if isspmatrix(m):
m = m.todense()
u, s, vh = svd(m)
if which == 'LM':
ii = np.argsort(s)[-k:]
elif which == 'SM':
ii = np.argsort(s)[:k]
else:
raise ValueError("unknown which=%r" % (which,))
return u[:, ii], s[ii], vh[ii]
def svd_estimate(u, s, vh):
return np.dot(u, np.dot(np.diag(s), vh))
n = 5
x = hilbert(n)
# u,s,vh = svd(x)
# s[2:-1] = 0
# x = svd_estimate(u, s, vh)
which = 'SM'
k = 2
u, s, vh = sorted_svd(x, k, which=which)
su,ss,svh = old_svds(x, k, which=which)
ssu,sss,ssvh = new_svds(x, k, which=which)
m_hat = svd_estimate(u, s, vh)
sm_hat = svd_estimate(su, ss, svh)
ssm_hat = svd_estimate(ssu, sss, ssvh)
ii = np.argsort(s)[:k]
else:
raise ValueError("unknown which=%r" % (which,))
return u[:, ii], s[ii], vh[ii]
def svd_estimate(u, s, vh):
return np.dot(u, np.dot(np.diag(s), vh))
x = np.array([[1, 2, 3],
[3, 4, 3],
[1, 0, 2],
[0, 0, 1]], np.float)
y = np.array([[1, 2, 3, 8],
[3, 4, 3, 5],
[1, 0, 2, 3],
[0, 0, 1, 0]], np.float)
z = csc_matrix(x)
m = x.T
k = 1
u, s, vh = sorted_svd(m, k)
su, ss, svh = old_svds(m, k)
ssu, sss, ssvh = new_svds(m, k)
s
ss
sss
u
su
ssu
if isspmatrix(m):
m = m.todense()
u, s, vh = svd(m)
if which == 'LM':
ii = np.argsort(s)[-k:]
elif which == 'SM':
ii = np.argsort(s)[:k]
else:
raise ValueError("unknown which=%r" % (which, ))
return u[:, ii], s[ii], vh[ii]
def svd_estimate(u, s, vh):
return np.dot(u, np.dot(np.diag(s), vh))
n = 5
x = hilbert(n)
# u,s,vh = svd(x)
# s[2:-1] = 0
# x = svd_estimate(u, s, vh)
which = 'SM'
k = 2
u, s, vh = sorted_svd(x, k, which=which)
su, ss, svh = old_svds(x, k, which=which)
ssu, sss, ssvh = new_svds(x, k, which=which)
m_hat = svd_estimate(u, s, vh)
sm_hat = svd_estimate(su, ss, svh)
ssm_hat = svd_estimate(ssu, sss, ssvh)
