Algorithms, the most comprehensive Python algorithm warehouse

pip3 install algorithms

from algorithms.sort import merge_sort if __name__ == "__main__": my_list = [1, 8, 3, 5, 6] my_list = merge_sort(my_list) print(my_list)

def bucket_sort(arr): ''' Bucket Sort Complexity: O(n^2) The complexity is dominated by nextSort ''' # The number of buckets and make buckets num_buckets = len(arr) buckets = [[] for bucket in range(num_buckets)] # Assign values into bucket_sort for value in arr: index = value * num_buckets // (max(arr) + 1) buckets[index].append(value) # Sort sorted_list = [] for i in range(num_buckets): sorted_list.extend(next_sort(buckets[i])) return sorted_list def next_sort(arr): # We will use insertion sort here. for i in range(1, len(arr)): j = i - 1 key = arr[i] while arr[j] > key and j >= 0: arr[j+1] = arr[j] j = j - 1 arr[j + 1] = key return arr

import math def distance(x,y): """[summary] HELPER-FUNCTION calculates the (eulidean) distance between vector x and y. Arguments: x {[tuple]} -- [vector] y {[tuple]} -- [vector] """ assert len(x) == len(y), "The vector must have same length" result = () sum = 0 for i in range(len(x)): result += (x[i] -y[i],) for component in result: sum += component**2 return math.sqrt(sum) def nearest_neighbor(x, tSet): """[summary] Implements the nearest neighbor algorithm Arguments: x {[tupel]} -- [vector] tSet {[dict]} -- [training set] Returns: [type] -- [result of the AND-function] """ assert isinstance(x, tuple) and isinstance(tSet, dict) current_key = () min_d = float('inf') for key in tSet: d = distance(x, key) if d < min_d: min_d = d current_key = key return tSet[current_key]

# Implement the encode and decode methods. def encode(strs): """Encodes a list of strings to a single string. :type strs: List[str] :rtype: str """ res = '' for string in strs.split(): res += str(len(string)) + ":" + string return res def decode(s): """Decodes a single string to a list of strings. :type s: str :rtype: List[str] """ strs = [] i = 0 while i < len(s): index = s.find(":", i) size = int(s[i:index]) strs.append(s[index+1: index+1+size]) i = index+1+size return strs

def get_histogram(input_list: list) -> dict: """ Get histogram representation :param input_list: list with different and unordered values :return histogram: dict with histogram of input_list """ # Create dict to store histogram histogram = {} # For each list value, add one to the respective histogram dict position for i in input_list: histogram[i] = histogram.get(i, 0) + 1 return histogram