def test_pretrain_gaussian_mixture_diffusion():
'''
Test Spatio-Temporal Point Process Generator equipped with
pretrained Gaussian mixture diffusion kernel
'''
params = np.load('data/mle_gaussian_mixture_params.npz')
mu = params['mu']
beta = params['beta']
kernel = GaussianMixtureDiffusionKernel(
n_comp=5, layers=[5], C=1., beta=beta,
SIGMA_SHIFT=.05, SIGMA_SCALE=.2, MU_SCALE=.01,
Wss=params['Wss'], bss=params['bss'], Wphis=params['Wphis'])
lam = HawkesLam(mu, kernel, maximum=1e+3)
pp = SpatialTemporalPointProcess(lam)
# # generate points
# points, sizes = pp.generate(
# T=[0., 10.], S=[[-1., 1.], [-1., 1.]],
# batch_size=2, verbose=True)
# print(points.shape)
# print(sizes)
# read or save to local npy file.
points = np.load('data/apd.robbery.permonth.npy')
da = DataAdapter(init_data=points)
points = da.normalize(points)
# np.save('results/gaussian_hpp_Mar_15_layer_5.npy', points)
# plot intensity of the process over the time
plot_spatial_intensity(lam, points[0], S=[[0., 10.], [-1., 1.], [-1., 1.]],
t_slots=1000, grid_size=50, interval=50)
def test_random_gaussian_mixture_diffusion():
'''
Test Spatio-Temporal Point Process Generator equipped with
random Gaussian mixture diffusion kernel
'''
mu = .2
kernel = GaussianMixtureDiffusionKernel(
n_comp=5, layers=[5, 5], C=1., beta=1.,
SIGMA_SHIFT=.2, SIGMA_SCALE=.05, MU_SCALE=.05)
lam = HawkesLam(mu, kernel, maximum=1e+3)
pp = SpatialTemporalPointProcess(lam)
# generate points
points, sizes = pp.generate(
T=[0., 10.], S=[[-1., 1.], [-1., 1.]],
batch_size=2, verbose=True)
print(points.shape)
print(sizes)
# read or save to local npy file.
# points = np.load('results/free_hpp_Mar_15_layer_5.npy')
# np.save('results/gaussian_hpp_Mar_15_layer_5.npy', points)
# plot intensity of the process over the time
plot_spatial_intensity(lam, points[0], S=[[0., 10.], [-1., 1.], [-1., 1.]],
t_slots=1000, grid_size=50, interval=50)
def test_std_diffusion():
'''
Test Spatio-Temporal Point Process Generator equipped with
standard diffusion kernel
'''
# parameters initialization
mu = .1
kernel = StdDiffusionKernel(C=1., beta=1., sigma_x=.1, sigma_y=.1)
lam = HawkesLam(mu, kernel, maximum=1e+3)
pp = SpatialTemporalPointProcess(lam)
# generate points
points, sizes = pp.generate(
T=[0., 10.], S=[[-1., 1.], [-1., 1.]],
batch_size=100, verbose=True)
print(points)
print(sizes)
# read or save to local npy file.
# points = np.load('results/tf_thining_samples.npy')
np.save('results/hpp_Feb_25.npy', points)
# plot intensity of the process over the time
plot_spatial_intensity(lam, points[0], S=[[0., 10.], [-1., 1.], [-1., 1.]],
t_slots=1000, grid_size=50, interval=50)
def test_gaussian_diffusion():
'''
Test Spatio-Temporal Point Process Generator equipped with
Gaussian diffusion kernel
'''
mu = .1
kernel = GaussianDiffusionKernel(
layers=[5, 5], C=1., beta=1.,
SIGMA_SHIFT=.2, SIGMA_SCALE=.05, MU_SCALE=.1, is_centered=True)
lam = HawkesLam(mu, kernel, maximum=1e+3)
pp = SpatialTemporalPointProcess(lam)
print(kernel.Ws)
print(kernel.bs)
# plot kernel parameters over the spatial region.
plot_spatial_kernel("results/kernel.pdf", kernel, S=[[-1., 1.], [-1., 1.]], grid_size=50)
# generate points
points, sizes = pp.generate(
T=[0., 10.], S=[[-1., 1.], [-1., 1.]],
batch_size=2, verbose=True)
print(points)
print(sizes)
# read or save to local npy file.
# points = np.load('results/free_hpp_Mar_15_layer_5.npy')
# np.save('results/gaussian_hpp_Mar_15_layer_5.npy', points)
# plot intensity of the process over the time
plot_spatial_intensity(lam, points[0], S=[[0., 10.], [-1., 1.], [-1., 1.]],
t_slots=1000, grid_size=50, interval=50)