# cocolour
> Color schemes generator based on machine learning
Latest version **0.1.0** (published 2014-10-25) · MIT license · 0 weekly downloads
## Install
```sh
npm install cocolour
pnpm add cocolour
yarn add cocolour
bun add cocolour
```
## Health
**Score 15/100 (F)** — status: abandoned.
Positive: no vulnerabilities.
Warnings: low downloads; no types; no esm support; pre 1.0.
Negative: abandoned; low maintenance score.
## Facts
| | |
|---|---|
| Version | 0.1.0 |
| Published | 2014-10-25 |
| First published | 2014-10-25 |
| Weekly downloads | 0 |
| License | MIT |
| TypeScript types | none |
| Module format | CommonJS |
| Dependencies | 7 |
| Known vulnerabilities | 0 (+8 in 2 direct dependencies) |
| Install scripts | no |
| Author | Zeno Zeng |
| Maintainers | zenozeng |
| Keywords | cocolour |
## Links
- npm: https://www.npmjs.com/package/cocolour
- Repository: https://github.com/zenozeng/cocolour
- Issues: https://github.com/zenozeng/cocolour/issues
- npm.io page: https://npm.io/package/cocolour
## Dependencies (7)
- [jquery](https://npm.io/package/jquery.md) >=2.1.0
- [lodash](https://npm.io/package/lodash.md) ^2.4.1
- [mathjs](https://npm.io/package/mathjs.md) ^1.0.1
- [gene-pool](https://npm.io/package/gene-pool.md) latest
- [avoscloud-sdk](https://npm.io/package/avoscloud-sdk.md) ^0.4.4
- [color-convert](https://npm.io/package/color-convert.md) latest
- [colors-clustering](https://npm.io/package/colors-clustering.md) >=0.1.1
## Alternatives
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- [coc-yank](https://npm.io/package/coc-yank.md) — 61 weekly downloads
## Recent versions
- 0.1.0 (latest) — 2014-10-25
## README
# Cocolour
Color schemes generator based on machine learning
## Development
```
sudo npm install -g grunt-cli
npm install
```
### Build
`grunt build`
### Watch
`grunt watch`
### Test
```
sudo npm install -g coffee-script
npm test
```
## FAQ
### Why HSL?
See also http://zh.wikipedia.org/wiki/HSL%E5%92%8CHSV%E8%89%B2%E5%BD%A9%E7%A9%BA%E9%97%B4
Section: HSL与HSV的比较
### 讨厌喜欢的分离
事实上，
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，只有三十几。
如果用 [喜欢，不喜欢，一般] 输出，大概61%
如果用 [喜欢, 不喜欢]，大概68-69%
https://github.com/zenozeng/cocolour/issues/77
## 经验
### BP神经网络命中率波动非常厉害，而且很低？
可能是数据过拟合了，调整 iterations 和 errorThresh 看看会不会有帮助。
## License
```
Copyright (C) 2014-2015 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 .
```
*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
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
### 2015-04-06 -- 2015-04-12
- add ANN.prototype.toJSON
- add ANN.fromJSON
### 2015-03-30 -- 2015-04-05
- force login to use setScore, fixes #123
### 2015-03-16 -- 2015-03-22
- add getError() in ANN.train
### 2015-03-09 -- 2015-03-15
- 模拟的举证里增加 `math.var(distance, 'biased')`
- 数据增加到 1441 组
- 将不同人的数据分离进行验证
### 2015-03-02 -- 2015-03-08
- [换用 convnetjs 的讨论](https://github.com/zenozeng/cocolour/issues/119)
- [关于个人训练模型的讨论](https://github.com/zenozeng/cocolour/issues/121)
- Switch to convnetjs
- Try SVM layer and switch back to softmax
- Add min distance
### 2015-03-23 -- 2015-03-01
- UI 细节调整、加载速度优化
- 现在切分训练数据与验证数据时会保持 positive 和 negative 的比例
### 2015-02-16 -- 2015-02-22
- 网站 UI 的进一步重构
- 代码组织方式调整、重构
### 2015-02-09 -- 2015-02-15
- 一些前端小调整
- 重写神经网络预测率测试
- 基于 cluster 模块，使用 process 的 message 通信
- 输出测试结果的 max, min, mean, median 和 var
- 调整 iterations 和 errorThresh 来避免过拟合问题
- 平均预测率基本可以到 71%
- 而且现在输出结果不再发生大幅度波动了
See also: https://github.com/zenozeng/cocolour/issues/92
- 移除色相、亮度、饱和度方差
- 平均预测率现在可达 72%
See also: https://github.com/zenozeng/cocolour/issues/78
- 输出讨厌和喜欢各自的预测率
- 目前喜欢可达 80%，讨厌仅为 56%
See also: https://github.com/zenozeng/cocolour/issues/90
- 尝试给估价函数增加一个 bias
```coffeescript
bias = 0.38
(positive - bias) * 1 / bias
```
对喜欢的平均预测率现在可达 93%，虽然对讨厌的平均预测率现在降到了 39%，综合判断率下降
See also: https://github.com/zenozeng/cocolour/issues/93
- 网站 UI 调整
### 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
- 调整学习速率到 0.05
### 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 (gene-pool@0.0.4)
### 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 CIE76 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
---
_Source: https://npm.io/package/cocolour · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._