Algorithms，最全的Python算法仓库

2023-02-28

for list arr

学习编程、学习Python最好的方式就是练习，哪怕是新手，只要不断地敲代码输出，肯定会有神效。Python的练手项目很多，特别是Github上，建议不管新手、老司机都去看看。这里推荐给大家一个Gitthub上练习的项目，算法仓库-algorithms。https://github.com/keon

学习编程、学习Python最好的方式就是练习，哪怕是新手，只要不断地敲代码输出，肯定会有神效。

Python的练手项目很多，特别是Github上，建议不管新手、老司机都去看看。

这里推荐给大家一个Gitthub上练习的项目，算法仓库-algorithms。

https://github.com/keon/algorithms

这里面集合众多核心算法的Python实现，比如排序、图计算、回溯、队列、流计算、堆、搜索、压缩等等。

该仓库支持第三方库安装，在python中进行调用，非常方便。

首先使用pip进行安装：

pip3 install algorithms1.

然后导入相关模块进行调用，比如sort模块里的merge_sort归并排序算法。

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)1.
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举几个常见的算法案例。

1. 排序算法-桶排序

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 arr1.
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2. 机器学习-最近邻插值法

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]1.
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3. 字符串解码编码

# 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 strs1.
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4. 直方分布

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 histogram1.
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