A Beginner's Guide to Machine Learning in PHP

With the rapid development of the Internet and artificial intelligence, machine learning has become one of the hot topics. As an important branch of computer science and artificial intelligence, machine learning provides us with many interesting methods and tools to explore and analyze data. In this field, there are many programming languages ​​and tools available, among which PHP is one of the most popular programming languages. In this article, we’ll introduce you to machine learning in PHP and provide some guidelines for getting started.

1. What is machine learning?

Machine learning is an application field of artificial intelligence that enables computers to automatically learn patterns and predict outcomes from data. Machine learning aims to discover useful information in data and use it to make good decisions. Among them, supervised learning, unsupervised learning and reinforcement learning are the three main types of machine learning.

2. Machine Learning Libraries in PHP

In PHP, there are several machine learning libraries available, the most popular of which is the PHP-ML library. PHP-ML is a PHP-based machine learning library that provides many commonly used machine learning algorithms, such as classification, clustering, regression, and dimensionality reduction. Additionally, PHP-ML's API is easy to use and the code is easy to understand.

3. Using Machine Learning in PHP

The first step in using machine learning in PHP is to choose a suitable machine learning library. If you choose PHP-ML, then you need to install it in your system. You can install PHP-ML using the following command from the command line:

composer require php-ai/php-ml

Once the installation is complete, you can introduce the PHP-ML library in your PHP code:

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

use PhpmlClassificationKNearestNeighbors;
用phpml库下ClassificationKNearestNeighbors， 加载到PHP脚本中

Now, let Let's see how to train a simple model using the PHP-ML library. In this example, we will use the K nearest neighbor algorithm to classify the Iris dataset.

Dataset IES Dataset
First, we need to load the data set. In this example, we use the Iris dataset from the PHP-ML library. To load the dataset, you can use the following code:

use PhpmlDatasetsIris;

$dataset = new Iris();

Next, we need to preprocess the data. In this example, we will normalize the data. To perform normalization you can use the following code:

use PhpmlPreprocessingStandardScaler;

$scaler = new StandardScaler();
$scaler->fit($samples);
$scaler->transform($samples); 

Then we need to split the dataset into training data and test data. In this example, we use 80% of the dataset for training and 20% for testing. To do this, you can use the following code:

use PhpmlCrossValidationStratifiedRandomSplit;

$split = new StratifiedRandomSplit($dataset->getSamples(), $dataset->getTargets(), 0.8); 

Next, we need to instantiate a K-nearest neighbor classifier and train it using the training data. To do this, you can use the following code:

$classifier = new KNearestNeighbors($k = 3);
$classifier->train($split->getTrainSamples(), $split->getTrainLabels());

Finally, we can use test data to test the performance of the model. To do this you can use the following code:

$predicted = $classifier->predict($split->getTestSamples());

4. Summary

Machine learning is an interesting and practical field that can provide us with many useful tools and technologies to explore and analyze data. In PHP, using machine learning libraries such as PHP-ML can help us easily train and test machine learning models. If you want to start using machine learning in PHP, using PHP-ML is a great way to get started.

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