Metadata-Version: 2.1
Name: prayikta
Version: 0.3
Summary: Gaussian and Binomial distributions
Home-page: UNKNOWN
Author: Manish Soni
Author-email: manish.soni8403@gmail.com
License: UNKNOWN
Description: ## Prayikta
        
        <p> By using Prayikta module you can  calculate Gaussian distribution, Binomial Probability, pdf and can visualization of them. </p>
        
        ### Classes
        
        * Gaussian Class
        * Binomial Class
          
        #### Gaussian Class
        
          ```
          # Example Fuctions
        
            >>> from prayikta import Gaussian
            
            # It has two arguments mean and standard daviation default (mean = 0 and stdev = 1)
        
            >>> gaussian = Gaussian()
            >>> gaussian.mean
            >>> gaussian.stdev
        
            # Read data from file and calculate mean and standard deviation
        
            >>> gaussian.read_data_file('filename.txt')
            >>> gaussian.calculate_mean()
            >>> gaussian.calculate_stdev()
            
            # Plot histogram of data
        
            >>> gaussian.plot_histogram()
        
            # Calculate probability density function and visualise it.
        
            >>> gaussian.pdf() # takes one argument
            >>> gaussian.plot_histogram_pdf()
            
        
            # Add to gaussian functions
        
            >>> gaussian_a = Gaussian(25,0)
            >>> gaussian_b = Gaussian(5,2)
            >>> gaussian_c = gaussian_a + gaussian_b
          ```
        
          #### Binomial Class
        
          ```
          # Example Fuctions
            >>> from prayikta import Binomial
        
            # It takes two arguments mean and standard daviation default (probability = 0.5 and size = 20)
        
            >>> binomial = Binomial()
            >>> binomial.calculate_mean()
            >>> binomial.calculate_stdev()
            
            # Plot bar
            
            >>> binomial.plot_bar()
        
            # Calculate pdf and visualise it.
        
            >>> binomial.pdf() # takes one argument
            >>> binomial.plot_bar_pdf()
        
            # Add to binomial functions
        
            >>> binomial_a = Binomial(0.5,10)
            >>> binomial_b = Binomial(0.25,20)
            >>> binomial_c = binomial_a + binomial_b
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
