self.left = None
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self.right = None
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def insert(self,val):
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def insert(self, val):
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if self.val:
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if val < self.val:
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if self.left is None:
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else:
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self.val = val
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def inorder(root, res):
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# Recursive travesal
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if root:
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inorder(root.left,res)
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inorder(root.left, res)
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res.append(root.val)
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inorder(root.right,res)
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inorder(root.right, res)
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def tree_sort(arr):
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# Build BST
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if len(arr) == 0:
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return arr
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root = node(arr[0])
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for i in range(1,len(arr)):
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for i in range(1, len(arr)):
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root.insert(arr[i])
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# Traverse BST in order.
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res = []
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inorder(root,res)
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inorder(root, res)
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return res
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if __name__ == '__main__':
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print(tree_sort([10,1,3,2,9,14,13]))
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print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
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"""
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Given an unsorted array nums, reorder it such that nums[0] < nums[1] > nums[2] < nums[3]....
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Wiggle Sort.
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Given an unsorted array nums, reorder it such
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that nums[0] < nums[1] > nums[2] < nums[3]....
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For example:
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if input numbers = [3, 5, 2, 1, 6, 4]
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one possible Wiggle Sorted answer is [3, 5, 1, 6, 2, 4].
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"""
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def wiggle_sort(nums):
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"""Perform Wiggle Sort."""
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for i in range(len(nums)):
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if (i % 2 == 1) == (nums[i-1] > nums[i]):
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nums[i-1], nums[i] = nums[i], nums[i-1]
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if (i % 2 == 1) == (nums[i - 1] > nums[i]):
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nums[i - 1], nums[i] = nums[i], nums[i - 1]
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if __name__ == '__main__':
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print("Enter the array elements:\n")
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array=list(map(int,input().split()))
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array = list(map(int, input().split()))
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print("The unsorted array is:\n")
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print(array)
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wiggle_sort(array)
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