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BigCodeBench / BigCodeBench v0.1.0_hf 38f5aac0-50ff-50fe-a3f0-c3d2a01c4d27

Problem

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

complete prompt

import re
import numpy as np
from collections import Counter
from sklearn.mixture import GaussianMixture


def task_func(text, num_gaussians=1, seed=42):
    '''
    Extract names from a string that aren't enclosed by square brackets, 
    tokenize the names into words, and count the frequency of each word.
    Finally, fit a mixture of num_gaussians 1-D Gaussian distributions to 
    the word frequencies and return the means and variances of the fitted 
    Gaussians.
    
    Parameters:
    text (str): The text from which to extract names and count word frequencies.
    num_gaussians (int, Optional): The number of Gaussian distributions to fit to 
                                   the word frequencies. Defaults to 1.
    seed (int, Optional): The seed for the random number generator. Defaults to 42.
    
    Returns:
    dict: A dictionary with the frequency of each word.
    
    Requirements:
    - re module for regular expression operations.
    - numpy for setting the random seed.
    - collections.Counter for counting word frequencies.
    - scipy.stats.gmm for fitting Gaussian mixture models.

    Raises:
    ValueError: If num_gaussians is less than or equal to 0.
    Exception: If num_gaussians is greater than the number of unique words.
    
    Examples:
    >>> freqs, means = task_func("Josie Smith [3996 COLLEGE AVENUE, SOMETOWN, MD 21003]Mugsy Dog Smith [2560 OAK ST, GLENMEADE, WI 14098]")
    >>> freqs
    {'Josie': 1, 'Smith': 2, 'Mugsy': 1, 'Dog': 1}
    '''

instruct prompt

Extract names from a string that aren't enclosed by square brackets, tokenize the names into words, and count the frequency of each word. Finally, fit a mixture of num_gaussians 1-D Gaussian distributions to the word frequencies and return the means and variances of the fitted Gaussians.
The function should raise the exception for: ValueError: If num_gaussians is less than or equal to 0. Exception: If num_gaussians is greater than the number of unique words.
The function should output with:
    dict: A dictionary with the frequency of each word.
You should write self-contained code starting with:

Code

import re
import numpy as np
from collections import Counter
from sklearn.mixture import GaussianMixture
def task_func(text, num_gaussians=1, seed=42):

code prompt

Code

import re
import numpy as np
from collections import Counter
from sklearn.mixture import GaussianMixture
def task_func(text, num_gaussians=1, seed=42):

entry point

task_func

libs

  • numpy
  • collections
  • re
  • sklearn

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Source and history

task_id
38f5aac0-50ff-50fe-a3f0-c3d2a01c4d27
task_key
default--v0~2e1~2e0~5fhf--38f5aac0-50ff-50fe-a3f0-c3d2a01c4d27
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import