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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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numpy/extending/README.md
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@ -28,3 +28,115 @@ numpy.tile(A, reps)
>
> Note : Although tile may be used for broadcasting, it is strongly recommended to use numpy’s broadcasting operations and functions.
**Very very important!!!​ First use numpy.newaxis​ to create the required additional axis and then use tile.**
Adding a new dimension:
```python
import numpy as np
a = np.arange(1, 5)
print(a) # -> [1 2 3 4]
print(a.shape) # -> (4,)
b = a[np.newaxis, :]
print(b.shape) # -> (1, 4)
print(np.may_share_memory(a, b)) # -> True (View)
c = np.tile(a, (1, 1))
print(c.shape) # -> (1, 4)
print(np.may_share_memory(a, c)) # -> False (Copy)
```
Examples:
```python
import numpy as np
a = np.arange(1, 5)[np.newaxis, :]
print(a) # -> [[1 2 3 4]]
print(a.shape) # -> (1, 4)
c = np.tile(a, (1, 1))
print(c) # -> [[1 2 3 4]]
print(c.shape) # -> (1, 4)
c = np.tile(a, (4, 1))
print(c)
print(c.shape) # -> (4, 4)
c = np.tile(a, (1, 4))
print(c) # -> [[1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4]]
print(c.shape) # -> (1, 16)
```
Output:
```python
[[1 2 3 4]
[1 2 3 4]
[1 2 3 4]
[1 2 3 4]]
```
Be very careful if you haven’t newaxis-ed...​
```python
import numpy as np
a = np.arange(1, 5)
print(a) # -> [1 2 3 4]
print(a.shape) # -> (4,)
c = np.tile(a, (4))
print(c) # -> [1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4]
print(c.shape) # -> (16,)
c = np.tile(a, (1, 4))
print(c) # -> [[1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4]]
print(c.shape) # -> (1, 16)
c = np.tile(a, (4, 1))
print(c)
print(c.shape) # -> (4, 4)
print()
c = np.tile(a, (4, 1, 1))
print(c)
print(c.shape) # -> (4, 1, 4)
print()
c = np.tile(a, (1, 4, 1))
print(c)
print(c.shape) # -> (1, 4, 4)
print()
c = np.tile(a, (1, 1, 4))
print(c) # -> [[[1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4]]]
print(c.shape) # -> (1, 1, 16)
```
Output:
```python
[[1 2 3 4]
[1 2 3 4]
[1 2 3 4]
[1 2 3 4]]
[[[1 2 3 4]]
[[1 2 3 4]]
[[1 2 3 4]]
[[1 2 3 4]]]
[[[1 2 3 4]
[1 2 3 4]
[1 2 3 4]
[1 2 3 4]]]
```