#0183

One-Hot Encode Integer Labels

MediumNumPy25 min20 XP

Problem

Given integer labels and num_classes, return a matrix with one 1 per row at the label index.

Why This Matters

One-hot matrices appear in classification labels, categorical features, and neural-network targets.

Function Signature

def one_hot(labels, num_classes):

Examples

Example 1

Inputlabels = [2, 0, 1], num_classes = 3

Output[[0,0,1],[1,0,0],[0,1,0]]

Each row marks the class index for that label.

Constraints

Return the exact requested result.
Handle common edge cases.
Keep the solution readable before optimizing.

CodePython

import numpy as np

def one_hot(labels, num_classes):
    """
    Input example: labels = [2, 0, 1], num_classes = 3
    Return: [[0,0,1],[1,0,0],[0,1,0]]
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return np.zeros((len(labels), num_classes))

import numpy as np

def one_hot(labels, num_classes):
    """
    Input example: labels = [2, 0, 1], num_classes = 3
    Return: [[0,0,1],[1,0,0],[0,1,0]]
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return np.zeros((len(labels), num_classes))

Visible browser tests run here when available.

Testcases1 visible / 3 hidden categories

Core example

Inputlabels = [2, 0, 1], num_classes = 3

Expected[[0,0,1],[1,0,0],[0,1,0]]

Each row marks the class index for that label.

Hidden Test Categories

minimal inputrepeated valueslarger realistic input