Help with Converting Spatio-Temporal Dataset for Consumption about rnnsharp HOT 3 OPEN

For each time frame (one line in your training corpus), if it only contains 5 features, you could build embedding model likes. That means each time frame has its unique id.
ID1 0.923 0.223 0.573 0.235 0.111
ID2 0.920 0.228 0.353 0.213 0.098
ID3 0.901 0.677 0.235 0.551 0.121
...
ID2 0.920 0.228 0.353 0.213 0.098

I just updated RNNSharp to support embedding model in raw text format, so you could use above format for training directly. Please replace WORDEMBEDDING_FILENAME with WORDEMBEDDING_RAW_FILENAME in configuration file.

For #2, yes. It looks good. For example, it may looks like
ID1 Wave
ID2 Label2
ID2 Wave
...
IDn LabelX

For each time frame, it has a corresponding label as result.

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Hello:

I'm getting closer. I've since extracted all my time frames that I want to train the dataset into a single file: rawModel.txt. It has the format:

\t\t\t\t\t
\t\t\t\t\t
...
\t\t\t\t\t

I've also created a train.txt file, and it is in the format:

\t
\t
\t
...
\t

Finally, I've also create a template.txt file. It looks like this:

U01:%x[0,0]
U02:%x[0,1]
U03:%x[0,2]
U04:%x[0,3]
U05:%x[0,4]
U06:%x[-1,0]
U07:%x[-1,1]
U08:%x[-1,2]
U09:%x[-1,3]
U10:%x[-1,4]
U11:%x[1,0]
U12:%x[1,1]
U13:%x[1,2]
U14:%x[1,3]
U15:%x[1,4]

I've modified the BAT file to use the new files, but it's not working the way I had planned.

1.) How does RNNSharp (RNNSharpConsole) know when one spatio-temporal entity has completed and a new one begins? I'm more talking about the edge cases. I've tried to split up them using a blank line, but an exception is thrown, stating the lengths are not the same.

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Since you are going to use continuous values as features, the template.txt should only keep one line: U01:%x[0,0]. All of other lines are used for discrete features only.

In training corpus, RNNSharp uses a blank line to split two entities, but embedding model (rawModel.txt in your example) needn't to use blank lines, since embedding model is just a key-value pair, RNNSharp access embedding model by keyword, and get dense features from embedding model for encoding or decoding.

RNNSharp already supports embedding model in raw text format, you could sync the latest code from depot and use it. In your case, the configuration file looks like:

#The file name for template feature set
TFEATURE_FILENAME: tfeature
#The context range for template feature set. In below, the context is current token, next token and next after next token
TFEATURE_CONTEXT: 0

WORDEMBEDDING_RAW_FILENAME: rawModel.txt
#The context range for word embedding.
WORDEMBEDDING_CONTEXT: -1, 0, 1
#The column index applied word embedding feature
WORDEMBEDDING_COLUMN: 0

I hope these information can help you. For exception you mentioned, could you please show more detailed information about it ?

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