single layer perceptron implementation using nn::Linear  
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#include <perceptron.h>
|  | 
|  | perceptron (std::vector< std::vector< double > > const &, const int num_classes, const int epochs=100, const double learning_rate=0.001) | 
|  | default constructor for perceptron class 
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| void | fit (const int batch_size) | 
|  | fit a single perceptron on the input data 
 | 
| double | predict (std::vector< double > const &) | 
|  | performs inference, classifying to 1 or -1 
 | 
single layer perceptron implementation using nn::Linear 
◆ perceptron()
  
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          | perceptron::perceptron | ( | std::vector< std::vector< double > > const & | data, |  
          |  |  | const int | num_classes, |  
          |  |  | const int | epochs = 100, |  
          |  |  | const double | learning_rate = 0.001 ) |  | inlineexplicit | 
 
default constructor for perceptron class 
- Parameters
- 
  
    | data | 2D vector, The input data. As usual, the last element of each sub-vector represents the label of the row |  | epochs(int) | The number of epochs |  | learning_rate(double) | The learning rate |  
 
 
 
◆ predict()
  
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          | double perceptron::predict | ( | std::vector< double > const & | input | ) |  |  | inline | 
 
performs inference, classifying to 1 or -1 
- Parameters
- 
  
    | input | 1D vector, the passed validation data |  
 
- Returns
- double: 1.0 or -1.0(binary) 
 
 
The documentation for this class was generated from the following file:
- /Users/runner/work/AlgoPlus/AlgoPlus/src/machine_learning/nn/perceptron.h