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# Keyboard And Mouse Interactive Dataset
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# Neural Network
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## Requirements
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The code is test in Ubuntu 20.04.
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pytorch 1.11.0
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matplotlib 3.3.2
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pickle 4.0
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pandas 1.4.3
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## Train
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Set training parameters in train.sh
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Run `sh train.sh` to train the model
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## Test
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Run `sh test.sh` to run test on trained model
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Predictions are saved under `prediction/task$i$/`
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# Bayesian Inference
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## Requirements
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R 4.2.1
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RStan [](https://mc-stan.org/users/interfaces/rstan.html)
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Run `sh sampler_user.sh` to split prediction to 10% to 90%
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Run `Rscript stan/strategy_inference_test.R` to get results of intention prediction for all users
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Run `sh stan/plot_user.sh` to plot the bar chart for user intention prediction results of all action sequences
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Run `Rscript stan/strategy_inference_test_full_length.R` to get results of intention prediction (0% to 100%) for all users
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Run `sh stan/plot_user_length_10_steps.sh` to plot the bar chart for user intention prediction results (0% to 100%) of all action sequences
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Run `sh sampler_single_act.sh` to get the predictions for each individual action sequence.
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Run `Rscript stan/strategy_inference_test_all_individual_act.R` to get all action sequences (0% to 100%) of all users for intention prediction
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Run `sh plot_user_all_individual.sh` to plot the bar chart for user intention prediction results of all action sequences
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Run `sh plot_user_length_10_steps_all_individual.sh` to plot the user intention prediction results (0% to 100%) of all action sequences
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Set training and test parameters in train.sh and test.sh
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Run sh train.sh to train the model.
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Run sh test.sh to run test on trained model.
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Predictions are saved under prediction/task$i$/
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Run sh sampler_user.sh to split prediction to 10% to 90%
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Run stan/strategy_inference_test.R to get results of intention prediction for all users
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Run stan/plot_user.py to plot the bar chart for user intention prediction results of all action sequences
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Run stan/strategy_inference_test_full_length.R to get results of intention prediction (0% to 100%) for all users
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Run stan/plot_user_length_10_users.py to plot the bar chart for user intention prediction results (0% to 100%) of all action sequences
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Run stan/strategy_inference_test_all_individual_act.R to get all action sequences (0% to 100%) of all users for intention prediction
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Run stan/plot_user_all_individual.py to plot the bar chart for user intention prediction results of all action sequences
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Run stan/plot_user_length_10_steps_all_individual.py to plot the user intention prediction results (0% to 100%) of all action sequences
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keyboard_and_mouse/README.md
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keyboard_and_mouse/README.md
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# Keyboard And Mouse Interactive Dataset
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# Neural Network
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## Train
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Set training parameters in train.sh
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Run `sh train.sh` to train the model
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## Test
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Run `sh test.sh` to run test on trained model
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Predictions are saved under `prediction/task$i$/`
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# Split Prediction
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Run `sh sampler_user.sh` to split prediction to 10% to 90%
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Run `sh sampler_single_act.sh` to split prediction individual action sequences.
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# Bayesian Inference
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Run inference to get results of intention prediction for all users and plot results
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```
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cd stan
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Rscript strategy_inference_test.R
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sh plot_user.sh
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```
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Run inference to get results of intention prediction (0% to 100%) for all users and plot results
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```
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Rscript strategy_inference_test_full_length.R
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sh plot_user_length_10_steps.sh
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```
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Run inference to get all action sequences (0% to 100%) of all users for intention prediction
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```
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Rscript strategy_inference_test_all_individual_act.R
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```
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Plot results of user intention prediction of all action sequences
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```
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sh plot_user_all_individual.sh
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```
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Plot the user intention prediction results (0% to 100%) of all action sequences
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```
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sh plot_user_length_10_steps_all_individual.sh
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@ -2,7 +2,7 @@
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Codes to reproduce results on WAH dataset[^1]
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[^1]: Modified based on WAH train and test codes (https://github.com/xavierpuigf/watch_and_help)[https://github.com/xavierpuigf/watch_and_help].
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[^1]: Modified based on WAH train and test codes, (see WAH)[https://github.com/xavierpuigf/watch_and_help]
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## Data
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