def unified_k3_m3(tensor_trans, tensor_begin):
#conteract first 3 dim tensor_trans[9,10,11] tensor_begin[6,7,8]
#join tensor_trans[3,4,5,6,7,8] tensor_begin[0,1,2,3,4,5]
#result= join[]+tensor_trans[0,1,2]
result = np.empty(tensor_begin.shape)
shape = tensor_trans.shape
for i in range(shape[0]):
for j in range(shape[1]):
for k in range(shape[2]):
for m in range(shape[3]):
for n in range(shape[4]):
for x in range(shape[5]):
for q in range(shape[6]):
for w in range(shape[7]):
for e in range(shape[8]):
result[
i, j, k, m, n, x, q, w,
e] = np.sum(
tensor_trans.data[i, j, k, m,
n, x, q, w,
e:, :, :] *
tensor_begin.data[m, n, x, q,
w,
e, :, :, :])
return tensor.tensor(result)
def ctor(verbose):
dat = numpy.arange(24).reshape([2,3,4]);
t = tensor.tensor(dat);
print (t);
if (verbose):
obj = tenmat.tenmat(t, [1,0]);
print (obj);
print (obj.copy());
dat = dat.reshape([4,6]);
t = tensor.tensor(dat);
if (verbose):
obj = tenmat.tenmat(t, [0], [1], [4,6]);
print (obj);
def totensorTests(verbose):
dat = numpy.arange(24).reshape([2,3,4]);
t = tensor.tensor(dat);
obj = tenmat.tenmat(t,[2,1]);
if(verbose):
print (obj);
print (obj.totensor());
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 tosptensor(verbose):
dat = numpy.array(
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0, 0, 13, 14, 15, 16, 17, 18])
siz = numpy.array([3, 3, 2])
obj = tensor.tensor(dat, siz)
if (verbose == 1):
print(obj)
print(obj.tosptensor())
def test2():
# A = numpy.array([[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]]);
# A = numpy.arange(12).reshape([4,3]) + 1;
A = numpy.arange(1000).reshape([10,10,10])+1;
(ans1, ans2) = DTA.DTA(tensor.tensor(A), [2,2,2]);
print (ans1);
for i in range(0, len(ans2)):
print ("{0} th array\n {1}".format(i, ans2[i]));
print (ans1.totensor());
def ttmTest(verbose):
dat = numpy.arange(24).reshape([2, 3, 4])
siz = numpy.array([2, 3, 4])
obj = tensor.tensor(dat, siz)
A = numpy.arange(18).reshape([6, 3])
print(obj.ttm(A, 1))
B = numpy.arange(12).reshape([3, 4])
print(obj.ttm([A, B], [1, 2]))
print("a")
print(obj.ttm([A.transpose(), B.transpose()], [1, 2], 't'))
def mathlogicops(verbose):
dat = numpy.arange(24).reshape([2, 3, 4])
siz = numpy.array([2, 3, 4])
obj = tensor.tensor(dat, siz)
print(obj + 100)
print(obj - 100)
print(obj + (obj + 10))
print(obj * 1.5)
print(obj < 10)
print(obj > 10)
print(obj == 10)
print(obj == obj)
def ctor(verbose):
dat = numpy.arange(24)
dat[10] = 100
dat[16] = -1
siz = numpy.array([4, 3, 2])
obj = tensor.tensor(dat, siz)
if (verbose == 1):
print(obj)
print(obj.ndims())
obj2 = tensor.tensor(dat.reshape([2, 3, 4]), siz)
if (verbose == 1):
print(obj)
print(obj.shape)
dat = numpy.array(
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0, 0, 13, 14, 15, 16, 17, 18])
obj2 = tensor.tensor(dat)
if (verbose == 1):
print(obj2)
print(obj2.shape)
def permutetest(verbose):
dat = numpy.arange(24).reshape([2, 3, 4])
siz = numpy.array([2, 3, 4])
obj = tensor.tensor(dat, siz)
if (verbose == 1):
print(obj)
print("permute by {0}".format("[2,0,1]"))
print(obj.permute([2, 0, 1]))
print("permute by {0}".format("[1,2,0]"))
print(obj.permute([1, 2, 0]))
print("ipermuted by {0}".format("[1,2,0]"))
print(obj.ipermute([1, 2, 0]))
print(obj.permute([1, 2, 0]).ipermute([1, 2, 0]))
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);
return np.linalg.norm(tensor_a.data - tensor_b.data)
def pow_approch(tensor_trans, tensor_begin, error):
resultx = unified_k3_m3(tensor_trans, tensor_begin)
#print(resultx)
resulty = unified_k3_m3(tensor_trans, resultx)
i = 0
while (checkError(resultx, resulty) > error and i < MAX_TIMES):
resultx = resulty
resulty = unified_k3_m3(tensor_trans, resulty)
return resulty
if __name__ == '__main__':
a = np.array(range(13824 * 2 * 3 * 4), dtype=np.float64).reshape(
[2, 3, 4, 2, 3, 4, 2, 3, 4, 2, 3, 4])
b = np.array(range(576 * 24),
dtype=np.float64).reshape([2, 3, 4, 2, 3, 4, 2, 3, 4])
tensor_trans = tensor.tensor(a)
tensor_begin = tensor.tensor(b)
#print(tensor_begin)
print('tensor_tran')
print(tensor_trans.data[0, 0, 0, 0, 0, 0, 0, 0, 0, :, :, :])
print('tensor_begin')
print(tensor_begin.data[0, 0, 0, 0, 0, 0, :, :, :])
result = unified_k3_m3(tensor_trans, tensor_begin)
print(result.data[0, 0, 0, 0, 0, 0, 0, 0, 0])
#error=10