chickenbestlover / online-recurrent-extreme-learning-machine Goto Github PK

Two issues related to each other:

1. As I can see in the code, self.beta is directly responsible for prediction. However, this matrix is being updated recursively up to the end of the process (to be more exact len(sequence)-predictionStep.
   For instance in FOS_ELM we have:
   self.beta = self.beta + np.dot(self.M, np.dot(Ht, targets - np.dot(H, self.beta)))
   and this train process is in a for loop with prediction of the next:
   for i in range(numLags, len(sequence)-predictionStep-1):
net.train(X[[i], :], T[[i], :])
Y = net.predict(X[[i+1], :])
predictedInput[i+1] = Y[-1]
   I believe this update should be continued up to the end of the training, then one needs to feed the testdata only to prediction function.

2. The whole data is normalized but if you divide the dataset to test and train, you cannot normalize test set. (you can use mean and variance of train set for normalizing test).

Based on these two issues, the NRMSE mentioned at the end of the process is not reliable.

Hi, I have successfully ran through your code, but I have a question I want to communicate with you.

As shown in codes below, why you choose to compare the gap between predictedinput [i+1] and targetInput [i+1] instead of comparing the gap between predictedinput [i+1] and trueData [i+1] .

I think predictedinput [i+1] is the prediction at time i+1 which is correspond to trueData [i+1]

Hello, i would like to learn, how to make to run or-elm classification with iris database.

Is it possible ?

Dear Jin,

First of all I would like to thank you for developing this repo. I read with great pleasure your paper on the OR-ELM algorithm and I would like to use it as part of my PhD thesis.
I was wondering if your code will work on Python3 or if you are aware of any R implementation of OR-ELM.

Thank you.

Best Wishes,
Ahmed

As a low-effort test of the ORELM, I substituted for NYC Taxi traffic, the attached ramp function from 0 to 20000 in increments of 10000, backed up the nyc_taxi.csv and copied therampified_nyc_taxi.csv in its place. It seems not to converge.

This isn't necessarily an issue with the code. It is interesting in that it raises the issue of parameter seeking in the ORELM algorithm. What is it about the ramp function that causes ORELM to diverge and how would one find the parameter(s) values that converge on a solution?

rampified_nyc_taxi.zip

For comparison, this is with a sine function rather than ramp function -- although in this case, I synchronized the time of day with the phase:

sine_nyc_taxi.zip