gwgundersen · April 26, 2020 16:44
diff --git a/viz_mvn.py b/viz_mvn.py
 # Because I always forget how to do this.
 #
 # Credit: https://scipython.com/blog/visualizing-the-bivariate-gaussian-distribution/

 import numpy as np
 import matplotlib.pyplot as plt
 from   matplotlib import cm
 from   mpl_toolkits.mplot3d import Axes3D
 from   scipy.stats import multivariate_normal

 # Our 2-dimensional distribution will be over variables X and Y
 N = 60
 X = np.linspace(-3, 3, N)
 Y = np.linspace(-3, 4, N)
 X, Y = np.meshgrid(X, Y)

 # Mean vector and covariance matrix
 mu = np.array([0., 1.])
 Sigma = np.array([[ 1. , -0.5], [-0.5,  1.5]])

 # Pack X and Y into a single 3-dimensional array
 pos = np.empty(X.shape + (2,))
 pos[:, :, 0] = X
 pos[:, :, 1] = Y

 # The distribution on the variables X, Y packed into pos.
 F = multivariate_normal(mu, Sigma)
 Z = F.pdf(pos)

 # Create a surface plot and projected filled contour plot under it.
 fig = plt.figure()
 ax = fig.gca(projection='3d')
 ax.plot_surface(X, Y, Z, rstride=3, cstride=3, linewidth=1, antialiased=True,
                cmap=cm.viridis)

 cset = ax.contourf(X, Y, Z, zdir='z', offset=-0.15, cmap=cm.viridis)

 # Adjust the limits, ticks and view angle
 ax.set_zlim(-0.15,0.2)
 ax.set_zticks(np.linspace(0,0.2,5))
 ax.view_init(27, -21)

 plt.show()
	# Because I always forget how to do this.
	#
	# Credit: https://scipython.com/blog/visualizing-the-bivariate-gaussian-distribution/

	import numpy as np
	import matplotlib.pyplot as plt
	from matplotlib import cm
	from mpl_toolkits.mplot3d import Axes3D
	from scipy.stats import multivariate_normal

	# Our 2-dimensional distribution will be over variables X and Y
	N = 60
	X = np.linspace(-3, 3, N)
	Y = np.linspace(-3, 4, N)
	X, Y = np.meshgrid(X, Y)

	# Mean vector and covariance matrix
	mu = np.array([0., 1.])
	Sigma = np.array([[ 1. , -0.5], [-0.5, 1.5]])

	# Pack X and Y into a single 3-dimensional array
	pos = np.empty(X.shape + (2,))
	pos[:, :, 0] = X
	pos[:, :, 1] = Y

	# The distribution on the variables X, Y packed into pos.
	F = multivariate_normal(mu, Sigma)
	Z = F.pdf(pos)

	# Create a surface plot and projected filled contour plot under it.
	fig = plt.figure()
	ax = fig.gca(projection='3d')
	ax.plot_surface(X, Y, Z, rstride=3, cstride=3, linewidth=1, antialiased=True,
	cmap=cm.viridis)

	cset = ax.contourf(X, Y, Z, zdir='z', offset=-0.15, cmap=cm.viridis)

	# Adjust the limits, ticks and view angle
	ax.set_zlim(-0.15,0.2)
	ax.set_zticks(np.linspace(0,0.2,5))
	ax.view_init(27, -21)

	plt.show()