def test_gaussian_kde():
pdf = MoG(a=[0.3, 0.7], ms=[[-1.0, 0.0], [1.0, 0.0]], Ss=[np.diag([0.1, 1.1]), np.diag([1.1, 0.1])])
xs = pdf.gen(5000)
kde = gaussian_kde(xs)
import util.plot
lims = [-4.0, 4.0]
util.plot.plot_pdf_marginals(pdf, lims=lims).suptitle('original')
util.plot.plot_hist_marginals(xs, lims=lims).suptitle('samples')
util.plot.plot_pdf_marginals(kde, lims=lims).suptitle('KDE')
util.plot.plt.show()
def compute_stat(self, img_array, data_type, file_type, k):
img_array = np.array(img_array)
pca = PCA(n_components=self.reduced_dimensions,
svd_solver='randomized').fit(img_array)
pca_array = pca.transform(img_array)
if data_type == 'positive':
model = MoG(pca_array, k)
model.exp_max(tolerance=1e-18)
for i in range(0, k):
mu = pca.inverse_transform(model.mu[i])
if (file_type == "rgb"):
mu = np.round(mu.reshape((100, 100, 3))).astype(np.uint8)
elif file_type == "hsv":
mu = np.round(mu.reshape((100, 100, 3))).astype(np.uint8)
mu = cv2.cvtColor(mu, cv2.COLOR_HSV2BGR)
else:
mu = np.round(mu.reshape((100, 100, 1))).astype(np.uint8)
# print(np.max(mu))
print(model.theta[i])
cv2.imshow('mean', mu)
cv2.waitKey(0)
cv2.destroyAllWindows()
# print(self.output_image_dir+file_type+"+"_mog_"+repr(i)
cv2.imwrite(
self.output_image_dir + "/" + file_type + "_mog_" +
repr(i + 1) + ".jpg", mu)
np.save(self.output_numpy_dir + "/" + file_type + "_mu", model.mu)
np.save(self.output_numpy_dir + "/" + file_type + "_cov",
model.cov)
np.save(self.output_numpy_dir + "/" + file_type + "_theta",
model.theta)
else:
mu = np.mean(img_array, axis=0)
if (file_type == "rgb"):
mu = np.round(mu.reshape((100, 100, 3))).astype(np.uint8)
elif file_type == "hsv":
mu = np.round(mu.reshape((100, 100, 3))).astype(np.uint8)
mu = cv2.cvtColor(mu, cv2.COLOR_HSV2BGR)
else:
mu = np.round(mu.reshape((100, 100, 1))).astype(np.uint8)
cv2.imwrite(
self.output_image_dir + "/" + file_type + "_mog_neg.jpg", mu)
pca_mu = np.mean(pca_array, axis=0)
cov = np.cov(pca_array.T)
np.save(self.output_numpy_dir + "/" + file_type + "_mu_neg",
pca_mu)
np.save(self.output_numpy_dir + "/" + file_type + "_cov_neg", cov)
def gaussian_kde(xs, ws=None, std=None):
"""
Returns a mixture of gaussians representing a kernel density estimate.
:param xs: rows are datapoints
:param ws: weights, optional
:param std: the std of the kernel, if None then a default is used
:return: a MoG object
"""
xs = np.array(xs)
assert xs.ndim == 2, 'wrong shape'
n_data, n_dims = xs.shape
ws = np.full(n_data, 1.0 / n_data) if ws is None else np.asarray(ws)
var = n_data ** (-2.0 / (n_dims + 4)) if std is None else std ** 2
return MoG(a=ws, ms=xs, Ss=[var * np.eye(n_dims) for _ in xrange(n_data)])
