a problem with using torchTT.interpolate.dmrg_cross()

[Boyuan-Shi-0607]

I tried to use interpolate.dmrg_cross() from a numpy array.

Generate a random array:
import numpy as np
import torch as tn
import torchtt as tntt
test = np.random.rand(10, 10, 10)

Define the function in this way:

def func(args):                                        
    return tn.tensor([tn.from_numpy(test)[*args[0]]], dtype=tn.complex128)         

When I tried to use dmg_cross to interpolate this exemplary random tensor,

N = list(test.shape)                                   
x = tntt.interpolate.dmrg_cross(func, N, eps=10**(-8)) 

I got the error message:

Traceback (most recent call last):
  File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/pydevconsole.py", line 364, in runcode
    coro = func()
           ^^^^^^
  File "<input>", line 1, in <module>
  File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/_pydev_bundle/pydev_umd.py", line 197, in runfile
    pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "/Users/boyuanshi/Desktop/second_project/equilibrium_v2/Screened_Interactions_Plot.py", line 61, in <module>
    x = tntt.interpolate.dmrg_cross(func, N, eps=10**(-8))
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/boyuanshi/.conda/envs/Desktop/lib/python3.11/site-packages/torchTT-2.0-py3.11-macosx-10.9-x86_64.egg/torchtt/interpolate.py", line 514, in dmrg_cross
    supercore = tn.reshape(function(eval_index),[rank[k],N[k],N[k+1],rank[k+2]])
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: shape '[1, 10, 10, 2]' is invalid for input of size 1

I am quite confused why it is the case?

[Boyuan-Shi-0607]

It seems that I magically solve the issue by adding 0./(2+tn.exp(tn.sum(I, 1))) to the function and also change the data type to tn.float64. Not known why.

[ion-g-ion]

Hi,

The output of the function handle should be float and of shape (m,) for an input of shape (m,d).
The provided hanndle returns a (1,) torch tensor. It can be modified as

def func(args):                                        
   return tn.tensor([tn.from_numpy(test)[*a] for a in args], dtype=tn.float64)    

Regarding the complex numbers, right now only the real p is approximated. One alternative is to cross approximate real and imaginary separately. I would need to do some work to support complex numbers in all operations.