def viz_rank(Oi,k=None):
if k is None:
U, s, VT = np.linalg.svd(Oi)
else:
U, s, VT = thin_svd_randomized(Oi,k)
plt.figure()
plt.stem(np.arange(s.shape[0]),s)
def viz_rank(Oi,k=None):
if k is None:
U, s, VT = np.linalg.svd(Oi)
else:
U, s, VT = thin_svd_randomized(Oi,k)
plt.figure()
plt.stem(np.arange(s.shape[0]),s)
def viz_rank2(y,i,k=None):
Y = AR_striding(y,i-1).T
l = Y.shape[0]//2
if k is None:
_, s, _ = np.linalg.svd(np.cov(Y)[:l,l:])
else:
_, s, _ = thin_svd_randomized(np.cov(Y)[:l,l:],k)
plt.figure()
plt.stem(np.arange(s.shape[0]),s)
def viz_rank2(y,i,k=None):
Y = AR_striding(y,i-1).T
l = Y.shape[0]//2
if k is None:
_, s, _ = np.linalg.svd(np.cov(Y)[:l,l:])
else:
_, s, _ = thin_svd_randomized(np.cov(Y)[:l,l:],k)
plt.figure()
plt.stem(np.arange(s.shape[0]),s)
def compute_gammas(Oi,nhat,p):
U,s,VT = thin_svd_randomized(Oi,nhat)
Gamma_i = U.dot(np.diag(np.sqrt(s)))
Gamma_im1 = Gamma_i[:-p]
return Gamma_i, Gamma_im1
def compute_gammas(Oi,nhat,p):
U,s,VT = thin_svd_randomized(Oi,nhat)
Gamma_i = U.dot(np.diag(np.sqrt(s)))
Gamma_im1 = Gamma_i[:-p]
return Gamma_i, Gamma_im1