def test_HawkesKernelPowerLaw_pickle(self):
"""...Test pickling ability of HawkesKernelPowerLaw
"""
obj = HawkesKernelPowerLaw(0.1, 0.01, 1.2)
pickled = pickle.loads(pickle.dumps(obj))
self.assertTrue(str(obj) == str(pickled))
np.testing.assert_array_equal(obj.get_values(self.random_times),
obj.get_values(self.random_times))
def setUp(self):
self.decay = 2
self.intensity = 3
self.hawkes_kernel_exp = HawkesKernelExp(self.intensity, self.decay)
self.multiplier = 0.1
self.cutoff = 0.01
self.exponent = 1.2
self.support = 10000
self.hawkes_kernel_power_law = HawkesKernelPowerLaw(
self.multiplier, self.cutoff, self.exponent, self.support)
def setUp(self):
np.random.seed(28374)
self.kernels = np.array([[HawkesKernel0(),
HawkesKernelExp(0.1, 3)],
[
HawkesKernelPowerLaw(0.2, 4, 2),
HawkesKernelSumExp([0.1, 0.4], [3, 4])
]])
self.baseline = np.random.rand(2)
def setUp(self):
np.random.seed(28374)
self.kernels = np.array([
[HawkesKernel0(), HawkesKernelExp(0.1, 3)],
[HawkesKernelPowerLaw(0.2, 4, 2),
HawkesKernelSumExp([0.1, 0.4], [3, 4])]
])
t_values = np.linspace(0, 10, 10)
y_values = np.maximum(0.5 + np.sin(t_values), 0)
self.time_func_kernel = HawkesKernelTimeFunc(t_values=t_values,
y_values=y_values)
self.baseline = np.random.rand(2)
def test_HawkesKernelPowerLaw_repr(self):
"""...Test HawkesKernelPowerLaw string in list representation
"""
multiplier = 0.1
cutoff = 0.01
exponent = 1.2
hawkes_kernel_power_law = HawkesKernelPowerLaw(multiplier, cutoff,
exponent)
self.assertEqual(str([hawkes_kernel_power_law]),
"[0.1*(0.01+t)^(-1.2)]")
multiplier = 0
cutoff = 0.01
exponent = 1.2
hawkes_kernel_power_law = HawkesKernelPowerLaw(multiplier, cutoff,
exponent)
self.assertEqual(str([hawkes_kernel_power_law]), "[0]")
multiplier = 0.1
cutoff = 0.01
exponent = 0
hawkes_kernel_power_law = HawkesKernelPowerLaw(multiplier, cutoff,
exponent)
self.assertEqual(str([hawkes_kernel_power_law]), "[0.1]")
def test_HawkesKernelPowerLaw_str(self):
"""...Test HawkesKernelPowerLaw string representation
"""
multiplier = 0.1
cutoff = 0.01
exponent = 1.2
hawkes_kernel_power_law = HawkesKernelPowerLaw(multiplier, cutoff,
exponent)
self.assertEqual(str(hawkes_kernel_power_law),
"0.1 * (0.01 + t)^(-1.2)")
multiplier = 0
cutoff = 0.01
exponent = 1.2
hawkes_kernel_power_law = HawkesKernelPowerLaw(multiplier, cutoff,
exponent)
self.assertEqual(str(hawkes_kernel_power_law), "0")
multiplier = 0.1
cutoff = 0.01
exponent = 0
hawkes_kernel_power_law = HawkesKernelPowerLaw(multiplier, cutoff,
exponent)
self.assertEqual(str(hawkes_kernel_power_law), "0.1")
class Test(unittest.TestCase):
def setUp(self):
self.decay = 2
self.intensity = 3
self.hawkes_kernel_exp = HawkesKernelExp(self.intensity, self.decay)
self.multiplier = 0.1
self.cutoff = 0.01
self.exponent = 1.2
self.support = 10000
self.hawkes_kernel_power_law = HawkesKernelPowerLaw(
self.multiplier, self.cutoff, self.exponent, self.support)
def test_is_zero(self):
"""...Test is_zero method of HawkesKernel"""
self.assertFalse(self.hawkes_kernel_exp.is_zero())
def test_get_support(self):
"""...Test get_support method of HawkesKernel"""
self.assertEqual(self.hawkes_kernel_power_law.get_support(),
self.support)
def test_get_plot_support(self):
"""...Test get_plot_support method of HawkesKernel"""
self.assertEqual(self.hawkes_kernel_exp.get_plot_support(),
self.intensity / self.decay)
def test_get_value(self):
"""...Test get_value method of HawkesKernel"""
self.assertEqual(self.hawkes_kernel_exp.get_value(3),
0.014872513059998151)
def test_get_values(self):
"""...Test get_values method of HawkesKernel"""
t_values = np.arange(5, dtype=float)
np.testing.assert_array_almost_equal(
self.hawkes_kernel_exp.get_values(t_values),
[6, 8.120117e-01, 1.098938e-01, 1.487251e-02, 2.012776e-03])
def test_get_norm(self):
"""...Test get_norm method of HawkesKernel"""
self.assertEqual(self.hawkes_kernel_exp.get_norm(), self.intensity)
import numpy as np
import matplotlib.pyplot as plt
from tick.hawkes import HawkesKernel0, HawkesKernelExp, HawkesKernelPowerLaw, \
HawkesKernelTimeFunc
kernel_0 = HawkesKernel0()
kernel_exp = HawkesKernelExp(.7, 1.3)
kernel_pl = HawkesKernelPowerLaw(.1, .2, 0.7)
t_values = np.array([0, 1, 1.5, 1.8, 2.7])
y_values = np.array([0, .6, .34, .2, .1])
kernel_tf = HawkesKernelTimeFunc(t_values=t_values, y_values=y_values)
kernels = [[kernel_0, kernel_exp], [kernel_pl, kernel_tf]]
fig, ax = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(10, 4))
t_values = np.linspace(0, 3, 100)
for i in range(2):
for j in range(2):
ax[i, j].plot(t_values, kernels[i][j].get_values(t_values),
label=kernels[i][j])
ax[i, j].legend()
plt.show()
"""
import numpy as np
import matplotlib.pyplot as plt
from tick.hawkes import SimuHawkes, HawkesKernelPowerLaw, HawkesConditionalLaw
from tick.plot import plot_hawkes_kernels
multiplier = np.array([0.012, 0.008, 0.004, 0.005])
cutoff = 0.0005
exponent = 1.3
support = 2000
hawkes = SimuHawkes(kernels=[[
HawkesKernelPowerLaw(multiplier[0], cutoff, exponent, support),
HawkesKernelPowerLaw(multiplier[1], cutoff, exponent, support)
],
[
HawkesKernelPowerLaw(multiplier[2], cutoff,
exponent, support),
HawkesKernelPowerLaw(multiplier[3], cutoff,
exponent, support)
]],
baseline=[0.05, 0.05],
seed=382,
verbose=False)
hawkes.end_time = 50000
hawkes.simulate()
e = HawkesConditionalLaw(claw_method="log",
from tick.hawkes import (SimuHawkes, SimuHawkesMulti, HawkesKernelExp,
HawkesKernelTimeFunc, HawkesKernelPowerLaw,
HawkesKernel0, HawkesSumGaussians)
end_time = 1000
n_nodes = 2
n_realizations = 10
n_gaussians = 5
timestamps_list = []
kernel_timefunction = HawkesKernelTimeFunc(
t_values=np.array([0., .7, 2.5, 3., 4.]),
y_values=np.array([.3, .03, .03, .2, 0.]))
kernels = [[HawkesKernelExp(.2, 2.),
HawkesKernelPowerLaw(.2, .5, 1.3)],
[HawkesKernel0(), kernel_timefunction]]
hawkes = SimuHawkes(baseline=[.5, .2],
kernels=kernels,
end_time=end_time,
verbose=False,
seed=1039)
multi = SimuHawkesMulti(hawkes, n_simulations=n_realizations)
multi.simulate()
learner = HawkesSumGaussians(n_gaussians, max_iter=10)
learner.fit(multi.timestamps)
def test_HawkesKernelPowerLaw_strtex(self):
"""...Test HawkesKernelPowerLaw latex string representation
"""
multiplier = 0.1
cutoff = 0.01
exponent = 1.2
hawkes_kernel_power_law = HawkesKernelPowerLaw(multiplier, cutoff,
exponent)
self.assertEqual(hawkes_kernel_power_law.__strtex__(),
"$0.1 (0.01+t)^{-1.2}$")
multiplier = 0
cutoff = 0.01
exponent = 1.2
hawkes_kernel_power_law = HawkesKernelPowerLaw(multiplier, cutoff,
exponent)
self.assertEqual(hawkes_kernel_power_law.__strtex__(), "$0$")
multiplier = 0.1
cutoff = 0.01
exponent = 0
hawkes_kernel_power_law = HawkesKernelPowerLaw(multiplier, cutoff,
exponent)
self.assertEqual(hawkes_kernel_power_law.__strtex__(), "$0.1$")
multiplier = 1
cutoff = 0.01
exponent = 1.2
hawkes_kernel_power_law = HawkesKernelPowerLaw(multiplier, cutoff,
exponent)
self.assertEqual(hawkes_kernel_power_law.__strtex__(),
"$(0.01+t)^{-1.2}$")
def setUp(self):
self.multiplier = 0.1
self.cutoff = 0.01
self.exponent = 1.2
self.hawkes_kernel_power_law = HawkesKernelPowerLaw(
self.multiplier, self.cutoff, self.exponent)