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<!DOCTYPE html>
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<title>python自动化测试人工智能</title>
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<h1><a href="/">python自动化测试人工智能 </a></h1>
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<h1><a href="/ml_quick2.html" rel="bookmark"
title="Permalink to 机器学习快速入门2分类的代码实现">机器学习快速入门2分类的代码实现</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2019-04-23T08:25:00+08:00">
Published: 二 23 四月 2019
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h3 id="_1">威斯康星乳腺癌数据集</h3>
<p>威斯康星乳腺癌（Breast Cancer Wisconsin）数据集共包含569个恶性或者良性肿瘤细胞样本。数据集的前两列分别存储了样本唯一的ID以及对样本的诊断结果（M代表恶性，B代表良性）。数据集的3～32列包含了30个从细胞核照片中提取、用实数值标识的特征，它们可以用于构建判定模型，对肿瘤是良性还是恶性做出预测。威斯康星乳腺癌数据集已经存储在UCI机器学习数据集库中，关于此数据集更多的信息请访问链接：http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/</p>
<p>sklearn已经包含了该数据集。</p>
<div class="highlight"><pre><span></span><span class="o">>>></span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="o">>>></span> <span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_breast_cancer</span>
<span class="o">>>></span> <span class="n">data</span> <span class="o">=</span> <span class="n">load_breast_cancer</span><span class="p">()</span>
<span class="o">>>></span> <span class="k">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="p">{</span><span class="s1">'data'</span><span class="p">:</span> <span class="n">array</span><span class="p">([[</span><span class="mf">1.799e+01</span><span class="p">,</span> <span class="mf">1.038e+01</span><span class="p">,</span> <span class="mf">1.228e+02</span><span class="p">,</span> <span class="o">...</span><span class="p">,</span> <span class="mf">2 …</span></pre></div>
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<header>
<h1><a href="/ml_quick1.html" rel="bookmark"
title="Permalink to 机器学习快速入门1分类">机器学习快速入门1分类</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2019-04-23T08:20:00+08:00">
Published: 二 23 四月 2019
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h2 id="_1">什么是分类？</h2>
<h3 id="mnist">MNIST</h3>
<p><img alt="图片.png" src="https://upload-images.jianshu.io/upload_images/12713060-a3dd7e25bfa0a166.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>MNIST(Modified National Institute of Standards and Technology database)</p>
<p>这是机器学习的经典案例。数据由一组手写0到9数字图像组成，多用于OCR(光学字符识别 optical character recognition)。</p>
<h3 id="_2">数据类型</h3>
<p>在机器学习中，您会发现有两种非常常见的数据类型。</p>
<p>MNIST由图像组成。图像通常属于“计算机视觉”领域。</p>
<p>机器学习中使用的另一种常见数据格式是文本。比如“自然语言处理”。</p>
<h3 id="_3">垃圾邮件分类</h3>
<p>Gmail，Yahoo，Hotmail等会自动为您分类垃圾邮件和非垃圾邮件。他们并不总能做对，但非常好。</p>
<p>这是怎么做到的？机器学习算法接收电子邮件作为输入，并输出预测：它将该电子邮件分类为垃圾邮件或非垃圾邮件。机器学习分类算法接受输入，并输出分类预测！</p>
<h3 id="_4">业务场景</h3>
<p>当然，必须有些不属于图像和文本的分类示例。企业正在利用机器学习来改善服务并赚更多钱。比如在线广告，这通常采用横幅广告或视频广告的形式。广告客户收集了一些您的数据。例如，他们知道您的年龄，性别和位置 …</p>
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<h1><a href="/pianfang.html" rel="bookmark"
title="Permalink to 偏方目录">偏方目录</a></h1>
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<footer class="post-info">
<abbr class="published" title="2019-04-04T08:20:00+08:00">
Published: 四 04 四月 2019
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/zhong-yi.html">中医</a>.</p>
</footer><!-- /.post-info --> <h3 id="_1">呼吸系统</h3>
<ul>
<li>
<p><a href="https://www.jianshu.com/p/ed0a59429ba5">感冒偏方</a></p>
</li>
<li>
<p><a href="https://www.jianshu.com/p/72b7e096397b">咳嗽、气管炎、哮喘偏方</a></p>
</li>
</ul>
<h3 id="_2">消化系统</h3>
<ul>
<li><a href="https://www.jianshu.com/p/633b8934e7f9">打嗝偏方</a></li>
</ul>
<h3 id="_3">外科皮肤科</h3>
<ul>
<li><a href="https://www.jianshu.com/p/f5fe4dc3bc02">烫伤冻伤冻疮偏方</a></li>
</ul>
<h3 id="_4">参考资料</h3>
<ul>
<li><a href="https://www.jianshu.com/p/19f4a5805199">偏方书籍汇总下载</a></li>
<li>讨论钉钉群21734177, qq群391441566 338228106</li>
<li><a href="https://github.com/china-testing/python-api-tesing/blob/master/society_books.md">本文相关海量书籍下载</a> </li>
<li>道家技术-手相手诊看相中医等钉钉群21734177 qq群：391441566 184175668 338228106 看八字、风水、手相、面相、舌相、抽签、体质识别。服务费50元每人次起。请联系钉钉或者微信pythontesting</li>
<li><a href="https://china-testing.github.io/testing_training.html">接口自动化性能测试线上培训大纲</a></li>
</ul>
<a class="readmore" href="/pianfang.html">read more</a>
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<header>
<h1><a href="/ai_project_books.html" rel="bookmark"
title="Permalink to 2019人工智能项目书籍汇总下载">2019人工智能项目书籍汇总下载</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2019-04-02T12:20:00+08:00">
Published: 二 02 四月 2019
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
</ul>
<h2 id="python-intelligent-projects-using-python-2019">书籍:python人工智能项目 Intelligent Projects Using Python - 2019</h2>
<h3 id="_1">简介</h3>
<p><img alt="图片.png" src="https://upload-images.jianshu.io/upload_images/12713060-4ff79b04fc15245b.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>实施机器学习和深度学习方法，使用Python构建智能，认知AI项目</p>
<p>主要特点
- 帮助您掌握AI算法和概念的入门指南
- 8个实际项目，解决医疗保健，电子商务和监控方面的各种挑战
- 使用TensorFlow，Keras和其他Python库来实现智能AI应用程序</p>
<p>图书说明</p>
<p>如果您想使用Python从领先的AI域构建富有洞察力的项目，本书将是一个完美的伴侣。</p>
<p>本书涵盖了AI所有核心学科项目的详细实施。我们首先介绍如何使用机器学习和深度学习技术创建智能系统的基础知识。您将吸收各种神经网络架构，如CNN，RNN，LSTM，以解决关键的新世界挑战。您将学习如何训练模型以检测人眼中的糖尿病视网膜病变状况，并创建用于执行视频到文本翻译的智能系统。您将在医疗保健领域中使用转移学习技术，并使用GAN实施样式转移。稍后您将学习构建基于AI的推荐系统，用于情感分析的移动应用程序以及用于承载客户服务的强大聊天机器人。您将在网络安全域中实施AI技术以生成Captchas。之后，您将使用强化学习训练和建造自动驾驶车辆以进行自驾车。您将使用来自Python生态系统的库（如TensorFlow，Keras等）来实现机器学习，深度学习和AI的核心方面。</p>
<p>在本书的最后，您将熟练地构建自己的智能模型，以解决任何类型的AI问题，而不会有任何麻烦。</p>
<h3 id="_2">参考资料</h3>
<ul>
<li>下载 …</li></ul>
<a class="readmore" href="/ai_project_books.html">read more</a>
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<h1><a href="/bazi.html" rel="bookmark"
title="Permalink to 使用python排八字 计算八字的相合相冲五行分值等">使用python排八字 计算八字的相合相冲五行分值等</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2019-03-01T07:20:00+08:00">
Published: 五 01 三月 2019
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
</ul>
<h3 id="_1">简介</h3>
<p>八字术是一种古老的算命术。它运用阴阳、五行、干支、八卦等理论,把人的出生年月日时转化为八个字,进而推算人的富贵贫贱、祸福吉凶、穷通寿天、名利地位、家庭六亲等方面的情况</p>
<p>八字源自古代的中国，是一种利用天干和地支来准确记录年、月、日、时的方式，由“年干，年支”、“月干，月支”、“日干，日支”、“时干，时支”，共八个干支所组成（共八个字），年、月、日、时的干支组合称为“柱”，形成“年柱”、“月柱”、“日柱”、“时柱”，故八字又称为“四柱”或“四柱八字”。</p>
<p>这种以干支记录时间的方式从夏朝开始就已经开始使用，但直至宋朝的徐子平才将人们出生时的干支八字用来论断人事祸福，因此命理学中的八字命理又称“子平八字学”，以纪念徐子平对八字命理学的贡献。 </p>
<p>公历 …</p>
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<header>
<h1><a href="/ai_news_2019-2.html" rel="bookmark"
title="Permalink to 人工智能及工具上周新闻2019-01-14">人工智能及工具上周新闻2019-01-14</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2019-01-14T08:20:00+08:00">
Published: 一 14 一月 2019
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h3 id="_1">书籍</h3>
<p>本周，畅销书作家马林福特{<a href="http://book.mfordfuture.com/">Marin Ford</a>}出版了一本新书:《<a href="http://book.mfordfuture.com/">Architects of Intelligence”</a>》，其中包括对AI行业一些顶尖人士的一系列采访。该书从一些主角的角度探讨了人工智能运动的过去，现在和未来。</p>
<p>书籍下载：https://itbooks.pipipan.com/fs/18113597-330958374</p>
<h3 id="ai">AI研究</h3>
<p>ElementAI的研究人员发表了文章，详细介绍了保护神经网络免受敌对攻击的技术。<a href="https://medium.com/element-ai-research-lab/securing-machine-learning-models-against-adversarial-attacks-b6cd5d2be8e2">链接</a></p>
<p><img alt="图片.png" src="https://upload-images.jianshu.io/upload_images/12713060-ec561649aef1739d.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>宾夕法尼亚大学，印第安纳大学和艾伦AI研究所的研究人员发表了论文，提出了一个框架，用于理解自然语言处理（NLP）代理的推理能力。 <a href="https://arxiv.org/abs/1901.02522">Read the research paper here</a></p>
<p>卡内基梅隆大学的研究人员提出了一种测量深度神经网络推广的新方法。 <a href="https://arxiv.org/abs/1901.01672">Read the research paper here</a></p>
<h3 id="_2">科技</h3>
<p>在消费电子展（CES）上，IBM Research展示了Project Debater的首次应用。 由Crowd命名的演讲，人工智能系统汇集了有关特定电子设备的意见，并能够辩论 …</p>
<a class="readmore" href="/ai_news_2019-2.html">read more</a>
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<h1><a href="/pptx.html" rel="bookmark"
title="Permalink to ppt制作工具python-pptx 快速入门">ppt制作工具python-pptx 快速入门</a></h1>
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<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-12-29T15:20:00+08:00">
Published: 六 29 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <h3 id="hello">hello</h3>
<div class="highlight"><pre><span></span><span class="ch">#!/usr/bin/env python3</span>
<span class="c1"># -*- coding: utf-8 -*-</span>
<span class="kn">from</span> <span class="nn">pptx</span> <span class="kn">import</span> <span class="n">Presentation</span>
<span class="n">prs</span> <span class="o">=</span> <span class="n">Presentation</span><span class="p">()</span>
<span class="n">title_slide_layout</span> <span class="o">=</span> <span class="n">prs</span><span class="o">.</span><span class="n">slide_layouts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">slide</span> <span class="o">=</span> <span class="n">prs</span><span class="o">.</span><span class="n">slides</span><span class="o">.</span><span class="n">add_slide</span><span class="p">(</span><span class="n">title_slide_layout</span><span class="p">)</span>
<span class="n">title</span> <span class="o">=</span> <span class="n">slide</span><span class="o">.</span><span class="n">shapes</span><span class="o">.</span><span class="n">title</span>
<span class="n">subtitle</span> <span class="o">=</span> <span class="n">slide</span><span class="o">.</span><span class="n">placeholders</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">title</span><span class="o">.</span><span class="n">text</span> <span class="o">=</span> <span class="s2">"Hello, World!"</span>
<span class="n">subtitle</span><span class="o">.</span><span class="n">text</span> <span class="o">=</span> <span class="s2">"python-pptx 可以轻松制作powerpoint!"</span>
<span class="n">prs</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'test.pptx'</span><span class="p">)</span>
</pre></div>
<p><img alt="图片.png" src="https://upload-images.jianshu.io/upload_images/12713060-a5f192eeed1c0c42.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<h3 id="bullet">Bullet</h3>
<div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pptx</span> <span class="kn">import</span> <span class="n">Presentation</span>
<span class="n">prs</span> <span class="o">=</span> <span class="n">Presentation</span><span class="p">()</span>
<span class="n">bullet_slide_layout …</span></pre></div>
<a class="readmore" href="/pptx.html">read more</a>
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<header>
<h1><a href="/docker_books.html" rel="bookmark"
title="Permalink to 2019最佳devops工具docker书籍">2019最佳devops工具docker书籍</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-12-27T07:20:00+08:00">
Published: 四 27 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
<li><a href="https://china-testing.github.io/python3_quick.html">python 3.7极速入门教程 - 目录</a></li>
</ul>
<h3 id="_1">汇总下载</h3>
<p><a href="https://itbooks.pipipan.com/fs/18113597-327750046">码农：Docker云图 - 2015.pdf</a></p>
<p><a href="https://itbooks.pipipan.com/fs/18113597-327750036">高性能Docker - 2016.pdf</a></p>
<p><a href="https://itbooks.pipipan.com/fs/18113597-327750030">第一本Docker书 修订版 - 2015.pdf</a></p>
<p><a href="https://itbooks.pipipan.com/fs/18113597-327750027">Docker全攻略 - 2016.pdf</a></p>
<p><a href="https://itbooks.pipipan.com/fs/18113597-327749959">Docker进阶与实战 - 2016.pdf</a></p>
<p><a href="https://itbooks.pipipan.com/fs/18113597-327749958">Docker技术入门与实战（第2版）- 2017.pdf</a></p>
<p><a href="https://itbooks.pipipan.com/fs/18113597-327749941">Docker - 从入门到实践 - 2017.pdf</a></p>
<p><a href="https://itbooks.pipipan.com/fs/18113597-327672319">The Docker Book - 2018.pdf</a></p>
<p><a href="https://itbooks.pipipan.com/fs/18113597-327672309">Practical Docker with Python - 2018.pdf</a></p>
<p><a href="https://itbooks.pipipan.com/fs/18113597-327672306">Learn Docker - Fundamentals of Docker 18.x …</a></p>
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<h1><a href="/cv_img_diff.html" rel="bookmark"
title="Permalink to python图像处理工具opencv练习:比较图像差异">python图像处理工具opencv练习:比较图像差异</a></h1>
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<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-12-26T08:25:00+08:00">
Published: 三 26 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
<li><a href="https://china-testing.github.io/python3_quick.html">python 3.7极速入门教程 - 目录</a></li>
</ul>
<p>为了计算两幅图像之间的差异，我们将利用Wang等人介绍的结构相似性指数：Image Quality Assessment: From Error Visibility to Structural Similarity。此方法已在scikit-image库中实现，论文下载：https://itbooks.pipipan.com/fs/18113597-326895607 。</p>
<p>首先需要安装Python，OpenCV，scikit-image和imutils。</p>
<h3 id="_1">计算图像差异</h3>
<p>你能发现这两个图像之间的区别吗？</p>
<p><img alt="图片.png" src="https://upload-images.jianshu.io/upload_images/12713060-7cc410de19979d80.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>代码: </p>
<p><img alt="" src="https://upload-images.jianshu.io/upload_images/12713060-b10ad1f9221c50d8.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>执行结果</p>
<p><img alt="图片.png" src="https://upload-images.jianshu.io/upload_images/12713060-a71a7686566153f4.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>源码： https://github.com/china-testing/python-api-tesing/blob/master/practices/cv/image_diff_pil.py
代码及配套图片：https://itbooks.pipipan.com/fs …</p>
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<h1><a href="/img_rotate.html" rel="bookmark"
title="Permalink to python图像处理工具pillow opencv等练习1 旋转">python图像处理工具pillow opencv等练习1 旋转</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-12-26T08:20:00+08:00">
Published: 三 26 十二月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <ul>
<li><a href="https://china-testing.github.io/practices.html">python测试开发项目实战-目录</a></li>
<li><a href="https://china-testing.github.io/python_books.html">python工具书籍下载-持续更新</a></li>
<li><a href="https://china-testing.github.io/python3_quick.html">python 3.7极速入门教程 - 目录</a></li>
</ul>
<h3 id="_1">对如下图片旋转</h3>
<p><img alt="image.png" src="https://upload-images.jianshu.io/upload_images/12713060-3b0e1960983e5502.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240"></p>
<p>将上面图片逆时针旋转45度，90度，要求图片内容完整。</p>
<h3 id="pillow">pillow</h3>
<div class="highlight"><pre><span></span><span class="ch">#!/usr/bin/env python3</span>
<span class="c1"># -*- coding: utf-8 -*-</span>
<span class="c1"># https://china-testing.github.io/pil1.html</span>
<span class="c1"># https://github.com/china-testing/python-api-tesing/blob/master/practices/pillow/rotate.py</span>
<span class="c1"># 项目实战讨论QQ群630011153 144081101</span>
<span class="c1"># CreateDate: 2018-12-26</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="n">im</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s2">"qun.jpg"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">im</span><span class="o">.</span><span class="n">size …</span></pre></div>
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