semvec/pypi-packages huggingface dataset:
adam-atan2 0.0.3
Adam-atan2 Fused Optimizer

# Adam-atan2 Fused Operator

## Usage

Drop-in replacement of `torch.optim.AdamW`.
 
 - Doesn't support `foreach`, `fused` argument, as the optimizer is already fused
 - Doesn't support `amsgrad`, `maximize`, `capturable`, `differentiable` argument yet

```bash
pip install adam-atan2
```

```python
from adam_atan2 import AdamATan2

# All supported arguments are listed below
optim = AdamATan2(model.parameters(),
    lr=1e-3,
    weight_decay=0.1,
    betas=(0.9, 0.95)
)
```

## Consistency Tests

We tested the consistency against reference AdamW-atan2 PyTorch implementation. To run tests, clone this repository, run pytest:

```bash
pip install -e .
pytest
```

## References

 - https://arxiv.org/pdf/2407.05872
TensorBeliefFunction 0.0.8
Tensor of Belief Function
Requires Python: >=3.6

# Tensor of Belief Function

## Install the package
```
pip install TensorBeliefFunction
```

## Example using TensorBeliefFunction (TensorBF)
```python
from TensorBeliefFunction.tensords import TensorMassFunction
TensorMassFunction.SetFrame(['a','b','c'])
tm1 = TensorMassFunction({'a':0.3, 'c':0.2, 'ab':0.2, 'ac':0.3}) 
tm2 = TensorMassFunction({'b':0.2, 'c':0.1, 'ab':0.5, 'abc':0.2}) 
elements = ['a', 'b', 'c', 'd']
m12 = tm1.Combine(tm2)
m12.ConvertToOriginalMassFunction()
print(m12)
```

## Example using TensorBeliefFunction with mask (TensorBF (mask))
```python
from TensorBeliefFunction.tensords_mask import TensorMassFunctionMask
TensorMassFunctionMask.SetFrame(['a','b','c'])
tm1 = TensorMassFunctionMask({'a':0.3, 'c':0.2, 'ab':0.2, 'ac':0.3}) 
tm2 = TensorMassFunctionMask({'b':0.2, 'c':0.1, 'ab':0.5, 'abc':0.2}) 
elements = ['a', 'b', 'c', 'd']
m12 = tm1.Combine(tm2)
m12.ConvertToOriginalMassFunction()
print(m12)
```

## Example using TensorBeliefFunction with sparse mask  (TensorBF (mask_csc))
```python
from TensorBeliefFunction.tensords_mask_csc import TensorMassFunctionMask_CSC
TensorMassFunctionMask_CSC.SetFrame(['a','b','c'])
tm1 = TensorMassFunctionMask_CSC({'a':0.3, 'c':0.2, 'ab':0.2, 'ac':0.3}) 
tm2 = TensorMassFunctionMask_CSC({'b':0.2, 'c':0.1, 'ab':0.5, 'abc':0.2}) 
elements = ['a', 'b', 'c', 'd']
m12 = tm1.Combine(tm2)
m12.ConvertToOriginalMassFunction()
print(m12)
```

## Example using TensorBeliefFunction with sparse vector (TensorBF (csc))
```python
from TensorBeliefFunction.tensords_csc import TensorMassFunction_CSC
TensorMassFunction_CSC.SetFrame(['a','b','c'])
tm1 = TensorMassFunction_CSC({'a':0.3, 'c':0.2, 'ab':0.2, 'ac':0.3}) 
tm2 = TensorMassFunction_CSC({'b':0.2, 'c':0.1, 'ab':0.5, 'abc':0.2}) 
elements = ['a', 'b', 'c', 'd']
m12 = tm1.Combine(tm2)
m12.ConvertToOriginalMassFunction()
print(m12)
```

## Example using TensorBeliefFunction with csc wise (TensorBF (csc_wise))
```python
from TensorBeliefFunction.tensords_csc_wise import TensorMassFunction_CSC_Wise
TensorMassFunction_CSC_Wise.SetFrame(['a','b','c'])
tm1 = TensorMassFunction_CSC_Wise({'a':0.3, 'c':0.2, 'ab':0.2, 'ac':0.3}) 
tm2 = TensorMassFunction_CSC_Wise({'b':0.2, 'c':0.1, 'ab':0.5, 'abc':0.2}) 
elements = ['a', 'b', 'c', 'd']
m12 = tm1.Combine(tm2)
m12.ConvertToOriginalMassFunction()
print(m12)
```