Scipy Library in Python:
- SciPy is a scientific computation package that uses the NumPy library underneath.
- SciPy is an abbreviation for Scientific Python.
- It includes additional utility functions for optimization, statistics, and signal processing.
- SciPy, like NumPy, is open source, so we can freely use it.
- Travis Olliphant, the developer of NumPy, created SciPy.
- SciPy has optimized and added/enhanced functions that are often used in NumPy and Data Science.
stats.halfgennorm.rvs() Function:
We can generate a random variate from a generalized normal distribution using the stats.halfgennorm.rvs() function.
Syntax:
stats.halfgennorm.rvs(beta)
Return Value:
A random variate value is returned by the stats.halfgennorm.rvs() Function.
Examples:
Example1:
Input:
Given Beta = 4
Output:
The random variate value for the given beta { 4 } value = 0.2221758090994274
Example2:
Input:
Given Beta = 2
Output:
The random variate value for the given beta { 2 } value = 0.9441734155526698
Scipy stats.halfgennorm.rvs() Function in Python
Method #1: Using rvs() Function (Static Input)
Approach:
- Import halfgennorm() method from stats of scipy module using the import keyword
- Give the beta value as static input and store it in a variable.
- Pass the given beta value as an argument to the rvs() function of halfgennorm to generate a random variate value from a generalized normal distribution.
- Store it in another variable.
- Print the random variate value for the given beta value.
- The Exit of the Program.
Below is the implementation:
# Import halfgennorm() method from stats of scipy module using the import keyword from scipy.stats import halfgennorm # Give the beta value as static input and store it in a variable. gvn_beta = 2 # Pass the given beta value as an argument to the rvs() function of halfgennorm to # generate a random variate value from a generalized normal distribution. # Store it in another variable. rslt = halfgennorm.rvs(gvn_beta) # Print the random variate value for the given beta value print("The random variate value for the given beta {", gvn_beta,"} value = ", rslt)
Output:
The random variate value for the given beta { 2 } value = 0.24472017026361062
Method #2: Using rvs() Function (User Input)
Approach:
- Import halfgennorm() method from stats of scipy module using the import keyword
- Give the beta value as user input using the int(input()) function and store it in a variable.
- Pass the given beta value as an argument to the rvs() function of halfgennorm to generate a random variate value from a generalized normal distribution.
- Store it in another variable.
- Print the random variate value for the given beta value.
- The Exit of the Program.
Below is the implementation:
# Import halfgennorm() method from stats of scipy module using the import keyword from scipy.stats import halfgennorm # Give the beta value as user input using the int(input()) function and store it in a variable. gvn_beta = int(input("Enter some random number = ")) # Pass the given beta value as an argument to the rvs() function of halfgennorm to # generate a random variate value from a generalized normal distribution. # Store it in another variable. rslt = halfgennorm.rvs(gvn_beta) # Print the random variate value for the given beta value print("The random variate value for the given beta {", gvn_beta,"} value = ", rslt)
Output:
Enter some random number = 4 The random variate value for the given beta { 4 } value = 0.8736129067330833