def uhdu(A, n):
"""
Calculates the UDUH decomposition of the Hermitian matrix A
such that U is unit upper triangular, D is diagonal and UDU'=A
(' = H = symmetric conjugated)
Now we avoid using the complex sqrt by instead introducing two complex add
Returns [U D]
"""
U = eye(n, dtype=A.dtype)
D = zeros(n, dtype=A.dtype)
for i in range(n):
upperColSum = 0
for k in range(i):
upperColSum += U[k, i] * U[k, i].conjugate() * D[k]
D[i] = A[i, i] - upperColSum
for j in range(i + 1, n):
upperColSum = 0
for k in range(i):
upperColSum += U[k, i].conjugate() * U[k, j] * D[k]
U[i, j] = (A[i, j] - upperColSum) / D[i]
return [U, D]
def woodbury(X, Ainv, m, up = True):
'''
Does a rank-k adjustment of Ainv using the woodbury matrix identity
(A + XX^H)^(-1) = A^(-1) - (A^(-1)X ( I + X^(H)A^(-1)X )^(-1)X^(H)A^(-1))
If up == True: A rank update is performed,
otherwise we do a rank degrade
NB! Not finished
'''
factor = 1
if up != True:
factor = -1
XH = mynp.conjugatetranspose(X)
XHAinv = mynp.dot(XH, Ainv)
AinvX = mynp.dot(Ainv, XH)
XHAinvX = mynp.dot(XHAinv, X)
IXHAinvX = mynp.eye(m) + (factor * XHAinvX)
# TODO: inv_IXHAinvX = we need to invert IXHAinvX!!!
AinvX_IXHAinvX_XHAinv = mynp.dot(mynp.dot(AinvX, IXHAinvX), XHAinv)
newAinv = Ainv + (-factor * AinvX_IXHAinvX_XHAinv)
return newAinv
def woodbury(X, Ainv, m, up=True):
'''
Does a rank-k adjustment of Ainv using the woodbury matrix identity
(A + XX^H)^(-1) = A^(-1) - (A^(-1)X ( I + X^(H)A^(-1)X )^(-1)X^(H)A^(-1))
If up == True: A rank update is performed,
otherwise we do a rank degrade
NB! Not finished
'''
factor = 1
if up != True:
factor = -1
XH = mynp.conjugatetranspose(X)
XHAinv = mynp.dot(XH, Ainv)
AinvX = mynp.dot(Ainv, XH)
XHAinvX = mynp.dot(XHAinv, X)
IXHAinvX = mynp.eye(m) + (factor * XHAinvX)
# TODO: inv_IXHAinvX = we need to invert IXHAinvX!!!
AinvX_IXHAinvX_XHAinv = mynp.dot(mynp.dot(AinvX, IXHAinvX), XHAinv)
newAinv = Ainv + (-factor * AinvX_IXHAinvX_XHAinv)
return newAinv
def uhdu(A, n):
'''
Calculates the UDUH decomposition of the Hermitian matrix A
such that U is unit upper triangular, D is diagonal and UDU'=A
(' = H = symmetric conjugated)
Now we avoid using the complex sqrt by instead introducing two complex add
Returns [U D]
'''
U = eye(n, dtype=A.dtype)
D = zeros(n, dtype=A.dtype)
for i in range(n):
upperColSum = 0
for k in range(i):
upperColSum += U[k, i] * U[k, i].conjugate() * D[k]
D[i] = A[i, i] - upperColSum
for j in range(i + 1, n):
upperColSum = 0
for k in range(i):
upperColSum += U[k, i].conjugate() * U[k, j] * D[k]
U[i, j] = (A[i, j] - upperColSum) / D[i]
return [U, D]
def testComplexCholeskySolver(self):
N = 32
x = np.random.normal(loc=0.0, scale=1.0, size=(N,)) + 1j * np.random.normal(loc=0.0, scale=1.0, size=(N,))
A = np.outer(x, x.conj()).astype("complex128")
A = A + 0.5 * np.eye(N)
b = np.ones(N, dtype=np.complex128)
# A = np.array([[3+0j,5+1j],[5-1j,14+0j]])
# b = np.array([1+0j,1+0j])
yi = mklcSolveCholeskyC(A, b)
print yi
print "Numpy reference:"
C = np.linalg.solve(A, b)
print C
print "hello"
def uhduGPUProto(A, n):
"""
Calculates the UDUH decomposition of the Hermitian matrix A
such that U is unit upper triangular, D is diagonal and UDU'=A
(' = H = symmetric conjugated)
Now we avoid using the complex sqrt by instead introducing two complex add
Returns [U D]
prototype CPU-code for how uhdu-composition should be done on the GPU
"""
U = eye([n, n], A.dtype)
D = zeros([n, 1], A.dtype)
upperColSum = zeros([n, 1], A.dtype) # shared column sum buffer
Ai = zeros([n, 1], A.dtype) # shared A row buffer
for i in range(n):
# read one row into "shared" memory
for k in range(n):
Ai[k, 0] = A[i, k]
upperColSum = 0
for k in range(i):
upperColSum += U[k, i] * U[k, i].conjugate() * D[k, 0]
D[i, 0] = A[i, i] - upperColSum
for j in range(i + 1, n):
upperColSum = 0
for k in range(i):
upperColSum += U[k, i].conjugate() * U[k, j] * D[k, 0]
U[i, j] = (A[i, j] - upperColSum) / D[i, 0]
return [U, D]
def uhduGPUProto(A, n):
'''
Calculates the UDUH decomposition of the Hermitian matrix A
such that U is unit upper triangular, D is diagonal and UDU'=A
(' = H = symmetric conjugated)
Now we avoid using the complex sqrt by instead introducing two complex add
Returns [U D]
prototype CPU-code for how uhdu-composition should be done on the GPU
'''
U = eye([n, n], A.dtype)
D = zeros([n, 1], A.dtype)
upperColSum = zeros([n, 1], A.dtype) # shared column sum buffer
Ai = zeros([n, 1], A.dtype) # shared A row buffer
for i in range(n):
# read one row into "shared" memory
for k in range(n):
Ai[k, 0] = A[i, k]
upperColSum = 0
for k in range(i):
upperColSum += U[k, i] * U[k, i].conjugate() * D[k, 0]
D[i, 0] = A[i, i] - upperColSum
for j in range(i + 1, n):
upperColSum = 0
for k in range(i):
upperColSum += U[k, i].conjugate() * U[k, j] * D[k, 0]
U[i, j] = (A[i, j] - upperColSum) / D[i, 0]
return [U, D]
def iterativeBuildRinv(X, M, L, Yavg, d):
'''
Builds Rinv from the data area X
using M elements, L long sub arrays, 2*Yavg+1 time samples and diagonal loading d.
X needs to be of size (2*Yavg+1, M).
The inverse is normalized with 1 / ((2*Yavg+1) * (M-l+1))
'''
diagonalLoadingFactor = 1
if d > 0: # if d is 0, use 1. Should use a smaller value, but it is not possible due to poor conditioning
diagonalLoadingFactor = 1 / d
Rinv = (diagonalLoadingFactor * mynp.eye(L));
N = 2*Yavg + 1;
K = M - L + 1;
for n in range(N):
Rinv = iterativeBuildUpRinv(Rinv, X[n], M, L)
Rinv = (N * K) * Rinv # normalize Rinv
return Rinv;
def iterativeBuildRinv(X, M, L, Yavg, d):
'''
Builds Rinv from the data area X
using M elements, L long sub arrays, 2*Yavg+1 time samples and diagonal loading d.
X needs to be of size (2*Yavg+1, M).
The inverse is normalized with 1 / ((2*Yavg+1) * (M-l+1))
'''
diagonalLoadingFactor = 1
if d > 0: # if d is 0, use 1. Should use a smaller value, but it is not possible due to poor conditioning
diagonalLoadingFactor = 1 / d
Rinv = (diagonalLoadingFactor * mynp.eye(L))
N = 2 * Yavg + 1
K = M - L + 1
for n in range(N):
Rinv = iterativeBuildUpRinv(Rinv, X[n], M, L)
Rinv = (N * K) * Rinv # normalize Rinv
return Rinv
def setUp(self):
self.n = 3
self.A = np.array([[2.0, -1.0, 0.0], [-1.0, 2.0, -1.0], [0.0, -1.0, 2.0]],dtype=complex)
self.A2 = np.array([[4.0, -2.0, 0.0], [-2.0, 4.0, -2.0], [0.0, -2.0, 4.0]],dtype=complex)
self.R = np.array([[1.414213562373095, -0.707106781186547, 0.0],
[0.0, 1.224744871391589, -0.816496580927726],
[0.0, 0.0, 1.154700538379252]],dtype=complex)
self.AA = np.array([[5, -4, 1], [-4, 6, -4], [1, -4, 5]],dtype=complex)
self.B = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]],dtype=complex)
self.BT = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]],dtype=complex)
self.b1 = np.array([1.0, 1.0, 1.0],dtype=complex)
self.x1 = np.array([3.0/2, 2.0, 3.0/2],dtype=complex)
self.b2 = np.array([1, 2, 3],dtype=complex)
self.x2 = np.array([5.0/2, 4, 7.0/2],dtype=complex)
self.C = np.array([[1, 2, 3], [0, 1, 1], [0, 0, 1]],dtype=complex)
self.b1c = np.array([1, 1, 1],dtype=complex)
self.x1c = np.array([-2, 0, 1],dtype=complex)
self.x1cT = np.array([1, -1, -1],dtype=complex)
self.x0_zero = np.zeros((3,), dtype=complex)
self.Ab2 = np.array([0, 0, 4],dtype=complex)
self.Ab1 = np.array([1, 0, 1],dtype=complex)
self.Ab1b1T = np.array([[3, 1, 3], [1, 6, 5], [3, 5, 11]],dtype=complex)
self.invA = np.array([[0.750, 0.50, 0.250], [0.50, 1.0, 0.50], [0.250, 0.50, 0.750]],dtype=complex)
self.invAb1b1T = np.array([[0.465909090909091, 0.045454545454545, -0.147727272727273],
[0.045454545454545, 0.272727272727273, -0.136363636363636],
[-0.147727272727273, -0.136363636363636, 0.193181818181818]],dtype=complex)
self.complexA = np.array([[2.0, 3.0 + 1.0j, 2.0 - 2.0j],
[3.0 - 1.0j, 9.0, -2.0j],
[2.0 + 2.0j, 2.0j, 14.0]], dtype=complex)
self.complexR = np.array([[1.414213562373095, 2.121320343559642 + 0.707106781186547j, 1.414213562373095 - 1.414213562373095j],
[0.0, 2.0, -1.0 + 1.0j],
[0.0, 0.0, 2.828427124746190]], dtype=complex)
self.complexb = np.array([1.0, 1.0 + 1.0j, 1.0 - 2.0j], dtype=complex)
self.complexy = np.array([0.707106781186547 - 0.0j,
-0.250 + 0.750j,
-0.353553390593273 - 0.883883476483184j], dtype=complex)
self.complexx = np.array([1.593749999999999 - 0.06250j,
-0.343750 + 0.281250j,
-0.1250 - 0.31250j], dtype=complex)
self.complexAb = np.array([2.0 - 2.0j, 8.0 + 6.0j, 14.0 - 24.0j], dtype=complex)
self.but4 = np.array([[0.5]*4, [0.5, -0.5j, -0.5, 0.5j], [0.5, -0.5]*2, [0.5, 0.5j, -0.5, -0.5j]], dtype=complex)
self.but3 = np.array([[0.577350269189626, 0.577350269189626, 0.577350269189626],
[0.577350269189626, -0.288675134594813 - 0.50j, -0.288675134594813 + 0.50j],
[0.577350269189626, -0.288675134594813 + 0.50j, -0.288675134594813 - 0.50j]], dtype=complex)
self.bsComplexb = np.array([1.732050807568878 - 0.577350269189626j, 1.50 + 0.288675134594813j], dtype=complex)
self.diag = 0.2
self.x = np.array([1.0, 1.0 + 1.0j, 1.0 - 2.0j, 2.0 + 1.0j], dtype=complex)
self.complexAbbH = np.array([[10.0, 11.0 + 1.0j, 10.0 - 2.0j],
[11.0 - 1.0j, 17.0, 8.0 - 2.0j],
[10.0 + 2.0j, 8.0 + 2.0j, 22.0]], dtype=complex)
self.complexInvAbbH = np.array([[1.067010309278351, -0.407216494845361 - 0.015463917525773j, -0.190721649484536 + 0.020618556701031j],
[-0.407216494845361 + 0.015463917525773j, 0.247422680412371, 0.077319587628866 - 0.015463917525773j],
[-0.190721649484536 - 0.020618556701031j, 0.077319587628866 + 0.015463917525773j, 0.087628865979381]], dtype=complex)
self.complexInvA = np.array([[1.906250, -0.593750 - 0.156250j, -0.250 + 0.18750j],
[-0.593750 + 0.156250j, 0.31250, 0.06250 - 0.06250j],
[-0.250 - 0.18750j, 0.06250 + 0.06250j, 0.1250]], dtype=complex)
self.complexA4x4 = np.array([[22.0, 8.0, 11.0 - 11.0j, 22.0 - 7.0j],
[8.0, 22.0, 17.0 - 2.0j, 11.0 - 7.0j],
[11.0 + 11.0j, 17.0 + 2.0j, 45.0, 23.0 - 5.0j],
[22.0 + 7.0j, 11.0 + 7.0j, 23.0 + 5.0j, 37.0]], dtype=complex)
self.U4x4 = np.array([[1.0000, 0.3636, 0.50 - 0.50j, 1.0 - 0.3182j],
[0.0, 1.0, 0.6810 + 0.1048j, 0.1571 - 0.2333j],
[0.0, 0.0, 1.0, 0.2776 - 0.3670j],
[0.0, 0.0, 0.0, 1.0]], dtype=complex)
self.D4x4 = np.array([22.0, 19.0909, 24.9381, 5.9806])
self.sonardata_R = np.array(np.load('./data/data_R.npy')) # created without diagonal loading
self.sonardata_a = np.array(np.load('./data/data_a.npy'))
self.sonardata_Ria = np.array(np.load('./data/data_Ria.npy'))
self.sonardata_ar = np.array(np.load('./data/data_ar.npy'))
self.sonardata_n = 32
# random data for testing
self.L = L = 24
self.d = d = 100
U = np.triu(np.random.randn(L,L) + np.random.randn(L,L)*1j) + np.eye(L)*d
self.randA = np.dot(U.conjugate().T, U)
self.randb = np.random.randn(L) + np.random.randn(L)*1j
