Project No. 2 Perceptron-based classifier

CMSC 409: Artificial Intelligence
Project No. 2

Pr.2.1 Perceptron-based classifier (10 pts)
In this assignment please use the datasets from Project 1. In the language of your preference (Python,
Java, Matlab, C++), implement a perceptron-based classifier that will iterate until the total error is:
Dataset Stopping criterion
A
Epsilon ε<10
-5
B
Epsilon ε<40
C
Epsilon ε<700
To do this, you need to introduce a stopping criterion. You should also introduce a limit on the maximum
number of iterations (let that be ni=5,000). Please normalize the datasets first. Initialize your neuron using
random values between (-0.5, 0.5).
Please use:
a) Hard unipolar activation function
b) Soft unipolar activation function
Page 1 of 3 CMSC 409 Artificial Intelligence, Project 2
CMSC 409: Artificial Intelligence
Fall 2022, Instructor: Dr. Milos Manic, http://www.people.vcu.edu/~mmanic
Project 2
Note: For either activation function, you may need to experiment with different learning rates (alpha).
Smaller alpha (especially with hard activation function) may yield smaller errors. When using a soft
activation function, in addition to alpha, you can adjust the gain (smaller gain may result in more
iterations, but more “stable” convergence).
For activation function in a) perform the following steps for each of the datasets.
1. Choose 75% of the data for training and the rest for testing. Train and test your neuron. Plot the
data and decision line for training and testing data (separately). Note and report the final Total
Error (TE) of training. Create a confusion matrix for the testing dataset, as well as rates (true
positive, false positive, etc), and compare those to the one from Project 1. (2 pts)
2. Choose 25% of the data for training and the rest for testing. Train and test your neuron. Plot the
data and decision line for training and testing data (separately). Note and report the final Total
Error (TE) of training. Create a confusion matrix for the testing dataset, as well as rates (true
positive, false positive, etc), and compare it to the one from Project 1. (2 pts)
3. Compare training TE between steps 1 & 2 above. For testing datasets, compare accuracy,
confusion matrices, and rates between steps 1 & 2 above. Answer the following questions: (1 pt)
a. Are error rates different, and if so, why?
b. What is the effect of different data sets and the effect of different training/testing
distributions of TEs on the accuracy, confusion matrices, and rates (true positive, false
positive, etc).?
c. When would you go with step 1 and when with step 2 from above?
d. Comment and discuss.
Repeat steps 1. through 3. for activation function in b).
Important: The data sets list the data points for both types of patterns (“small” car and “big” car).
Extra credit question: How would you choose which data points to use for training, and which for
testing? Note that the algorithm may fail if trained on one type of pattern, and tested on another. (1 pt)
Pr. 2.2 Soft vs. hard activation function (5 pts)
Compare and discuss results when hard unipolar activation was used, vs. when soft unipolar activation
function was used. You should include plots and be specific (provide quantitative comparisons when
comparing). (3 pts)
Comment on each training/testing distribution and each data set. (2 pt)
This assignment may be updated!
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Note:
1. The code must be user-friendly. The TA must be able to test it simply by executing the code.
2. Project deliverable should be two files:
a. Written report with all the plots and answers to all of the questions above, in pdf format.
b. A zip file containing:
i. Training/testing data sets as decided in Pr. 2.1 steps 1 & 2
ii. Source code. Python code can be in a Python notebook file (.ipynb) or a Python file (.py).
3. Submit your deliverables (Canvas). Please name files as
a. GroupName_Project2.pdf and
Page 2 of 3 CMSC 409 Artificial Intelligence, Project 2
CMSC 409: Artificial Intelligence
Fall 2022, Instructor: Dr. Milos Manic, http://www.people.vcu.edu/~mmanic
Project 2
b. GroupName_Project2.zip
Page 3 of 3 CMSC 409 Artificial Intelligence, Project 2