dictionary

Appending to dict of lists adds value to every key [duplicate]

由 假装没事ソ 提交于 2021-02-18 21:43:11
问题 This question already has answers here : dict.fromkeys all point to same list (4 answers) Closed 5 years ago . I have a dictionary of empty lists with all keys declared at the beginning: >>> keys = ["k1", "k2", "k3"] >>> d = dict.fromkeys(keys, []) >>> d {'k2': [], 'k3': [], 'k1': []} When I try to add a coordinate pair (the list ["x1", "y1"] ) to one of the key's lists, it instead adds to all the keys' lists: >>> d["k1"].append(["x1", "y1"]) >>> d {'k1': [['x1', 'y1']], 'k2': [['x1', 'y1']],

equivalent to a sorted dictionary that allows duplicate keys

由 为君一笑 提交于 2021-02-18 20:15:49
问题 I need a data structure that can sort objects by the float keys they're associated with, lowest first. The trouble is that the keys represent cost so there are often duplicates, I don't care about this because if two have the same cost I'll just grab the first as it makes no difference, the problem is that the compiler complains. Is there a data structure that behaves in the same way but allows duplicate keys? EDIT - I still need the duplicates though because if one turns out to be a dead-end

equivalent to a sorted dictionary that allows duplicate keys

由 蓝咒 提交于 2021-02-18 20:15:16
问题 I need a data structure that can sort objects by the float keys they're associated with, lowest first. The trouble is that the keys represent cost so there are often duplicates, I don't care about this because if two have the same cost I'll just grab the first as it makes no difference, the problem is that the compiler complains. Is there a data structure that behaves in the same way but allows duplicate keys? EDIT - I still need the duplicates though because if one turns out to be a dead-end

equivalent to a sorted dictionary that allows duplicate keys

由 放肆的年华 提交于 2021-02-18 20:13:33
问题 I need a data structure that can sort objects by the float keys they're associated with, lowest first. The trouble is that the keys represent cost so there are often duplicates, I don't care about this because if two have the same cost I'll just grab the first as it makes no difference, the problem is that the compiler complains. Is there a data structure that behaves in the same way but allows duplicate keys? EDIT - I still need the duplicates though because if one turns out to be a dead-end

word frequency program in python

由 三世轮回 提交于 2021-02-18 19:07:10
问题 Say I have a list of words called words i.e. words = ["hello", "test", "string", "people", "hello", "hello"] and I want to create a dictionary in order to get word frequency. Let's say the dictionary is called 'counts' counts = {} for w in words: counts[w] = counts.get(w,0) + 1 The only part of this I don't really understand is the counts.get(w.0). The book says, normally you would use counts[w] = counts[w] + 1 but the first time you encounter a new word, it won't be in counts and so it would

word frequency program in python

由 女生的网名这么多〃 提交于 2021-02-18 19:06:28
问题 Say I have a list of words called words i.e. words = ["hello", "test", "string", "people", "hello", "hello"] and I want to create a dictionary in order to get word frequency. Let's say the dictionary is called 'counts' counts = {} for w in words: counts[w] = counts.get(w,0) + 1 The only part of this I don't really understand is the counts.get(w.0). The book says, normally you would use counts[w] = counts[w] + 1 but the first time you encounter a new word, it won't be in counts and so it would

How to parallelize computation on “big data” dictionary of lists?

由 守給你的承諾、 提交于 2021-02-18 19:00:17
问题 I have a question here regarding doing calculations on a python dictionary----in this case, the dictionary has millions of keys, and the lists are similarly long. There seems to be disagreement whether one could use parallelization here, so I'll ask the question here more explicitly. Here is the original question: Optimizing parsing of massive python dictionary, multi-threading This is a toy (small) python dictionary: example_dict1 = {'key1':[367, 30, 847, 482, 887, 654, 347, 504, 413, 821],

How to parallelize computation on “big data” dictionary of lists?

由 只谈情不闲聊 提交于 2021-02-18 18:58:47
问题 I have a question here regarding doing calculations on a python dictionary----in this case, the dictionary has millions of keys, and the lists are similarly long. There seems to be disagreement whether one could use parallelization here, so I'll ask the question here more explicitly. Here is the original question: Optimizing parsing of massive python dictionary, multi-threading This is a toy (small) python dictionary: example_dict1 = {'key1':[367, 30, 847, 482, 887, 654, 347, 504, 413, 821],

Noah Mt4跟单系统制作第十篇 锁篇

由 試著忘記壹切 提交于 2021-02-18 16:43:53
using System; using System.Collections.Concurrent; using System.Collections.Generic; using System.Linq; using System.Text; using System.Threading; using System.Threading.Tasks; namespace Copier.Core { public sealed class KeyLocker<T> : IDisposable { private static readonly object _lockerDictionary = new object(); private static readonly Dictionary<T, LockerObject> _lockerObjects = new Dictionary<T, LockerObject>(); private T _key; public KeyLocker(T key) { _key = key; LockerObject lockerObject; lock (_lockerDictionary) { if (!_lockerObjects.TryGetValue(_key, out lockerObject)) { lockerObject =

Vulkan(1)用apispec生成Vulkan库

由 假装没事ソ 提交于 2021-02-18 12:30:12
Vulkan(1)用apispec生成Vulkan库 我的Vulkan.net库已在（ https://github.com/bitzhuwei/Vulkan.net ）开源，欢迎交流。 apispec.html 在Vulkan SDK的安装文件夹里，有一个Documentation\ apispec.html 文件。这是一个由代码 生成 的对Vulkan API的说明。它包含了Vulkan API的枚举类型、结构体、函数声明以及这一切的详细 注释 。 由于它是自动生成的，所以其格式非常规则。只需将少数几处<br>改为<br />，几处<col .. >改为<col .. />，就可以直接用 XElement 来加载和解析它。 由于它包含了每个枚举类型及其成员的注释，包含了每个结构体及其成员的注释，包含了每个函数声明及其参数的注释，我就想，如果我能将它转换为C#代码，那会是多么美妙的一个Vulkan库啊！ 我在网上找到的几个Vulkan库，基本上都没有什么注释，这让我使用起来很不方便，严重妨碍了学习速度。很多结构体的成员类型都是粗糙的 IntPtr ，而不是具体类型的指针，这也使得用起来很麻烦。 那么就动手做自己的Vulkan库吧！ 分类 首先，要将巨大的apispec.html文件里的内容分为几个类别，即C宏定义、Command（函数声明）、Enum、Extension、Flag
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