# tensorplay.functional.vander

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.vander.html

```python
tensorplay.functional.vander(x, N=None, increasing=False) → Tensor
```

Generates a Vandermonde matrix.

The columns of the output matrix are elementwise powers of the input vector $x^{(N-1)}, x^{(N-2)}, ..., x^0$.
If increasing is True, the order of the columns is reversed $x^0, x^1, ..., x^{(N-1)}$. Such a
matrix with a geometric progression in each row is named for Alexandre-Theophile Vandermonde.

Parameters:

- x ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – 1-D input tensor.

- N ([int](https://docs.python.org/3/builtins/functions.html#int), optional) – Number of columns in the output. If N is not specified, a square array is returned $(N = len(x))$.

- increasing ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – Order of the powers of the columns. If True, the powers increase from left to right, if False (the default) they are reversed.

Returns:

Vandermonde matrix. If increasing is False, the first column is $x^{(N-1)}$,
the second $x^{(N-2)}$ and so forth. If increasing is True, the columns
are $x^0, x^1, ..., x^{(N-1)}$.

Return type:

[Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)

Example:

```
>>> x = tensorplay.tensor([1, 2, 3, 5])
>>> tensorplay.vander(x)
tensor([[  1,   1,   1,   1],
        [  8,   4,   2,   1],
        [ 27,   9,   3,   1],
        [125,  25,   5,   1]])
>>> tensorplay.vander(x, N=3)
tensor([[ 1,  1,  1],
        [ 4,  2,  1],
        [ 9,  3,  1],
        [25,  5,  1]])
>>> tensorplay.vander(x, N=3, increasing=True)
tensor([[ 1,  1,  1],
        [ 1,  2,  4],
        [ 1,  3,  9],
        [ 1,  5, 25]])
```