GMM_scratch.py

model = GMM(3, n_runs = 30)

fitted_values = model.fit(Y_sklearn)
predicted_values = model.predict(Y_sklearn)

# # compute centers as point of highest density of distribution
centers = np.zeros((3,2))
for i in range(model.C):
    density = mvn(cov=model.sigma[i], mean=model.mu[i]).logpdf(Y_sklearn)
    centers[i, :] = Y_sklearn[np.argmax(density)]
    
plt.figure(figsize = (10,8))
plt.scatter(Y_sklearn[:, 0], Y_sklearn[:, 1],c=predicted_values ,s=50, cmap='viridis', zorder=1)

plt.scatter(centers[:, 0], centers[:, 1],c='black', s=300, alpha=0.5, zorder=2);

w_factor = 0.2 / model.pi.max()
for pos, covar, w in zip(model.mu, model.sigma, model.pi):
    draw_ellipse(pos, covar, alpha = w)