import numpy as np import matplotlib.pyplot as plt from dppy.multivariate_jacobi_ope import MultivariateJacobiOPE # The .plot() method outputs smtg only in dimension d=1 or 2 # Number of points / dimension N, d = 200, 2 # Jacobi parameters in [-0.5, 0.5]^{d x 2} jac_params = np.array([[0.5, 0.5], [-0.3, 0.4]]) dpp = MultivariateJacobiOPE(N, jac_params) # Get an exact sample sampl = dpp.sample() # Display # the cloud of points # the base probability densities # the marginal empirical histograms dpp.plot(sample=sampl, weighted=False) plt.tight_layout() dpp.plot(sample=sampl, weighted='BH') plt.tight_layout() dpp.plot(sample=sampl, weighted='EZ') plt.tight_layout()
import numpy.random as rndm import matplotlib.pyplot as plt from dppy.multivariate_jacobi_ope import MultivariateJacobiOPE # The .plot() method outputs smtg only in dimension d=1 or 2 # Number of points / dimension N, d = 200, 2 # Jacobi parameters in [-0.5, 0.5]^{d x 2} jac_params = 0.5 - rndm.rand(d, 2) dpp = MultivariateJacobiOPE(N, jac_params) # Get an exact sample sampl = dpp.sample() # Display # the cloud of points # the base probability densities # the marginal empirical histograms dpp.plot(sample=sampl, weighted=False) plt.tight_layout() # Attach a weight 1/K(x,x) to each of the points dpp.plot(sample=sampl, weighted=True) plt.tight_layout()