How to model decision trees and neuron networks in PHP?

With the rapid development of machine learning, decision trees and neuron networks have become one of the most widely used models. They have applications in various fields, such as finance, medical care, e-commerce, etc. How to model decision trees and neuron networks in PHP? We will introduce it to you in detail in this article.

1. Decision tree modeling

The decision tree is a classification model with a tree structure. Its core is to select the features in the data set that can best classify the data. The nodes of a decision tree can be leaf nodes that represent yes/no answers, or nodes that represent decisions. The construction process of the decision tree is to start from the root and gradually select the best features for segmentation until the preset stopping conditions are reached.

To implement decision tree modeling in PHP, you can use the PHP-ML library. The PHP-ML library provides a decision tree classifier: DecisionTreeClassifier. The following is a simple sample code:

<?php
use PhpmlClassificationDecisionTree;
use PhpmlModelManager;

require_once __DIR__ . '/vendor/autoload.php';

$trainingSamples = [[1, 2], [1, 4], [3, 1], [3, 3], [2, 2], [4, 1], [4, 3]];
$trainingLabels = ['a', 'a', 'a', 'b', 'a', 'b', 'b'];

$classifier = new DecisionTree();
$classifier->train($trainingSamples, $trainingLabels);

$modelManager = new ModelManager();
$modelManager->saveToFile($classifier, 'classifier.phpml');

In the above code, we use PHP-ML's DecisionTree classifier to train a simple classification model, and use the model manager to save the trained model into a file for subsequent use.

2. Neuron network modeling

The neuron network is a model that imitates the human brain nervous system. It has non-linear characteristics and can adapt to different inputs through learning. Neuronal networks consist of units (neurons) and weighted edges connecting them, and can be trained using the backpropagation algorithm.

To implement neural network modeling in PHP, you can use the Neural Network PHP extension. The following is a simple sample code:

<?php
use FFI;

$ffi = FFI::cdef("
    typedef struct {
        double* input;
        double* hidden;
        double output;
    } neuron;

    void init_neurons(neuron* ns);
    void train(neuron* ns, double* inputs, double output);
    double test(neuron* ns, double* inputs);
", "nn.c");

$ns = FFI::new("neuron[4]");

$ffi->init_neurons($ns);

for ($i = 0; $i < 10000; ++$i) {
    $ffi->train($ns, [0, 0], 0);
    $ffi->train($ns, [0, 1], 1);
    $ffi->train($ns, [1, 0], 1);
    $ffi->train($ns, [1, 1], 0);
}

$result = $ffi->test($ns, [0, 0]); // 0
$result = $ffi->test($ns, [0, 1]); // 1
$result = $ffi->test($ns, [1, 0]); // 1
$result = $ffi->test($ns, [1, 1]); // 0

In the above code, we use the Neural Network PHP extension to train a simple neuron network and use it to perform XOR logical operations.

Conclusion

Decision trees and neural networks are very important modeling methods in machine learning. These two methods can be implemented in PHP using the PHP-ML library and the Neural Network PHP extension respectively. To gain a deeper understanding of these two methods, readers are recommended to continue learning the relevant content so that they can be better applied to actual projects.

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