{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"bigcodebench","formal_name":"BigCodeBench","introduction":"BigCodeBench poses 1,140 function-level tasks drawn across 139 libraries. It tests whether a model can compose several real APIs correctly rather than write one self-contained function.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://bigcode-bench.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"60050d7d-9c56-58d3-a045-a3cd952bbd1a","task_key":"default--v0~2e1~2e0~5fhf--60050d7d-9c56-58d3-a045-a3cd952bbd1a","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 60050d7d-9c56-58d3-a045-a3cd952bbd1a","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom statsmodels.formula.api import ols\\ndef task_func(mu, sigma, seed=0, num_samples=1000, num_bins=30):\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom statsmodels.formula.api import ols\\n\\n\\ndef task_func(mu, sigma, seed=0, num_samples=1000, num_bins=30):\\n    '''\\n    Create a histogram of a normal distribution with a given mean and standard deviation, and overlay the \\n    probability density function (PDF) of the normal distribution on the histogram. Additionally, overlay a \\n    second order polynomial function on the histogram fitted bin-wise using ordinary least squares (OLS) \\n    regression. The random seed is set for reproducibility. The color of the PDF line is red, and the color of the OLS line is green.\\n    \\n    Parameters:\\n    - mu (float): The mean of the distribution.\\n    - sigma (float): The standard deviation of the distribution.\\n    - seed (int, Optional): The random seed for reproducibility. Defaults to 0.\\n    - num_samples (int, Optional): The number of samples to generate from the distribution. Defaults to 1000.\\n    - num_bins (int, Optional): The number of bins to use in the histogram. Defaults to 30.\\n    \\n    Returns:\\n    - matplotlib.axes.Axes: The Axes object with the histogram and overlaid PDF.\\n    \\n    Requirements:\\n    - numpy\\n    - matplotlib.pyplot\\n    - statsmodels.formula.api\\n    \\n    Example:\\n    >>> ax = task_func(0, 1)\\n    >>> type(ax)\\n    <class 'matplotlib.axes._axes.Axes'>\\n    '''\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Create a histogram of a normal distribution with a given mean and standard deviation, and overlay the probability density function (PDF) of the normal distribution on the histogram. Additionally, overlay a second order polynomial function on the histogram fitted bin-wise using ordinary least squares (OLS) regression. The random seed is set for reproducibility. The color of the PDF line is red, and the color of the OLS line is green.\\nThe function should output with:\\n    matplotlib.axes.Axes: The Axes object with the histogram and overlaid PDF.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom statsmodels.formula.api import ols\\ndef task_func(mu, sigma, seed=0, num_samples=1000, num_bins=30):\\n```\",\"libs\":\"['numpy', 'matplotlib', 'statsmodels']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}