orange

Using MySQL socket with Orange3

老子叫甜甜 提交于 2021-02-10 14:17:48
问题 The Orange3 "SQL Table" Widget allows to connect to a MSSQL server, but is it possible to use it with a local MySQL installation? I would like to make use of a MySQL socket. 回答1: MySql is not supported. See https://github.com/biolab/orange3/issues/3565 and linked issues. 来源: https://stackoverflow.com/questions/56866891/using-mysql-socket-with-orange3

Deploying Orange 3 models

不羁岁月 提交于 2021-02-05 11:35:06
问题 Yes, I've read https://datascience.stackexchange.com/questions/23100/is-there-an-orange-server-to-deploy-the-models-developed-in-orange-3 . What I mean I do not have to have Orange server (though obviously it would be nice to have it), but rather I would like to deploy the model developed in Orane somehow, e.g. replace an input data file and rerun the model built in Orange from command line to get some predictions out (or import Orange in Python code, load the model and tell it to use new set

Orange的扩展插件Widgets开发(五)-Utilities

半世苍凉 提交于 2020-04-06 23:52:58
Utilities Progress Bar Operations that take more than a split second indicate their progress with a progress bar and in the title bar of the widget’s window. There are three mechanisms for implementing this. Context handlers The recommended and the simplest approach is to wrap the code into context handler progressBar . If the operation consists of a known number of steps that take similar time, the context handler can be used as follows: with self.progressBar(n_steps) as progress: for k in range(n_steps): self.do_some_processing() progress.advance() In general, the argument of self

Orange的扩展插件Widgets开发(七)-GUI Control

南笙酒味 提交于 2020-04-06 23:52:39
Orange的扩展插件Widgets开发(七) -Library of Common GUI Controls gui is a library of functions which allow constructing a control (like check box, line edit or a combo), inserting it into the parent’s layout, setting tooltips, callbacks and so forth, establishing synchronization with a Python object’s attribute (including saving and retrieving when the widgets is closed and reopened) ... in a single call. Almost all functions need three arguments: the widget into which the control is inserted, the master widget with one whose attributes the control’s value is synchronized, the name of that attribute (

Orange的扩展插件Widgets开发(四)-Channels和Tokens

拜拜、爱过 提交于 2020-04-06 22:39:44
Orange的扩展插件Widgets开发(四) Channels 和 Tokens 我们上次介绍的数据抽样的widget例子,在数据传输通道上是简单和直接的。widget 被设计从一个widget接收数据,处理后将Token通过另外一个Channel发送出去。像下面这个图一样: 关于channels和tokens的管理,其实这里有一些更多的情况,这里我们将更复杂的事情做一个概览,这些了解可以帮助你做出一些复杂的widgets,用于处理多路输出、多路输入的一些处理逻辑。 多输入通道:Multi-Input Channels 简单来说,“multi-input” channels 就是这个widget可以与多个widgets的多个output channels进行连接。这样子的话,多个来源的数据可以被 feed 到一个Widget中进行处理,就像一个函数可以输入多个参数一样的情况。 比如说,我们想构建一个widget,将获取数据并且通过多种预测模型在之上进行测试。widget必须有 input data channel, 我们已经知道如何进行处理。但是,不同的是,我们希望连接多个widgets,像下图定义的逻辑: 我们将了解如何定义learning curve widget的多个channels,以及如何管理多个input tokens。但在此之前,先简单说明一下: learning

Orange的扩展插件Widgets开发(六)-OWWidget

有些话、适合烂在心里 提交于 2020-04-06 22:39:27
Orange的扩展插件Widgets开发(六) -OWWidget The OWWidget is the main component for implementing a widget in the Orange Canvas workflow. It both defines the widget input/output capabilities and implements it’s functionality within the canvas. Widget Meta Description Every widget in the canvas framework needs to define it’s meta definition. This includes the widget’s name and text descriptions but more importantly also its input/output specification. This is done by defining constants in the widget’s class namespace: class IntConstant(OWWidget): name = "Integer Constant" description "A simple integer

Orange的扩展插件Widgets开发(一)-快速入门

你说的曾经没有我的故事 提交于 2020-04-06 22:39:14
亲手翻译,欢迎转载。动态修订,请附原址: http://my.oschina.net/u/2306127/admin/edit-blog?blog=595479 原文(英)来自于: http://orange-development.readthedocs.org/tutorial.html 关于Orange Widgets的开发完整教程参见: http://orange-development.readthedocs.org/ 注意:原文有一些错误,经作者试验后此文已经修正,并附上一些经验和运行结果图。 快速开始 Orange Canvas是Orange的可视化程序环境,而Widgets 是Orange Canvas中运行的组件。Canvas提供了Widgets自包含的功能性函数,并且提供了一个图形用户界面,可以通过拖拽来快速构建数据处理流程和数据分析的工作流。Widgets互相之间可以通讯、可以传递对象,通过一个通讯Channel来实现。 这里,我们将介绍一个简单的例子,并且展示如何构建一个简单的Widgets的方法,然后让它在Canvas中运行起来。 本文的例程完整运行的情况(译者注:此处为作者试验的结果,将输入和加法操作执行了两遍): 预备知识 每一个Orange widget属于category并且有一个在category中的优先级。当打开Orange Canvas

Orange的扩展插件Widgets开发(二)-制作流程

感情迁移 提交于 2020-04-06 22:38:59
亲手翻译,欢迎转载。动态修订,请附原址: http://my.oschina.net/u/2306127/admin/edit-blog?blog=596025 关于Orange Widgets的开发完整教程参见: http://orange-development.readthedocs.org/ 创建一个完整的Widgets 在创建了一个简单的widgets帮助了解了基本概念之后,我们来制作一个真正有用的widgets,可以安装到系统与其它的widgets一起协同工作。 我们先从简单的开始,在input中接收data set然后output输出10%的数据实例。 将其命名为OWDataSamplerA 。 创建一个包“Demo” 首先,为了在Orange Canvas的toolbox显示,我们创建 python project 名字为: orange-demo 工程文件目录如下: orange-demo/ setup.py orangedemo/ __init__.py OWDataSamplerA.py 这个 orange-demo/setup.py 文件包括: from setuptools import setup setup(name="Demo", packages=["orangedemo"], package_data={"orangedemo": ["icons

Orange的扩展插件Widgets开发(三)-设置和控件

岁酱吖の 提交于 2020-04-06 22:01:47
Settings and Controls In the previous section of our tutorial we have just built a simple sampling widget. Let us now make this widget a bit more useful, by allowing a user to set the proportion of data instances to be retained in the sample. Say we want to design a widget that looks something like this: What we added is an Options box, with a spin entry box to set the sample size, and a check box and button to commit (send out) any change we made in setting. If the check box with “Commit data on selection change” is checked, than any change in the sample size will make the widget send out the

Orange脚本调用Data Mining Library

允我心安 提交于 2020-03-24 08:30:27
3 月,跳不动了?>>> 原文(英): http://docs.orange.biolab.si/3/data-mining-library/ Orange Data Mining Library 教程 这是一个有好的关于 Orange 脚本使用的教程, 而Orange是一个基于Python 3的数据挖掘支持库。假定你已经下载和安装了 Orange, 可以从 github repository 获得的版本,并且有一个可以正常运行的Python环境,注意需要python3,python3.4将是目前推荐的版本。使用Python shell,输入下面的代码: % python >>> import Orange >>> Orange.version.version '3.2.dev0+8907f35' >>> 如果没有错误和警告信息,Orange 和 Python 就已经正确安装,可以继续下面的教程。 The Data Data Input Saving the Data Exploration of the Data Domain Data Instances Orange Data Sets and NumPy Meta Attributes Missing Values Data Selection and Sampling Classification Learners and