def test_simu_hawkes_multi_time_func(self):
"""...Test that hawkes multi works correctly with HawkesKernelTimeFunc
"""
run_time = 100
t_values1 = np.array([0, 1, 1.5], dtype=float)
y_values1 = np.array([0, .2, 0], dtype=float)
tf1 = TimeFunction([t_values1, y_values1],
inter_mode=TimeFunction.InterConstRight, dt=0.1)
kernel1 = HawkesKernelTimeFunc(tf1)
t_values2 = np.array([0, 2, 2.5], dtype=float)
y_values2 = np.array([0, .6, 0], dtype=float)
tf2 = TimeFunction([t_values2, y_values2],
inter_mode=TimeFunction.InterConstRight, dt=0.1)
kernel2 = HawkesKernelTimeFunc(tf2)
baseline = np.array([0.1, 0.3])
hawkes = SimuHawkes(baseline=baseline, end_time=run_time,
verbose=False, seed=2334)
hawkes.set_kernel(0, 0, kernel1)
hawkes.set_kernel(0, 1, kernel1)
hawkes.set_kernel(1, 0, kernel2)
hawkes.set_kernel(1, 1, kernel2)
hawkes_multi = SimuHawkesMulti(hawkes, n_simulations=5, n_threads=4)
hawkes_multi.simulate()
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_simu_hawkes_no_seed(self):
"""...Test hawkes multi can be simulated even if no seed is given
"""
T1 = np.array([0, 2, 2.5], dtype=float)
Y1 = np.array([0, .6, 0], dtype=float)
tf = TimeFunction([T1, Y1], inter_mode=TimeFunction.InterConstRight,
dt=0.1)
kernel = HawkesKernelTimeFunc(tf)
hawkes = SimuHawkes(baseline=[.1], end_time=100, verbose=False)
hawkes.set_kernel(0, 0, kernel)
multi_hawkes_1 = SimuHawkesMulti(hawkes, n_simulations=5)
multi_hawkes_1.simulate()
multi_hawkes_2 = SimuHawkesMulti(hawkes, n_simulations=5)
multi_hawkes_2.simulate()
# If no seed are given, realizations must be different
self.assertNotEqual(multi_hawkes_1.timestamps[0][0][0],
multi_hawkes_2.timestamps[0][0][0])
import numpy as np
import matplotlib.pyplot as plt
from tick.inference import HawkesEM
from tick.simulation import SimuHawkes, HawkesKernelTimeFunc, HawkesKernelExp
from tick.base import TimeFunction
from tick.plot import plot_hawkes_kernels
run_time = 30000
t_values1 = np.array([0, 1, 1.5, 2., 3.5], dtype=float)
y_values1 = np.array([0, 0.2, 0, 0.1, 0.], dtype=float)
tf1 = TimeFunction([t_values1, y_values1],
inter_mode=TimeFunction.InterConstRight,
dt=0.1)
kernel1 = HawkesKernelTimeFunc(tf1)
t_values2 = np.linspace(0, 4, 20)
y_values2 = np.maximum(0., np.sin(t_values2) / 4)
tf2 = TimeFunction([t_values2, y_values2])
kernel2 = HawkesKernelTimeFunc(tf2)
baseline = np.array([0.1, 0.3])
hawkes = SimuHawkes(baseline=baseline,
end_time=run_time,
verbose=False,
seed=2334)
hawkes.set_kernel(0, 0, kernel1)
hawkes.set_kernel(0, 1, HawkesKernelExp(.5, .7))
import numpy as np
import matplotlib.pyplot as plt
from tick.simulation 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()
from tick.plot import plot_hawkes_kernels
from tick.simulation import (SimuHawkes, SimuHawkesMulti, HawkesKernelExp,
HawkesKernelTimeFunc, HawkesKernelPowerLaw,
HawkesKernel0)
from tick.inference import 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()
Simulation of Hawkes processes with usage of custom kernels
"""
import matplotlib.pyplot as plt
import numpy as np
from tick.base import TimeFunction
from tick.simulation import SimuHawkes, HawkesKernelExp, HawkesKernelTimeFunc
from tick.plot import plot_point_process
t_values = np.array([0, 1, 1.5], dtype=float)
y_values = np.array([0, .2, 0], dtype=float)
tf1 = TimeFunction([t_values, y_values],
inter_mode=TimeFunction.InterConstRight,
dt=0.1)
kernel_1 = HawkesKernelTimeFunc(tf1)
t_values = np.array([0, .1, 2], dtype=float)
y_values = np.array([0, .4, -0.2], dtype=float)
tf2 = TimeFunction([t_values, y_values],
inter_mode=TimeFunction.InterLinear,
dt=0.1)
kernel_2 = HawkesKernelTimeFunc(tf2)
hawkes = SimuHawkes(kernels=[[kernel_1, kernel_1],
[HawkesKernelExp(.07, 4), kernel_2]],
baseline=[1.5, 1.5],
verbose=False,
seed=23983)
run_time = 40