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.logpdf() Function:
We can obtain the log value of the probability density function by using the stats.halfgennorm.logpdf() method.
The formula of probability density function for halfgennorm:
Syntax:
stats.halfgennorm.logpdf(x, beta)
Return Value:
The log value of the probability density function is returned by the stats.halfgennorm.logpdf() Function.
Scipy stats.halfgennorm.logpdf() Function in Python
Method #1: Using logpdf() 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.
- Calculate the log value of probability density function using the logpdf() function of halfgennorm by passing some random value(x), given beta value as arguments to it.
- Store it in another variable.
- Print the log value of the probability density function 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 = 3 # Calculate the log value of probability density function using the logpdf() function # of halfgennorm by passing some random value(x), given beta value as arguments to it. # Store it in another variable. rslt = halfgennorm.logpdf(0.2, gvn_beta) # Print the log value of probability density function for the given beta value print("The log value of probability density function for the given beta {", gvn_beta,"} value = ", rslt)
Output:
The log value of probability density function for the given beta { 3 } value = 0.10519164174034268
Method #2: Using logpdf() 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.
- Calculate the log value of probability density function using the logpdf() function of halfgennorm by passing some random value(x), given beta value as arguments to it.
- Store it in another variable.
- Print the log value of the probability density function 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 = ")) # Calculate the log value of probability density function using the logpdf() function # of halfgennorm by passing some random value(x), given beta value as arguments to it. # Store it in another variable. rslt = halfgennorm.logpdf(0.3, gvn_beta) # Print the log value of probability density function for the given beta value print("The log value of probability density function for the given beta {", gvn_beta,"} value = ", rslt)
Output:
Enter some random number = 6 The log value of probability density function for the given beta { 6 } value = 0.07429703414981458