Package com.jml.linear_models

Class Perceptron

java.lang.Object
com.jml.core.Model<double[][],​double[][]>
com.jml.linear_models.Perceptron
public class Perceptron extends Model<double[][],​double[][]>
A perceptron is a linear model that is equivalent to a single layer neural network.

When a perceptron model is saved, it will be saved as a neural network model. When the activation of a perceptron is the sigmoid function, it is a linear classifier that is analogous to logistic regression.

  • Constructor Summary

    Constructors
    Constructor
    Description
    Perceptron()
    Creates a perceptron with default hyper-parameters.
    Perceptron​(double learningRate, int epochs, int batchSize, double threshold)
    Creates a perceptron with specified hyper-parameters and the sigmoid activation function.
    Perceptron​(double learningRate, int epochs, int batchSize, double threshold, ActivationFunction activation)
    Creates a perceptron with specified hyper-parameters and activation function.

  • Method Summary

    Modifier and Type
    Method
    Description
    Perceptron
    fit​(double[][] features, double[][] targets)
    Fits or trains the model with the given features and targets.
    linalg.Matrix
    getParams()
    Gets the parameters of the trained model.
    String
    inspect()
    Forms a string of the important aspects of the model.
    same as Model.toString()
    double[][]
    predict​(double[][] features)
    Uses fitted/trained model to make prediction on single feature.
    linalg.Matrix
    predict​(linalg.Matrix X, linalg.Matrix w)
    Makes a prediction using a model by specifying the parameters of the model.
    void
    saveModel​(String filePath)
    Saves a trained model to the specified file path.
    String
    toString()
    Forms a string of the important aspects of the model.

    Methods inherited from class com.jml.core.Model

    load

    Methods inherited from class java.lang.Object

    equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

  • Constructor Details

    • Perceptron

      public Perceptron()
      Creates a perceptron with default hyper-parameters. The default parameters are listed below. 
           Learning Rate: 0.01
     Epochs: 10
     Batch Size: 1
     Threshold: 1e-5
     Activation: Sigmoid Function

    • Perceptron

      public Perceptron​(double learningRate, int epochs, int batchSize, double threshold)
      Creates a perceptron with specified hyper-parameters and the sigmoid activation function.
      To specify an activation function, see Perceptron(double, int, int, double, ActivationFunction).
      Parameters:
      learningRate - Learning rate to use during training.
      epochs - Number of epochs to train the perceptron for.
      batchSize - Batch size to use during training.
      threshold - Threshold for early stopping of training. If the loss of the perceptron model falls below this value during training, training will end before the specified number of epochs.

    • Perceptron

      public Perceptron​(double learningRate, int epochs, int batchSize, double threshold, ActivationFunction activation)
      Creates a perceptron with specified hyper-parameters and activation function.
      Parameters:
      learningRate - Learning rate to use during training.
      epochs - Number of epochs to train the perceptron for.
      batchSize - Batch size to use during training.
      threshold - Threshold for early stopping of training. If the loss of the perceptron model falls below this value during training, training will end before the specified number of epochs.
      activation - Activation function to use in the perceptron.

  • Method Details

    • fit

      public Perceptron fit​(double[][] features, double[][] targets)
      Fits or trains the model with the given features and targets. For both the features and targets parameters, if they are 2D arrays, then the number of rows in each must match and will be the number of samples in the data. The number of columns in each will be the number of features and targets in a single sample.

      For instance, if the features array has shape n-by-m and the targets array has shape n-by-k. Then there are n samples in the dataset, each individual sample has m features, and each individual sample has k targets.
      Specified by:
      fit in class Model<double[][],​double[][]>
      Parameters:
      features - The features of the training set.
      targets - The targets of the training set.
      Returns:
      This. i.e. the trained model.

    • predict

      public double[][] predict​(double[][] features)
      Uses fitted/trained model to make prediction on single feature.
      Specified by:
      predict in class Model<double[][],​double[][]>
      Parameters:
      features - The features to make predictions on.
      Returns:
      The models predicted labels.

    • predict

      public linalg.Matrix predict​(linalg.Matrix X, linalg.Matrix w)
      Makes a prediction using a model by specifying the parameters of the model. Unlike the other predict method, no model needs to be trained to use this method since the parameters provided define a model.
      Specified by:
      predict in class Model<double[][],​double[][]>
      Parameters:
      X - Features to make prediction on.
      w - Parameters of the model.
      Returns:
      prediction on the features using the given model parameters.

    • getParams

      public linalg.Matrix getParams()
      Gets the parameters of the trained model.
      Specified by:
      getParams in class Model<double[][],​double[][]>
      Returns:
      A matrix containing the parameters of the trained model.

    • saveModel

      public void saveModel​(String filePath)
      Saves a trained model to the specified file path.
      Specified by:
      saveModel in class Model<double[][],​double[][]>
      Parameters:
      filePath - File path, including extension, to save fitted / trained model to.

    • inspect

      public String inspect()
      Forms a string of the important aspects of the model.
      same as Model.toString()
      Specified by:
      inspect in class Model<double[][],​double[][]>
      Returns:
      Details of model as string.

    • toString

      public String toString()
      Forms a string of the important aspects of the model.
      Specified by:
      toString in class Model<double[][],​double[][]>
      Returns:
      String representation of model.