def fit(self, data): # 进行svd 分解操作
if type(data) is np.ndarray: # 如果是数组
data = mat(data)
else:
ValueError("data type must be np.ndarray or np.matrix")
U, D, V= svd(data)
return U, D, V
def tt_svd_old(tt, A, eps=1e-9):
d = len(A.shape)
tt.n = A.shape
frob_norm = frobenius_norm(A)
delta = frob_norm * eps / math.sqrt(d - 1)
N = A.size
ns = np.array(A.shape)
C = A
tt.cores = []
ranks = np.zeros(d + 1, dtype=np.int)
ranks[0] = 1
for k in xrange(d - 1):
C = reshape(C, (ranks[k] * ns[k], N / (ranks[k] * ns[k])))
U, s, V = svd(C, full_matrices=False)
ranks[k + 1] = rank_chop(s, delta)
r_new = ranks[k + 1]
if r_new == 0:
# Tensor becomes zero as convolution by rank 0
tt = tt_zeros(tt.n)
U = U[:, :ranks[k + 1]]
tt.cores.append(reshape(U, (ranks[k], ns[k], r_new)))
V = V[:r_new, :]
s = s[:r_new]
V = dot(np.diag(s), V)
C = V
N = N * r_new / (ns[k] * ranks[k])
r = r_new
tt.cores.append(C.reshape(C.shape + (1,)))
tt.d = d
tt.r = ranks
tt.r[-1] = 1
def get_reverse_transforms(mat1, mat2):
# get the reverse procrustes transformation to align m2 with m1
m1 = array(mat1)
m2 = array(mat2)
# centralize matrices
mean_m1 = [mean(column) for column in m1.T]
m1 = m1-mean_m1
mean_m2 = [mean(column) for column in m2.T]
m2 = m2-mean_m2
t = array(mean_m2)-array(mean_m1)
# scaling
scale_m1 = sqrt(sum(square(m1))/(m1.shape[1]))
scale_m2 = sqrt(sum(square(m2))/(m2.shape[1]))
m2 = (m2/scale_m2)*scale_m1
sc = scale_m2/scale_m1
# rotation
u, s, v = svd(dot(m1.T, m2))
r = inv(dot(v.T, u.T))
return sc, r, mean_m1, mean_m2
def get_reverse_transforms(mat1, mat2):
# get the reverse procrustes transformation to align m2 with m1
m1 = array(mat1)
m2 = array(mat2)
# centralize matrices
mean_m1 = [mean(column) for column in m1.T]
m1 = m1 - mean_m1
mean_m2 = [mean(column) for column in m2.T]
m2 = m2 - mean_m2
t = array(mean_m2) - array(mean_m1)
# scaling
scale_m1 = sqrt(sum(square(m1)) / (m1.shape[1]))
scale_m2 = sqrt(sum(square(m2)) / (m2.shape[1]))
m2 = (m2 / scale_m2) * scale_m1
sc = scale_m2 / scale_m1
# rotation
u, s, v = svd(dot(m1.T, m2))
r = inv(dot(v.T, u.T))
return sc, r, mean_m1, mean_m2
def learn(self, data, numsteps, visualize=False):
if type(data) is not type([]): # learn on a single sequence
numdims, numpoints = data.shape
assert numdims == self.numdims
logXi = zeros((numpoints-1,self.numstates,self.numstates),
dtype=float)
lastlogprob = -inf
for iteration in range(numsteps):
print "EM iteration: %d" % iteration
# E-step
logAlpha, logBeta = self.alphabeta(data)
# compute xi and gamma
for t in range(numpoints-1):
for i in range(self.numstates):
for j in range(self.numstates):
logXi[t,i,j] = logAlpha[i,t] +\
self.logTransitionProbs[i,j] +\
self._loggaussian(data[:,t+1],j) +\
logBeta[j,t+1]
logXi[t,:,:] -= logsumexp(logXi[t,:,:].flatten())
logGamma = vstack((logsumexp(logXi, 2),
logsumexp(logXi[-1,:,:], 1)))
logprob = logsumexp(logAlpha[:,-1])
print "logprob = %f" % logprob
if abs(logprob - lastlogprob) <= 10**-6:
print "converged"
break
lastlogprob = logprob
# M-step
self.logInitProbs[:] = logGamma[0,:]
self.logTransitionProbs[:] = logsumexp(logXi, 0) - \
logsumexp(logGamma[:-1,:],0)[:,newaxis]
G = exp(logGamma - logsumexp(logGamma, 0)[newaxis,:])
for k in range(self.numstates):
self.means[:,k] = sum(G[:,k][newaxis,:]*data,1)
data_m = data - self.means[:,k][:,newaxis]
self.covs[:,:,k] = dot((data_m*G[:,k][newaxis,:]), data_m.T)
# threshold eigenvalues
for k in range(self.numstates):
U, D, Vt = svd(self.covs[:,:,k])
D[D<0.01] = 0.01
self.covs[:,:,k] = dot(U, dot(diag(D), Vt))
# vsiualize:
if visualize and self.numdims == 2:
cla()
scatter(data[0,:], data[1,:])
for k in range(self.numstates):
plotGaussian(self.means[:,k], self.covs[:,:,k])
def __init__(self, distribution, num_eigen=2, \
mean_est=array([-2.0, -2.0]), cov_est=0.05 * eye(2), \
sample_discard=500, sample_lag=10, accstar=0.234):
AdaptiveMetropolis.__init__(self, distribution=distribution, \
mean_est=mean_est, cov_est=cov_est, \
sample_discard=sample_discard, sample_lag=sample_lag, accstar=accstar)
assert (num_eigen <= distribution.dimension)
self.num_eigen = num_eigen
self.dwscale = self.globalscale * ones([self.num_eigen])
u, s, _ = svd(self.cov_est)
self.eigvalues = s[0:self.num_eigen]
self.eigvectors = u[:, 0:self.num_eigen]
def __init__(self, distribution, kernel, Z, nu2=0.1, gamma=0.1, num_eigen=10):
Kameleon.__init__(self, distribution, kernel, Z, nu2, gamma)
self.num_eigen = num_eigen
if Z is None:
self.Kc = None
self.eigvalues = None
self.eigvectors = None
else:
K = self.kernel.kernel(Z)
H = Kernel.centring_matrix(len(self.Z))
self.Kc = H.dot(K.dot(H))
u, s, _ = svd(self.Kc)
self.eigvalues = s[0 : self.num_eigen]
self.eigvectors = u[:, 0 : self.num_eigen]
def triangulate(f1, f2, m1, m2):
u1 = f1.x
v1 = f1.y
u2 = f2.x
v2 = f2.y
Q = np.array([
u1 * m1[2] - m1[0], v1 * m1[2] - m1[1], u2 * m2[2] - m2[0],
v2 * m2[2] - m2[1]
])
U, E, V = svd(Q)
V /= V[-1:, -1:]
return V[3, :]
def sqrt_ginv(a, rcond=1e-15):
a, wrap = _makearray(a)
rcond = asarray(rcond)
if _isEmpty2d(a):
m, n = a.shape[-2:]
res = empty(a.shape[:-2] + (n, m), dtype=a.dtype)
return wrap(res)
a = a.conjugate()
u, s, vt = svd(a, full_matrices=False)
# discard small singular values
cutoff = rcond[..., newaxis] * amax(s, axis=-1, keepdims=True)
large = s > cutoff
s = divide(1, s**0.5, where=large, out=s)
s[~large] = 0
res = matmul(transpose(vt), multiply(s[..., newaxis], transpose(u)))
return wrap(res)
def cpt_Moore_Penrose(L):
U, S, V = linalg.svd(L) # 求出的V实际是（共轭转置，实数相当于直接转置）转置后的结果！
num = len(S) # 非零奇异值的个数,肯定不大于min_num
num_U = len(U)
num_V = len(V)
min_num = min(num_U, num_V)
S_matrix = np.zeros((num_U, num_V)) # 构造二维S矩阵
S_plus = np.zeros((num_U, num_V))
for i in range(num):
S_matrix[i][i] = S[i]
S_plus[i][i] = 1.0 / S[i]
L_plus = np.dot(V.T, S_plus.T).dot(U.T) # weki
print('cpt_Moore_Penrose to compute u_ij,done!')
return L_plus
def svdFrog(ampFrog): ''' Extracts the pulse and gate as functions of time from the FROG with the singular value decomposition method ''' E = fftpack.fftshift( fftpack.fft( fftpack.ifftshift(ampFrog, (0,)), axis=0 ) ) nbPoints = shape(E)[0] for i in arange(1,nbPoints): E[i,:] = roll(E[i,:], i) [U, S, V] = la.svd(E) pulseField = U[:,0] gateFunction = conj(V[0,:]) return [pulseField, gateFunction]
def svdFrog(ampFrog):
'''
Extracts the pulse and gate as functions of time from the FROG
with the singular value decomposition method
'''
E = fftpack.fftshift(fftpack.fft(fftpack.ifftshift(ampFrog, (0, )),
axis=0))
nbPoints = shape(E)[0]
for i in arange(1, nbPoints):
E[i, :] = roll(E[i, :], i)
[U, S, V] = la.svd(E)
pulseField = U[:, 0]
gateFunction = conj(V[0, :])
return [pulseField, gateFunction]
def testDecomposition(self):
Wba = [[-1. / 3, 0], [0, -1. / 3]]
Wbx = [[0, 0, 2. / 3, 2. / 3], [2. / 3, 2. / 3, 0, 0]]
Wxx = [[0, 0, 2. / 3, -1. / 3], [0, 0, -1. / 3, 2. / 3],
[2. / 3, -1. / 3, 0, 0], [-1. / 3, 2. / 3, 0, 0]]
Wxa = [[2. / 3, 0], [2. / 3, 0], [0, 2. / 3], [0, 2. / 3]]
I = identity(4).tolist()
S = (matrix(I) - matrix(Wxx)).tolist()
U, s, VH = svd(S)
U = U.tolist()
s = s.tolist()
VH = VH.tolist()
res = (matrix(U) * matrix(diag(s)) * matrix(VH)).tolist()
difference = linalg.norm(matrix(res) - matrix(S))
self.assertTrue(difference < 1e-10, 'svd exapansion incorrect')
def __init__(self,
distribution,
kernel,
Z,
nu2=0.1,
gamma=0.1,
num_eigen=10):
Kameleon.__init__(self, distribution, kernel, Z, nu2, gamma)
self.num_eigen = num_eigen
if Z is None:
self.Kc = None
self.eigvalues = None
self.eigvectors = None
else:
K = self.kernel.kernel(Z)
H = Kernel.centring_matrix(len(self.Z))
self.Kc = H.dot(K.dot(H))
u, s, _ = svd(self.Kc)
self.eigvalues = s[0:self.num_eigen]
self.eigvectors = u[:, 0:self.num_eigen]
def procrustes(m1, m2):
# centralize matrices
mean_m1 = [mean(column) for column in m1.T]
m1 = m1-mean_m1
mean_m2 = [mean(column) for column in m2.T]
m2 = m2-mean_m2
# scaling
scale_m1 = sqrt(sum(square(m1))/(m1.shape[1]))
scale_m2 = sqrt(sum(square(m2))/(m2.shape[1]))
m2 = (m2/scale_m2)*scale_m1
# rotation
u, s, v = svd(dot(m1.T, m2))
r = dot(v.T, u.T)
m2 = dot(m2, r)
# set m2 center to m1 center
m2 = m2+mean_m1
return m2
def optimize_vector(self, i_vec):
"""
Optimizes torsion and bond angles by rotation around
one atom i.
1. moves coordinate origin to that atom.
2. generate a rotation matrix from a singular value decompositon
3. rotate all atoms after i.
"""
center = self.moving[i_vec]
moving_coords = self.get_moving_coords(center)
fixed_coords = self.get_fixed_coords(center)
# Do singular value decomposition
a = dot(fixed_coords, transpose(moving_coords))
u, d, vt = linalg.svd(a)
# Check reflection
if (linalg.det(u) * linalg.det(vt)) < 0:
u = dot(u, S)
# Calculate rotation
rot_matrix = dot(u, vt)
# Apply rotation
if self.accept_rotation():
self.apply_rotation(rot_matrix, i_vec, center)
def procrustes(m1, m2):
# centralize matrices
mean_m1 = [mean(column) for column in m1.T]
m1 = m1 - mean_m1
mean_m2 = [mean(column) for column in m2.T]
m2 = m2 - mean_m2
# scaling
scale_m1 = sqrt(sum(square(m1)) / (m1.shape[1]))
scale_m2 = sqrt(sum(square(m2)) / (m2.shape[1]))
m2 = (m2 / scale_m2) * scale_m1
# rotation
u, s, v = svd(dot(m1.T, m2))
r = dot(v.T, u.T)
m2 = dot(m2, r)
# set m2 center to m1 center
m2 = m2 + mean_m1
return m2
def optimize_vector(self, i_vec):
"""
Optimizes torsion and bond angles by rotation around
one atom i.
1. moves coordinate origin to that atom.
2. generate a rotation matrix from a singular value decompositon
3. rotate all atoms after i.
"""
center = self.moving[i_vec]
moving_coords = self.get_moving_coords(center)
fixed_coords = self.get_fixed_coords(center)
# Do singular value decomposition
a = dot(fixed_coords, transpose(moving_coords))
u, d, vt = linalg.svd(a)
# Check reflection
if (linalg.det(u) * linalg.det(vt))<0:
u = dot(u, S)
# Calculate rotation
rot_matrix = dot(u, vt)
# Apply rotation
if self.accept_rotation():
self.apply_rotation(rot_matrix, i_vec, center)
def cseparate(x,
M=None,
N=4096,
H=1024,
W=4096,
max_iter=200,
pre_emphasis=True,
magnitude_only=False,
svd_only=False,
transpose_spectrum=False):
"""
complex-valued frequency domain separation by independent components
using relative phase representation
inputs:
x - the audio signal to separate (1 row)
M - the number of sources to extract
options:
N - fft length in samples [4096]
H - hop size in samples [1024]
W - window length in samples (fft padded with N-W zeros) [4096]
max_iter - maximum JADE ICA iterations [200]
pre_emphasis - apply an exponential spectral pre-emphasis filter [False]
magnitude_only - whether to use magnitude-only spectrum (real-valued factorization)
svd_only - whether to use SVD instead of JADE
transpose_spectrum - whether to transpose the spectrum prior to factorization
output:
xhat - the separated signals (M rows)
xhat_all - the M separated signals mixed (1 row)
Copyright (C) 2014 Michael A. Casey, Bregman Media Labs,
Dartmouth College All Rights Reserved
"""
def pre_func(x, a, b, c):
return a * np.exp(-b * x) + c
M = 20 if M is None else M
phs_rec = lambda rp, dp: (np.angle(rp) + np.tile(
np.atleast_2d(dp).T, rp.shape[1])).cumsum(1)
F = LinearFrequencySpectrum(x, nfft=N, wfft=W, nhop=H)
U = F._phase_map()
XX = np.absolute(F.STFT)
if pre_emphasis:
xx = np.arange(F.X.shape[0])
yy = XX.mean(1)
popt, pcov = curve_fit(pre_func, xx, yy)
XX = (XX.T * (1 / pre_func(xx, *popt))).T
# w = np.r_[np.ones(64), .05*xx[64:]]
# XX = (XX.T * w).T
if magnitude_only:
X = XX
else:
X = XX * np.exp(1j * np.array(F.dPhi)) # Relative phase STFT
if transpose_spectrum:
X = X.T
if svd_only:
u, s, v = svd(X.T)
A = np.dot(u[:, :M], np.diag(s)[:M, :M])
S = v[:M, :] # v = V.H in np.linalg.svd
AS = np.dot(A,
S).T # Non Hermitian transpose avoids complex conjugation
else:
A, S = cjade(X.T, M, max_iter) # complex-domain JADE by J. F. Cardoso
AS = np.array(
A * S).T # Non Hermitian transpose avoids complex conjugation
if transpose_spectrum:
AS = AS.T
X_hat = np.absolute(AS)
if pre_emphasis:
#X_hat = (XX.T / (w)).T
X_hat = (XX.T * pre_func(xx, *popt)).T
Phi_hat = phs_rec(AS, F.dphi)
x_hat_all = F.inverse(X_hat=X_hat, Phi_hat=Phi_hat, usewin=True)
x_hat = []
for k in np.arange(M):
if svd_only:
AS = np.dot(A[:, k][:, np.newaxis], S[k, :][np.newaxis, :]).T
else:
AS = np.array(A[:, k] * S[k, :]).T
if transpose_spectrum:
AS = AS.T
X_hat = np.absolute(AS)
if pre_emphasis:
#X_hat = (XX.T / (w)).T
X_hat = (XX.T * pre_func(xx, *popt)).T
Phi_hat = phs_rec(AS, F.dphi)
x_hat.append(F.inverse(X_hat=X_hat, Phi_hat=Phi_hat, usewin=True))
return x_hat, x_hat_all
def cseparate(x, M=None, N=4096, H =1024, W=4096, max_iter=200, pre_emphasis=True, magnitude_only=False, svd_only=False, transpose_spectrum=False): """ complex-valued frequency domain separation by independent components using relative phase representation inputs: x - the audio signal to separate (1 row) M - the number of sources to extract options: N - fft length in samples [4096] H - hop size in samples [1024] W - window length in samples (fft padded with N-W zeros) [4096] max_iter - maximum JADE ICA iterations [200] pre_emphasis - apply an exponential spectral pre-emphasis filter [False] magnitude_only - whether to use magnitude-only spectrum (real-valued factorization) svd_only - whether to use SVD instead of JADE transpose_spectrum - whether to transpose the spectrum prior to factorization output: xhat - the separated signals (M rows) xhat_all - the M separated signals mixed (1 row) Copyright (C) 2014 Michael A. Casey, Bregman Media Labs, Dartmouth College All Rights Reserved """ def pre_func(x, a, b, c): return a * np.exp(-b * x) + c M = 20 if M is None else M phs_rec = lambda rp,dp: (np.angle(rp)+np.tile(np.atleast_2d(dp).T,rp.shape[1])).cumsum(1) F = LinearFrequencySpectrum(x, nfft=N, wfft=W, nhop=H) U = F._phase_map() XX = np.absolute(F.STFT) if pre_emphasis: xx = np.arange(F.X.shape[0]) yy = XX.mean(1) popt, pcov = curve_fit(pre_func, xx, yy) XX = (XX.T * (1/pre_func(xx,*popt))).T # w = np.r_[np.ones(64), .05*xx[64:]] # XX = (XX.T * w).T if magnitude_only: X = XX else: X = XX * np.exp(1j * np.array(F.dPhi)) # Relative phase STFT if transpose_spectrum: X = X.T if svd_only: u,s,v = svd(X.T) A = np.dot(u[:,:M], np.diag(s)[:M,:M]) S = v[:M,:] # v = V.H in np.linalg.svd AS = np.dot(A,S).T # Non Hermitian transpose avoids complex conjugation else: A,S = cjade(X.T, M, max_iter) # complex-domain JADE by J. F. Cardoso AS = np.array(A*S).T # Non Hermitian transpose avoids complex conjugation if transpose_spectrum: AS = AS.T X_hat = np.absolute(AS) if pre_emphasis: #X_hat = (XX.T / (w)).T X_hat = (XX.T * pre_func(xx,*popt)).T Phi_hat = phs_rec(AS, F.dphi) x_hat_all = F.inverse(X_hat=X_hat, Phi_hat=Phi_hat, usewin=True) x_hat = [] for k in np.arange(M): if svd_only: AS = np.dot(A[:,k][:,np.newaxis],S[k,:][np.newaxis,:]).T else: AS = np.array(A[:,k]*S[k,:]).T if transpose_spectrum: AS = AS.T X_hat = np.absolute(AS) if pre_emphasis: #X_hat = (XX.T / (w)).T X_hat = (XX.T * pre_func(xx,*popt)).T Phi_hat = phs_rec(AS, F.dphi) x_hat.append(F.inverse(X_hat=X_hat, Phi_hat=Phi_hat, usewin=True)) return x_hat, x_hat_all
def Dagger(self, A, Left=None, Right=None, Mul=False):
"""
Special computation of \f$\mathbf{A}^\dagger\f$ where
\f$\mathbf{L}\cdot\mathbf{A}^\dagger\cdot\mathbf{R}\f$ needs to be computed\n
@param A matrix \f$\mathbf{A}\f$ to be inverted
@param Left optional matrix that appears to the left of \f$\mathbf{A}^\dagger\f$
@param Right optional matrix that appears to the right of \f$\mathbf{A}^\dagger\f$
@param Mul (optional) whether to provide the result \f$\mathbf{L}\cdot\mathbf{A}^\dagger\cdot\mathbf{R}\f$.
Otherwise, by default, \$A^\dagger\f$ is returned.
@return matrix \f$\mathbf{A}^\dagger\f$
@throw LinAlgError if matrix cannot be inverted
@remark All matrices supplied can be either list of list or numpy matrix, but
the return type is always a numpy matrix
@details if trySVD if False and alwaysUseSVD is False, then the Left and Right
arguments are ignored and an attempt is made at calculating the Moore-Penrose
pseudo-inverse of \f$\mathbf{A}\f$. if the condition number of the resulting inverse is less
than the conditionNumberLimit, then this method fails.\n
If alwaysUseSVD is True or there is a failure and trySVD is True, then the svd
is used. The svd is not _better_ than the pseudo-inverse, per se, but it is able
to make use of the left and right matrices.\n
Many of the problems are of the form \f$\mathbf{L}\cdot\mathbf{A}^{-1}\cdot\mathbf{R}\f$.
In many cases, the matrices \f$\mathbf{L}\f$
and/or \f$\mathbf{R}\f$ are such that the all of the elements of the inverse of
\f$\mathbf{A}\f$ are not used.
Think of it as we only want to find certain elements of the inverse. Situations like
this arise, for example, when we have two wires in parallel connected to two circuit
nodes. We are not able to calculate the current through each of the wires, but we
are able to calculate the current into and out of the parallel combination. Another
example is a circuit with no ground reference provided and we are calculating the
differential voltage across an element in the circuit. In cases like this, it is
not possible to calculate the values of each circuit node, yet the answer exists and
can be found.\n
Using the svd, \f$\mathbf{A}\f$ is decomposed into \f$\mathbf{U}\cdot diag\left(\sigma\right)\cdot\mathbf{V}^H\f$
where if \f$\mathbf{U}\f$ is \f$R\times C\f$, \f$diag\left(\sigma\right)\f$
is \f$C\times C\f$ and \f$\mathbf{V}\f$ is \f$C\times C\f$. The inverse of \f$\mathbf{A}\f$
can be written as \f$\mathbf{V}\cdot diag\left(\sigma\right)^{-1}\cdot\mathbf{U}^H\f$.\n
Here, we multiply the matrix \f$\mathbf{L}\cdot\mathbf{V}\f$ and the matrix \f$\mathbf{U}^H\cdot\mathbf{R}\f$.
Then, if a column \f$rc\f$ of \f$\mathbf{L}\cdot\mathbf{V}\f$ is
all zeros or a row \f$rc\f$ of \f$\mathbf{U}^H\cdot\mathbf{R}\f$ is zero,
we know that the singular value \f$\sigma\left[rc\right]\f$ is
not used and is irrelevant - we set it to one so that it can't harm us and return
the inverse.
@see trySVD
@see alwaysUseSVD
@see conditionNumberLimit
@see singularValueLimit
@throw LinAlgError if anything fails.
"""
from numpy import linalg, array, diag, ndarray
from numpy.linalg.linalg import LinAlgError, svd
if A is None: return None
if isinstance(A, list): A = array(A)
if not self.alwaysUseSVD:
try:
# without this check, there is a gray zone where the matrix is really uninvertible
# yet, produces total garbage without raising the exception.
if 1.0 / linalg.cond(A) < self.conditionNumberLimit:
raise LinAlgError
Adagger = linalg.inv(A)
if Mul:
if Left is None: Left = array(1.)
elif isinstance(Left, list):
Left = array(Left)
if Left.shape == (1, 1):
Left = array(Left[0, 0])
if Right is None: Right = array(1.)
elif isinstance(Right, list):
Right = array(Right)
if Right.shape == (1, 1):
Right = array(Right[0, 0])
return Left.dot(Adagger).dot(Right)
else:
return Adagger
except:
# the regular matrix inverse failed
pass # will get another try at it
if self.trySVD:
try:
U, sigma, VH = svd(A, full_matrices=False)
sigma = sigma.tolist()
if Left is None: Left = array(1.)
elif isinstance(Left, (list, ndarray)):
Left = array(Left)
if Left.shape == (1, 1):
Left = array(Left[0, 0])
if Right is None: Right = array(1.)
elif isinstance(Right, (list, ndarray)):
Right = array(Right)
if Right.shape == (1, 1):
Right = array(Right[0, 0])
V = VH.conj().T
lv = Left.dot(V)
UH = U.conj().T
uhr = UH.dot(Right)
# assume that the singular value is unused according to left matrix
sl = [False] * len(sigma)
# if there is any element in column c that is nonzero
# then the singular value is used
for r in range(len(lv)):
for c in range(len(lv[0])):
if abs(lv[r][c]) > self.singularValueLimit:
sl[c] = True
# assume that the singular value is unused according to the right matrix
sr = [False] * len(sigma)
# if there is any element in column c that is nonzero
# then the singular value is used
for r in range(len(uhr)):
for c in range(len(uhr[0])):
if abs(uhr[r][c]) > self.singularValueLimit:
sr[r] = True
sUsed = [l and r for l, r in zip(sl, sr)]
for u, s in zip(sUsed, sigma):
if u and (s < self.singularValueLimit):
raise LinAlgError
sigmaInv = [
1. / self.singularValueLimit if
(not sUsed[i] and s < self.singularValueLimit) else 1. /
sigma[i] for i in range(len(sigma))
]
if Mul:
return lv.dot(diag(sigmaInv)).dot(uhr)
else:
return V.dot(diag(sigmaInv)).dot(UH)
except:
raise LinAlgError
else:
raise LinAlgError
def pinv(M):
U, D, Vt = svd(M)
wellConditioned = D > 0.000000001
return dot(U[:, wellConditioned],
dot(diag(D[wellConditioned]**-1.0), Vt[wellConditioned, :]))
def absdet(M):
U,D,Vt = svd(M)
wellConditioned = D>0.000000001
return prod(D[wellConditioned])
def svd_creator(self):
from numpy.linalg.linalg import svd
self.U, self.S, self.Vt = svd(self.matrix)
X_mean = np.mean(X, axis=0)
X_std = X - X_mean
return X_std, X_mean
'''initial_data'''
digit = 0
num = 5000
X = load_data(digit, num)
X_std, X_mean = zeroMean(X)
'''plot mean picture'''
fig_name = '0_mean'
display(X_mean, fig_name)
'''SVD decompositon'''
U, S, V = linalg.svd(X_std)
#eigvals=np.diag(S)
print(S.shape)
'''plot eigvals-dimensions'''
x = np.arange(100)
y = S[x]
plt.figure(figsize=(8, 5))
plt.plot(x, y, 'or')
plt.xlabel('dimension')
plt.ylabel('egivalues')
plt.savefig('eigenvalues_dimensions.png')
plt.show()
'''plot the first 20 eigenvectors'''
for i in xrange(20):
fig_name = 'eigenvector_' + str(i + 1) + '.png'
def svd_creator(self):
from numpy.linalg.linalg import svd
self.U, self.S, self.Vt = svd(self.matrix)
def calc(self):
self.U, self.S, self.Vt = svd(self.A)
def calc(self):
self.U, self.S, self.Vt = svd(self.A)
def learn(self, data, numsteps, visualize=False):
if type(data) is not type([]): #learn on a single sequence
numdims, numpoints = data.shape
assert numdims == self.numdims
logXi = zeros((numpoints - 1, self.numstates, self.numstates),
dtype=float)
lastlogprob = -inf
for iteration in range(numsteps):
print "EM iteration: %d" % iteration
#E-step:
logAlpha, logBeta = self.alphabeta(data)
#compute xi and gamma:
for t in range(numpoints - 1):
for i in range(self.numstates):
for j in range(self.numstates):
logXi[t, i, j] = logAlpha[i, t]+\
self.logTransitionProbs[i, j]+\
self._loggaussian(data[:, t+1], j)+\
logBeta[j, t+1]
logXi[t, :, :] -= logsumexp(logXi[t, :, :].flatten())
logGamma = vstack(
(logsumexp(logXi, 2), logsumexp(logXi[-1, :, :], 1)))
logprob = logsumexp(logAlpha[:, -1])
print "logprob = %f" % logprob
if abs(logprob - lastlogprob) <= 10**-6:
print "converged"
break
lastlogprob = logprob
#M-step:
self.logInitProbs[:] = logGamma[0, :]
self.logTransitionProbs[:] = logsumexp(logXi, 0) - \
logsumexp(logGamma[:-1,:], 0)[:, newaxis]
G = exp(logGamma - logsumexp(logGamma, 0)[newaxis, :])
for k in range(self.numstates):
self.means[:, k] = sum(G[:, k][newaxis, :] * data, 1)
data_m = data - self.means[:, k][:, newaxis]
self.covs[:, :, k] = dot((data_m * G[:, k][newaxis, :]),
data_m.T)
#threshold eigenvalues:
for k in range(self.numstates):
U, D, Vt = svd(self.covs[:, :, k])
D[D < 0.01] = 0.01
self.covs[:, :, k] = dot(U, dot(diag(D), Vt))
#visualize:
if visualize and self.numdims == 2:
data = concatenate(data)
cla()
scatter(*data)
for k in range(self.numstates):
plotGaussian(self.means[:, k], self.covs[:, :, k])
else: #got a list -- learn on multiple sequences
numseqs = len(data)
lastaverageLogprob = -inf
logAlphas = [None] * numseqs
logBetas = [None] * numseqs
logXis = [None] * numseqs
logGammas = [None] * numseqs
logprobs = [None] * numseqs
dataarray = concatenate(data)
for iteration in range(numsteps):
print "EM iteration: %d" % iteration
#E-step:
for seqindex, d in enumerate(data):
numdims, numpoints = d.shape
logAlphas[seqindex], logBetas[seqindex] = self.alphabeta(d)
#compute xi and gamma:
assert numdims == self.numdims
logXis[seqindex] = zeros(
(numpoints - 1, self.numstates, self.numstates),
dtype=float)
for t in range(numpoints - 1):
for i in range(self.numstates):
for j in range(self.numstates):
logXis[seqindex][t, i, j] = \
logAlphas[seqindex][i, t]+\
self.logTransitionProbs[i, j]+\
self._loggaussian(d[:, t+1], j)+\
logBetas[seqindex][j, t+1]
logXis[seqindex][t, :, :] -= logsumexp(
logXis[seqindex][t, :, :].flatten())
logGammas[seqindex] = vstack(
(logsumexp(logXis[seqindex],
2), logsumexp(logXis[seqindex][-1, :, :],
1)))
logprobs[seqindex] = logsumexp(logAlphas[seqindex][:, -1])
averageLogprob = mean(logprobs)
print "logprob = %f" % averageLogprob
if abs(averageLogprob - lastaverageLogprob) <= 10**-6:
print "converged"
break
lastaverageLogprob = averageLogprob
#M-step:
logInitProbs = []
logTransitionProbs = []
oldmeans = self.means.copy()
self.logInitProbs = \
logsumexp(array([l[0,:] for l in logGammas]),0)\
-log(double(numseqs))
logXisArray = concatenate(logXis, 0)
logGammasArray_ = \
concatenate(map(lambda x: x[:-1,:],logGammas), 0)
self.logTransitionProbs = logsumexp(logXisArray, 0) \
- logsumexp(logGammasArray_, 0)[:, newaxis]
dataarray = concatenate(data, 1)
logGammasArray = concatenate(logGammas, 0)
G = exp(logGammasArray -
logsumexp(logGammasArray, 0)[newaxis, :])
for k in range(self.numstates):
self.means[:, k] = sum(
exp(logGammasArray[:, k] -
logsumexp(logGammasArray[:, k], 0))[:, newaxis] *
dataarray.T, 0)
data_m = dataarray - oldmeans[:, k][:, newaxis]
self.covs[:, :, k] = dot((data_m * G[:, k][newaxis, :]),
data_m.T)
#threshold eigenvalues:
for k in range(self.numstates):
U, D, Vt = svd(self.covs[:, :, k])
D[D < 0.01] = 0.01
self.covs[:, :, k] = dot(U, dot(diag(D), Vt))
#visualize:
if visualize and self.numdims == 2:
cla()
scatter(*dataarray)
for k in range(self.numstates):
plotGaussian(self.means[:, k], self.covs[:, :, k])
def learn(self, data, numsteps, visualize=False):
if type(data) is not type([]): #learn on a single sequence
numdims, numpoints = data.shape
assert numdims == self.numdims
logXi = zeros((numpoints-1,self.numstates,self.numstates),
dtype=float)
lastlogprob = -inf
for iteration in range(numsteps):
print "EM iteration: %d" % iteration
#E-step:
logAlpha, logBeta = self.alphabeta(data)
#compute xi and gamma:
for t in range(numpoints-1):
for i in range(self.numstates):
for j in range(self.numstates):
logXi[t, i, j] = logAlpha[i, t]+\
self.logTransitionProbs[i, j]+\
self._loggaussian(data[:, t+1], j)+\
logBeta[j, t+1]
logXi[t, :, :] -= logsumexp(logXi[t, :, :].flatten())
logGamma = vstack( (logsumexp(logXi, 2),
logsumexp(logXi[-1,:,:], 1)) )
logprob = logsumexp(logAlpha[:, -1])
print "logprob = %f" % logprob
if abs(logprob-lastlogprob)<=10**-6:
print "converged"
break
lastlogprob = logprob
#M-step:
self.logInitProbs[:] = logGamma[0, :]
self.logTransitionProbs[:] = logsumexp(logXi, 0) - \
logsumexp(logGamma[:-1,:], 0)[:, newaxis]
G = exp(logGamma - logsumexp(logGamma, 0)[newaxis,:])
for k in range(self.numstates):
self.means[:, k] = sum(G[:, k][newaxis,:]*data, 1)
data_m = data-self.means[:, k][:,newaxis]
self.covs[:, :, k] = dot((data_m*G[:, k][newaxis,:]),
data_m.T)
#threshold eigenvalues:
for k in range(self.numstates):
U, D, Vt = svd(self.covs[:, :, k])
D[D<0.01] = 0.01
self.covs[:, :, k] = dot(U, dot(diag(D), Vt))
#visualize:
if visualize and self.numdims == 2:
data = concatenate(data)
cla()
scatter(*data)
for k in range(self.numstates):
plotGaussian(self.means[:,k], self.covs[:, :, k])
else: #got a list -- learn on multiple sequences
numseqs = len(data)
lastaverageLogprob = -inf
logAlphas = [None] * numseqs
logBetas = [None] * numseqs
logXis = [None] * numseqs
logGammas = [None] * numseqs
logprobs = [None] * numseqs
dataarray = concatenate(data)
for iteration in range(numsteps):
print "EM iteration: %d" % iteration
#E-step:
for seqindex, d in enumerate(data):
numdims, numpoints = d.shape
logAlphas[seqindex], logBetas[seqindex] = self.alphabeta(d)
#compute xi and gamma:
assert numdims == self.numdims
logXis[seqindex] = zeros(
(numpoints-1,self.numstates,self.numstates), dtype=float)
for t in range(numpoints-1):
for i in range(self.numstates):
for j in range(self.numstates):
logXis[seqindex][t, i, j] = \
logAlphas[seqindex][i, t]+\
self.logTransitionProbs[i, j]+\
self._loggaussian(d[:, t+1], j)+\
logBetas[seqindex][j, t+1]
logXis[seqindex][t, :, :] -= logsumexp(
logXis[seqindex][t, :, :].flatten())
logGammas[seqindex] = vstack(
(logsumexp(logXis[seqindex], 2),
logsumexp(logXis[seqindex][-1,:,:], 1)) )
logprobs[seqindex] = logsumexp(logAlphas[seqindex][:, -1])
averageLogprob = mean(logprobs)
print "logprob = %f" % averageLogprob
if abs(averageLogprob-lastaverageLogprob)<=10**-6:
print "converged"
break
lastaverageLogprob = averageLogprob
#M-step:
logInitProbs = []
logTransitionProbs = []
oldmeans = self.means.copy()
self.logInitProbs = \
logsumexp(array([l[0,:] for l in logGammas]),0)\
-log(double(numseqs))
logXisArray = concatenate(logXis, 0)
logGammasArray_ = \
concatenate(map(lambda x: x[:-1,:],logGammas), 0)
self.logTransitionProbs = logsumexp(logXisArray, 0) \
- logsumexp(logGammasArray_, 0)[:, newaxis]
dataarray = concatenate(data, 1)
logGammasArray = concatenate(logGammas, 0)
G = exp(logGammasArray-logsumexp(logGammasArray,0)[newaxis,:])
for k in range(self.numstates):
self.means[:, k] = sum( exp(logGammasArray[:,k]-
logsumexp(logGammasArray[:,k],0))[:,newaxis]
* dataarray.T, 0)
data_m = dataarray-oldmeans[:, k][:,newaxis]
self.covs[:, :, k] = dot((data_m*G[:,k][newaxis,:]),
data_m.T)
#threshold eigenvalues:
for k in range(self.numstates):
U, D, Vt = svd(self.covs[:, :, k])
D[D<0.01] = 0.01
self.covs[:, :, k] = dot(U, dot(diag(D), Vt))
#visualize:
if visualize and self.numdims == 2:
cla()
scatter(*dataarray)
for k in range(self.numstates):
plotGaussian(self.means[:,k], self.covs[:, :, k])
def absdet(M):
U, D, Vt = svd(M)
wellConditioned = D > 0.000000001
return prod(D[wellConditioned])
def eigen_adapt(self):
u, s, _ = svd(self.cov_est)
self.eigvalues = s[0:self.num_eigen]
# print "estimated eigenvalues: ", self.eigvalues
self.eigvectors = u[:, 0:self.num_eigen]
def pinv(M):
U,D,Vt = svd(M)
wellConditioned = D>0.000000001
return dot(U[:,wellConditioned],
dot(diag(D[wellConditioned]**-1.0), Vt[wellConditioned,:]))
