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Update README.md

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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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David Rotermund 2023-12-19 10:31:18 +01:00 committed by GitHub
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numpy/dimensions/README.md
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@ -54,6 +54,19 @@ print(data[0, 0, :].shape) # -> (2,)
print(data[0, 0, 0].shape) # -> ()
print(type(data[0, 0, 0])) # -> <class 'numpy.float64'>
```
### keepdims
There are functions like
```python
ndarray.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)
ndarray.mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)
ndarray.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)
ndarray.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
ndarray.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
ndarray.argmax(axis=None, out=None, *, keepdims=False)
ndarray.argmin(axis=None, out=None, *, keepdims=False)
```
that normally make one dimension vanish. However, often this type of functions have an argument **keepdims** that keeps this dimension alive.
```python
import numpy as np
@ -74,4 +87,12 @@ print(data.sum(axis=1, keepdims=True).shape) # -> (5, 1, 2)
print(data.sum(axis=2, keepdims=True).shape) # -> (5, 3, 1)
```
As as reminder, shape is only availabe for np.ndarray and torch.Tensor matrices:
```python
z = int(7)
print(np.array(z).shape) # -> ()
print(type(np.array(z))) # -> <class 'numpy.ndarray'>
print(type(z)) # -> <class 'int'>
print(z.shape) # -> AttributeError: 'int' object has no attribute 'shape'
```