This is a Python script that implements the Perceptron Learning Algorithm (PLA) for a binary classification problem. The script allows you to train a simple perceptron model on a training dataset and evaluate its performance on a testing dataset. Additionally, it provides a visualization of the classification results in a 2D plot.
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Ensure you have Python installed on your system.
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Clone or download this repository to your local machine.
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Open a terminal or command prompt and navigate to the directory where the script is located.
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Run the script using the following command:
python simple_pla.py -
Follow the on-screen prompts to provide input for the training and testing datasets.
The script expects input in the following format:
<num_examples> <num_features>
<feature1_value> <featurex_value> <class_label>
<feature1_value> <featurex_value> <class_label>
Where:
num_examples: The number of input samples in the dataset.
num_features: The number of features for each input sample.
feature1_value, feature2_value, ...: The values of the features for each input sample.
class_label: The class label for each input sample, which should be either -1 or 1 for binary classification.
Here is an example training set:
6 2
77 66 1
86 14 1
50 21 -1
42 82 1
28 78 1
22 32 -1
And a testing set:
10 2
23 47 -1
26 2 -1
96 78 1
1 65 -1
64 40 1
13 68 1
24 5 -1
69 32 1
21 19 -1
65 88 1
- The script will display the accuracy of the perceptron model on the testing dataset.
- It will also generate a 2D plot to visualize the classification results. The plot shows correctly classified points in green and incorrectly classified points in red.
- If you input more than 2 features you will be asked to provide two feature indices for the plotting.