Metadata-Version: 2.1
Name: rand-number-gen
Version: 0.1
Summary: Random Number Generator
Home-page: https://github.com/maneeshammm/
Author: Maneesha Majeed
Author-email: maneesha_majeed@hotmail.com
License: UNKNOWN
Description: 
        # Random Number Package
        ---
        
        ### random number generator 
        
        This module implement random number from the specified distribution.
        
        ### Real-valued distributions
        ---
        The following functions generate specific real-valued distributions. Function parameters are named after the corresponding variables in the distributionâ€™s equation.
        
        1. uniform(size)
        
            Return the 'size random floating point numbers in the range [0.0, 1.0).
            
        2. gaussian(size)
        
        3. binomial(trials,probability,size)
        
           return samples from a binomial distribution, where each sample is equal to the number of successes over the n trials. 
           
        4. chisquare(df,size)
            
            return samples from a chi-square distribution.
            Parameters:  
                     a.  df : int or array_like of ints;Number of degrees of freedom.
                      b.  size : int or tuple of ints, Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is                  None (default), a single value is returned if df is a scalar. Otherwise, np.array(df).size samples are drawn.
           
        5. weibull(size)
            return  samples from a Weibull distribution.
            Parameters:
            a : float or array_like of floats
                       Shape of the distribution. Should be greater than zero.
                       size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if a is a scalar. Otherwise, np.array(a).size samples are drawn.
            
        6. exponential(scale,size)
           return samples from an exponential distribution.
           Parameters:
           scale : float or array_like of floats
                      The scale parameter, \beta = 1/\lambda.
           size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if scale is a scalar. Otherwise, np.array(scale).size samples are drawn.
                      
        7. poisson(lam,size)
           return samples from a poisson distribution.
           Parameters:
           lam : float or array_like of floats
                      Expectation of interval, should be >= 0. A sequence of expectation intervals must be broadcastable over the requested size.
                      size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if lam is a scalar. Otherwise, np.array(lam).size samples are drawn.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
