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quantized.utils Module

Classes

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Parabola

Parabola(a: 'float', b: 'float', c: 'float')

Fields

a (float)

Constraints: , , Range(min=-1000.0, max=1000.0)

b (float)

Constraints: , , Range(min=-1000.0, max=1000.0)

c (float)

Constraints: , , Range(min=-1000.0, max=1000.0)

Properties

has_vertex

vertex

Static Methods

from_points

Parabola.from_points(
    point1: 'Tuple[float, float]',
    point2: 'Tuple[float, float]',
    point3: 'Tuple[float, float]'
) -> Parabola

Create a Parabola passing through 3 points.

Parameters

point1 x, y point point2 x, y point point3 x, y point

Returns

Parabola Parabola with coefficients fit to the points.

Dunder Methods

__call__

Parabola.__call__(self, x) -> <class 'inspect._empty'>

Call self as a function.

Functions

---

cache

cache(f: 'Callable') -> Callable

None

pairwise_array_from_func

pairwise_array_from_func(
    items: 'Sequence[T]',
    func: 'Callable[[T, T], float]',
    symmetric=False
) -> np.ndarray

Create a pairwise array by applying a function to all pairs of items.

Parameters

items A container from which pairs will be generated. Must support len() and integer indexing over range(len(items)) func A function f(first, second, args, *kwargs) which takes 2 items and returns a float. symmetric Whether the resulting matrix should be symmetric. If true, will only compute each (i, j) pair once and set both [i, j] and [j, i] to that value.

Returns

np.array The resulting matrix

Examples

from quantized.utils import pairwise_array_from_func def distance(i, j): ... return abs(i - j) ... pairwise_array_from_func([1, 2, 4], distance) array([[0., 1., 3.], [1., 0., 2.], [3., 2., 0.]]) pairwise_array_from_func([1, 2, 4], distance, symmetric=True) array([[0., 1., 3.], [1., 0., 2.], [3., 2., 0.]])