Python是一种高级编程语言，它用于创建网页，移动应用程序，脚本和机器学习模型。它拥有强大的类库，允许开发人员快速编写功能强大的应用程序。
Python有许多优点，其中包括：它是相对容易学习和使用的动态编程语言；它拥有丰富的内置库和模块；它拥有广泛的社区支持；它支持跨平台；它可以进行快速原型开发；它可以有效地利用内存；它可以使用C / C ++扩展；它支持大量的开源框架和库；它具有强大的编程能力和可读性；它支持多种编程风格；它可以进行测试驱动开发，以及其他许多优点。
Azul : A Competitive Game Environment for COMP90054, Semester 2, 2023 Table of contents Introduction Key files to read: Other supporting files (do not modify): Rules Winning: Computation Time: Getting Started Restrictions: Warning: Ranking Introduction For COMP90054 this semester, you will be competing against agent teams in Azul, a strategic board game. There are many […]
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Kraken API 您将创建两个单独的程序（python，C / C++，ruby，Golang或其他程序），报告来自流行的加密货币交换服务器（称为Kraken）的返回消息。请注意，绝对没有必要花钱。您的程序将仅返回您的钱包余额和另一个信息，其中这些请求需要特殊密钥来加密/解密/验证。本练习将向我们展示如何使用加密密钥和HTTP协议，发送格式正确的数据包。任何格式不正确的消息都不会通过到服务器的完整性检查。 转到 https://www.kraken.com/。 如果您还没有登录帐户，请创建它。然后登录。 转到 https://www.kraken.com/u/security/api ->添加密钥。创建您的 API 密钥和密钥。 只留给自己！您将需要这些 API 密钥才能继续。 现在你已经准备好了。 重要提示：考试后删除您的 API 密钥。 程序应显示以下两个请求的响应消息： A. API 文档 -> REST API ->钱包 -> 获取余额 （https://docs.kraken.com/rest/#tag/User-Data/operation/getAccountBalance） 您的余额应该是空的，假设您在Kraken没有任何硬币或金钱。 显示此 API 调用的返回消息。 B. API 文档 -> REST API -> 下订单 （https://docs.kraken.com/rest/#tag/User-Trading/operation/addOrder） 仅发送“卖出”订单。同样，任何市场符号都可以。 显示此 API 调用的返回消息。 要完成A和B，请参阅 https://docs.kraken.com/rest/#section/Authentication。但是，您需要将此示例代码与密钥和机密相结合。然后，您将能够向服务器发送经过验证的“http请求”消息。仔细阅读 API 文档。请勿使用现有的库，如CCXT或任何其他与Kraken相关的库。 **任务 1：在 python
Introduction In this project, you will be implementing a simple interpreter for a new programming language, called Brewin. Brewin is an object-oriented variant of the LISP language. You guessed it – that means there are lots of parentheses! :^) You¡¯ll be implementing your interpreter in Python. This project is the first of three – in
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Assignment 3: Azul Project You must read fully and carefully the assignment specification and instructions detailed in this file. You are NOT to modify this file in any way. Course: COMP90054 AI Planning for Autonomy @ Semester 1, 2023 Instructor: Tim Miller and Nir Lipovetzky Deadline Team Registration: Monday 1 May, 2023 @ 18:00 (start
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# tm-sim.py # author: bjr # date: 21 mar 2020 # last update: 22 mar 2020 # 16 mar 2021, updated # 3 apr 2021, return conventions for accept/reject # verbose_levels reimplemented # character # is not allowed as a tape symbol # for magical reasons, then ” is also not allowed # added class
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COMP24412 Coursework 3 Step 0: Setup Make sure to set up a dedicated python environment for this project. You can either use anaconda or venv to create a dedicated environment. With anaconda: with venv: Step 1: Obtain the Code In your base repo: Install the requirements pip install -r requirements.txt Familiarise yourself with the code.
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step_functions Using Step Functions to Orchestrate Massively Parallel Batch Jobs¶ You’ll recall from our serverless demonstration a couple of weeks ago, that we can upload Lambda functions programmatically: import boto3 import json import time import requests from concurrent.futures import ThreadPoolExecutor import matplotlib.pyplot as plt # Access our class IAM role, which allows Lambda # to
Total points: 6 (+1… the total will be capped at 6) This last hw is on supervised machine learning! As you now know, it’s data-related (lots, and lots, and lots of it), after all 🙂 Here is a summary of what you’ll do: on Google’s Colab, train a neural network on differentiating between a cat
HW5: Data Mining [tool-based] Total points: 6 Summary: In this homework, you are going to use three UI-based tools (no coding!), to carry out data mining: WEKA, KNIME, RapidMiner (the last two are dataflow-driven!). There are 3 questions you need to answer about linear regression, one using each tool. Description WEKA Start by downloading WEKA,
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“””Inference functions used with backtracking search. COMP3620/6320 Artificial Intelligence The Australian National University Authors: COMP-3620 team Date: 2022 Student Details ————— Student Name: Student Number: import collections from typing import Callable, Dict, List, Optional, Tuple from csp import CSP Assignment = Dict[str, str] Pruned = List[Tuple[str, str]] def forward_checking(var: str, assignment: Assignment, gamma: CSP) ->
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