def test_2D_cov():
s1 = 1.3
s2 = 1.0
alpha = 0.2
cov = convert_2D_cov(s1, s2, alpha)
assert_array_almost_equal([s1, s2, alpha], convert_2D_cov(cov))
def test_2D_cov():
s1 = 1.3
s2 = 1.0
alpha = 0.2
cov = convert_2D_cov(s1, s2, alpha)
assert_array_almost_equal([s1, s2, alpha], convert_2D_cov(cov))
#------------------------------------------------------------
# Set up the dataset
# We'll use scikit-learn's Gaussian Mixture Model to sample
# data from a mixture of Gaussians. The usual way of using
# this involves fitting the mixture to data: we'll see that
# below. Here we'll set the internal means, covariances,
# and weights by-hand.
# we'll define clusters as (mu, sigma1, sigma2, alpha, frac)
clusters = [((50, 50), 20, 20, 0, 0.1), ((40, 40), 10, 10, np.pi / 6, 0.6),
((80, 80), 5, 5, np.pi / 3, 0.2), ((60, 60), 30, 30, 0, 0.1)]
gmm_input = GMM(len(clusters), covariance_type='full')
gmm_input.means_ = np.array([c[0] for c in clusters])
gmm_input.covars_ = np.array([convert_2D_cov(*c[1:4]) for c in clusters])
gmm_input.weights_ = np.array([c[4] for c in clusters])
gmm_input.weights_ /= gmm_input.weights_.sum()
#------------------------------------------------------------
# Compute and plot the results
fig = plt.figure(figsize=(5, 5))
fig.subplots_adjust(left=0.11,
right=0.9,
bottom=0.11,
top=0.9,
hspace=0,
wspace=0)
ax_list = [fig.add_subplot(s) for s in [221, 223, 224]]
ax_list.append(fig.add_axes([0.62, 0.62, 0.28, 0.28]))
# Set up the dataset
# We'll use scikit-learn's Gaussian Mixture Model to sample
# data from a mixture of Gaussians. The usual way of using
# this involves fitting the mixture to data: we'll see that
# below. Here we'll set the internal means, covariances,
# and weights by-hand.
# we'll define clusters as (mu, sigma1, sigma2, alpha, frac)
clusters = [((50, 50), 20, 20, 0, 0.1),
((40, 40), 10, 10, np.pi / 6, 0.6),
((80, 80), 5, 5, np.pi / 3, 0.2),
((60, 60), 30, 30, 0, 0.1)]
gmm_input = GMM(len(clusters), covariance_type='full')
gmm_input.means_ = np.array([c[0] for c in clusters])
gmm_input.covars_ = np.array([convert_2D_cov(*c[1:4]) for c in clusters])
gmm_input.weights_ = np.array([c[4] for c in clusters])
gmm_input.weights_ /= gmm_input.weights_.sum()
#------------------------------------------------------------
# Compute and plot the results
fig = plt.figure(figsize=(5, 5))
fig.subplots_adjust(left=0.11, right=0.9, bottom=0.11, top=0.9,
hspace=0, wspace=0)
ax_list = [fig.add_subplot(s) for s in [221, 223, 224]]
ax_list.append(fig.add_axes([0.62, 0.62, 0.28, 0.28]))
linestyles = ['-', '--', ':']
grid = np.linspace(-5, 105, 70)
Xgrid = np.array(np.meshgrid(grid, grid))
