<?php
require_once 'vendor/autoload.php';
use PHPML\Preprocessing\DataPreprocessor;
use PHPML\Models\LinearRegression;
use PHPML\Models\KMeans;
use PHPML\Analysis\DataAnalyzer;
use PHPML\Evaluation\ModelEvaluator;
// Sample data
$data = [
[1, 2, 3],
[2, 3, 4],
[3, 4, 5],
[4, 5, 6],
[5, 6, 7],
[6, 7, 8],
[7, 8, 9],
[8, 9, 10],
[9, 10, 11],
[10, 11, 12]
];
$labels = [4, 5, 6, 7, 8, 9, 10, 11, 12, 13];
// Data preprocessing
$preprocessor = new DataPreprocessor($data);
$normalizedData = $preprocessor->normalize();
$standardizedData = $preprocessor->standardize();
// Linear Regression
$regression = new LinearRegression(0.01, 1000);
$regression->fit($normalizedData, $labels);
$predictions = $regression->predict($normalizedData);
$score = $regression->score($normalizedData, $labels);
echo "Linear Regression Score: " . $score . "\n";
// K-Means Clustering
$kmeans = new KMeans(3, 100);
$centroids = $kmeans->fit($normalizedData);
$clusters = $kmeans->predict($normalizedData);
echo "K-Means Centroids:\n";
print_r($centroids);
echo "K-Means Clusters:\n";
print_r($clusters);
// Data Analysis
$analyzer = new DataAnalyzer($data);
$summary = $analyzer->getSummary();
$correlationMatrix = $analyzer->correlationMatrix();
echo "Data Summary:\n";
print_r($summary);
echo "Correlation Matrix:\n";
print_r($correlationMatrix);
// Model Evaluation
$accuracy = ModelEvaluator::accuracy($labels, $predictions);
$mse = ModelEvaluator::meanSquaredError($labels, $predictions);
echo "Accuracy: " . $accuracy . "\n";
echo "Mean Squared Error: " . $mse . "\n";
|
Download
