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BigCodeBench / BigCodeBench v0.1.0_hf 28c9c96e-1187-5d6e-b871-2e832012c515
Problem
Answer published by the source. Consult the official source to check your work against its answer.
complete prompt
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
def task_func(mu=0, sigma=1, sample_size=1000, seed=0):
"""
Generate a sample from a normal distribution with a given mean and a standard deviation and plot the histogram
together with the probability density function. Returns the Axes object representing the plot and the empirical
mean and standard deviation of the sample.
Parameters:
- mu (float): The mean of the normal distribution. Default is 0.
- sigma (float): The standard deviation of the normal distribution. Default is 1.
- sample_size (int): The size of the sample to generate. Default is 1000.
Returns:
- ax (matplotlib.axes._axes.Axes): Axes object with the plotted histogram and normal PDF, with the title format of 'Normal Distribution with
$\\mu = %0.2f, \\sigma = %0.2f$'.
- float: The empirical mean of the sample.
- float: The empirical standard deviation of the sample.
Requirements:
- numpy for data generation.
- scipy.stats for statistical functions.
- matplotlib.pyplot for plotting.
Example:
>>> ax, mean, std = task_func(0, 1, 1000)
>>> type(ax)
<class 'matplotlib.axes._axes.Axes'>
>>> print(round(mean, 3))
-0.045
>>> print(round(std, 3))
0.987
"""
Plain-text mathematical notation (without MathML)
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
def task_func(mu=0, sigma=1, sample_size=1000, seed=0):
"""
Generate a sample from a normal distribution with a given mean and a standard deviation and plot the histogram
together with the probability density function. Returns the Axes object representing the plot and the empirical
mean and standard deviation of the sample.
Parameters:
- mu (float): The mean of the normal distribution. Default is 0.
- sigma (float): The standard deviation of the normal distribution. Default is 1.
- sample_size (int): The size of the sample to generate. Default is 1000.
Returns:
- ax (matplotlib.axes._axes.Axes): Axes object with the plotted histogram and normal PDF, with the title format of 'Normal Distribution with $\\mu = %0.2f, \\sigma = %0.2f$'.
- float: The empirical mean of the sample.
- float: The empirical standard deviation of the sample.
Requirements:
- numpy for data generation.
- scipy.stats for statistical functions.
- matplotlib.pyplot for plotting.
Example:
>>> ax, mean, std = task_func(0, 1, 1000)
>>> type(ax)
<class 'matplotlib.axes._axes.Axes'>
>>> print(round(mean, 3))
-0.045
>>> print(round(std, 3))
0.987
"""
Original LaTeX notation
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
def task_func(mu=0, sigma=1, sample_size=1000, seed=0):
"""
Generate a sample from a normal distribution with a given mean and a standard deviation and plot the histogram
together with the probability density function. Returns the Axes object representing the plot and the empirical
mean and standard deviation of the sample.
Parameters:
- mu (float): The mean of the normal distribution. Default is 0.
- sigma (float): The standard deviation of the normal distribution. Default is 1.
- sample_size (int): The size of the sample to generate. Default is 1000.
Returns:
- ax (matplotlib.axes._axes.Axes): Axes object with the plotted histogram and normal PDF, with the title format of 'Normal Distribution with $\\mu = %0.2f, \\sigma = %0.2f$'.
- float: The empirical mean of the sample.
- float: The empirical standard deviation of the sample.
Requirements:
- numpy for data generation.
- scipy.stats for statistical functions.
- matplotlib.pyplot for plotting.
Example:
>>> ax, mean, std = task_func(0, 1, 1000)
>>> type(ax)
<class 'matplotlib.axes._axes.Axes'>
>>> print(round(mean, 3))
-0.045
>>> print(round(std, 3))
0.987
"""
instruct prompt
Generate a sample from a normal distribution with a given mean and a standard deviation and plot the histogram together with the probability density function. Returns the Axes object representing the plot and the empirical mean and standard deviation of the sample.
The function should output with:
ax (matplotlib.axes._axes.Axes): Axes object with the plotted histogram and normal PDF, with the title format of 'Normal Distribution with
$\\mu = %0.2f, \\sigma = %0.2f$'.
float: The empirical mean of the sample.
float: The empirical standard deviation of the sample.
You should write self-contained code starting with:
Plain-text mathematical notation (without MathML)
Generate a sample from a normal distribution with a given mean and a standard deviation and plot the histogram together with the probability density function. Returns the Axes object representing the plot and the empirical mean and standard deviation of the sample.
The function should output with:
ax (matplotlib.axes._axes.Axes): Axes object with the plotted histogram and normal PDF, with the title format of 'Normal Distribution with $\\mu = %0.2f, \\sigma = %0.2f$'.
float: The empirical mean of the sample.
float: The empirical standard deviation of the sample.
You should write self-contained code starting with:
Original LaTeX notation
Generate a sample from a normal distribution with a given mean and a standard deviation and plot the histogram together with the probability density function. Returns the Axes object representing the plot and the empirical mean and standard deviation of the sample.
The function should output with:
ax (matplotlib.axes._axes.Axes): Axes object with the plotted histogram and normal PDF, with the title format of 'Normal Distribution with $\\mu = %0.2f, \\sigma = %0.2f$'.
float: The empirical mean of the sample.
float: The empirical standard deviation of the sample.
You should write self-contained code starting with:
Code
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
def task_func(mu=0, sigma=1, sample_size=1000, seed=0):
code prompt
Code
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
def task_func(mu=0, sigma=1, sample_size=1000, seed=0):
entry point
task_func
libs
- numpy
- matplotlib
- scipy
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initial import