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BigCodeBench / BigCodeBench v0.1.0_hf 8f9bb0da-7df0-5bf5-8e3c-ebf891f62abe

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans
# Constants for configuration
RANGE = 100
SIZE = 1000
CLUSTERS = 5


def task_func():
    """
    Generates a set of 2D random points within a specified range and size,
    applies KMeans clustering to these points, and plots the results with
    cluster centroids.

    The function creates a scatter plot of the clustered points with each
    cluster displayed in a different color and the centroids of these clusters
    highlighted.

    Requirements:
        - numpy
        - sklearn.cluster
        - matplotlib.pyplot

    Returns:
        A tuple containing the numpy array of data points and the fitted KMeans model.

    Example:
    >>> data, kmeans = task_func()
    >>> isinstance(data, np.ndarray)  # Check if data is a numpy array
    True
    >>> data.shape == (1000, 2)  # Verify the shape of the data array
    True
    >>> isinstance(kmeans, KMeans)  # Confirm kmeans is an instance of KMeans
    True
    >>> len(kmeans.cluster_centers_) == 5  # Check the number of clusters
    True
    """

instruct prompt

Generates a set of 2D random points within a specified range and size, applies KMeans clustering to these points, and plots the results with cluster centroids. The function creates a scatter plot of the clustered points with each cluster displayed in a different color and the centroids of these clusters highlighted.
The function should output with:
    A tuple containing the numpy array of data points and the fitted KMeans model.
You should write self-contained code starting with:

Code

import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans
# Constants for configuration
RANGE = 100
SIZE = 1000
CLUSTERS = 5
def task_func():

code prompt

Code

import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans
# Constants for configuration
RANGE = 100
SIZE = 1000
CLUSTERS = 5
def task_func():

entry point

task_func

libs

  • numpy
  • matplotlib
  • sklearn

Discussion

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Source and history

task_id
8f9bb0da-7df0-5bf5-8e3c-ebf891f62abe
task_key
default--v0~2e1~2e0~5fhf--8f9bb0da-7df0-5bf5-8e3c-ebf891f62abe
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import