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AI & MACHINE LEARNING PROGRAM • LEVEL 07 — LOGISTIC CLASSIFICATION

Calculate the Sigmoid Function with Python

Learn calculate the sigmoid function with python with a short, executable Python example.

IntermediateSigmoidProbability

PROBLEM UNDERSTANDING

Input and expected output

Sample input
No input required
Sample output
0.8808

COMPLETE PYTHON PROGRAM

Complete Python implementation

ai-ml-07-sigmoid.py
Open in compiler
from math import exp
value = 2
print(round(1 / (1 + exp(-value)), 4))

GUIDED CODE TOUR • NOT LIVE EXECUTION

Study the program line by line

Use the real compiler button above to run and debug with different inputs.

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

0.8808
0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

  1. Import the required standard-library tools and prepare the sample data for calculate the sigmoid function.
  2. Apply Sigmoid and Probability to compute the required result.
  3. Display the result for calculate the sigmoid function and compare it with the documented sample output.

This example of calculate the sigmoid function computes the result directly from the prepared sample data. It demonstrates Sigmoid and Probability and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(1)

Auxiliary space

O(1)

DEBUGGING CHECKLIST

Common mistakes

Check this

For calculate the sigmoid function, keep the data shape and value types consistent with Sigmoid.

Check this

Apply Sigmoid in the same order shown by the algorithm; changing the order can change the result.

Check this

Check denominators, numeric ranges and rounding before comparing the calculated value.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.