Numpy-A1.py
- Create a two-dimensional array A, of MxN, of 24 elements in the range of 0 to 1. Using a uniform distribution, and store it in a .csv file. Read the array again, from the file, change the structure to PxR a nd save it as B. Apply the Sigmoid function to each value of the array B. Convert B into a 1D array called C Store the positions of all major values in a tuple at 0.5 C.
Numpy-A2.py
- Create a two-dimensional AP array, of MxN, of 24 elements in the range of 10 to 50. Create a two-dimensional AR array, of MxN, of 24 elements in the range of 10 to 50. Store AP and AR in different .csv files. Applies the root mean square (RMSE) function, between AP (prediction) and AR (actual) values. And it shows the result in a 1D array