def mse_nlms(x, d, h, mu):
import numpy as np
from adaptive_filters import NLMS
from filter_sim_routines import run_filter
nlms = NLMS(h.shape[0], mu=mu)
w = run_filter(x, d, nlms)
e = np.linalg.norm(h - w, axis=1)**2
return e
def mse_srls(x, d, h, lmbd, delta, N, pr):
import numpy as np
from sketch_rls import SketchRLS
from filter_sim_routines import run_filter
srls = SketchRLS(h.shape[0], lmbd=lmbd, delta=delta, N=N, p=pr)
w = run_filter(x, d, srls)
e = np.linalg.norm(h - w, axis=1)**2
return e
def mse_brls(x, d, h, lmbd, delta, L):
import numpy as np
from adaptive_filters import BlockRLS
from filter_sim_routines import run_filter
brls = BlockRLS(h.shape[0], lmbd=lmbd, delta=delta, L=L)
w = run_filter(x, d, brls)
e = np.linalg.norm(h - w, axis=1)**2
return e
def mse_srls(x, d, h, lmbd, delta, N, pr):
import numpy as np
from sketch_rls import SketchRLS
from filter_sim_routines import run_filter
srls = SketchRLS(h.shape[0], lmbd=lmbd, delta=delta, N=N, p=pr)
w = run_filter(x, d, srls)
e = np.linalg.norm(h - w, axis=1)**2
return e
def mse_brls(x, d, h, lmbd, delta, L):
import numpy as np
from adaptive_filters import BlockRLS
from filter_sim_routines import run_filter
brls = BlockRLS(h.shape[0], lmbd=lmbd, delta=delta, L=L)
w = run_filter(x, d, brls)
e = np.linalg.norm(h - w, axis=1)**2
return e
def mse_nlms(x, d, h, mu):
import numpy as np
from adaptive_filters import NLMS
from filter_sim_routines import run_filter
nlms = NLMS(h.shape[0], mu=mu)
w = run_filter(x, d, nlms)
e = np.linalg.norm(h - w, axis=1)**2
return e
