Update README.md

README.md

+The conDetSEC approach is implemented in the main_varyingLength.py file.
+The Recurrent Neural Network autoencoder is implemented in the model.py file.
+
+The main_varyingLength.py file takes as input three information:
+- The Directory in which data are stored
+- The number of labelled samples to derive MUST and CANNOT Link constraints (this is used to access to the related fiile)
+- The run identifier to add to the output file name
+
+In the data directory (i.e. JapWov) there are different files:
+- data.npy: contains the data in numpy format as a tensor. (JapWov: (640, 29, 12) )
+- class.npy: contains the label assignment. (JapWov: (640,) )
+- seqLength.npy: contains the valid timestamps for each multi-variate time series contained in data.npy (JapWov: (640,)). The value of each row of this file is between 1 and the maximum lenght of the time series of the dataset
+
+
 Example to run the code:
-python main.py JapWov 10 0
+python main_varyingLength.py JapWov 10 0
+
+This means that we run the clustering algorithm on the Japanese Wovel dataset, with a number of constraints derived using 10 labelled instances per classes. The final 0 indicates the run number in order to record which is the specific run (for experimental evaluation purposes).
+Running this line of code makes the hypothesis that in the JapWov folder, the file 10_0.npy exists.
+The file 10_0.npy contains the identifier (position) of 10 samples per class that will be used to derive constraints.
+
+The "python main_varyingLength.py JapWov 10 0" line command will produce two files:
+- JapWov/res_10_0.npy: The results of the K-Means clustering algorithm applied on the learnt embeddings
+- JapWov/emb_10_0.npy: The data embedding produced by our framework
 
-This means that we run the clustering algorithm on the Japanese Wovel dataset, with a number of constraints derived using 10 labelled instances per classes. The final 0 indicates the run number in order to record which is the specific run (for experimental evaluation purposes)