Application of PHP functions in artificial intelligence and machine learning

PHP functions are widely used in AI and machine learning, including: Data preprocessing: Use array_map() and in_array() to standardize and filter data. Feature engineering: Use array_intersect() and array_column() to calculate feature correlation and extract training data. Model training: array_rand() and mb_strtolower() are used to divide the training set and clean the text data. Model evaluation: Functions such as log() and exp() calculate negative log-likelihood loss.

Application of PHP functions in artificial intelligence and machine learning

With its strong ecosystem and extensive function library, PHP has been widely used in artificial intelligence (AI) ) and machine learning (ML) fields find a wide range of applications. This article will explore some useful PHP functions and demonstrate through practical examples how they can enhance the functionality of AI and ML applications.

1. Data preprocessing

• array_map(): Apply the specified function to each element in the array.
• in_array(): Check whether an element is in the array.

// 使用 array_map() 标准化数据
$data = array_map('strtoupper', $data);

// 使用 in_array() 过滤无效数据
$valid_data = array_filter($data, function ($item) { return in_array($item, ['VALID_VALUE1', 'VALID_VALUE2']); });

2. Feature Engineering

• array_intersect(): Returns the intersection of two arrays.
• array_column(): Extract specific columns from a multidimensional array.

// 使用 array_intersect() 计算特征相关性
$features1 = array_keys($data1);
$features2 = array_keys($data2);
$correlated_features = array_intersect($features1, $features2);

// 使用 array_column() 提取训练数据
$X = array_column($data, 'feature1', 'feature2');

3. Model training

• array_rand(): Randomly select one or more elements from the array.
• mb_strtolower(): Convert the string to lowercase.

// 使用 array_rand() 划分训练集和验证集
$dataset_size = count($data);
$num_train = round($dataset_size * 0.7);
$train_indices = array_rand($data, $num_train);
$test_indices = array_diff(range(0, $dataset_size - 1), $train_indices);

// 使用 mb_strtolower() 清理文本数据
$text_data = array_map('mb_strtolower', $text_data);

4. Model evaluation

• log(): Calculate the natural logarithm.
• exp(): Calculate the exponent.

// 使用 log() 和 exp() 计算负对数似然损失
$y_pred = log($model->predict_proba($X_test)[:, 1]);
$y_true = log($Y_test);
$loss = -exp(mean($y_pred - y_true));

Conclusion

By leveraging PHP’s powerful library of functions, developers can build robust and efficient AI and ML applications. These functions provide flexibility and convenience, allowing programmers to focus on the logic and algorithms of machine learning tasks.

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