gmm | Gaussian Mixture Models in Python | Machine Learning library

I've encountered a situation where the code generates different results in the case of having arrays defined inside the loop on index i (case #1) and in the case of declaring them outside the loop on the i index and using the clause private (case #2). Case #2 generates the same results of the code running on CPU only.

Case #1

My problem is this: I have GMM model with K multi-variate gaussians, and also I have N samples. I want to create a N*K numpy matrix, which in it's [i,k] cell there is the pdf function of the k'th gaussian on the i'th sample, i.e. in this cell there is In short, I'm intrested in the following matrix: pdf matrix

This what I have now (I'm working with python):

Q = np.array([scipy.stats.multivariate_normal(mu_t[k], cov_t[k]).pdf(X) for k in range(self.K)]).T

X in the code is a matrix whose lines are my samples.

It's works fine on small toy dataset from small dimension, but the dataset I'm working with is 10,000 28*28 pictures, and on it this line run extremely slowly...

I want to find a solution that doesn't envolve loops but only vector\matrix operation (i.e. vectorization). The scipy 'multivariate_normal' function cannot parameters of more than 1 gaussians, as far as I understand it (but it's 'pdf' function can calculates on multiple samples at once).

Does someone have an Idea?

Getting the below error on the code snippet that follows the error.

Any ideas on how to solve this?

Pretty much brand new to using Numpy - have spent most of my time using Pandas but trying to move away from using Pandas for numerous performance related issues.

End goal is to run a LEFT JOIN on the two structed arrays.

The error seems to be prompted by the ret[i] = tuple(row1[f1]) + tuple(row2[f1]) expression, but honestly not certain why i'd be getting this error.

Tested the row1 and row2 to check the number of fields vs. the f1 which contains the dtype keys, and it all seems to line up from what I can tell.

Any thoughts would be appreciated!

ERROR

So if I ran an experiment with multiple treatment groups and a control, I would analyse the results using Statsmodel ols to see if any of the treatment group were statistically different from the control group:

y ~ C(treatment_group, Treatment('Control')

I would then run results.t_test_pairwise() to find out if the coefficients of each treatment group were equal. I.e. to know whether the results of each treatment group were statistically significantly different to one another.

In the current situation, rather than just running a standard ols, I am using Statsmodel/Linearmodel's 2SLS to analyse an instrumental variable. I can run the analysis perfectly fine, and I get the results. But now I need to see whether the coefficients of the different instruments (the three different treatment groups) are the same, so I know whether the different treatment groups vary in their effect.

Code for statsmodel:

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