The Perceptron algorithm is a two-class (binary) classification machine learning algorithm.
It is a type of neural network model, perhaps the simplest type of neural network model.
It consists of a single node or neuron that takes a row of data as input and predicts a class label. This is achieved by calculating the weighted sum of the inputs and a bias (set to 1). The weighted sum of the input of the model is called the activation.
- Activation = Weights * Inputs + Bias
If the activation is above 0.0, the model will output 1.0; otherwise, it will output 0.0.
- Predict 1: If Activation > 0.0
- Predict 0: If Activation <= 0.0
Given that the inputs are multiplied by model coefficients, like linear regression and logistic regression, it is good practice to normalize or standardize data prior to using the model.
Here, I have a file with "gradientdescentforperceptron_nor.py" name that implements the gradient descent algorithm for the perceptron, which is designed for NOR binary logic function and uses it to update its weights.
also I have another file with "perceptron_algorithm.py" name that implements the Perceptron algorithm and run it on the attached data.
The classification result:
The error rate per iteration:
