def ctorTests(verbose):
arr = numpy.arange(24).reshape([2, 3, 4])
A = numpy.array([[1, 2], [3, 4], [5, 6]])
B = numpy.array([[1, 2, 3], [4, 5, 6]])
C = numpy.array([[1, 2, 3, 4]])
obj = ttensor.ttensor(tensor.tensor(arr), [A, B, C])
print obj
print obj.shape
def ctorTests(verbose):
arr = numpy.arange(24).reshape([2,3,4]);
A = numpy.array([[1,2],[3,4],[5,6]]);
B = numpy.array([[1,2,3],[4,5,6]]);
C = numpy.array([[1,2,3,4]]);
obj = ttensor.ttensor(tensor.tensor(arr), [A, B, C]);
print obj;
print obj.shape;
def test1():
A = ttensor.ttensor(tensor.tenrands([2,3,4]),
[numpy.random.random([10,2]),
numpy.random.random([30,3]),
numpy.random.random([40,4])]).totensor();
[a,b] = DTA.DTA(A, [1,2,3]);
#print a;
#print b;
Core = numpy.arange(24).reshape([2,3,4]);
#Core = numpy.array([[1,3,5],[2,4,6]] , [[7,9,11],[8,10,12]])
u1 = numpy.array([[1,2],[3,4]]);
u2 = numpy.array([[0,1,0],[1,0,1],[1,1,1]]);
u3 = numpy.array([[1,1,1,1],[1,2,3,4],[1,1,1,1]]);
tt = ttensor.ttensor(tensor.tensor(Core), [u1,u2,u3]);
print tt;
[a,b] = DTA.DTA(tt.totensor(), [1,2,3]);
print a;
print a.totensor();
print b;
def test1():
A = ttensor.ttensor(tensor.tenrands([2, 3, 4]), [
numpy.random.random([10, 2]),
numpy.random.random([30, 3]),
numpy.random.random([40, 4])
]).totensor()
[a, b] = DTA.DTA(A, [1, 2, 3])
#print a;
#print b;
Core = numpy.arange(24).reshape([2, 3, 4])
#Core = numpy.array([[1,3,5],[2,4,6]] , [[7,9,11],[8,10,12]])
u1 = numpy.array([[1, 2], [3, 4]])
u2 = numpy.array([[0, 1, 0], [1, 0, 1], [1, 1, 1]])
u3 = numpy.array([[1, 1, 1, 1], [1, 2, 3, 4], [1, 1, 1, 1]])
tt = ttensor.ttensor(tensor.tensor(Core), [u1, u2, u3])
print tt
[a, b] = DTA.DTA(tt.totensor(), [1, 2, 3])
print a
print a.totensor()
print b
def DTA(Xnew, R, C=None, alpha=None):
"""DTA analysis"""
# number of dimensions of the input tensor.
N = Xnew.ndims()
# If the co-variacne matrices are not given,
# initialize all of them to be 0
if (C == None):
C = []
dv = Xnew.shape
for i in range(0, N):
C.extend([sparse.coo_matrix(([], ([], [])), [dv[i], dv[i]])])
# If the forgetting factor is not given, it is 1.
if (alpha == None):
alpha = 1
U = []
Cnew = []
for i in range(0, N):
if (Xnew.__class__ == tensor.tensor):
XM = tenmat.tenmat(Xnew, [i]).tondarray()
elif (Xnew.__class__ == sptensor.sptensor):
XM = sptenmat.sptenmat(Xnew, [i]).tosparsemat()
elif (Xnew.__class__ == ttensor.ttensor):
raise TypeError("It is not supported yet.")
else:
raise TypeError(
"1st argument must be tensor, sptensor, or ttensor")
Cnew.extend(
[numpy.array(alpha * C[i] + numpy.dot(XM, XM.transpose()))])
(w, v) = eigwrapper(Cnew[i], R[i])
U.extend([numpy.array(v)])
core = Xnew.ttm(U, None, 't')
T = ttensor.ttensor(core, U)
return (T, Cnew)
def DTA(Xnew, R, C = None, alpha = None):
"""DTA analysis"""
# number of dimensions of the input tensor.
N = Xnew.ndims();
# If the co-variacne matrices are not given,
# initialize all of them to be 0
if(C == None):
C = [];
dv = Xnew.shape;
for i in range(0, N):
C.extend([ sparse.coo_matrix(([],([],[])),[dv[i], dv[i]]) ]);
# If the forgetting factor is not given, it is 1.
if(alpha == None):
alpha = 1;
U = [];
Cnew = [];
for i in range (0,N):
if(Xnew.__class__ == tensor.tensor):
XM = tenmat.tenmat(Xnew,[i]).tondarray();
elif(Xnew.__class__ == sptensor.sptensor):
XM = sptenmat.sptenmat(Xnew,[i]).tosparsemat();
elif(Xnew.__class__ == ttensor.ttensor):
raise TypeError("It is not supported yet.");
else:
raise TypeError("1st argument must be tensor, sptensor, or ttensor");
Cnew.extend([ numpy.array(alpha*C[i] + numpy.dot(XM, XM.transpose())) ]);
(w,v) = eigwrapper(Cnew[i], R[i]);
U.extend([ numpy.array(v) ]);
core = Xnew.ttm(U, None, 't');
T = ttensor.ttensor(core, U);
return (T, Cnew);