| ingredient1 | ingredient2 | quality | |
|---|---|---|---|
| chard | anchovies | heaven | |
| chard | bacon | high_rec | |
| chard | basil | freq_rec | |
| chard | bcheese | rec |
| //Knapsack algorithm | |
| //================== | |
| // wikipedia: [Knapsack (0/1)](http://en.wikipedia.org/wiki/Knapsack_problem#0.2F1_Knapsack_Problem) | |
| // Given a set `[{weight:Number, benefit:Number}]` and a capacity, | |
| // find the maximum value possible while keeping the weight below | |
| // or equal to the capacity | |
| // **params**: | |
| // `capacity` : Number, | |
| // `items` : [{w:Number, b:Number}] | |
| // **returns**: |
The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.
The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.