__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()
def main_async_method(queue, n):
track = trainer(50, ds, vs, n)
queue.put({'mse': track[-1]})
coun = 3800
FB = map(lambda x: float(x.split(';')[7]),
open('/home/martslaaf/Pictures/Finance/GOOG.csv').readlines()[:3880])
shift_1 = FB[4:coun + 4]
shift_2 = FB[3:coun + 3]
shift_3 = FB[2:coun + 2]
shift_4 = FB[1:coun + 1]
shift_5 = FB[:coun]
no_shift = FB[5:coun + 5]
tr = []
va = []
for i in xrange(coun - 1000):
tr.append(
(np.array([shift_1[i], shift_2[i], shift_3[i], shift_4[i],
shift_5[i]]), np.array([no_shift[i]])))
for i in xrange(coun - 1000, coun):
va.append(
(np.array([shift_1[i], shift_2[i], shift_3[i], shift_4[i],
shift_5[i]]), np.array([no_shift[i]])))
n = wavelon_class_constructor(frame=(-200, 200), period=100)
n = n(5, 1, 19)
k = 0
track = trainer(100, tr, va, n)
outew = []
print track
for j in va:
outew.append(n.forward(j[0])[0][0])
plot(no_shift[coun - 1000:coun], 'g')
plot(outew, 'r')
show()
def main_async_method(queue, n):
track = trainer(50, ds, vs, n)
queue.put({"mse": track[-1]})
__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()
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()
