def text_apr(data, d1data=None, d2data=None):
N = 10
T = data.shape[-1]
C, D, DD = dcheb(T, N)
K = fblas.dgemm(alpha=1., a=data.T, b=C.T, trans_a=True)
apr = fblas.dgemm(alpha=1., a=C.T, b=K, trans_a=False, trans_b=True).T
d1apr = fblas.dgemm(alpha=1., a=D.T, b=K, trans_a=False, trans_b=True).T
d2apr = fblas.dgemm(alpha=1., a=DD.T, b=K, trans_a=False, trans_b=True).T
print ((data - apr) ** 2).sum() / (data**2).sum()
if d1data is not None:
print ((d1data - d1apr) ** 2).sum() / (d1data**2).sum()
if d2data is not None:
print ((d2data - d2apr) ** 2).sum() / (d2data**2).sum()
def mygemm(alpha,A,B,dtype=None,**kwargs):
'''
my gemm function that uses scipy fblas functions.
'''
from scipy.linalg.fblas import dgemm, sgemm
if dtype is None:
dtype=A.dtype
if dtype != np.float32 and dtype != np.float64:
print 'Error: this function cannot deal with such dtype.'
exit()
if not (A.flags['F_CONTIGUOUS'] or A.flags['C_CONTIGUOUS']) \
or not (B.flags['F_CONTIGUOUS'] or B.flags['C_CONTIGUOUS']):
print 'Matrices should either be C or F contiguous.'
exit()
if A.dtype != dtype:
A=np.asarray(A,dtype=dtype)
if B.dtype != dtype:
B=np.asarray(B,dtype=dtype)
if A.flags['F_CONTIGUOUS']:
trans_a=0
else:
A=A.T
trans_a=1
if B.flags['F_CONTIGUOUS']:
trans_b=0
else:
B=B.T
trans_b=1
if dtype==np.float32:
return sgemm(alpha,A,B,trans_a=trans_a,trans_b=trans_b,**kwargs)
else:
return dgemm(alpha,A,B,trans_a=trans_a,trans_b=trans_b,**kwargs)
def mean_cov(X):
n, p = X.shape
m = X.mean(axis=0)
# covariance matrix with correction for rounding error
# S = (cx'*cx - (scx'*scx/n))/(n-1)
# Am Stat 1983, vol 37: 242-247.
cx = X - m
cxT_a = zeros((p - 1, n), float64)
cxT_b = zeros((p - 1, n), float64)
cxT_a[:, :] = (cx.T)[0:p - 1, :]
cxT_b[:, :] = (cx.T)[1:p, :]
scx = cx.sum(axis=0)
scx_op = dger(-1.0 / n, scx, scx)
S = dgemm(1.0,
cx.T,
cx.T,
beta=1.0,
c=scx_op,
trans_a=0,
trans_b=1,
overwrite_c=1)
#S = dgemm(1.0, cxT_a, cxT_b, beta=1.0,
# c=scx_op, trans_a=0, trans_b=1, overwrite_c=1)
S[:] *= 1.0 / (n - 1)
return m, S.T
def __setJac2(self):
'''Calculates :math:`J^T J` and :math:`J^T e` for the training data.
Used for Levenberg-Marquardt method.'''
self.jac2.fill(0)
self.jacDiff.fill(0)
for i in range(self.tTD.nBlocks):
data = self.tTD.getDataBlock(i)
vals,valOut,hiddenOut = self.__processDataBlock(data)
diffs = numexpr.evaluate('valOut - vals')
jac = np.empty((data.shape[0], (self.nHid) * (self.nIn+1) + self.nHid + 1))
d0 = numexpr.evaluate('-vals * (1 - vals)')
ot = (np.outer(d0, self.l2))
dj = numexpr.evaluate('hiddenOut * (1 - hiddenOut) * ot')
I = np.tile(np.arange(data.shape[0]), (self.nHid + 1, 1)).flatten('F')
J = np.arange(data.shape[0] * (self.nHid + 1))
Q = ss.csc_matrix((dj.flatten(), np.vstack((J, I))), (data.shape[0] * (self.nHid + 1), data.shape[0]))
jac[:, 0:self.nHid + 1] = ss.spdiags(d0, 0, data.shape[0], data.shape[0]).dot(hiddenOut)
Q2 = np.reshape(Q.dot(data), (data.shape[0],(self.nIn+1) * (self.nHid + 1)))
jac[:, self.nHid + 1:jac.shape[1]] = Q2[:, 0:Q2.shape[1] - (self.nIn+1)]
if hasfblas:
self.jac2 += fblas.dgemm(1.0,a=jac.T,b=jac.T,trans_b=True)
self.jacDiff += fblas.dgemv(1.0,a=jac.T,x=diffs)
else:
self.jac2 += np.dot(jac.T,jac)
self.jacDiff += np.dot(jac.T,diffs)
def __setJac2(self):
'''Calculates :math:`J^T J` and :math:`J^T e` for the training data.
Used for Levenberg-Marquardt method.'''
self.jac2.fill(0)
self.jacDiff.fill(0)
for i in range(self.tTD.nBlocks):
data = self.tTD.getDataBlock(i)
vals, valOut, hiddenOut = self.__processDataBlock(data)
diffs = numexpr.evaluate('valOut - vals')
jac = np.empty(
(data.shape[0], (self.nHid) * (self.nIn + 1) + self.nHid + 1))
d0 = numexpr.evaluate('-vals * (1 - vals)')
ot = (np.outer(d0, self.l2))
dj = numexpr.evaluate('hiddenOut * (1 - hiddenOut) * ot')
I = np.tile(np.arange(data.shape[0]),
(self.nHid + 1, 1)).flatten('F')
J = np.arange(data.shape[0] * (self.nHid + 1))
Q = ss.csc_matrix((dj.flatten(), np.vstack((J, I))),
(data.shape[0] * (self.nHid + 1), data.shape[0]))
jac[:, 0:self.nHid + 1] = ss.spdiags(d0, 0, data.shape[0],
data.shape[0]).dot(hiddenOut)
Q2 = np.reshape(Q.dot(data),
(data.shape[0], (self.nIn + 1) * (self.nHid + 1)))
jac[:, self.nHid + 1:jac.shape[1]] = Q2[:, 0:Q2.shape[1] -
(self.nIn + 1)]
if hasfblas:
self.jac2 += fblas.dgemm(1.0, a=jac.T, b=jac.T, trans_b=True)
self.jacDiff += fblas.dgemv(1.0, a=jac.T, x=diffs)
else:
self.jac2 += np.dot(jac.T, jac)
self.jacDiff += np.dot(jac.T, diffs)
def mygemm(alpha, A, B, dtype=None, **kwargs):
'''
my gemm function that uses scipy fblas functions.
'''
from scipy.linalg.fblas import dgemm, sgemm
if dtype is None:
dtype = A.dtype
if dtype != np.float32 and dtype != np.float64:
print 'Error: this function cannot deal with such dtype.'
exit()
if not (A.flags['F_CONTIGUOUS'] or A.flags['C_CONTIGUOUS']) \
or not (B.flags['F_CONTIGUOUS'] or B.flags['C_CONTIGUOUS']):
print 'Matrices should either be C or F contiguous.'
exit()
if A.dtype != dtype:
A = np.asarray(A, dtype=dtype)
if B.dtype != dtype:
B = np.asarray(B, dtype=dtype)
if A.flags['F_CONTIGUOUS']:
trans_a = 0
else:
A = A.T
trans_a = 1
if B.flags['F_CONTIGUOUS']:
trans_b = 0
else:
B = B.T
trans_b = 1
if dtype == np.float32:
return sgemm(alpha, A, B, trans_a=trans_a, trans_b=trans_b, **kwargs)
else:
return dgemm(alpha, A, B, trans_a=trans_a, trans_b=trans_b, **kwargs)
def calc_argmax(data):
T = data.shape[-1]
N = T // 5
t0 = time()
C, D, DD = dcheb(T, N)
K = fblas.dgemm(alpha=1., b=data.T, a=C.T).T
apr = fblas.dgemm(alpha=1., a=C.T, b=K.T, trans_a=True).T
argmaxs = apr.argmax(axis=-1)
# Ds = DD[argmaxs] # makes copies
# DDs = DD[argmaxs]
# pers = inner1d(K, Ds) / inner1d(K, DDs)
pers = np.empty_like(argmaxs, dtype=float)
for i, ax in enumerate(argmaxs): # does not make copies. slower.
pers[i] = fblas.ddot(K[i], D[ax]) / fblas.ddot(K[i], DD[ax])
pers[pers > 1] = 0
return argmaxs - pers
def linear_least_squares(a, b, residuals=False):
"""
Return the least-squares solution to a linear matrix equation.
Solves the equation `a x = b` by computing a vector `x` that
minimizes the Euclidean 2-norm `|| b - a x ||^2`. The equation may
be under-, well-, or over- determined (i.e., the number of
linearly independent rows of `a` can be less than, equal to, or
greater than its number of linearly independent columns). If `a`
is square and of full rank, then `x` (but for round-off error) is
the "exact" solution of the equation.
Parameters
----------
a : (M, N) array_like
"Coefficient" matrix.
b : (M,) array_like
Ordinate or "dependent variable" values.
residuals : bool
Compute the residuals associated with the least-squares solution
Returns
-------
x : (M,) ndarray
Least-squares solution. The shape of `x` depends on the shape of
`b`.
residuals : int (Optional)
Sums of residuals; squared Euclidean 2-norm for each column in
``b - a*x``.
"""
if type(a) != np.ndarray or not a.flags['C_CONTIGUOUS']:
warn('Matrix a is not a C-contiguous numpy array. The solver will create a copy, which will result' + \
' in increased memory usage.')
a = np.asarray(a, order='c')
i = dgemm(alpha=1.0, a=a.T, b=a.T, trans_b=True)
x = np.linalg.solve(i, dgemm(alpha=1.0, a=a.T, b=b)).flatten()
if residuals:
return x, np.linalg.norm(np.dot(a, x) - b)
else:
return x
def linear_least_squares(a, b, residuals=False):
"""
Return the least-squares solution to a linear matrix equation.
Solves the equation `a x = b` by computing a vector `x` that
minimizes the Euclidean 2-norm `|| b - a x ||^2`. The equation may
be under-, well-, or over- determined (i.e., the number of
linearly independent rows of `a` can be less than, equal to, or
greater than its number of linearly independent columns). If `a`
is square and of full rank, then `x` (but for round-off error) is
the "exact" solution of the equation.
Parameters
----------
a : (M, N) array_like
"Coefficient" matrix.
b : (M,) array_like
Ordinate or "dependent variable" values.
residuals : bool
Compute the residuals associated with the least-squares solution
Returns
-------
x : (M,) ndarray
Least-squares solution. The shape of `x` depends on the shape of
`b`.
residuals : int (Optional)
Sums of residuals; squared Euclidean 2-norm for each column in
``b - a*x``.
"""
if type(a) != np.ndarray or not a.flags['C_CONTIGUOUS']:
warn('Matrix a is not a C-contiguous numpy array. The solver will create a copy, which will result' + \
' in increased memory usage.')
a = np.asarray(a, order='c')
i = dgemm(alpha=1.0, a=a.T, b=a.T, trans_b=True)
x = np.linalg.solve(i, dgemm(alpha=1.0, a=a.T, b=b)).flatten()
if residuals:
return x, np.linalg.norm(np.dot(a, x) - b)
else:
return x
def verify_ga_gemm(ta, tb, num_m, num_n, num_k, alpha, g_a, g_b, beta, g_c):
tmpa = np.ndarray((num_m, num_k), dtype=np.float64)
tmpb = np.ndarray((num_k, num_n), dtype=np.float64)
tmpc = np.ndarray((num_m, num_n), dtype=np.float64)
tmpa = ga.get(g_a, buffer=tmpa)
tmpb = ga.get(g_b, buffer=tmpb)
tmpc = ga.get(g_c, buffer=tmpc)
if not ta and not tb:
result = dgemm(alpha, tmpa, tmpb, beta=beta, trans_a=ta, trans_b=tb)
elif ta and not tb:
result = dgemm(alpha, tmpa, tmpb, beta=beta, trans_a=ta, trans_b=tb)
elif not ta and tb:
result = dgemm(alpha, tmpa, tmpb, beta=beta, trans_a=ta, trans_b=tb)
elif ta and tb:
result = dgemm(alpha, tmpa, tmpb, beta=beta, trans_a=ta, trans_b=tb)
else:
raise ValueError, "shouldn't get here"
abs_value = np.abs(tmpc-result)
if np.any(abs_value > 1):
ga.error('verify ga.gemm failed')
def mean_cov(X):
n,p = X.shape
m = X.mean(axis=0)
# covariance matrix with correction for rounding error
# S = (cx'*cx - (scx'*scx/n))/(n-1)
# Am Stat 1983, vol 37: 242-247.
cx = X - m
cxT_a = zeros((p-1,n),float64)
cxT_b = zeros((p-1,n),float64)
cxT_a[:,:] = (cx.T)[0:p-1,:]
cxT_b[:,:] = (cx.T)[1:p,:]
scx = cx.sum(axis=0)
scx_op = dger(-1.0/n,scx,scx)
S = dgemm(1.0, cx.T, cx.T, beta=1.0,
c=scx_op, trans_a=0, trans_b=1, overwrite_c=1)
#S = dgemm(1.0, cxT_a, cxT_b, beta=1.0,
# c=scx_op, trans_a=0, trans_b=1, overwrite_c=1)
S[:] *= 1.0/(n-1)
return m,S.T
def gemm(alpha,A,B,dtype=None,**kwargs):
'''A gemm function that uses scipy fblas functions, avoiding matrix copy
when the input is transposed.
The returned matrix is designed to be C_CONTIGUOUS.
'''
from scipy.linalg.fblas import dgemm, sgemm
if A.ndim != 2 or B.ndim != 2:
raise TypeError, 'mygemm only deals with 2-D matrices.'
if dtype is None:
dtype=A.dtype
if dtype != np.float32 and dtype != np.float64:
raise TypeError, 'Error: this function cannot deal with dtype {}.'.format(dtype)
if not (A.flags['F_CONTIGUOUS'] or A.flags['C_CONTIGUOUS']) \
or not (B.flags['F_CONTIGUOUS'] or B.flags['C_CONTIGUOUS']):
raise TypeError, 'Matrices should either be C or F contiguous.'
if A.dtype != dtype:
A=np.asarray(A,dtype=dtype)
if B.dtype != dtype:
B=np.asarray(B,dtype=dtype)
# In fact, what we are doing here is (1) compute B*A, and (2) transpose the
# result. The reason is that fblas returns F_CONTINUOUS matrices, so doing
# this enables us to get a final output that is C_CONTIGUOUS.
if not B.flags['F_CONTIGUOUS']:
B = B.T
trans_b=0
else:
trans_b=1
if not A.flags['F_CONTIGUOUS']:
A = A.T
trans_a=0
else:
trans_a=1
if dtype==np.float32:
return sgemm(alpha,B,A,trans_a=trans_b,trans_b=trans_a,**kwargs).T
else:
return dgemm(alpha,B,A,trans_a=trans_b,trans_b=trans_a,**kwargs).T
def admmDP(C, a=1.0, props={}, warmX=None, warmU=None):
n = C.shape[0]
logger = logging.getLogger("search.admm.dp")
beta = props.get('priorWeight', 0.01)
maxIter = props.get("maxParamaterOptIters", 1000)
epsilon = props.get("epsilon", 1e-6)
returnIntermediate = props.get("returnIntermediate", False)
regDiagonal = props.get("regDiagonal", False)
degreePriorADMM = props.get("degreePrior", True)
adaptiveMu = props.get("adaptiveMu", True)
props['dualDecompEpsilon'] = props.get('dualDecompEpsilon', 1e-10)
sTime = time.time()
logger.info("Starting ADMM learning. n=%d beta=%1.4f", n, beta)
mu = props.get("mu", 0.1) #10.0
muChange = props.get("muChange", 0.1)
if props.get("normalizeForADMM", True):
logger.info("-- NORMALIZING C --")
covNormalizer = sqrt(diagonal(C))
C = C / outer(covNormalizer, covNormalizer)
else:
logger.info("NOT NORMALIZING C")
# Rescale to make beta's range a better fit
maxOffDiag = numpy.max(numpy.abs(tril(C, -1)))
C = array(C / maxOffDiag)
if warmX is not None:
X = warmX
else:
X = eye(n)
if warmU is not None:
U = warmU
else:
U = eye(n)
Z = copy(X)
ll = inf
Xs = []
gs = []
ds = []
if degreePriorADMM:
adelta = -a[:n] + a[1:]
adeltaMat = outer(ones(n), adelta)
beta = beta / 2
logger.error("adelta: %s", adelta[:6])
warmV = zeros((n,n))
if a is None:
a = 1.0
for i in range(maxIter):
#####################################################################
##### Eigenvalue update to X
logger.debug("Performing eigenvalue decomposition")
for retry in range(6):
try:
A = mu*(Z - U) - C
(lamb, Q) = linalg.eigh(A)
logger.debug("Decomposition finished")
break
except numpy.linalg.linalg.LinAlgError as err:
# If A is not in the PSD cone, we reduce the step size mu
logger.error("Failed eigendecomposition with mu=%2.2e", mu)
mu *= 0.5
U /= 0.5
logger.error("Retry %d, halving mu to: %2.5f", retry, mu)
newEigs = (lamb + sqrt(lamb*lamb + 4*mu)) / (2*mu)
X = FB.dgemm(alpha=1.0, a=(Q*newEigs), b=Q, trans_b=True)
#### Soft thresholding update Z
logger.debug("Starting Proximal step")
Zpreshrink = X + U
Zlast = copy(Z)
if degreePriorADMM:
Z = proxSubDualDecomp(Zpreshrink, beta/mu, adelta, adeltaMat, props, warmV)
else:
Z = shrink(Zpreshrink, beta*a/mu, regDiagonal)
if props.get("nonpositivePrecision", False):
Z = Z * (Z < 0) + diag(diag(Z))
### Update U ( U is the sum of residuals so far )
logger.debug("Updating U")
U += X - Z
#####################################################################
dualResidual = linalg.norm(Z - Zlast)
residual = linalg.norm(X-Z)
if adaptiveMu:
# if the two residuals differ my more than this factor, adjust mu (p20)
differenceMargin = 10
if residual > dualResidual*differenceMargin:
mu *= 1.0 + muChange
U /= 1.0 + muChange
logger.debug("*** Increasing mu to %2.6f", mu)
elif dualResidual > residual*differenceMargin:
mu *= 1.0 - muChange
U /= 1.0 - muChange
logger.debug("*** Decreasing mu to %2.6f", mu)
# Ensure that the dual decomp procedure is run with enough accuracy
ddeps = props['dualDecompEpsilon']
margin = 50.0
if residual < margin*ddeps or dualResidual < margin*ddeps:
props['dualDecompEpsilon'] = min(residual, dualResidual)/margin
if returnIntermediate:
ds.append(dualResidual)
gs.append(residual)
if residual < epsilon and dualResidual < epsilon:
logger.info("Converged to %2.3e in %i iters", residual, i+1)
break
edges = (count_nonzero(Z) - n) / 2
logger.info("Iter %d, res: %2.2e, dual res: %2.2e, mu=%1.1e, %d edges free",
i+1, residual, dualResidual, mu, edges)
eTime = time.time()
timeTaken = eTime-sTime
logger.info("Time taken(s): %5.7f", timeTaken)
if residual > epsilon or dualResidual > epsilon:
logger.error("NONCONVERGENCE!!, res: %2.2e, dres: %2.2e, iters: %d",
residual, dualResidual, i)
edges = (count_nonzero(Z) - n) / 2
logger.info("regDiagonal: %s, beta: %2.4f", regDiagonal, beta)
logger.info("Edges %d out of %d | eps=%1.1e", edges, (n*n - n)/2, epsilon)
logger.info("Final residual=%2.2e, dual res=%2.2e", residual, dualResidual)
return {'X': Z, 'U': U, 'obj': ll, 'iteration': i+1, 'Xs': Xs, 'gs': gs, 'ds': ds,
'timeTaken': timeTaken, 'edges': edges, 'Zpreshrink': Zpreshrink, 'bm': beta/mu}
def admmDP(C, a=1.0, props={}, warmX=None, warmU=None):
n = C.shape[0]
logger = logging.getLogger("search.admm.dp")
beta = props.get('priorWeight', 0.01)
maxIter = props.get("maxParamaterOptIters", 1000)
epsilon = props.get("epsilon", 1e-6)
returnIntermediate = props.get("returnIntermediate", False)
regDiagonal = props.get("regDiagonal", False)
degreePriorADMM = props.get("degreePrior", True)
adaptiveMu = props.get("adaptiveMu", True)
props['dualDecompEpsilon'] = props.get('dualDecompEpsilon', 1e-10)
sTime = time.time()
logger.info("Starting ADMM learning. n=%d beta=%1.4f", n, beta)
mu = props.get("mu", 0.1) #10.0
muChange = props.get("muChange", 0.1)
if props.get("normalizeForADMM", True):
logger.info("-- NORMALIZING C --")
covNormalizer = sqrt(diagonal(C))
C = C / outer(covNormalizer, covNormalizer)
else:
logger.info("NOT NORMALIZING C")
# Rescale to make beta's range a better fit
maxOffDiag = numpy.max(numpy.abs(tril(C, -1)))
C = array(C / maxOffDiag)
if warmX is not None:
X = warmX
else:
X = eye(n)
if warmU is not None:
U = warmU
else:
U = eye(n)
Z = copy(X)
ll = inf
Xs = []
gs = []
ds = []
if degreePriorADMM:
adelta = -a[:n] + a[1:]
adeltaMat = outer(ones(n), adelta)
beta = beta / 2
logger.error("adelta: %s", adelta[:6])
warmV = zeros((n, n))
if a is None:
a = 1.0
for i in range(maxIter):
#####################################################################
##### Eigenvalue update to X
logger.debug("Performing eigenvalue decomposition")
for retry in range(6):
try:
A = mu * (Z - U) - C
(lamb, Q) = linalg.eigh(A)
logger.debug("Decomposition finished")
break
except numpy.linalg.linalg.LinAlgError as err:
# If A is not in the PSD cone, we reduce the step size mu
logger.error("Failed eigendecomposition with mu=%2.2e", mu)
mu *= 0.5
U /= 0.5
logger.error("Retry %d, halving mu to: %2.5f", retry, mu)
newEigs = (lamb + sqrt(lamb * lamb + 4 * mu)) / (2 * mu)
X = FB.dgemm(alpha=1.0, a=(Q * newEigs), b=Q, trans_b=True)
#### Soft thresholding update Z
logger.debug("Starting Proximal step")
Zpreshrink = X + U
Zlast = copy(Z)
if degreePriorADMM:
Z = proxSubDualDecomp(Zpreshrink, beta / mu, adelta, adeltaMat,
props, warmV)
else:
Z = shrink(Zpreshrink, beta * a / mu, regDiagonal)
if props.get("nonpositivePrecision", False):
Z = Z * (Z < 0) + diag(diag(Z))
### Update U ( U is the sum of residuals so far )
logger.debug("Updating U")
U += X - Z
#####################################################################
dualResidual = linalg.norm(Z - Zlast)
residual = linalg.norm(X - Z)
if adaptiveMu:
# if the two residuals differ my more than this factor, adjust mu (p20)
differenceMargin = 10
if residual > dualResidual * differenceMargin:
mu *= 1.0 + muChange
U /= 1.0 + muChange
logger.debug("*** Increasing mu to %2.6f", mu)
elif dualResidual > residual * differenceMargin:
mu *= 1.0 - muChange
U /= 1.0 - muChange
logger.debug("*** Decreasing mu to %2.6f", mu)
# Ensure that the dual decomp procedure is run with enough accuracy
ddeps = props['dualDecompEpsilon']
margin = 50.0
if residual < margin * ddeps or dualResidual < margin * ddeps:
props['dualDecompEpsilon'] = min(residual, dualResidual) / margin
if returnIntermediate:
ds.append(dualResidual)
gs.append(residual)
if residual < epsilon and dualResidual < epsilon:
logger.info("Converged to %2.3e in %i iters", residual, i + 1)
break
edges = (count_nonzero(Z) - n) / 2
logger.info(
"Iter %d, res: %2.2e, dual res: %2.2e, mu=%1.1e, %d edges free",
i + 1, residual, dualResidual, mu, edges)
eTime = time.time()
timeTaken = eTime - sTime
logger.info("Time taken(s): %5.7f", timeTaken)
if residual > epsilon or dualResidual > epsilon:
logger.error("NONCONVERGENCE!!, res: %2.2e, dres: %2.2e, iters: %d",
residual, dualResidual, i)
edges = (count_nonzero(Z) - n) / 2
logger.info("regDiagonal: %s, beta: %2.4f", regDiagonal, beta)
logger.info("Edges %d out of %d | eps=%1.1e", edges, (n * n - n) / 2,
epsilon)
logger.info("Final residual=%2.2e, dual res=%2.2e", residual, dualResidual)
return {
'X': Z,
'U': U,
'obj': ll,
'iteration': i + 1,
'Xs': Xs,
'gs': gs,
'ds': ds,
'timeTaken': timeTaken,
'edges': edges,
'Zpreshrink': Zpreshrink,
'bm': beta / mu
}
