single layer perceptron implementation using nn::Linear
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#include <perceptron.h>
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| | 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
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| double | predict (std::vector< double > const &) |
| | performs inference, classifying to 1 or -1
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single layer perceptron implementation using nn::Linear
◆ perceptron()
| perceptron::perceptron |
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std::vector< std::vector< double > > const & | data, |
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const int | num_classes, |
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const int | epochs = 100, |
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const double | learning_rate = 0.001 ) |
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inlineexplicit |
default constructor for perceptron class
- Parameters
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| 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()
| double perceptron::predict |
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std::vector< double > const & | input | ) |
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inline |
performs inference, classifying to 1 or -1
- Parameters
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| 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