BigCodeBench / BigCodeBench v0.1.0_hf 2040273f-6ace-5d73-93e2-c1418268a9ea

complete prompt

import numpy as np
import matplotlib.pyplot as plt

def task_func(mu, sigma, sample_size):
    """
    Generates a numpy array of random samples drawn from a normal distribution
    and plots the histogram of these samples. This function specifies the mean (mu), 
    standard deviation (sigma), and sample size (sample_size), making it useful 
    for simulating data, conducting statistical experiments, or initializing 
    algorithms that require normally distributed data with visualization.

    Parameters:
        mu (float): The mean of the normal distribution.
        sigma (float): The standard deviation of the normal distribution.
        sample_size (int): The number of samples to draw from the distribution.

    Returns:
        ndarray: A numpy array of shape (sample_size,) containing samples drawn from the
                 specified normal distribution.

    Notes:
        Plots a histogram of the generated samples to show the distribution. The histogram
        features:
        - X-axis labeled "Sample values", representing the value of the samples.
        - Y-axis labeled "Frequency", showing how often each value occurs.
        - Title "Histogram of Generated Samples", describing the content of the graph.
        - Number of bins set to 30, to discretize the sample data into 30 intervals.
        - Alpha value of 0.75 for bin transparency, making the histogram semi-transparent.
        - Color 'blue', giving the histogram a blue color.

    Requirements:
    - numpy
    - matplotlib.pyplot

    Examples:
    >>> data = task_func(0, 1, 1000)
    >>> len(data)
    1000
    >>> isinstance(data, np.ndarray)
    True
    """

instruct prompt

Generates a numpy array of random samples drawn from a normal distribution and plots the histogram of these samples. This function specifies the mean (mu), standard deviation (sigma), and sample size (sample_size), making it useful for simulating data, conducting statistical experiments, or initializing algorithms that require normally distributed data with visualization.
Note that: Notes: Plots a histogram of the generated samples to show the distribution. The histogram features: X-axis labeled "Sample values", representing the value of the samples. Y-axis labeled "Frequency", showing how often each value occurs. Title "Histogram of Generated Samples", describing the content of the graph. Number of bins set to 30, to discretize the sample data into 30 intervals. Alpha value of 0.75 for bin transparency, making the histogram semi-transparent. Color 'blue', giving the histogram a blue color.
The function should output with:
    ndarray: A numpy array of shape (sample_size,) containing samples drawn from the
    specified normal distribution.
You should write self-contained code starting with:

Code

import numpy as np
import matplotlib.pyplot as plt
def task_func(mu, sigma, sample_size):

code prompt

Code

import numpy as np
import matplotlib.pyplot as plt
def task_func(mu, sigma, sample_size):

entry point

task_func

libs

• numpy
• matplotlib