cocolour

v0.1.0

Published

2 years ago

Color schemes generator based on machine learning

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zenozeng

cocolour

Cocolour

Color schemes generator based on machine learning

Development

sudo npm install -g grunt-cli
npm install

Build

grunt build

Watch

grunt watch

License

Copyright (C) 2014 Zeno Zeng

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.

This program incorporates work covered by the following copyright and permission notices:

jQuery
Copyright 2005, 2014 jQuery Foundation, Inc. and other contributors
Released under the MIT license
color-convert
Copyright (c) 2011 Heather Arthur [email protected]
Released under the MIT license
colors-clustering
Copyright (C) 2014 Zeno Zeng
Released under the MIT license
gene-pool
Copyright (C) 2014 Zeno Zeng
Released under the MIT license
brain
Copyright (c) 2010 Heather Arthur
Released under the MIT license

项目日程

2014-10-20 -- 2014-11-09

确定神经网络库的选择为 Brain
确定输入格式为一个 HSL 矩阵的 flatten: H1 S1 L1 H2 S2 L2 H3 S3 L3 H4 S4 L4 H5 S5 L5
See also: https://github.com/zenozeng/cocolour/issues/72
一次概念验证性测试
```
Length:  164
Match Cound:  106
Unmatch Cound:  58
Rate(%):  64.63414634146342
```
具体的测试详情：https://github.com/zenozeng/cocolour/issues/76
确定输出格式
似乎喜欢和讨厌的机制是很不一样的，所以他们应该被丢到两个堆中去。
如果直接用单个score输出，正确率非常低，只有30%-40% 如果用 [喜欢，不喜欢，一般] 输出，大概60-61%，以及一般这一档的数据非常少，不怎么可靠。如果用 [喜欢, 不喜欢]，大概60-68%
具体的测试详情：https://github.com/zenozeng/cocolour/issues/77
增加数据到 813 组
数据分组成 train 和 verify 组的时候引入随机性
https://github.com/zenozeng/cocolour/issues/81
这个 Issue 会导致之前的测定结果存在一定的偏差
增加数据到 1378 组
调整 learningRate 到 0.1
似乎结果的稳定性提升了一些、正确率也提升了一些
基于 master-slave 的多进程结果验证
充分利用多核性能

尝试引入色相方差、饱和度方差、明度方差

[ { tests: 242, passed: 153, rate: 0.6322314049586777 },
  { tests: 242, passed: 165, rate: 0.6818181818181818 },
  { tests: 242, passed: 140, rate: 0.5785123966942148 },
  { tests: 242, passed: 168, rate: 0.6942148760330579 },
  { tests: 242, passed: 162, rate: 0.6694214876033058 },
  { tests: 242, passed: 161, rate: 0.6652892561983471 },
  { tests: 242, passed: 147, rate: 0.6074380165289256 },
  { tests: 242, passed: 155, rate: 0.640495867768595 },
  { tests: 242, passed: 166, rate: 0.6859504132231405 },
  { tests: 242, passed: 163, rate: 0.6735537190082644 },
  { tests: 242, passed: 156, rate: 0.6446280991735537 },
  { tests: 242, passed: 157, rate: 0.6487603305785123 } ]
{ tests: 2904, passed: 1893, rate: 0.6518595041322314 }

[ { tests: 242, passed: 157, rate: 0.6487603305785123 },
  { tests: 242, passed: 157, rate: 0.6487603305785123 },
  { tests: 242, passed: 151, rate: 0.6239669421487604 },
  { tests: 242, passed: 159, rate: 0.6570247933884298 },
  { tests: 242, passed: 163, rate: 0.6735537190082644 },
  { tests: 242, passed: 160, rate: 0.6611570247933884 },
  { tests: 242, passed: 171, rate: 0.7066115702479339 },
  { tests: 242, passed: 154, rate: 0.6363636363636364 },
  { tests: 242, passed: 158, rate: 0.6528925619834711 },
  { tests: 242, passed: 160, rate: 0.6611570247933884 },
  { tests: 242, passed: 170, rate: 0.7024793388429752 },
  { tests: 242, passed: 167, rate: 0.6900826446280992 } ]
{ tests: 2904, passed: 1927, rate: 0.6635674931129476 }

调整学习速率到 0.05

[ { tests: 242, passed: 157, rate: 0.6487603305785123 },
  { tests: 242, passed: 165, rate: 0.6818181818181818 },
  { tests: 242, passed: 145, rate: 0.5991735537190083 },
  { tests: 242, passed: 153, rate: 0.6322314049586777 },
  { tests: 242, passed: 160, rate: 0.6611570247933884 },
  { tests: 242, passed: 159, rate: 0.6570247933884298 },
  { tests: 242, passed: 157, rate: 0.6487603305785123 },
  { tests: 242, passed: 159, rate: 0.6570247933884298 },
  { tests: 242, passed: 146, rate: 0.6033057851239669 },
  { tests: 242, passed: 154, rate: 0.6363636363636364 },
  { tests: 242, passed: 160, rate: 0.6611570247933884 },
  { tests: 242, passed: 158, rate: 0.6528925619834711 } ]
  { tests: 2904, passed: 1873, rate: 0.6449724517906336 }

// SLAVE#64 closed
[ { tests: 242, passed: 151, rate: 0.6239669421487604 },
  { tests: 242, passed: 159, rate: 0.6570247933884298 },
  { tests: 242, passed: 150, rate: 0.6198347107438017 },
  { tests: 242, passed: 158, rate: 0.6528925619834711 },
  { tests: 242, passed: 166, rate: 0.6859504132231405 },
  { tests: 242, passed: 150, rate: 0.6198347107438017 },
  { tests: 242, passed: 156, rate: 0.6446280991735537 },
  { tests: 242, passed: 162, rate: 0.6694214876033058 },
  { tests: 242, passed: 163, rate: 0.6735537190082644 },
  { tests: 242, passed: 162, rate: 0.6694214876033058 },
  { tests: 242, passed: 141, rate: 0.5826446280991735 },
  { tests: 242, passed: 160, rate: 0.6611570247933884 },
  { tests: 242, passed: 146, rate: 0.6033057851239669 },
  { tests: 242, passed: 159, rate: 0.6570247933884298 },
  { tests: 242, passed: 153, rate: 0.6322314049586777 },
  { tests: 242, passed: 150, rate: 0.6198347107438017 },
  { tests: 242, passed: 162, rate: 0.6694214876033058 },
  { tests: 242, passed: 155, rate: 0.640495867768595 },
  { tests: 242, passed: 151, rate: 0.6239669421487604 },
  { tests: 242, passed: 154, rate: 0.6363636363636364 },
  { tests: 242, passed: 152, rate: 0.628099173553719 },
  { tests: 242, passed: 151, rate: 0.6239669421487604 },
  { tests: 242, passed: 156, rate: 0.6446280991735537 },
  { tests: 242, passed: 156, rate: 0.6446280991735537 },
  { tests: 242, passed: 158, rate: 0.6528925619834711 },
  { tests: 242, passed: 158, rate: 0.6528925619834711 },
  { tests: 242, passed: 157, rate: 0.6487603305785123 },
  { tests: 242, passed: 159, rate: 0.6570247933884298 },
  { tests: 242, passed: 157, rate: 0.6487603305785123 },
  { tests: 242, passed: 152, rate: 0.628099173553719 },
  { tests: 242, passed: 158, rate: 0.6528925619834711 },
  { tests: 242, passed: 149, rate: 0.6157024793388429 },
  { tests: 242, passed: 163, rate: 0.6735537190082644 },
  { tests: 242, passed: 155, rate: 0.640495867768595 },
  { tests: 242, passed: 154, rate: 0.6363636363636364 },
  { tests: 242, passed: 166, rate: 0.6859504132231405 },
  { tests: 242, passed: 153, rate: 0.6322314049586777 },
  { tests: 242, passed: 154, rate: 0.6363636363636364 },
  { tests: 242, passed: 161, rate: 0.6652892561983471 },
  { tests: 242, passed: 156, rate: 0.6446280991735537 },
  { tests: 242, passed: 150, rate: 0.6198347107438017 },
  { tests: 242, passed: 156, rate: 0.6446280991735537 },
  { tests: 242, passed: 160, rate: 0.6611570247933884 },
  { tests: 242, passed: 146, rate: 0.6033057851239669 },
  { tests: 242, passed: 157, rate: 0.6487603305785123 },
  { tests: 242, passed: 159, rate: 0.6570247933884298 },
  { tests: 242, passed: 150, rate: 0.6198347107438017 },
  { tests: 242, passed: 150, rate: 0.6198347107438017 },
  { tests: 242, passed: 162, rate: 0.6694214876033058 },
  { tests: 242, passed: 154, rate: 0.6363636363636364 },
  { tests: 242, passed: 161, rate: 0.6652892561983471 },
  { tests: 242, passed: 155, rate: 0.640495867768595 },
  { tests: 242, passed: 154, rate: 0.6363636363636364 },
  { tests: 242, passed: 167, rate: 0.6900826446280992 },
  { tests: 242, passed: 160, rate: 0.6611570247933884 },
  { tests: 242, passed: 151, rate: 0.6239669421487604 },
  { tests: 242, passed: 161, rate: 0.6652892561983471 },
  { tests: 242, passed: 157, rate: 0.6487603305785123 },
  { tests: 242, passed: 156, rate: 0.6446280991735537 },
  { tests: 242, passed: 151, rate: 0.6239669421487604 },
  { tests: 242, passed: 162, rate: 0.6694214876033058 },
  { tests: 242, passed: 156, rate: 0.6446280991735537 },
  { tests: 242, passed: 155, rate: 0.640495867768595 },
  { tests: 242, passed: 160, rate: 0.6611570247933884 } ]
{ tests: 15488, passed: 9983, rate: 0.6445635330578512 }

[TODO] Verify 的断点续跑
[TODO] Verify 时间记录
[TODO] Verify 中途查看结果

2014-10-13 -- 2014-10-19

Fix bugs in UI
Script for fetching all color schemes in database
500+ more color schemes
Normalize colors

2014-10-06 -- 2014-10-12

引入 AVOS Cloud SDK
user.signup, user.login, user.logout & user.passwordReset
DB: Class Scheme
ACL for Scheme
Log heart and trash

2014-08-18 -- 2014-08-24

神经网络库的选择讨论
See also: https://github.com/zenozeng/cocolour/issues/53
界面增加动画
尝试引入遗传算法，以便在更短时间获得更好结果
构建遗传算法库
https://github.com/zenozeng/gene-pool
Move static/font-awesome to cdn.staticfile.org
各家 Baas 服务商的比较，打算使用 avoscloud
https://cn.avoscloud.com/docs/js_guide.html
遗传算法可视化
http://zenozeng.github.io/gene-pool/demo/
引入遗传算法 ([email protected])

2014-08-11 -- 2014-08-17

Go back using CoffeeScript
See also https://github.com/zenozeng/cocolour/issues/37
update header, fixes #41
聚类算法可视化 (D3)
https://github.com/zenozeng/colors-clustering-visualization

2014-06-23 -- 2014-06-29

About whether to use DBaaS or Baas
See also https://github.com/zenozeng/cocolour/issues/46
See also https://github.com/zenozeng/cocolour/issues/44
Consider using Genetic Algorithms
See also https://github.com/zenozeng/cocolour/issues/49

2014-06-16 -- 2014-06-22

Consider using Web Worker
New Arch Design (ClojureScript for Pure Calculation & CoffeeScript for UI and Communication)

2014-06-09 -- 2014-06-15

关于应用容器化的构想，及相关服务提供商的比较
Linode + Ubuntu + Docker / DigitalOcean + Ubuntu + Docker / Stackdock / Tutum

2014-06-02 -- 2014-06-08

New UI Design for colors clustering (in Zeno's loose notes 2014-06-08)

2014-05-12 -- 2014-05-18

Simple JSON based user system
Simple loging system for replaying requests later

2014-05-05 -- 2014-05-11

New name: cocolour
New domain: cocolour.com
Deploy on Github Pages
Move clustering/ to new repo: zenozeng/colors-clustering
Use seeds from CSS Color Module Level 3
Use CIEDE2000 for calc color difference
Add RGBA Support for Colors Clustering
Switch to CIE67 for perfermence
see https://github.com/zenozeng/colors-clustering/issues/7
Add nodejs support for colors-clustering
Npm publish zenozeng/colors-clustering
Rewrite cocolour using zenozeng/colors-clustering
New UI for cocolour
Use Grunt as task runner
UI for 1920 * 1080
New Repo: cocolour-server

2014-04-28 -- 2014-05-04

基于 K-Means 算法以及 HSL 色彩空间实现基本色彩聚类
Init UI (based on HTML5 drag & drop API)

2014-03-17 -- 2014-04-27

基本调研
初始化项目
服务器基本部署
色彩聚类代码初步

2014-03-05 -- 2014-03-16

Init Repo