perceptron: Perceptron

In UAHDataScienceSC: Learn Supervised Classification Methods Through Examples and Code

Perceptron

Description

Binary classification algorithm that learns to separate two classes of data points by finding an optimal decision boundary (hyper plane) in the feature space.

Usage

perceptron(
  training_data,
  to_clasify,
  activation_method,
  max_iter,
  learning_rate,
  learn = FALSE,
  waiting = TRUE
)

Arguments

training_data	Data frame with already classified observations. Each column represents a parameter of the values. The last column contains the output, this means, the expected output when the other column values are inputs. Each row is a different observation. It works as training data.
to_clasify	Vector containing the parameters of the new value that we want to classify.
activation_method	Activation function to be used. It must be one of "step", "sine", "tangent", "linear", "relu", "gelu" or "swish".
max_iter	Maximum epoch during the training phase.
learning_rate	Value at which the perceptron will learn from previous epochs mistakes.
learn	Boolean value. If it is set to "TRUE" multiple clarifications and explanations are printed along the code
waiting	If TRUE while learn = TRUE. The code will stop in each "block" of code and wait for the user to press "enter" to continue.

Details

Functioning:

Step 1

Generate a random weight for each independent variable.

Step 2

Check if the weights classify correctly. If they do, go to step 4

Step 3

Adjust weights based on the error between the expected output and the real output. If max_iter is reached go to step 4. If not, go to step 2.

Step 4

Return the weights and use them to classify the new value

Value

List with the weights of the inputs.

Author(s)

Víctor Amador Padilla, [e-mail]

Examples

# example code
perceptron(db_per_or, c(1, 1, 1), "gelu", 1000, 0.1)
perceptron(db_per_and, c(0,0,1), "swish", 1000, 0.1, TRUE, FALSE)

UAHDataScienceSC documentation built on April 3, 2025, 8:58 p.m.