fib_gen = fibonacci_generator() for _ in range(10): print(next(fib_gen)) 上下文管理器(Context Managers) 上下文管理器用于管理资源的获取和释放,确保在代码块执行完毕后正确地释放资源。Python 中的with语句就是用于使用上下文管理器的。以下是一个使用上下文管理器来管理文件操作的示例: 代码语言:
数据处理：pandas、numpy 数据建模：scipy、scikit-learn、statesmodel、keras 数据可视化：matplotlib、seabor...
from stylecloud import gen_stylecloud import jieba import re import random # 读取数据 with open('datas.txt', encoding='utf-8') as f: data = f.read() # 文本预处理 去除一些无用的字符 只提取出中文出来 new_data = re.findall('[\u4e00-\u9fa5]+', data, re.S) new_data = "/".join...
encoding="utf-8", mode="r") read = f.read() f.close() """ 第二种写法，通过with语句，...
Mix multiple adapters with coefficients via alpha blending All scenarios are run on top of OpenVINO Runtime that supports inference on CPU, GPU and NPU. Seeherefor platform support matrix. Supported Generative AI optimization methods OpenVINO™ GenAI library provides a transparent way to use state...
Master Generative AI, ChatGPT, ChatGPT APIs, learn Python, Claude, MidJourney, Gemini, Perplexity, Gen AI APIs & more What you’ll learn: Introduction to AI: What Artificial Intelligence Is & The Basics of AI Detailed prompt engineering for Midjourney, ChatGPT, Dall-E, Stable Diffusion, Ad...
(15) ] self.gameover = 0 self.overvalue = 0 self.maxdepth = 3 # 产生当前棋局的走法 def genmove (self, turn): moves = [] board = self.board POSES = self.evaluator.POS for i in xrange(15): for j in xrange(15): if board[i][j] == 0: score = POSES[i][j] moves.append...
Episode 248: Experiments With Gen AI, Knowledge Graphs, Workflows, and Python May 09, 2025 59m Are you looking for some projects where you can practice your Python skills? Would you like to experiment with building a generative AI app or an automated knowledge graph sentiment analysis tool?
(linewidth) label:标签文本 color: 线条颜色 } **kwargs: 第二组或更多(x,y,format_string) plt.plot(x, y, format_string, x,y,format_string,x,y,format_string,x,y,format_string) 当绘制多条曲线时,各条曲线的x不能省略 https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot....
Python SDK azure-ai-ml v2 (current) You can generate a Responsible AI dashboard and scorecard via a pipeline job by using Responsible AI components. There are six core components for creating Responsible AI dashboards, along with a couple of helper components. Here's a sample experiment graph...