Pochung Chen
Pochung Chen
I think in this case one should use `is_grp: bool = True` when combing bonds. Then I realize that that `cytnx.Bond.combineBonds` and `cytnx.Bond.combineBonds` support `is_grp: bool`, but `cytnx.UniTensor.combineBonds` DONOT support...
I think we need to implement combiner and splitter to fully fix this issus and related ones. However, for now can we change the default value of `is_grp` to `false`...
I reopen this issue to discuss `reshape` and `reshape_` for UniTensor. In response to one of the referee's question, my current thinking is as follows. * cytnx.Tensor should behave like...
This is current behaviour. Depending on how you do the reshape, you got different default row-rank. ```` X = cytnx.UniTensor.zeros([2,3,4]) X.print_diagram() Y=X.reshape(4,3,2) X.print_diagram() X.reshape_(4,3,2) Y.print_diagram() ----------------------- tensor Name : tensor...
Then this means that the syntax for inplace and non-inplace version is very different, which can be confusing.
If I have a UniTensor `uT`, how do I add random noise to all of the elements?
It seems that non-inplace version only output `cytnx.Tensor`. ```` 1. normal(Nelem: int, mean: float, std: float, device: int = -1, seed: int = -1, dtype: int = 3) -> cytnx.cytnx.Tensor...
I am ok with using `clone`. If we want to add another API, I would suggest to use `cytnx.random.uniform_like(uT, low=, high=).`
Thanks. I will provide some feedback later.
I see. We should probably mention this somewhere in the (new) documentation.