def deleter(Hawkes):
k = 1
Hawkes.propogate_by_amount(10)
while (Hawkes.density(Hawkes.Events[-1]) < 100) and (k < 100):
Hawkes.propogate_by_amount(10)
k += 1
return k
def deleter(Hawkes, alpha, th):
k = 1
if (alpha*Hawkes.param[0]/Hawkes.param[1] < 1) and (not Hawkes.Nonlin(1) == np.exp(1)):
Hawkes.propogate_by_amount(10)
while Hawkes.Events[-1] < 500:
Hawkes.propogate_by_amount(1)
else:
Hawkes.propogate_by_amount(10)
while (Hawkes.density(Hawkes.Events[-1]) < th) and (k < 100):
Hawkes.propogate_by_amount(10)
k += 1
return k
def deleter(Hawkes, alpha, th):
k = 1
if alpha * Hawkes.param[0] * Hawkes.param[1] / 2 < 1:
Hawkes.propogate_by_amount(10)
while Hawkes.Events[-1] < 500:
Hawkes.propogate_by_amount(1)
else:
Hawkes.propogate_by_amount(10)
while (Hawkes.density(Hawkes.Events[-1]) < th) and (k < 1000):
Hawkes.propogate_by_amount(10)
k += 1
return k
def DataGenerator(inp):
param = inp
alpha = 1
th = 50
expl_proc = []
count_1 = 0
def deleter(Hawkes, alpha, th):
k = 1
if (alpha * Hawkes.param[0] / Hawkes.param[1] <
1) and (not Hawkes.Nonlin(1) == np.exp(1)):
Hawkes.propogate_by_amount(10)
while Hawkes.Events[-1] < 100:
Hawkes.propogate_by_amount(1)
else:
Hawkes.propogate_by_amount(10)
while (Hawkes.density(Hawkes.Events[-1]) < th) and (k < 100):
Hawkes.propogate_by_amount(10)
k += 1
return k
def HawkesIntensity_temporal(time, params):
Ind = (time >= 0)
return params[0] * np.exp(-params[1] * time) * Ind
for i in range(100):
print('Für Parameter ({},{}) count_nr: {}'.format(
param[0], param[1], count_1))
H = Hawkes(HawkesIntensity_temporal,
param,
phi=lambda s: (1 + s)**2 / (2 + s),
mon_kernel=True)
deleter(H, alpha, th)
if (H.density(H.Events[-1]) >=
th): #or (alpha*H.param[0]/H.param[1] < 1):
vars()['H_' + str(count_1)] = H
expl_proc.append('H_' + str(count_1))
count_1 += 1
return count_1
return params[0]*np.exp(-params[1]*time)*Ind
# alpha gives boundry of stability given by theorems by bremaud & masoulie
param = [1/4,5]
alpha = 1/2
th = 100
expl_proc = []
count_1 = 0
for i in range(1000):
print(count_1)
H = Hawkes(HawkesIntensity_temporal, param, phi = lambda s: np.exp(s), mon_kernel = True)
deleter(H, alpha, th)
if (H.density(H.Events[-1]) >= th) or (alpha*H.param[0]/H.param[1] < 1):
vars()['H_' + str(count_1)] = H
expl_proc.append('H_' + str(count_1))
count_1 += 1
for item in expl_proc:
vars()['dense_' + item] = []
t = np.linspace(0,vars()[item].Events[-1],1000)
for i in t:
vars()['dense_' + item].append(vars()[item].density(i))
plt.plot(t,vars()['dense_' + item])
def HawkesIntensity_temporal(time, params):
Ind = (time >= 0)
return params[0] * np.exp(-params[1] * time) * Ind
# alpha gives boundry of stability given by theorems by bremaud & masoulie
param = [2, 1]
alpha = 1
th = 100
expl_proc = []
count_1 = 0
for i in range(100):
print(count_1)
H = Hawkes(HawkesIntensity_temporal, param, mon_kernel=True)
deleter(H, alpha, th)
if (H.density(H.Events[-1]) >= th) or (alpha * H.param[0] / H.param[1] <
1):
vars()['H_' + str(count_1)] = H
expl_proc.append('H_' + str(count_1))
count_1 += 1
for item in expl_proc:
vars()['dense_' + item] = []
t = np.linspace(0, vars()[item].Events[-1], 1000)
for i in t:
vars()['dense_' + item].append(vars()[item].density(i))
plt.plot(t, vars()['dense_' + item])
#plt.plot(t, 2*np.ones(len(t)), 'k')