def test_different_methods():
mean_reggresion = zeros((5,6))
pl.figure()
word = "samochod"
metods = ["momentum", "tnc", "bfgs", "cg", "rprop"]
_words=[u"Backpropagation z momentem", u"Alg. BFGS (multicore)",
u"Alg. BFGS", u"Gradient sprzezony", "Alg. RProp"]
colors= ["#FF2800".lower(), "#FFB800".lower(), "#1729B0".lower(),
"#007929".lower(), "#60016D"]
w = 3
p_names = ["Slope", "Intercept", "R-value", "P-value", "Slope Error",
"Estimation Error"]
for n, met in zip(range(5), metods):
fn = "{}_network_{}.net".format(met, voice_sample.timestamp() )
(network, samples) = learn_net(fn, word, rs=1, re=5, save=False,
method=met)
(inp, target) = get_inputs_and_outputs(samples, word, 6,10,False)
(output, reggresion) = network.test(inp, target, iprint=0)
mean_reggresion[n,:] = np.mean(reggresion,0)
x = np.arange(6)*7 + n
pl.bar(x, mean_reggresion[n,:], color=colors[n], width=0.9,
label=_words[n])
print mean_reggresion
pl.xticks( np.arange(6)*7+2, p_names )
pl.xlim( -1, 44 )
pl.legend()
pl.show()
pass
def test_and_plot():
mean_reggresion = zeros((4, 6))
pl.figure()
_words = [u"Imie", u"Nazwisko", u"Samochód", u"Kot"]
colors = [
"#FF2800".lower(), "#FFB800".lower(), "#1729B0".lower(),
"#007929".lower()
]
w = 3
p_names = [
"Slope", "Intercept", "R-value", "P-value", "Slope Error",
"Estimation Error"
]
for n, word in zip(range(4), voice_sample.WORDS):
fn = "{}_network_{}.net".format(word, voice_sample.timestamp())
(network, samples) = learn_net(fn, word, rs=1, re=5, save=False)
(inp, target) = get_inputs_and_outputs(samples, word, 6, 10, False)
(output, reggresion) = network.test(inp, target, iprint=0)
mean_reggresion[n, :] = np.mean(reggresion, 0)
x = np.arange(6) * 6 + n
pl.bar(x,
mean_reggresion[n, :],
color=colors[n],
width=0.9,
label=_words[n])
print mean_reggresion
pl.xticks(np.arange(6) * 6 + 2, p_names)
pl.xlim(-1, 35)
pl.legend()
pl.show()
def test_different_methods():
mean_reggresion = zeros((5, 6))
pl.figure()
word = "samochod"
metods = ["momentum", "tnc", "bfgs", "cg", "rprop"]
_words = [
u"Backpropagation z momentem", u"Alg. BFGS (multicore)", u"Alg. BFGS",
u"Gradient sprzezony", "Alg. RProp"
]
colors = [
"#FF2800".lower(), "#FFB800".lower(), "#1729B0".lower(),
"#007929".lower(), "#60016D"
]
w = 3
p_names = [
"Slope", "Intercept", "R-value", "P-value", "Slope Error",
"Estimation Error"
]
for n, met in zip(range(5), metods):
fn = "{}_network_{}.net".format(met, voice_sample.timestamp())
(network, samples) = learn_net(fn,
word,
rs=1,
re=5,
save=False,
method=met)
(inp, target) = get_inputs_and_outputs(samples, word, 6, 10, False)
(output, reggresion) = network.test(inp, target, iprint=0)
mean_reggresion[n, :] = np.mean(reggresion, 0)
x = np.arange(6) * 7 + n
pl.bar(x,
mean_reggresion[n, :],
color=colors[n],
width=0.9,
label=_words[n])
print mean_reggresion
pl.xticks(np.arange(6) * 7 + 2, p_names)
pl.xlim(-1, 44)
pl.legend()
pl.show()
pass
def test_and_plot():
mean_reggresion = zeros((4,6))
pl.figure()
_words=[u"Imie", u"Nazwisko", u"Samochód", u"Kot"]
colors= ["#FF2800".lower(), "#FFB800".lower(), "#1729B0".lower(),
"#007929".lower()]
w = 3
p_names = ["Slope", "Intercept", "R-value", "P-value", "Slope Error",
"Estimation Error"]
for n, word in zip(range(4), voice_sample.WORDS):
fn = "{}_network_{}.net".format(word, voice_sample.timestamp() )
(network, samples) = learn_net(fn, word, rs=1, re=5, save=False)
(inp, target) = get_inputs_and_outputs(samples, word, 6,10,False)
(output, reggresion) = network.test(inp, target, iprint=0)
mean_reggresion[n,:] = np.mean(reggresion,0)
x = np.arange(6)*6 + n
pl.bar(x, mean_reggresion[n,:], color=colors[n], width=0.9,
label=_words[n])
print mean_reggresion
pl.xticks( np.arange(6)*6+2, p_names )
pl.xlim( -1, 35 )
pl.legend()
pl.show()