def test(data=None,
precision_bp=2000,
nb_bp=3,
taille_fenetre=10,
breakp=None,
abscisse=None):
"""Paramètres"""
#donnees
if data == None:
data = [
580.38, 581.86, 580.97, 580.8, 579.79, 580.39, 580.42, 580.82,
581.4, 581.32, 581.44, 581.68, 581.17, 580.53, 580.01, 579.91,
579.14, 579.16, 579.55, 579.67, 578.44, 578.24, 579.1, 579.09,
579.35, 578.82, 579.32, 579.01, 579, 579.8, 579.83, 579.72, 579.89,
580.01, 579.37, 578.69, 578.19, 578.67, 579.55, 578.92, 578.09,
579.37, 580.13, 580.14, 579.51, 579.24, 578.66, 578.86, 578.05,
577.79, 576.75, 576.75, 577.82, 578.64, 580.58, 579.48, 577.38,
576.9, 576.94, 576.24, 576.84, 576.85, 576.9, 577.79, 578.18,
577.51, 577.23, 578.42, 579.61, 579.05, 579.26, 579.22, 579.38,
579.1, 577.95, 578.12, 579.75, 580.85, 580.41, 579.96, 579.61,
578.76, 578.18, 577.21, 577.13, 579.1, 578.25, 577.91, 576.89,
575.96, 576.8, 577.68, 578.38, 578.52, 579.74, 579.31, 579.89,
579.96, 579.96, 579.96
]
#valeur du découpage pour trouver les breakpoints
#nombre de breakpoints > 0
#Affichage variance et moyenne des données
print("variance = ", np_var(data))
print("ecart type = ", np_var(data)**0.5)
print("moyenne = ", np_mean(data))
#Calcul de l'intégrale de la gaussienne trouvé
mu = np_mean(data)
sig = np_var(data)
ecart = (max(data) - min(data))
integral_g = quad(gaussian,
min(data) - ecart,
max(data) + ecart,
args=(mu, sig))
print("integrale gauss", integral_g)
print(mu, sig, ecart)
#Appel de la fonctio SAX
vector_c, vector_c_fit = sax(data, taille_fenetre)
def _get_data_saxed(self):
return [sax.sax(dim, self.alphabet_size) for dim in self.data_paaed]
def test(data=None,
precision_bp=2000,
nb_bp=3,
taille_fenetre=10,
breakp=None,
abscisse=None):
"""Paramètres"""
#donnees
if data == None:
data = [
580.38, 581.86, 580.97, 580.8, 579.79, 580.39, 580.42, 580.82,
581.4, 581.32, 581.44, 581.68, 581.17, 580.53, 580.01, 579.91,
579.14, 579.16, 579.55, 579.67, 578.44, 578.24, 579.1, 579.09,
579.35, 578.82, 579.32, 579.01, 579, 579.8, 579.83, 579.72, 579.89,
580.01, 579.37, 578.69, 578.19, 578.67, 579.55, 578.92, 578.09,
579.37, 580.13, 580.14, 579.51, 579.24, 578.66, 578.86, 578.05,
577.79, 576.75, 576.75, 577.82, 578.64, 580.58, 579.48, 577.38,
576.9, 576.94, 576.24, 576.84, 576.85, 576.9, 577.79, 578.18,
577.51, 577.23, 578.42, 579.61, 579.05, 579.26, 579.22, 579.38,
579.1, 577.95, 578.12, 579.75, 580.85, 580.41, 579.96, 579.61,
578.76, 578.18, 577.21, 577.13, 579.1, 578.25, 577.91, 576.89,
575.96, 576.8, 577.68, 578.38, 578.52, 579.74, 579.31, 579.89,
579.96, 579.96, 579.96
]
#valeur du découpage pour trouver les breakpoints
#nombre de breakpoints > 0
fig = plt.figure(figsize=(15, 10))
# TODO arranger les axes ici !!!
years = mdates.YearLocator()
yearsFmt = mdates.DateFormatter('%Y')
months = mdates.MonthLocator()
days = mdates.DayLocator()
daysFmt = mdates.DateFormatter('%d')
hours = mdates.HourLocator()
minutes = mdates.MinuteLocator()
gauss = fig.add_subplot(2, 1, 1)
gauss.xaxis.set_major_locator(daysFmt)
gauss.xaxis.set_minor_locator(hours)
#plt.ylabel('some numbers')
#Affichage variance et moyenne des données
print("variance = ", np_var(data))
print("ecart type = ", np_var(data)**0.5)
print("moyenne = ", np_mean(data))
#Calcul de l'intégrale de la gaussienne trouvé
mu = np_mean(data)
sig = np_var(data)
ecart = (np.amax(data) - np.amin(data))
integral_g = quad(gaussian,
np.amin(data) - ecart,
np.amax(data) + ecart,
args=(mu, sig))
print("integrale gauss", integral_g)
#Appel de la fonctio SAX
vector_c, vector_c_fit = sax(data, taille_fenetre)
if abscisse is None:
plt.plot(vector_c)
plt.plot(data)
else:
plt.plot(abscisse, vector_c)
plt.plot(abscisse, data)
if breakp is None:
breakp = breakpoints(integral_g, min(data), np_mean(data),
precision_bp, nb_bp, mu, sig)
for bp in breakp:
print("seuil : ", bp)
plt.axhline(bp, c='grey')
#print("seuil 2 : ",bp+2*(mu-bp))
#plt.axhline(bp+2*(mu-bp))
#Affichage de la gaussienne
#fig.add_subplot(2,1,2)
"""
x = np_linspace(min(data)-ecart,max(data)+ecart,100)
plt.plot(x,gaussian(x,mu,sig))
for bp in breakp:
plt.axvline(bp)
fig.add_subplot(2,1,1)
"""
tab_classif = [0] * (len(breakp) + 1)
print(tab_classif)
"""
for val in vector_c:
it = 0
for var in breakp:
if val < var:
tab_classif[it] = tab_classif[it] + 1
break
it = it + 1
"""
pos_x = 1
for val in vector_c_fit:
it = 0
test = 0
int_char = 0
for var in breakp:
if val < var:
tab_classif[it] = tab_classif[it] + 1
test = 1
# conversion en binaire str(bin(int_char))[2:]
plt.annotate(
str(bin(int_char))[2:],
xy=(taille_fenetre * pos_x - taille_fenetre / 2, val),
xytext=(taille_fenetre * pos_x - taille_fenetre / 2,
val + 2),
arrowprops=dict(facecolor='white', shrink=0.05),
)
break
it = it + 1
int_char = int_char + 1
if test == 0:
tab_classif[it] = tab_classif[it] + 1
# conversion en binaire str(bin(int_char))[2:]
plt.annotate(
str(bin(int_char))[2:],
xy=(taille_fenetre * pos_x - taille_fenetre / 2, val),
xytext=(taille_fenetre * pos_x - taille_fenetre / 2, val + 2),
arrowprops=dict(facecolor='white', shrink=0.05),
)
pos_x = pos_x + 1
print(tab_classif)
plt.show()
return breakp
--------
>>> import numpy as np
>>> from sciquence.representation import paa
>>> np.random.seed(42)
>>> random_time_series = np.random.rand(50)
>>> print paa(random_time_series, window=10)
[ 0.52013674 0.39526784 0.40038724 0.50927069 0.40455702]
References
----------
.. [1] https://jmotif.github.io/sax-vsm_site/morea/algorithm/PAA.html
'''
# TODO: Check adjust option
# TODO: Consider optimization: output = np.zeros(len(sequence))
output = []
for w in wingen(sequence, window, window, raw=not adjust):
output.append(np.mean(w))
return np.array(output)
if __name__ == "__main__":
from sax import sax
np.random.seed(42)
random_time_series = np.random.rand(50)
print sax(random_time_series, window=10)
