Feat: add new Markdown parser (#39800)
and change all the challenges to new `md` format.
This commit is contained in:
Oliver Eyton-Williams
committed by
GitHub
GitHub
parent
a07f84c8ec
commit
0bd52f8bd1
@ -5,56 +5,159 @@ challengeType: 5
|
||||
forumTopicId: 302295
|
||||
---
|
||||
|
||||
## Description
|
||||
# --description--
|
||||
|
||||
<section id='description'>
|
||||
A k-d tree (short for *k*-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space. k-d trees are a useful data structure for several applications, such as searches involving a multidimensional search key (e.g. range searches and nearest neighbor searches). k-d trees are a special case of binary space partitioning trees. k-d trees are not suitable, however, for efficiently finding the nearest neighbor in high dimensional spaces. As a general rule, if the dimensionality is *k*, the number of points in the data, *N*, should be *N* ≫ 2<sup><i>k</i></sup>. Otherwise, when k-d trees are used with high-dimensional data, most of the points in the tree will be evaluated and the efficiency is no better than exhaustive search, and other methods such as approximate nearest-neighbor are used instead.
|
||||
|
||||
A k-d tree (short for <i>k</i>-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space.
|
||||
k-d trees are a useful data structure for several applications, such as searches involving a multidimensional search key (e.g. range searches and nearest neighbor searches).
|
||||
k-d trees are a special case of binary space partitioning trees. k-d trees are not suitable, however, for efficiently finding the nearest neighbor in high dimensional spaces. As a general rule, if the dimensionality is <i>k</i>, the number of points in the data, <i>N</i>, should be <i>N</i> ≫ 2<sup><i>k</i></sup>.
|
||||
Otherwise, when k-d trees are used with high-dimensional data, most of the points in the tree will be evaluated and the efficiency is no better than exhaustive search, and other methods such as approximate nearest-neighbor are used instead.
|
||||
|
||||
</section>
|
||||
|
||||
## Instructions
|
||||
|
||||
<section id='instructions'>
|
||||
# --instructions--
|
||||
|
||||
Write a function to perform a nearest neighbour search using k-d tree. The function takes two parameters: an array of k-dimensional points, and a single k-dimensional point whose nearest neighbour should be returned by the function. A k-dimensional point will be given as an array of k elements.
|
||||
|
||||
</section>
|
||||
# --hints--
|
||||
|
||||
## Tests
|
||||
`kdNN` should be a function.
|
||||
|
||||
<section id='tests'>
|
||||
|
||||
```yml
|
||||
tests:
|
||||
- text: <code>kdNN</code> should be a function.
|
||||
testString: assert(typeof kdNN == 'function');
|
||||
- text: <code>kdNN([[[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [9, 2])</code> should return an array.
|
||||
testString: assert(Array.isArray(kdNN([[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [9, 2])));
|
||||
- text: <code>kdNN([[[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [9, 2])</code> should return <code>[ 8, 1 ]</code>.
|
||||
testString: assert.deepEqual(kdNN([[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [9, 2]), [8, 1]);
|
||||
- text: <code>kdNN([[[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [7, 1])</code> should return <code>[ 8, 1 ]</code>.
|
||||
testString: assert.deepEqual(kdNN([[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [7, 1]), [8, 1]);
|
||||
- text: <code>kdNN([[[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [3, 2])</code> should return <code>[ 2, 3 ]</code>.
|
||||
testString: assert.deepEqual(kdNN([[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [3, 2]), [2, 3]);
|
||||
- text: <code>kdNN([[2, 3, 1], [9, 4, 5], [4, 6, 7], [1, 2, 5], [7, 8, 9], [3, 6, 1]], [1, 2, 3])</code> should return <code>[ 1, 2, 5 ]</code>.
|
||||
testString: assert.deepEqual(kdNN([[2, 3, 1], [9, 4, 5], [4, 6, 7], [1, 2, 5], [7, 8, 9], [3, 6, 1]], [1, 2, 3]), [1, 2, 5]);
|
||||
- text: <code>kdNN([[2, 3, 1], [9, 4, 5], [4, 6, 7], [1, 2, 5], [7, 8, 9], [3, 6, 1]], [4, 5, 6])</code> should return <code>[ 4, 6, 7 ]</code>.
|
||||
testString: assert.deepEqual(kdNN([[2, 3, 1], [9, 4, 5], [4, 6, 7], [1, 2, 5], [7, 8, 9], [3, 6, 1]], [4, 5, 6]), [4, 6, 7]);
|
||||
- text: <code>kdNN([[2, 3, 1], [9, 4, 5], [4, 6, 7], [1, 2, 5], [7, 8, 9], [3, 6, 1]], [8, 8, 8])</code> should return <code>[ 7, 8, 9 ]</code>.
|
||||
testString: assert.deepEqual(kdNN([[2, 3, 1], [9, 4, 5], [4, 6, 7], [1, 2, 5], [7, 8, 9], [3, 6, 1]], [8, 8, 8]), [7, 8, 9]);
|
||||
```js
|
||||
assert(typeof kdNN == 'function');
|
||||
```
|
||||
|
||||
</section>
|
||||
`kdNN([[[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [9, 2])` should return an array.
|
||||
|
||||
## Challenge Seed
|
||||
```js
|
||||
assert(
|
||||
Array.isArray(
|
||||
kdNN(
|
||||
[
|
||||
[2, 3],
|
||||
[5, 4],
|
||||
[9, 6],
|
||||
[4, 7],
|
||||
[8, 1],
|
||||
[7, 2]
|
||||
],
|
||||
[9, 2]
|
||||
)
|
||||
)
|
||||
);
|
||||
```
|
||||
|
||||
<section id='challengeSeed'>
|
||||
`kdNN([[[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [9, 2])` should return `[ 8, 1 ]`.
|
||||
|
||||
<div id='js-seed'>
|
||||
```js
|
||||
assert.deepEqual(
|
||||
kdNN(
|
||||
[
|
||||
[2, 3],
|
||||
[5, 4],
|
||||
[9, 6],
|
||||
[4, 7],
|
||||
[8, 1],
|
||||
[7, 2]
|
||||
],
|
||||
[9, 2]
|
||||
),
|
||||
[8, 1]
|
||||
);
|
||||
```
|
||||
|
||||
`kdNN([[[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [7, 1])` should return `[ 8, 1 ]`.
|
||||
|
||||
```js
|
||||
assert.deepEqual(
|
||||
kdNN(
|
||||
[
|
||||
[2, 3],
|
||||
[5, 4],
|
||||
[9, 6],
|
||||
[4, 7],
|
||||
[8, 1],
|
||||
[7, 2]
|
||||
],
|
||||
[7, 1]
|
||||
),
|
||||
[8, 1]
|
||||
);
|
||||
```
|
||||
|
||||
`kdNN([[[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]], [3, 2])` should return `[ 2, 3 ]`.
|
||||
|
||||
```js
|
||||
assert.deepEqual(
|
||||
kdNN(
|
||||
[
|
||||
[2, 3],
|
||||
[5, 4],
|
||||
[9, 6],
|
||||
[4, 7],
|
||||
[8, 1],
|
||||
[7, 2]
|
||||
],
|
||||
[3, 2]
|
||||
),
|
||||
[2, 3]
|
||||
);
|
||||
```
|
||||
|
||||
`kdNN([[2, 3, 1], [9, 4, 5], [4, 6, 7], [1, 2, 5], [7, 8, 9], [3, 6, 1]], [1, 2, 3])` should return `[ 1, 2, 5 ]`.
|
||||
|
||||
```js
|
||||
assert.deepEqual(
|
||||
kdNN(
|
||||
[
|
||||
[2, 3, 1],
|
||||
[9, 4, 5],
|
||||
[4, 6, 7],
|
||||
[1, 2, 5],
|
||||
[7, 8, 9],
|
||||
[3, 6, 1]
|
||||
],
|
||||
[1, 2, 3]
|
||||
),
|
||||
[1, 2, 5]
|
||||
);
|
||||
```
|
||||
|
||||
`kdNN([[2, 3, 1], [9, 4, 5], [4, 6, 7], [1, 2, 5], [7, 8, 9], [3, 6, 1]], [4, 5, 6])` should return `[ 4, 6, 7 ]`.
|
||||
|
||||
```js
|
||||
assert.deepEqual(
|
||||
kdNN(
|
||||
[
|
||||
[2, 3, 1],
|
||||
[9, 4, 5],
|
||||
[4, 6, 7],
|
||||
[1, 2, 5],
|
||||
[7, 8, 9],
|
||||
[3, 6, 1]
|
||||
],
|
||||
[4, 5, 6]
|
||||
),
|
||||
[4, 6, 7]
|
||||
);
|
||||
```
|
||||
|
||||
`kdNN([[2, 3, 1], [9, 4, 5], [4, 6, 7], [1, 2, 5], [7, 8, 9], [3, 6, 1]], [8, 8, 8])` should return `[ 7, 8, 9 ]`.
|
||||
|
||||
```js
|
||||
assert.deepEqual(
|
||||
kdNN(
|
||||
[
|
||||
[2, 3, 1],
|
||||
[9, 4, 5],
|
||||
[4, 6, 7],
|
||||
[1, 2, 5],
|
||||
[7, 8, 9],
|
||||
[3, 6, 1]
|
||||
],
|
||||
[8, 8, 8]
|
||||
),
|
||||
[7, 8, 9]
|
||||
);
|
||||
```
|
||||
|
||||
# --seed--
|
||||
|
||||
## --seed-contents--
|
||||
|
||||
```js
|
||||
function kdNN(fpoints, fpoint) {
|
||||
@ -62,12 +165,7 @@ function kdNN(fpoints, fpoint) {
|
||||
}
|
||||
```
|
||||
|
||||
</div>
|
||||
</section>
|
||||
|
||||
## Solution
|
||||
|
||||
<section id='solution'>
|
||||
# --solutions--
|
||||
|
||||
```js
|
||||
function kdNN(fpoints, fpoint) {
|
||||
@ -368,5 +466,3 @@ function kdNN(fpoints, fpoint) {
|
||||
return tree.nearest(fpoint, 1, 1000)[0][0];
|
||||
}
|
||||
```
|
||||
|
||||
</section>
|
||||
|
||||
Reference in New Issue
Block a user