def test_signal():
length = 120
x = np.sign(RandomState(0).normal(size=length))
x[-20:] = 0 # make sure the signal has bounded support
den = np.array([1, -0.6, 0.1])
num = np.array([0.5])
y = utils.lfilter(x=x, b=num, a=den)
lookahead = 2
h = equalizer.train(
signal=y, expected=x, order=len(den), lookahead=lookahead)
assert dsp.norm(h[:lookahead]) < 1e-12
h = h[lookahead:]
assert_approx(h, den / num)
x_ = utils.lfilter(x=y, b=h, a=[1])
assert_approx(x_, x)
def test_signal():
length = 120
x = np.sign(RandomState(0).normal(size=length))
x[-20:] = 0 # make sure the signal has bounded support
den = np.array([1, -0.6, 0.1])
num = np.array([0.5])
y = utils.lfilter(x=x, b=num, a=den)
lookahead = 2
h = equalizer.train(signal=y,
expected=x,
order=len(den),
lookahead=lookahead)
assert dsp.norm(h[:lookahead]) < 1e-12
h = h[lookahead:]
assert_approx(h, den / num)
x_ = utils.lfilter(x=y, b=h, a=[1])
assert_approx(x_, x)
def assert_approx(x, y, e=1e-12):
x = x.flatten()
y = y.flatten()
assert dsp.norm(x - y) < e * dsp.norm(x)
def assert_approx(x, y, e=1e-12):
x = x.flatten()
y = y.flatten()
assert dsp.norm(x - y) < e * dsp.norm(x)