<commit_before>## TRo 2017 Experiments

* [Preliminary](#preliminary)
* [Installation](#installation)
* [Experiments](#experiments)
* [Troubleshooting](#troubleshooting)

### Preliminary <a name="preliminary"></a>
This folder contains the source code to reproduce the experimental results presented in the paper.

* It contains sub folders with each folder containing an experiment.
* The modeling is done in Python language using the [GPy library](https://github.com/SheffieldML/GPy).
* The experiments can be run interactively using [IPython notebooks](https://ipython.org/) for each experiment.
* The evaluation dataset is available separately at this [link](https://github.com/buntyke/TRo2017/releases/download/v1.0/Data.zip).

### Usage Instructions <a name="installation"></a>

* Installation of Ipython to run notebooks (For Ubuntu):
```
$ sudo pip install matplotlib --upgrade
$ sudo pip install numpy --upgrade
$ sudo pip install scipy --upgrade
$ sudo pip install jupyter --upgrade
$ sudo pip install ipython --upgrade
$ sudo pip install ipython[notebook] --upgrade
```
For Windows, MAC OSX: Please install [Anaconda](https://www.continuum.io/downloads) as it contains all the required packages by default.
* Installation of GPy and its dependencies (For Ubuntu):
```
$ sudo pip install GPy --upgrade
```
For Windows, Mac OSX: Please follow the installation instructions from the [GPy page](https://github.com/SheffieldML/GPy).
* Clone/[Download](https://github.com/buntyke/TRo2017/archive/master.zip) the repository to your PC:
```
$ git clone https://github.com/buntyke/TRo2017.git
```
* Download evaluation dataset into the Experiments folder:
```
$ cd TRo2017/Experiments/
$ wget "https://github.com/buntyke/TRo2017/releases/download/v1.0/Data.zip"
$ unzip Data.zip
```
* Read an experiment overview and then run the desired experiment in Ipython:
```
$ cd Exp1/
$ gedit README.md
$ ipython experiment.ipynb
```

### Experiments <a name="experiments"></a>

* [Preprocessing](README.md): Run the preprocessing.ipynb notebook prior to the actual experiments to generate the pickle files of the dataset.

* [BGPLVM Experiment](Exp1/README.md): Comparison of BGPLVM with Principal Component Analysis (PCA). Used to generate Figures 8,9 and Table I in paper.

* [MRD Model Visualization](Exp4/README.md): Visualization of an example MRD model with test inference. Used to generate Figure 10.

* [MRD Model Comparison](Exp2/README.md): Comparison of MRD with other linear regression models. Used to generate Figure 11.

* [Inference Strategies](Exp3/README.md): Comparison of various inference strategies for MRD. Used to generate Figures 12,13.

* [Feature Representations](Exp5/README.md): Comparison of different feature representations for both observation spaces. Used to generate Figures 15,16.

* [Postures Comparison](Exp6/README.md): Evaluation of generalizability of MRD to various postures of mannequin. Used to generate Figure 17.

* [T-shirts Comparison](Exp7/README.md): Evaluation of generalizability of MRD to various T-shirts. Used to generate Figure 18.

* [Framework Video](Exp8/README.md): Feature representations and latent space visualization. Used to generate later half of Framework.mp4 video.

* [Latent Features Video](Exp9/READM.md): Visualization of latent features learned by BGPLVM. Used to generate LatentFeatures.mp4 video.

### Troubleshooting <a name="troubleshooting"></a>

* The Ipython notebooks should run out of the box. However, the GPy API could change for future versions and cause some errors. This code was tested for the following versions of GPy: 0.8.x,1.0.x,1.5.x. Please switch to '1.5.x' if the error persists.
* The training time could take between 3-4 hrs per model. If the users would like to access the models trained by us, please contact the authors. [<NAME>](buntyke.github.io), [<NAME>](brain.kyutech.ac.jp/~tom).<commit_msg>[minor] update instructions to run experiments<commit_after>## TRo 2017 Experiments

* [Preliminary](#preliminary)
* [Installation](#installation)
* [Experiments](#experiments)
* [Troubleshooting](#troubleshooting)

### Preliminary <a name="preliminary"></a>
This folder contains the source code to reproduce the experimental results presented in the paper.

* It contains sub folders with each folder containing an experiment.
* The modeling is done in Python language using the [GPy library](https://github.com/SheffieldML/GPy).
* The experiments can be run interactively using [IPython notebooks](https://ipython.org/) for each experiment.
* The evaluation dataset is available separately at this [link](https://github.com/buntyke/TRo2017/releases/download/v1.0/Data.zip).

### Usage Instructions <a name="installation"></a>

* Installation of Ipython to run notebooks (For Ubuntu):
```
$ sudo pip install matplotlib --upgrade
$ sudo pip install numpy --upgrade
$ sudo pip install scipy --upgrade
$ sudo pip install jupyter --upgrade
$ sudo pip install ipython --upgrade
$ sudo pip install ipython[notebook] --upgrade
```
For Windows, MAC OSX: Please install [Anaconda](https://www.continuum.io/downloads) as it contains all the required packages by default.
* Installation of GPy and its dependencies (For Ubuntu):
```
$ sudo pip install GPy --upgrade
```
For Windows, Mac OSX: Please follow the installation instructions from the [GPy page](https://github.com/SheffieldML/GPy).
* Clone/[Download](https://github.com/buntyke/TRo2017/archive/master.zip) the repository to your PC:
```
$ git clone https://github.com/buntyke/TRo2017.git
```
* Download evaluation dataset into the Experiments folder:
```
$ cd TRo2017/Experiments/
$ wget "https://github.com/buntyke/TRo2017/releases/download/v1.0/Data.zip"
$ unzip Data.zip
```
* Read an experiment overview and then run the desired experiment in Ipython:
```
$ cd Exp1/
$ gedit README.md
$ ipython experiment.ipynb
```

### Experiments <a name="experiments"></a>

Please follow these steps in order to sequentially generate the results of the paper.

* [Preprocessing](preprocessing.ipynb): Run the preprocessing.ipynb notebook prior to the actual experiments to generate the pickle files of the dataset.

* [BGPLVM Experiment](Exp1/README.md): Comparison of BGPLVM with Principal Component Analysis (PCA). Used to generate Figures 8,9 and Table I in paper.

* [MRD Model Visualization](Exp4/README.md): Visualization of an example MRD model with test inference. Used to generate Figure 10.

* [Model Training](models.ipynb): Run the models.ipynb notebook prior to the cross validation experiments. The execution of this notebook is highly time consuming (about 3 days). Please contact the co-authors, if you would like to obtain the pre-trained models.

* [MRD Model Comparison](Exp2/README.md): Comparison of MRD with other linear regression models. Used to generate Figure 11.

* [Inference Strategies](Exp3/README.md): Comparison of various inference strategies for MRD. Used to generate Figures 12,13.

* [Feature Representations](Exp5/README.md): Comparison of different feature representations for both observation spaces. Used to generate Figures 15,16.

* [Postures Comparison](Exp6/README.md): Evaluation of generalizability of MRD to various postures of mannequin. Used to generate Figure 17.

* [T-shirts Comparison](Exp7/README.md): Evaluation of generalizability of MRD to various T-shirts. Used to generate Figure 18.

* [Framework Video](Exp8/README.md): Feature representations and latent space visualization. Used to generate later half of Framework.mp4 video.

* [Latent Features Video](Exp9/READM.md): Visualization of latent features learned by BGPLVM. Used to generate LatentFeatures.mp4 video.

### Troubleshooting <a name="troubleshooting"></a>

* The Ipython notebooks should run out of the box. However, the GPy API could change for future versions and cause some errors. This code was tested for the following versions of GPy: 0.8.x,1.0.x,1.5.x. Please switch to '1.5.x' if the error persists.
* The training time could take between 3-4 hrs per model. If the users would like to access the models trained by us, please contact the authors. [<NAME>](buntyke.github.io), [<NAME>](brain.kyutech.ac.jp/~tom).
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