# Python multi-gaussian Fitting - ValueError: GMM estimation with 2 components, but got only 1 samples

I have two Gaussian Distributions that I want to fit. As the two distributions can be mixed differently I wanted the fit to be as universal as possible. I found the code below here:

Gaussian fit to a histogram data in python: Trust Region v/s Levenberg Marquardt - the first answer.

However, it does not work with my data or the original data generated in the code below and spits out the error:

ValueError: GMM estimation with 2 components, but got only 1 samples

I am hoping its something simple. My data is simply a 2D array that plots a histogram, time vs. amplitude.

import numpy as np from sklearn import mixture import matplotlib.pyplot as plt comp0 = np.random.randn(1000) - 5 # samples of the 1st component comp1 = np.random.randn(1000) + 5 # samples of the 2nd component x = np.hstack((comp0, comp1)) # merge them gmm = mixture.GMM(n_components=2) # gmm for two components gmm.fit(x) # train it! linspace = np.linspace(-10, 10, 1000) fig, ax1 = plt.subplots() ax2 = ax1.twinx() ax1.hist(x, 100) # draw samples ax2.plot(linspace, np.exp(gmm.score_samples(linspace)[0]), 'r') plt.show()

## Answers

Use:

x = np.vstack((comp0, comp1))

instead of hstack

Because each row should denote a sample, and each column - feature of samples.

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