运行高斯混合模型时出现以下错误：
ValueError: Fitting the mixture model failed because some components have ill-defined empirical covariance (for instance caused by singleton or collapsed samples). Try to decrease the number of components, or increase reg_covar.
我使用的矩阵具有相对较大的形状，因此很难在此页面上显示，但是，这里是一个概述
[[ 6.10086000e+05 1.58787000e+05 0.00000000e+00 ..., 8.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 2.36273000e+05 1.48953000e+05 0.00000000e+00 ..., 5.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 1.70486000e+05 1.53083000e+05 0.00000000e+00 ..., 3.50000000e+01
0.00000000e+00 0.00000000e+00]
...,
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 2.00000000e+01
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 2.00000000e+00
0.00000000e+00 1.00000000e+00]]
这是运行GMM的代码段：
def perform_gaussian_mixture():
colors = ["navy", "cyan", "darkorange", "orchid", "lime"]
def make_ellipses(gmm, ax):
for n, color in enumerate(colors):
if gmm.covariance_type == 'full':
covariances = gmm.covariances_[n][:2, :2]
elif gmm.covariance_type == 'tied':
covariances = gmm.covariances_[:2, :2]
elif gmm.covariance_type == 'diag':
covariances = np.diag(gmm.covariances_[n][:2])
elif gmm.covariance_type == 'spherical':
covariances = np.eye(gmm.means_.shape[1]) * gmm.covariances_[n]
v, w = np.linalg.eigh(covariances)
u = w[0] / np.linalg.norm(w[0])
angle = np.arctan2(u[1], u[0])
angle = 180 * angle / np.pi # convert to degrees
v = 2. * np.sqrt(2.) * np.sqrt(v)
ell = mpl.patches.Ellipse(gmm.means_[n, :2], v[0], v[1],
180 + angle, color=color)
ell.set_clip_box(ax.bbox)
ell.set_alpha(0.5)
ax.add_artist(ell)
data = matricize()
x_train, x_test, y_train, y_test = train_test_split(data["data"], data["target"], test_size=0.25, random_state=42)
n_classes = len(np.unique(data["target"]))
# Try GMMs using different types of covariances.
estimators = dict((cov_type, mixture.GaussianMixture(n_components=n_classes,
covariance_type=cov_type, max_iter=20, random_state=0))
for cov_type in ["full"])
n_estimators = len(estimators)
plt.figure(figsize=(3 * n_estimators // 2, 6))
plt.subplots_adjust(bottom=.01, top=0.95, hspace=.15, wspace=.05,
left=.01, right=.99)
for index, (name, estimator) in enumerate(estimators.items()):
# Since we have class labels for the training data, we can
# initialize the GMM parameters in a supervised manner.
estimator.means_init = np.array([x_train[np.where(y_train == i)].mean(axis=0)
for i in range(n_classes)])
# Train the other parameters using the EM algorithm.
estimator.fit(x_train)
h = plt.subplot(2, n_estimators // 2, index + 1)
make_ellipses(estimator, h)
for n, color in enumerate(colors):
d = data["data"][np.where(data["target"] == n)]
plt.scatter(d[:, 0], d[:, 1], s=0.8, color=color,
label=data["target_names"][n])
# Plot the test data with crosses
for n, color in enumerate(colors):
d = x_test[np.where(y_test == n)]
plt.scatter(d[:, 0], d[:, 1], marker='x', color=color)
y_train_pred = estimator.predict(x_train)
train_accuracy = np.mean(y_train_pred.ravel() == y_train.ravel()) * 100
plt.text(0.05, 0.9, 'Train accuracy: %.1f' % train_accuracy,
transform=h.transAxes)
y_test_pred = estimator.predict(x_test)
test_accuracy = np.mean(y_test_pred.ravel() == y_test.ravel()) * 100
plt.text(0.05, 0.8, 'Test accuracy: %.1f' % test_accuracy,
transform=h.transAxes)
plt.xticks(())
plt.yticks(())
plt.title(name)
plt.legend(scatterpoints=1, loc='lower right', prop=dict(size=12))
plt.show()
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