perceptron {LearnSL} | R Documentation |
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,
details = 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
|
max_iter |
Maximum epoch during the training phase. |
learning_rate |
Value at which the perceptron will learn from previous epochs mistakes. |
details |
Boolean value. If it is set to "TRUE" multiple clarifications and explanations are printed along the code |
waiting |
If TRUE while |
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)
VĂctor Amador Padilla, victor.amador@edu.uah.es
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)