__author__ = 'martslaaf'
import numpy as np
from matplotlib.pyplot import plot, show
from wavenet import wavelon_class_constructor, trainer
coun = 3000
inp_s = map(lambda x: float(x.split(',')[0]), open('/home/martslaaf/Learn_Coding/pybrain/sig.csv').readlines())
outp_s = map(lambda x: float(x.split(',')[1]), open('/home/martslaaf/Learn_Coding/pybrain/sig.csv').readlines())
inp_o = map(lambda x: float(x.split(',')[0])-150, open('/home/martslaaf/Learn_Coding/pybrain/orig.csv').readlines())
outp_o = map(lambda x: float(x.split(',')[1]), open('/home/martslaaf/Learn_Coding/pybrain/orig.csv').readlines())
ds = []
for i in xrange(coun):
ds.append((np.array([inp_s[i]]), np.array([outp_s[i]])))
vs = []
for i in xrange(301):
vs.append((np.array([inp_o[i]]), np.array([outp_o[i]])))
n = wavelon_class_constructor()
n = n(1, 1, 19)
k = 0
track = trainer(300, ds, vs, n)
outew = []
print track
for j in vs:
outew.append(n.forward(j[0])[0][0])
plot(outp_o, 'g')
plot(outew, 'r')
show()
__author__ = "martslaaf"
import numpy as np
from matplotlib.pyplot import plot, show
from wavenet import wavelon_class_constructor, trainer
inp_1 = map(lambda x: float(x), open("/home/martslaaf/Pictures/old_data/nonlinear_xor_1.csv").readlines())
inp_2 = map(lambda x: float(x), open("/home/martslaaf/Pictures/old_data/nonlinear_xor_2.csv").readlines())
inp_3 = map(lambda x: float(x), open("/home/martslaaf/Pictures/old_data/nonlinear_sum.csv").readlines())
outp = map(lambda x: float(x), open("/home/martslaaf/Pictures/old_data/nonlinear_target.csv").readlines())
coun = 1000
tr = []
va = []
for i in xrange(coun - 250):
tr.append((np.array([inp_1[i], inp_2[i], inp_3[i]]), np.array([outp[i]])))
for i in xrange(coun - 250, coun):
va.append((np.array([inp_1[i], inp_2[i], inp_3[i]]), np.array([outp[i]])))
n = wavelon_class_constructor(frame=(-200, 200), period=100)
n = n(3, 1, 19)
k = 0
track = trainer(10000, tr, va, n)
outew = []
print track
for j in va:
outew.append(n.forward(j[0])[0][0])
plot(outp[coun - 250 : coun], "g")
plot(outew, "r")
show()
vs = []
for i in xrange(301):
vs.append((np.array([inp_o[i]]), np.array([outp_o[i]])))
seed()
# networks initialization
exp_1_n = [] # default set
exp_2_n = [] # default but Mhat
exp_3_n = [] # random limits
exp_4_n = [] # hidden layer x2
exp_5_n = [] # right limits for translation
exp_6_n = [] # period data (Nyqist)
exp_7_n = [] # fourier analysis
exp_8_n = [] # hidden layer /2
mini, maxi = -150, 150
e_1 = wavelon_class_constructor()
e_2 = wavelon_class_constructor(motherfunction=Mhat)
e_3 = wavelon_class_constructor(frame=(uniform(-100, 0), uniform(0, 100)), period=uniform(0, 100))
e_4 = wavelon_class_constructor()
e_5 = wavelon_class_constructor(frame=(mini, maxi))
e_6 = wavelon_class_constructor(period=120)
e_7 = wavelon_class_constructor(period=120, signal=outp_s, fa=True)
e_8 = wavelon_class_constructor()
seed()
for i in xrange(repeat_count):
exp_1_n.append(e_1(1, 1, 19))
exp_2_n.append(e_2(1, 1, 19))
exp_3_n.append(e_3(1, 1, 38))
exp_4_n.append(e_4(1, 1, 19))
exp_5_n.append(e_5(1, 1, 19))
exp_6_n.append(e_6(1, 1, 19))
from matplotlib.pyplot import plot, show
from wavenet import wavelon_class_constructor, trainer
coun = 3000
inp_s = map(lambda x: float(x.split(',')[0]),
open('/home/martslaaf/Learn_Coding/pybrain/sig.csv').readlines())
outp_s = map(lambda x: float(x.split(',')[1]),
open('/home/martslaaf/Learn_Coding/pybrain/sig.csv').readlines())
inp_o = map(lambda x: float(x.split(',')[0]) - 150,
open('/home/martslaaf/Learn_Coding/pybrain/orig.csv').readlines())
outp_o = map(lambda x: float(x.split(',')[1]),
open('/home/martslaaf/Learn_Coding/pybrain/orig.csv').readlines())
ds = []
for i in xrange(coun):
ds.append((np.array([inp_s[i]]), np.array([outp_s[i]])))
vs = []
for i in xrange(301):
vs.append((np.array([inp_o[i]]), np.array([outp_o[i]])))
n = wavelon_class_constructor()
n = n(1, 1, 19)
k = 0
track = trainer(300, ds, vs, n)
outew = []
print track
for j in vs:
outew.append(n.forward(j[0])[0][0])
plot(outp_o, 'g')
plot(outew, 'r')
show()
