fsc-distributions 1.4

Gaussian and binomial distribution classes

fsc-distributions package

Package that contains classes for binomial and gaussian distributions, created while taking the AI Programming with Python - Bertelsmann course on Udacity

Installation instructions

pip install fsc_distributions

Gaussian class

Gaussian distribution class for calculating and visualizing a Gaussian distribution.

Attributes:

mean (float) representing the mean value of the distribution

stdev (float) representing the standard deviation of the distribution

data_list (list of floats) a list of floats extracted from the data file

Methods:

read_data_file(file_name) Function to read in data from a txt file. The txt file should have one number (float) per line. The numbers are stored in the data attribute.
	Args:
		file_name (string): name of a file to read from

	Returns:
		None

calculate_mean() Function to calculate the mean of the data set.
	Args: 
		None

	Returns: 
		float: mean of the data set

calculate_stdev(sample=True) Function to calculate the standard deviation of the data set.
	Args: 
		sample (bool): whether the data represents a sample or population

	Returns: 
		float: standard deviation of the data set
		
plot_histogram() Function to output a histogram of the instance variable data using matplotlib pyplot library.
	Args:
		None
	
	Returns:
		None

pdf(x) Probability density function calculator for the gaussian distribution.
	Args:
		x (float): point for calculating the probability density function
	
	Returns:
		float: probability density function output

plot_histogram_pdf(n_spaces = 50) Function to plot the normalized histogram of the data and a plot of the probability density function along the same range
	Args:
		n_spaces (int): number of data points 
	
	Returns:
		list: x values for the pdf plot
		list: y values for the pdf plot

from fsc_distributions import Gaussian

gaussian1 = Gaussian(25,2) #create a new Gaussian object and initialize the mean with 25 and stdev with 2

gaussian2 = Gaussian(35,8) #create a new Gaussian object and initialize the mean with 25 and stdev with 8

gaussian3 = gaussian1 + gaussian2 #sum the two distributions with an overloaded add operator

Binomial class

Binomial distribution class for calculating and visualizing a Binomial distribution.

Attributes:

mean (float) representing the mean value of the distribution

stdev (float) representing the standard deviation of the distribution

data_list (list of floats) a list of floats to be extracted from the data file

p (float) representing the probability of an event occurring

n (int) number of trials

Methods:

read_data_file(file_name) Function to read in data from a txt file. The txt file should have one number (float) per line. The numbers are stored in the data attribute.
	Args:
		file_name (string): name of a file to read from

	Returns:
		None

calculate_mean() Function to calculate the mean from p and n
    Args: 
        None

    Returns: 
        float: mean of the data set

calculate_stdev(sample=True) Function to calculate the standard deviation from p and n.
    Args: 
        None

    Returns: 
        float: standard deviation of the data set

replace_stats_with_data() Function to calculate p and n from the data set
    Args: 
        None

    Returns: 
        float: the p value
        float: the n value

plot_bar() Function to output a histogram of the instance variable data using matplotlib pyplot library.
    Args:
        None

    Returns:
        None

pdf(k) Probability density function calculator for the binomial distribution.
	Args:
		k (float): point for calculating the probability density function

	Returns:
		float: probability density function output

plot_bar_pdf()Function to plot the pdf of the binomial distribution
    Args:
        None

    Returns:
        list: x values for the pdf plot
        list: y values for the pdf plot

from fsc_distributions import Binomial

binomial1 = Binomial(0.5,20) #create a new Binomial object and initialize the prob with 0.5 and size with 20

binomial2 = Binomial(0.5,30) #create a new Binomial object and initialize the prob with 0.5 and size with 30

binomial3 = binomial1 + binomial2 #sum the two distributions with an overloaded add operator (only sums binomials with the same p)