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I have data of 2 coloumns . 1st coloumn is data for Inflow of water reservoir with respect to time t and 2nd coloumn is demand with respect to time.
Objective function is to minimise spill of water.
Constraints are
Storage(t)=storage(t-1) +I(t)-D(t)-Spill(t) for all time t
Here storage and spill is unknown parameter.. and we have to minimize spill...
How can I do this in genetic algorithm optimization in python/matlab.
I am expecting the code to do this.
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I am running a programme about Real-time Road Congestion Detection Based on Image Texture Analysis that calculates the traffic density which is added up by the average energy and average entropy from the GLCM of a greyscale.
I am using scikit-image to calculate the GLCM and the energy from greyscale but it cannot calculate the entropy.
skimage.feature.graycomatrix
skimage.feature.graycoprops
How can I write this equation of calculating entropy feature into Python code? I am not good at math and I am a beginner in Python.
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On geeksforgeeks in Selection sort algorithms it says that "It can cause poor branch prediction due to its high branch misprediction rate" and "Selection sort algorithm needs to iterate over the list multiple times, thus it can lead to an unbalanced branch" as disadvantage. What does it mean by branch or unbalanced branch or poor branch prediction?
I am expecting expaination for the concept of branch in any programming langauge.
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I have following algorithm I will implement in Python:
I'm not sure how to build it up and especially to deal with the minimum function. Can anyone help me?
I have self made the norm of the gradiant calculated out from matrix A and vector b by following:
r=b-A#x
r_new=np.inner(r,r)
np.sqrt(r_new)
But how do I deal with the minimum function and the setup. Can anyone help me?
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I am working on a FFT program, and I would like to get the frequency which has the largest amplitude or intensity in a sound. I checked out some codes on internet, but I couldn't find how to get the amplitude on a python program.
Check librosa.core.stft This gives magnitude of each frequency bin at given time. More details here:
https://librosa.github.io/librosa/generated/librosa.core.stft.html?highlight=stft#librosa.core.stft
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I have a question about the Levenberg-Marquardt optimize method in Python:
Generally, the Lavenberg Maquardt is used for deterministic systems. Can I use it for stochastic model to estimate unknown parameters (inputs of my model).
Thanks
The requirement for the Levenberg Marquard algorithm is that you need to be able to calculate the jacoboan (derivative with respect to your parameter).
If this is the case for your problem then yes. I guess that it is not.
Perhaps the simplex algorithm is what you are looking for.