gmm.py

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from sklearn.mixture import GaussianMixture
import scipy

data = np.load("./data/traj.npy")

dim,_ = data.shape

n_bins = 100
plt.figure()
n, bins, patches = plt.hist(data, bins=n_bins, density=True, histtype='bar', facecolor='g')
plt.title('Histogram')
plt.xlabel('Value')
plt.ylabel('Frequency')

num_comp = 4
gmm = GaussianMixture(n_components = num_comp)
gmm.fit(data)
means = gmm.means_
covar = gmm.covariances_
probs = gmm.predict_proba(data)

for i in range(num_comp):
    num = sum(probs[:,i])
    mu = means[i]
    sigma = np.sqrt(covar[i])
    y = scipy.stats.norm.pdf(bins, mu, sigma)
    l = plt.plot(bins, np.transpose(y)*num/dim, 'r--', linewidth=1)


plt.savefig("histogram"+str(num_comp)+".pdf")