init: 验证robust算法
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mayge
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core/sk_iter.py
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250
core/sk_iter.py
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import numpy as np
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from dataclasses import dataclass
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from typing import List, Sequence, Tuple, Optional
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@dataclass
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class OPVFConfig:
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n_poles: int
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max_iter: int = 5
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tol: float = 1e-3
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add_constraint: bool = True
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real_split: bool = True
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lambda_reg: float = 0.0
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verbose: bool = True
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# ---------- 参数正交基 ----------
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class ParamOrthoBasis:
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def __init__(self, params: np.ndarray, total_degree: int):
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# params: (Ns, d)
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self.mean = params.mean(0)
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self.std = params.std(0) + 1e-15
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self.exps = self._gen_exps(params.shape[1], total_degree)
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M = self._build_monomial_matrix(params) # (Ns,M)
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Q,R = np.linalg.qr(M)
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self.Q = Q
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self.R = R
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def _gen_exps(self, dim, D):
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exps=[]
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def rec(cur, i, rem):
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if i==dim:
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exps.append(tuple(cur)); return
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for k in range(rem+1):
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cur.append(k); rec(cur, i+1, rem-k); cur.pop()
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rec([],0,D)
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return exps
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def _build_monomial_matrix(self, params):
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X = (params - self.mean)/self.std
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Ns = X.shape[0]
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M = np.zeros((Ns, len(self.exps)))
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for idx,e in enumerate(self.exps):
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v = np.ones(Ns)
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for j,p in enumerate(e):
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if p: v *= X[:,j]**p
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M[:,idx]=v
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return M
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def eval(self, g: np.ndarray) -> np.ndarray:
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x = (g - self.mean)/self.std
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mono=[]
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for e in self.exps:
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val=1.0
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for j,p in enumerate(e):
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if p: val*= x[j]**p
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mono.append(val)
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mono=np.array(mono)
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# φ = mono @ R^{-1}
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return mono @ np.linalg.inv(self.R)
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# ---------- 频率正交(有理)基 ----------
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class RationalFreqBasis:
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def __init__(self, freqs: np.ndarray, poles: np.ndarray):
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# 构造原始矩阵 G: 列0 常数 1, 后面 1/(s-a_k)
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s = 1j*2*np.pi*freqs
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cols=[np.ones_like(s)]
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for a in poles:
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cols.append(1.0/(s - a))
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G = np.vstack(cols).T # (Nf, K+1)
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# 加权可加 w_f，这里统一 1
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Q, R = np.linalg.qr(G)
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self.Q = Q # Φ(f) (Nf, K+1) 频率正交基取样
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self.R = R # 变换: G = Q R
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self.freqs = freqs
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def eval(self) -> np.ndarray:
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return self.Q # 直接返回已正交基取样 (Nf, K+1)
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# ---------- 主模型 ----------
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class OrthonormalParametricVF:
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"""
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H(s, μ) = N(s,μ)/D(s,μ)
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N(s,μ)= Σ_k c_k(μ) φ_k(s); D(s,μ)= Σ_k ĉ_k(μ) φ_k(s)
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其中 φ_k(s) 是频率正交有理基; c_k(μ), ĉ_k(μ) 在参数正交基上展开:
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c_k(μ)= Σ_q C[k,q] ψ_q(μ); ĉ_k(μ)= Σ_q Ctilde[k,q] ψ_q(μ)
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"""
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def __init__(self, cfg: OPVFConfig, freq_basis: RationalFreqBasis, param_basis: ParamOrthoBasis):
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self.cfg = cfg
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self.fb = freq_basis
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self.pb = param_basis
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Kp1 = self.fb.Q.shape[1]
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Qp = self.pb.Q.shape[1]
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self.C = np.zeros((Kp1, Qp), dtype=complex) # 分子
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self.Ct = np.zeros((Kp1, Qp), dtype=complex) # 分母
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# 初始化分母：ĉ_0 ≈ 1，其余 0
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self.Ct[0,0] = 1.0
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def _assemble_phi_param(self, params: np.ndarray) -> np.ndarray:
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# 返回 (Ns, Qp)
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return self.pb.Q
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def fit(self, H_samples: List[np.ndarray], params: np.ndarray):
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"""
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H_samples: list 长度 Ns, 每个 (Nf,) 复
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params: (Ns,d)
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"""
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Φf = self.fb.eval() # (Nf, K+1)
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Ns = len(H_samples)
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Nf, Kp1 = Φf.shape
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Φμ = self._assemble_phi_param(params) # (Ns, Qp)
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Qp = Φμ.shape[1]
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# t=0: 只解 C (式3, D=1)
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self._solve_t0(H_samples, Φf, Φμ)
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D_prev = self._eval_D(Φf, Φμ) # (Nf, Ns)
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for it in range(1, self.cfg.max_iter+1):
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A, b = self._build_iter_system(H_samples, Φf, Φμ, D_prev)
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if self.cfg.lambda_reg>0:
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lam = self.cfg.lambda_reg
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A = np.vstack([A, lam*np.eye(A.shape[1])])
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b = np.concatenate([b, np.zeros(A.shape[1])])
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x, *_ = np.linalg.lstsq(A, b, rcond=None)
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self._unpack_iter_solution(x, Kp1, Qp)
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D_new = self._eval_D(Φf, Φμ)
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rel = np.max(np.abs(D_new-D_prev)/(np.abs(D_prev)+1e-12))
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if self.cfg.verbose:
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print(f"[OPVF-Iter {it}] rel_change={rel:.3e}")
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D_prev = D_new
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if rel < self.cfg.tol:
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if self.cfg.verbose:
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print(f"[OPVF] converged at {it}")
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break
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return self
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def _solve_t0(self, H_samples, Φf, Φμ):
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Ns = len(H_samples); Nf,Kp1 = Φf.shape; Qp = Φμ.shape[1]
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# 设计矩阵行数 Ns*Nf；未知数 Kp1*Qp
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cols = Kp1*Qp
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A = np.zeros((Ns*Nf, cols), dtype=complex)
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b = np.zeros(Ns*Nf, dtype=complex)
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r = 0
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for n,Hn in enumerate(H_samples):
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phiμ = Φμ[n] # (Qp,)
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# Φf (Nf,Kp1) 外积 phiμ -> (Nf, Kp1*Qp)
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blk = np.einsum('fk,q->fkq', Φf, phiμ).reshape(Nf, cols)
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A[r:r+Nf,:] = blk
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b[r:r+Nf] = Hn
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r += Nf
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x, *_ = np.linalg.lstsq(A, b, rcond=None)
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self.C = x.reshape(Kp1, Qp)
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# 分母保持初始 (ĉ_0=1)
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def _build_iter_system(self, H_samples, Φf, Φμ, D_prev):
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Ns = len(H_samples); Nf,Kp1 = Φf.shape; Qp=Φμ.shape[1]
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# 未知：C (Kp1*Qp) + Ct (Kp1*Qp) 但固定 Ct[0,0]=1，可去掉该变量
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mask_fix = np.zeros((Kp1,Qp), dtype=bool)
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mask_fix[0,0]=True
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idx_map={}
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col=0
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for i in range(Kp1):
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for j in range(Qp):
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idx_map[('C',i,j)] = col; col+=1
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for i in range(Kp1):
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for j in range(Qp):
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if mask_fix[i,j]: continue
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idx_map[('Ct',i,j)] = col; col+=1
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cols = col
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rows = Ns*Nf
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A = np.zeros((rows, cols), dtype=complex)
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b = np.zeros(rows, dtype=complex)
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r=0
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for n,Hn in enumerate(H_samples):
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phiμ = Φμ[n]
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Dp = D_prev[:,n] # (Nf,)
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invDp = 1.0/(Dp + 1e-12)
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# 分子块: (Φf φμ^T) / D_prev
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Num_blk = np.einsum('fk,q->fkq', Φf, phiμ).reshape(Nf, Kp1*Qp)
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Num_blk = (Num_blk.T * invDp).T
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# 填 C 部分
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for i in range(Kp1):
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for j in range(Qp):
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A[r:r+Nf, idx_map[('C',i,j)]] = Num_blk[:, i*Qp + j]
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# 分母块: -(H * Φf φμ^T)/D_prev
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Den_blk = (Num_blk.T * Hn).T * (-1.0)
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for i in range(Kp1):
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for j in range(Qp):
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if mask_fix[i,j]: continue
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A[r:r+Nf, idx_map[('Ct',i,j)]] = Den_blk[:, i*Qp + j]
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# 右端 0
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r += Nf
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# 约束行 (式5)(8) 可选：Re( Σ_k D^{(t)}/D^{(t-1)} ) = K+1
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if self.cfg.add_constraint:
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row_c = np.zeros(cols, dtype=complex)
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# D^{(t)} ≈ Σ Ct_k φ_k(s) ; 用代表样本 n=0, 对所有频率平均
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n0=0
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phiμ0=Φμ[n0]
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mean_phiμ = phiμ0 # 可换平均
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# φ_k(s) 平均
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mean_phif = Φf.mean(0) # (Kp1,)
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for i in range(Kp1):
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for j in range(Qp):
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if mask_fix[i,j]: continue
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row_c[idx_map[('Ct',i,j)]] = mean_phif[i]*mean_phiμ[j]
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rhs = (Kp1) # K+1
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A = np.vstack([A, np.real(row_c) if self.cfg.real_split else row_c])
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b = np.concatenate([b, [rhs]])
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# 拆实虚
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if self.cfg.real_split:
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A_real = np.vstack([np.real(A), np.imag(A)])
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b_real = np.concatenate([np.real(b), np.imag(b)])
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else:
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A_real, b_real = A, b
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return A_real, b_real
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def _unpack_iter_solution(self, x, Kp1, Qp):
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# 重新填回 C, Ct (保持 Ct[0,0]=1)
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# 构建与 _build_iter_system 相同的 idx_map
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mask_fix = np.zeros((Kp1,Qp), dtype=bool); mask_fix[0,0]=True
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idx_C = Kp1*Qp
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# 注意：我们当时 C 索引从 0..(Kp1*Qp-1)
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self.C = x[:idx_C].reshape(Kp1, Qp)
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Ct_new = self.Ct.copy()
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pos = idx_C
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for i in range(Kp1):
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for j in range(Qp):
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if mask_fix[i,j]: continue
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Ct_new[i,j]=x[pos]; pos+=1
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self.Ct = Ct_new
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def _eval_C_mu(self, phiμ):
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# 返回 c_k(μ) (Kp1,)
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return self.C @ phiμ
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def _eval_Ct_mu(self, phiμ):
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return self.Ct @ phiμ
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def _eval_D(self, Φf, Φμ):
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# D(f, sample)= Σ_k ĉ_k(μ_n) φ_k(f)
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Kp1 = Φf.shape[1]
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Ns = Φμ.shape[0]
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D = np.zeros((Φf.shape[0], Ns), dtype=complex)
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for n in range(Ns):
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ct_mu = self._eval_Ct_mu(Φμ[n])
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D[:,n] = Φf @ ct_mu
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return D
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def evaluate(self, freqs: np.ndarray, g: np.ndarray):
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assert np.allclose(freqs, self.fb.freqs), "频率需在训练网格上 (示例简化)"
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Φf = self.fb.Q # (Nf,K+1)
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phiμ = self.pb.eval(g) # (Qp,)
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num = Φf @ (self.C @ phiμ)
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den = Φf @ (self.Ct @ phiμ)
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return num / (den + 1e-15)
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