matiasvlevi / Dannjs-Pokemon

Deep Neural Network predicts pokemon combat outcomes with Dannjs

Dannjs Pokemon Combat Guesser

A Dann model learns to predict pokemon combat winners.
Try it out!

Here are the values trained to the neural network (48 999 samples trained)

Inputs:
 0: Pokemon 1 element 1 (Value mapped to each element)
 1: Pokemon 1 element 2 (Value mapped to each element)
 2: Pokemon 1 legendary (Boolean)
 3: Pokemon 1 HP (Normalized)
 4: Pokemon 1 Attack (Normalized)
 5: Pokemon 1 Defense (Normalized)
 6: Pokemon 1 Sp Attack (Normalized)
 7: Pokemon 1 Sp Defense (Normalized)
 8: Pokemon 1 speed (Normalized)

 9: Pokemon 2 element 1 (Value mapped to each element)
10: Pokemon 2 element 2 (Value mapped to each element)
11: Pokemon 2 legendary (True or False)
12: Pokemon 2 HP (Normalized)
13: Pokemon 2 Attack (Normalized)
14: Pokemon 2 Defense (Normalized)
15: Pokemon 2 Sp Attack (Normalized)
16: Pokemon 2 Sp Defense (Normalized)
17: Pokemon 2 speed (Normalized)

Output:
 0: Winner (Boolean)

The dataset can be found [here](https://www.kaggle.com/tuannguyenvananh/pokemon-dataset-with-team-combat)

Links

Dataset

Combat dataset

Dann Guesses Combas winner

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NPM SCRIPTS

Run the webapp on a local server

npm start

Parse dataset (csv to json)

npm run parse

Train the model, this command will create a new model if you haven't trained one yet. If you allready saved a model, this command will train the model with the most epochs (from file name).

npm run train [epochs]

Minify the latest model to a JS function in public/scripts/minifiedModel.js

npm run minify