How to use machine learning functions in PHP

With the continuous development of artificial intelligence technology, machine learning has become an important part of the application of artificial intelligence technology. In the field of web development, PHP is a widely used programming language. Therefore, understanding how to use machine learning functions in PHP can not only improve our programming skills, but also provide more intelligent functions to our web applications. This article explains how to use machine learning functions in PHP.

1. Basic concepts of machine learning functions

Before using machine learning functions in PHP, you first need to understand the difference between machine learning functions and ordinary functions. Machine learning functions are different from ordinary functions in that they require data as input rather than just processing data. In machine learning, one of the most common tasks is classification. Classification is a technique that divides input data into two or more categories. Machine learning models can be trained to learn patterns and patterns in data to classify new data.

2. Steps to use machine learning functions in PHP

1. Install the PHP-ML library

PHP-ML is a library specially designed for PHP Machine learning library. It supports most common machine learning algorithms such as decision trees, K-nearest neighbors, naive Bayes, etc. To use the PHP-ML library, you first need to install it. Can be installed using Composer. Run the following command in the terminal:

composer require php-ai/php-ml

2. Loading data

To use machine learning functions in PHP for classification tasks, you need to have a dataset. A dataset is a collection of data consisting of inputs and outputs. Among them, input data is also called features, which are used to describe the attributes of the data. The output data is called a target and describes the category to which the data belongs.

In PHP-ML, a data set is represented by an array, and each element is an array containing input and output. For example, we can create a dataset with two features and one target as follows:

$dataset = [
    [0, 0, 'negative'],
    [0, 1, 'positive'],
    [1, 0, 'positive'],
    [1, 1, 'negative']
];

3. Preparing training data

Before training the model, by Splitting the data set into training data and test data allows us to evaluate the performance of the model. In PHP-ML, you can use the Split class to split a dataset into training and test data. Here is the code example:

use PhpmlCrossValidationStratifiedRandomSplit;

$split = new StratifiedRandomSplit($dataset, 0.5);
$trainDataset = $split->getTrainSamples();
$trainLabels = $split->getTrainLabels();
$testDataset = $split->getTestSamples();
$testLabels = $split->getTestLabels();

In this example, we split $dataset into training data and test data with a ratio of 0.5. $trainDataset and $trainLabels contain training data and corresponding target values, and $testDataset and $testLabels contain test data and corresponding target values.

4. Training the model

Once the training data is prepared, the model can be trained. In PHP-ML, various machine learning algorithms can be used to train models. The following is a code example that uses the neural network algorithm to train a model:

use PhpmlNeuralNetworkNetwork;
use PhpmlNeuralNetworkLayer;

$layers = [
    new Layer(2),
    new Layer(3),
    new Layer(1)
];

$neuralNetwork = new Network(...$layers);

$neuralNetwork->train($trainDataset, $trainLabels);

In this example, we define a model based on the neural network algorithm and use the $neuralNetwork->train() method to train it train. The training data and corresponding target values ​​are passed as parameters to this method.

5. Use the model to make predictions

Once training is complete, you can use the model to classify new data. In PHP-ML, you can use the predict() method to make predictions on new data. Here is the code example:

$predictedLabels = [];
foreach ($testDataset as $sample) {
    $predictedLabels[] = $neuralNetwork->predict($sample);
}

In this example, we use the $neuralNetwork->predict() method to make predictions on the test data and store the results in the $predictedLabels array.

3. Summary

This article introduces how to use machine learning functions in PHP for classification tasks. To use the PHP-ML library, you need to install it first. Next, the dataset needs to be loaded and split into training and test data. The training data can then be trained using various machine learning algorithms. Finally, the trained model can be used to classify new data. Using machine learning functions can help us build smarter web applications.

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