Python Scipy stats.halfgennorm.entropy() Function

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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.

Scipy stats.halfgennorm.entropy() Function:

We can obtain the value of entropy of a random variate by using the stats.halfgennorm.entropy() function.

Syntax:

stats.halfgennorm.entropy(beta)

Return Value:

The entropy value of a random variate is returned by the stats.halfgennorm.entropy() Function.

What is Entropy?

When entropy is discussed in information theory, it refers to the randomness in data. Another way to think about entropy is as the data’s unpredictability. So a high entropy indicates that the data is scattered, whereas a low entropy indicates that nearly all of the data is the same.

Scipy stats.halfgennorm.entropy() Function in Python

Method #1: Using entropy 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 entropy value using the entropy() function of halfgennorm by passing the given beta value as an argument to it.
  • Store it in another variable.
  • Print the entropy 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 = 3

# Calculate the entropy value using the entropy() function of halfgennorm by passing
# the given beta value as an argument to it.
# Store it in another variable.
rslt = halfgennorm.entropy(gvn_beta)

# Print the entropy value for the given beta value
print("The entropy value for the given beta {", gvn_beta,"} value = ", rslt)

Output:

The entropy value for the given beta { 3 } value = 0.22014169159299057

Method #2: Using entropy 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 entropy value using the entropy() function of halfgennorm by passing the given beta value as an argument to it.
  • Store it in another variable.
  • Print the entropy 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 = "))

# Calculate the entropy value using the entropy() function of halfgennorm by passing
# the given beta value as an arguments to it.
# Store it in another variable.
rslt = halfgennorm.entropy(gvn_beta)

# Print the entropy value for the given beta value
print("The entropy value for the given beta {", gvn_beta,"} value = ", rslt)

Output:

Enter some random number = 5
The entropy value for the given beta { 5 } value = 0.1146259099966842