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K-Nearest Neighbors Classifier

Predict a label by majority vote of the k closest training points.

สิ่งที่คุณจะได้เรียนรู้

  • Find the closest training points
  • Predict by majority vote
  • Break ties deterministically

โจทย์

ดูฉบับแปล

K-nearest neighbors (KNN) classifies a new point by looking at the k training points closest to it and taking the most common label.

  1. Compute the Euclidean distance from query to every point.
  2. Sort the points by distance; if two distances are equal, the point with the smaller index comes first.
  3. Take the first k points and count their labels.
  4. Return the label with the most votes. If labels tie, return the tied label whose closest point comes first in the sorted order.

Implement knn_predict(points, labels, query, k).

Input

points: The training points, each a list of coordinates.

labels: The label of each point.

query: The point to classify.

k: How many neighbors vote.

Output

Return the predicted label.

เงื่อนไข

1 ≤ k ≤ number of points ≤ 1,000

1 ≤ dimensions ≤ 20

-1,000 ≤ each coordinate ≤ 1,000

ตัวอย่าง

ตัวอย่างที่ 1
อินพุต:
k = 3
query = [3.0, 3.0]
labels = ["A", "A", "B", "B"]
points = [
    [1.0, 1.0],
    [2.0, 2.0],
    [8.0, 8.0],
    [9.0, 9.0]
]

ผลลัพธ์ที่คาดหวัง:
expected = "A"
อินพุต:
long long k = 3;
std::vector<double> query = {3.0, 3.0};
std::vector<std::string> labels = {"A", "A", "B", "B"};
std::vector<std::vector<double>> points = {
    {1.0, 1.0},
    {2.0, 2.0},
    {8.0, 8.0},
    {9.0, 9.0}
};

ผลลัพธ์ที่คาดหวัง:
std::string expected = "A";
อินพุต:
long k = 3;
double[] query = new double[] {3.0, 3.0};
string[] labels = new string[] {"A", "A", "B", "B"};
double[][] points = new double[][] {
    new double[] {1.0, 1.0},
    new double[] {2.0, 2.0},
    new double[] {8.0, 8.0},
    new double[] {9.0, 9.0}
};

ผลลัพธ์ที่คาดหวัง:
string expected = "A";

คำอธิบาย

Distances from (3, 3): (2, 2) ≈ 1.41, (1, 1) ≈ 2.83, (8, 8) ≈ 7.07. The 3 nearest labels are A, A and B, so the answer is A.

ตัวอย่างที่ 2
อินพุต:
k = 2
query = [1.0, 1.0]
labels = ["red", "blue", "blue", "red"]
points = [
    [0.0, 0.0],
    [4.0, 0.0],
    [0.0, 4.0],
    [4.0, 4.0]
]

ผลลัพธ์ที่คาดหวัง:
expected = "red"
อินพุต:
long long k = 2;
std::vector<double> query = {1.0, 1.0};
std::vector<std::string> labels = {"red", "blue", "blue", "red"};
std::vector<std::vector<double>> points = {
    {0.0, 0.0},
    {4.0, 0.0},
    {0.0, 4.0},
    {4.0, 4.0}
};

ผลลัพธ์ที่คาดหวัง:
std::string expected = "red";
อินพุต:
long k = 2;
double[] query = new double[] {1.0, 1.0};
string[] labels = new string[] {"red", "blue", "blue", "red"};
double[][] points = new double[][] {
    new double[] {0.0, 0.0},
    new double[] {4.0, 0.0},
    new double[] {0.0, 4.0},
    new double[] {4.0, 4.0}
};

ผลลัพธ์ที่คาดหวัง:
string expected = "red";

คำอธิบาย

The nearest point is (0, 0) (red); the next two, (4, 0) and (0, 4), are at the same distance, so the smaller index (4, 0) (blue) wins the tie. Red and blue have one vote each, and red's point came first, so the answer is red.

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