GMM | Gothic Mod Manager - Manage your modifications team | Game Engine library

I have encountered some problems with dockerizing my rebar3 app. As I'm trying to run the app (after building) I receive an error /prod/bin/prod: line 272: /prod/erts-11.2.2.1/bin/erl: not found

This is my rebar.config:

i get the GMM models of generation of electricity for my SPS (solar power station) through scikit-learn and search Probability Density Function (PDF, black line):

But i want get a probability function (CDF or Cumulative distribution function). In other words, i want to get a function like an example:

that can receive values on axes y in range [0, 1] and grows on all x-axis. Does scikit-learn allow it or not?

I am using the GMM-algorithm (BackgroundSubtractorMOG2) to find moving objects in a video. The output of this GMM-algorithm is a binary image with white pixels as the pixels that moved, and black pixels as background. I am looking for a way to ignore novel objects entering the field-of-view as long as they are not fully in the field-of-view. Here is an example picture I created:

Here, all the white pixels represent moving pixels. The circles are fully in the field-of-view, while the 2 triangles are just about to enter the field-of-view.The left bottom triangle has nearly completely entered the field-of-view, but is still not 100% in the field-of-view. Therefore I still want the left bottom triangle to be completely erased from the frame. Does anyone know a method to deal with this problem?

Thank you

I import IV2SLS from statsmodels.sandbox.regression.gmm,

but it failed to do heteroscedasticity robust covariance.

I'm trying to cluster a group of points in a probabilistic manner. Using below, I have a single set of xy points, which are recorded in X and Y. I want to cluster into groups using a reference point, which is displayed in X2 and Y2.

With the help of an answer the current approach is to measure the distance from the reference point and group using k-means. Although, it provides a method to cluster using the reference point, the hard cutoff and adherence to k clusters makes it somewhat unsuitable when dealing with numerous datasets. For instance, the number of clusters needed for this example is probably 3. But a separate example may different. I'd have to manually go through and alter k every time.

Given the non-probabilistic nature of k-means a separate option could be GMM. Is it possible to account for the reference point when modelling? If I attach the output below the underlying model isn't clustering as I'm hoping for.

If I look at the probability each point is within a group it's not clustered as I'd hoped. With this I run into the same problem with manually altering the amount of components. Because the points are distributed randomly, using “AIC” or “BIC” to select the appropriate number of clusters doesn't work. There is no optimal number.

I'm running Cygwin on Windows 7 and trying to build a program I downloaded. I cd to where I have my file.tar.gz and type

tar -xvf file.tar.gz

and Cygwin successfully spits out a list of what's in there. (point of confusion: for some reason, -xvzf doesn't work, even though the file claims to be zipped. Also, I expected there to be an untarred folder put somewhere in my directory, but there's not.)

Then I type

make

and get

c++ -O -c gmm.c -o gmm.o

make: c++: No such file or directory

make: *** [makefile:19: gmm.o] Error 127

I expected this to create a gmm.exe (according to the documentation of this program). What's going on?

I actually want to estimate a normalizing flow with a mixture of gaussians as the base distribution, so I'm sort of stuck with torch. However you can reproduce my error in my code by just estimating a mixture of Gaussian model in torch. My code is below:

I am trying to fit a GMM in sklearn and i see that the model converges at around epoch 3 but i cannot seems to access the log-likelihood score computed at each epoch.

I have implemented EM algorithm for GMM using this post GMMs and Maximum Likelihood Optimization Using NumPy unsuccessfully as follows: