CODEWITHSUNDEEP
pythonscikit-learn
komodovaran_
komodovaran_

Reputation: 2012

Sklearn Gaussian Mixture lock parameters?

I'm trying to fit some gaussians, of which I already have a pretty good idea about the initial parameters (in this case, I'm generating the distributions, so I should always be able to fit these). However, I can't seem to figure out how to force the mean to be e.g. 0 for both gaussians. Is it possible? m.means_ = ... doesn't work.

from sklearn import mixture
import numpy as np
import math
import matplotlib.pyplot as plt
from scipy import stats

a = np.random.normal(0, 0.2, 500)
b = np.random.normal(0, 2, 800)

obs = np.concatenate([a,b]).reshape(-1,1)
plt.hist(obs, bins = 100, normed = True, color = "lightgrey")

min_range = -8
max_range = 8

n_gaussians = 2

m = mixture.GaussianMixture(n_components = n_gaussians)
m.fit(obs)

# # Get the gaussian parameters
weights = m.weights_
means = m.means_
covars = m.covariances_

# Plot all gaussians

n_gaussians = 2

gaussian_sum = []
for i in range(n_gaussians):
    mean = means[i]
    sigma = math.sqrt(covars[i])

    plotpoints = np.linspace(min_range,max_range, 1000)

    gaussian_points = weights[i] * stats.norm.pdf(plotpoints, mean, sigma)
    gaussian_points = np.array(gaussian_points)

    gaussian_sum.append(gaussian_points)

    plt.plot(plotpoints,
             weights[i] * stats.norm.pdf(plotpoints, mean, sigma))

sum_gaussian = np.sum(gaussian_sum, axis=0)
plt.plot(plotpoints, sum_gaussian, color = "black", linestyle = "--")
plt.xlim(min_range, max_range)

plt.show()

Upvotes: 2

Views: 1251

Answers (2)

komodovaran_
komodovaran_

Reputation: 2012

So what I was actually after was known priors, which means that it should actually be fitted with BayesianGaussianMixture, which allows one to set a mean_prior and a mean_prior_precision

Fitting with

m = mixture.BayesianGaussianMixture(n_components = n_gaussians, mean_prior = np.array([0]), mean_precision_prior = np.array([1]))

One can force it to work out even this: enter image description here

Upvotes: 1

sascha
sascha

Reputation: 33532

(Assuming you don't want to force, but give an initial-guess. The fixed-case probably needs to touch the whole code and it's highly questionable if the whole EM-approach is of use then. It probably collapes into some optimization problem approachable by scipy's optimize module.)

Just follow the docs. It's supported at time of GaussianMixture-creation.

weights_init : array-like, shape (n_components, ), optional

The user-provided initial weights, defaults to None. If it None, weights are initialized using the init_params method.

means_init : array-like, shape (n_components, n_features), optional

The user-provided initial means, defaults to None, If it None, means are initialized using the init_params method.

Upvotes: 1

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