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feat: 整合了basicQR和relaxedQR

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mayge
2025-09-22 06:37:28 -04:00
parent 966adfe140
commit 05a9702b88
1 changed files with 80 additions and 37 deletions
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117
core/relaxed_basisQR.py
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@@ -4,9 +4,10 @@ from scipy.linalg import block_diag
import skrf as rf
from skrf import VectorFitting
from core.freqency import auto_select
import random as rnd
class RelaxedBasicBasisQR:
def __init__(self,H,freqs,poles,weights=None,passivity=True,dc_enforce=True,fit_constant=True):
def __init__(self,H,freqs,poles,weights=None,passivity=True,dc_enforce=True,fit_constant=True,fit_proportional=False):
self.least_squares_rms_error = None
self.least_squares_condition = None
self.eigenval_condition = None
@@ -16,6 +17,7 @@ class RelaxedBasicBasisQR:
self.dc_enforce = dc_enforce
self.fit_constant = fit_constant
self.fit_proportional = fit_proportional
# self.H = H
# self.freqs = freqs
@@ -27,7 +29,7 @@ class RelaxedBasicBasisQR:
self.Phi = self.generate_basis(self.s, self.poles)
self.A = self.matrix_A(self.poles)
self.B = self.vector_B(self.poles)
self.Cw,self.w0 = self.fit_denominator(self.H, weights=weights)
self.Cw,self.w0,self.e = self.fit_denominator(self.H, weights=weights)
self.D = self.w0
self.Cr = None
@@ -119,7 +121,7 @@ class RelaxedBasicBasisQR:
B = Bb if B is None else np.vstack([B, Bb])
return B
def fit_denominator(self, H, weights=None):
def fit_denominator(self, H, weights=None, d0 = 1.0):
"""
Solve formula (70) on the real basis Φ to obtain:
- d (real) → packs into C for this state's block structure
@@ -129,6 +131,10 @@ class RelaxedBasicBasisQR:
H = np.asarray(H, np.complex128).reshape(-1,1)
K, N = self.Phi.shape
one = np.ones((K, 1), np.complex128)
Phi = self.Phi
dc_tol = 1e-18
has_dc = self.dc_enforce and self.freqs[0] < dc_tol
keep = np.ones(K, dtype=bool)
# SK weighting (applied only to the (73) rows we keep in LS)
if weights is None:
@@ -136,17 +142,16 @@ class RelaxedBasicBasisQR:
else:
weights = np.diag([1/res for res in weights])
Phi = self.Phi
Phi_w = np.hstack([one, Phi])
M = np.hstack([Phi, -(H * Phi_w)]) # (K, 2N+1), complex
dc_tol = 1e-18
has_dc = self.dc_enforce and self.freqs[0] < dc_tol
if self.fit_constant:
Phi_w = np.hstack([one, Phi])
M = np.hstack([Phi, -(H * Phi_w)]) # (K, 2N+1), complex
else:
M = np.hstack([Phi, -(H * Phi)]) # (K, 2N), complex
if has_dc:
# Enforce DC response exactly:
k0 = int(np.argmin(np.abs(self.freqs)))
keep = np.ones(K, dtype=bool); keep[k0] = False
keep[k0] = False
M_w = weights @ M
A_re = np.real(M_w[keep, :])
A_im = np.imag(M_w[keep, :])
@@ -154,49 +159,78 @@ class RelaxedBasicBasisQR:
# exact (unweighted) DC rows:
A_dc_re = np.real(M[k0, :]).reshape(1, -1)
A_dc_im = np.imag(M[k0, :]).reshape(1, -1)
else:
M_w = weights @ M
A_re = np.real(M_w)
A_im = np.imag(M_w)
A_dc_re = A_dc_im = None
beta = float(np.sqrt(np.sum(np.abs(H)**2)))
mean_row = (beta / K) * np.sum(Phi_w, axis=0)
A_w0 = np.concatenate([np.zeros(N, float),
np.real(mean_row).astype(float)]
).reshape(1, -1)
b_w0 = np.array([beta], float)
A_blocks = [A_re, A_im]
if self.fit_constant:
beta = float(np.sqrt(np.sum(np.abs(H)**2)))
mean_row = (beta / K) * np.sum(Phi_w, axis=0)
A_w0 = np.concatenate([np.zeros(N, float),
np.real(mean_row).astype(float)]
).reshape(1, -1)
b_w0 = np.array([beta], float)
A_blocks += [A_w0]
m = A_re.shape[0] + A_im.shape[0]
b = np.zeros(m, float)
b = np.concatenate([b, b_w0])
else:
H_kp = (weights @ H)[keep,:]
b_re = np.real(d0 * H_kp)
b_im = np.imag(d0 * H_kp)
b = np.concatenate([b_re.ravel(), b_im.ravel()]).astype(float)
# ---- build final stacked-real system ----
A_blocks = [A_re, A_im]
# if A_dc_re is not None:
# A_blocks += [A_dc_re, A_dc_im]
A_blocks += [A_w0]
A = np.vstack(A_blocks).astype(float)
m = A_re.shape[0] + A_im.shape[0]
b = np.zeros(m, float)
# if A_dc_re is not None:
# b = np.concatenate([b, np.zeros(2, float)]) # DC rows → 0
b = np.concatenate([b, b_w0])
# ---- QR solve for x = [c_H (N); c_w (N+1)] ----
A = np.vstack(A_blocks).astype(float)
Q, R = np.linalg.qr(A, mode="reduced")
x = np.linalg.solve(R, Q.T @ b)
if self.fit_constant:
Q2 = Q[:,A.shape[1]//2:]
R22 = R[A.shape[1]//2:,A.shape[1]//2:]
else:
Q2 = Q[:,A.shape[1]//2:]
R22 = R[A.shape[1]//2:,A.shape[1]//2:]
x = np.linalg.solve(R22, Q2.T @ b)
# diagnostics
resid = A @ x - b
resid = Q2 @ R22 @ x - b
self.least_squares_rms_error = float(np.sqrt(np.mean(resid**2)))
self.least_squares_condition = float(np.linalg.cond(R))
# split cw and return
cw = x[N:] # last (N+1) entries = [w0, w_1..w_N]
w0 = float(cw[0])
Cw = cw[1:].reshape(1, N) # row vector (1, N)
return Cw, w0
# cw = x[N:] # last (N+1) entries = [w0, w_1..w_N]
# w0 = float(cw[0])
# Cw = cw[1:].reshape(1, N) # row vector (1, N)
return self.extract_Cw_d_e(x,N,d0)
def extract_Cw_d_e(self,C,N,d0=1.0):
if self.fit_proportional and self.fit_constant:
d = C[1]
e = C[0]
return C[2:].reshape(1, -1), d, e
elif self.fit_proportional and not self.fit_constant:
d = 0.0
e = C[0]
return C[1:].reshape(1, -1), d, e
elif not self.fit_proportional and self.fit_constant:
d = C[0]
e = 0.0
return C[1:].reshape(1, -1), d, e
else:
return C.reshape(1, -1), d0, 0.0
def non_bias_Cr(self,w0):
@@ -215,12 +249,22 @@ class RelaxedBasicBasisQR:
num = phi @ self.Cr
H = num / den
return H.ravel()
def noise(n:complex,coeff:float=0.05):
noise_r = rnd.gauss(-coeff * n.real, coeff * n.real)
noise_i = rnd.gauss(-coeff * n.imag, coeff * n.imag)
return complex(n.real + noise_r, n.imag + noise_i)
if __name__ == "__main__":
start_point = 0
network = rf.Network("/tmp/paramer/simulation/3000/3000.s2p")
K = 5
H11,freqs = auto_select([network.y[i][0][0] for i in range(start_point,len(network.y))],network.f[start_point:],max_points=20)
K = 10
full_freqences = network.f[start_point:]
noised_sampled_points = [(network.y[i][0][0]) for i in range(start_point,len(network.y))]
sampled_points = [network.y[i][0][0] for i in range(start_point,len(network.y))]
H11,freqs = auto_select(noised_sampled_points,full_freqences,max_points=20)
poles = generate_starting_poles(2,beta_min=1e4,beta_max=freqs[-1]*1.1)
Dt_1 = np.ones((len(freqs),1),np.complex128)
@@ -266,10 +310,9 @@ if __name__ == "__main__":
fig, axes = plt.subplots(3, 2, figsize=(15, 16), sharex=False)
ax00 = axes[0][0]
full_freqences = network.f[start_point:]
sliced_freqences = freqs
sampled_points = [network.y[i][0][0] for i in range(start_point,len(network.y))]
fitted_points = H11_evaluated
sliced_freqences = freqs
input_points = H11
ax00.plot(full_freqences, np.abs(sampled_points), 'o', ms=4, color='red', label='Samples')
ax00.plot(full_freqences, np.abs(fitted_points), '-', lw=2, color='k', label='Fit')