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https://github.com/xinychen/academic-drawing

Providing codes (including Matlab and Python) for visualizing numerical experiment results.
https://github.com/xinychen/academic-drawing

heatmap matlab-figure visulization

Last synced: about 2 months ago
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Providing codes (including Matlab and Python) for visualizing numerical experiment results.

Host: GitHub
URL: https://github.com/xinychen/academic-drawing
Owner: xinychen
Created: 2018-06-14T05:24:59.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2019-09-27T15:11:21.000Z (about 5 years ago)
Last Synced: 2024-07-18T16:33:05.892Z (3 months ago)
Topics: heatmap, matlab-figure, visulization
Language: MATLAB
Homepage:
Size: 3.12 MB
Stars: 193
Watchers: 6
Forks: 39
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        Academic drawing

-----------------

This is a project providing source codes (including Matlab and Python) for presenting experiment results.

Contents

--------

-   [Usage](#usage)

-   [Our examples](#our-examples)

-   [Our publications](#our-publications)

Usage

--------------

> It is not necessary to open each file in this repository because you can follow this readme document to find your needs.

Our examples

--------------

- **Download**

  - [mu10.mat](https://github.com/xinychen/academic-drawing/blob/master/curves/mu10.mat)

  - [mu_curve10.m](https://github.com/xinychen/academic-drawing/blob/master/curves/mu_curve10.m)

and evaluate these in Matlab, then, you will see the following picture:

![mu_curve10](https://github.com/xinychen/academic-drawing/blob/master/curves/mu_curve10.png)

- **Download**

  - [BCPF_fiber_rmselb_m30_r5.csv](https://github.com/xinychen/academic-drawing/blob/master/curves/BCPF_fiber_rmselb_m30_r5.csv)

  - [BCPF_fiber_rmselb_m30_r10.csv](https://github.com/xinychen/academic-drawing/blob/master/curves/BCPF_fiber_rmselb_m30_r10.csv)

  - [overfitting.m](https://github.com/xinychen/academic-drawing/blob/master/curves/overfitting.m)

and evaluate these in Matlab, then, you will see the following pictures:

![overfitting_ms30_r5](https://github.com/xinychen/academic-drawing/blob/master/curves/overfitting_ms30_r5.png)

![overfitting_ms30_r10](https://github.com/xinychen/academic-drawing/blob/master/curves/overfitting_ms30_r10.png)

- **Download**

  - [bias10.mat](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/bias10.mat)

  - [heat_map10.m](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/heat_map10.m)

and evaluate these in Matlab, then, you will see the following picture:

![heat_map10](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/heat_map10.png)

- **Download**

  - [latent_factors.mat](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/latent_factors.mat)

  - [latent_factors.m](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/latent_factors.m)

and evaluate these in Matlab, then, you will see the following pictures:

  ![factor2](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/factor2.png)

  ![factor3](https://github.com/xinychen/academic-drawing/blob/master/heat-maps/factor3.png)

- **Download**

  - [rmse.mat](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse.mat)

  - [rmse_curves.m](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse_curves.m)

and evaluate these in Matlab, then, you will see the following picture:

![rmse_curve](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse_curve.png)

- **Download**

  - [rmse10.mat](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse10.mat)

  - [rmse_curve10.m](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse_curve10.m)

and evaluate these in Matlab, then, you will see the following picture:

![rmse_curve10](https://github.com/xinychen/academic-drawing/blob/master/rmse-curves/rmse_curve10.png)

- **Download**

  - [road1_fiber_ms50_r10.csv](https://github.com/xinychen/academic-drawing/blob/master/time-series/road1_fiber_ms50_r10.csv)

  - [time_series_speed1.py](https://github.com/xinychen/academic-drawing/blob/master/time-series/time_series_speed1.py)

  - [time_series_speed2.py](https://github.com/xinychen/academic-drawing/blob/master/time-series/time_series_speed2.py)

and evaluate these in Python, then, you will see the following pictures:

![time_series_speed1](https://github.com/xinychen/academic-drawing/blob/master/time-series/time_series_speed1.png)

![time_series_speed2](https://github.com/xinychen/academic-drawing/blob/master/time-series/time_series_speed2.png)

- **Download**

  - [road1_fiber_ms50_r10.csv](https://github.com/xinychen/academic-drawing/blob/master/time-series/road1_fiber_ms50_r10.csv)

  - [speed_curve.m](https://github.com/xinychen/academic-drawing/blob/master/time-series/speed_curve.m)

and evaluate these in Matlab, then, you will see the following picture:

![speed_curve](https://github.com/xinychen/academic-drawing/blob/master/time-series/speed_curve.png)

- **Download**

  - [nyc_data_completeness.mat](https://github.com/xinychen/academic-drawing/blob/master/time-series/nyc_data_completeness.mat)

  - [nyc_data_completeness.m](https://github.com/xinychen/academic-drawing/blob/master/time-series/nyc_data_completeness.m)

and evaluate these in Matlab, then, you will see the following picture:

![nyc_data_completeness](https://github.com/xinychen/academic-drawing/blob/master/time-series/nyc_data_completeness.png)

Our Publications

--------------

Most of these examples are from our publications:

- **Xinyu Chen**, Zhaocheng He, Yixian Chen, Yuhuan Lu, Jiawei Wang (2019). **Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model**. Transportation Research Part C: Emerging Technologies, 104: 66-77. [[preprint](https://xinychen.github.io/paper/BATF.pdf)] [[doi](https://doi.org/10.1016/j.trc.2019.03.003)] [[slide](https://doi.org/10.5281/zenodo.2632552)] [[data](http://doi.org/10.5281/zenodo.1205229)] [[Matlab code](https://github.com/sysuits/BATF)]

- **Xinyu Chen**, Zhaocheng He, Lijun Sun (2019). **A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation**. Transportation Research Part C: Emerging Technologies, 98: 73-84. [[preprint](https://www.researchgate.net/publication/329177786_A_Bayesian_tensor_decomposition_approach_for_spatiotemporal_traffic_data_imputation)] [[doi](https://doi.org/10.1016/j.trc.2018.11.003)] [[data](http://doi.org/10.5281/zenodo.1205229)] [[Matlab code](https://github.com/lijunsun/bgcp_imputation)] [[Python code](https://github.com/xinychen/transdim/blob/master/experiments/Imputation-BGCP.ipynb)]

  > Please consider citing our papers if you find these codes help your research.