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| Hi Dr. Ghorbani, Note that I quickly created the following data to run MLP and KAN and it is **ill-conditioned** data, thus there will be infinite number of solutions (the columns are perfect linear combination of other columns), thus any least... |
| When explaining PCA, I often find that people struggle to grasp the concept of principal components. They often ask, "What are principal components, again?" This difficulty arises because, in traditional methods, we typically define variables,... |
| Kotaro-san, Thank you very. I appreciate. |
| Dear Giulio, Thank you for your valuable information -- I really appreciate it. I have one more question. In my current setup, the response variable is a numerical value rather than a categorical one. I've created a simple training dataset and... |
| I removed the constants to make the ODE simple and used constants for initial conditions, which can be modified, accordingly. (* 1st approach *) dsolSDL03 = DSolve[ {D[y[x], {x, 4}] + D[y[x], {x, 2}] == 0, y[0] == 4,... |
| I am not sure about the conditions: why "0" and x->"0" with quotation? su["0", y] == 1/Cosh[y], D[su[x, y], x] == 0 /. x -> "0". I replaced the quotations and the PDE can be solved su[0, y] == 1/Cosh[y], D[su[x, y], x] == 0 ... |
| Thanks for this great notebook. |
| The "matrice" in your example is ill - conditioned; thus, the computation of its inverse matrix is unstable (e.g., a small change in a matrix causes huge changes in the inverse matrix). I believe you already know the followings but just a reminder. ... |
| NumericalArray[] returns an object, not a matrix. Use Normal[], e.g., f2=Fourier[Normal[dataNum]] |
| I just find out the "Exploratory Factor Analysis" from the Wolfram Demonstrations Project, that provides the varimax rotation. [http://demonstrations.wolfram.com/ExploratoryFactorAnalysis/][1] [1]:... |