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gradient_boosting updated with healthdata/src/lib updated.rs

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+use forust_ml::{GradientBooster, Matrix};
+use std::error::Error;
+use std::fs::File;
+use std::io::BufReader;
+use serde::Deserialize;
+use serde_json::Value;
+
+#[derive(Debug, Deserialize)]
+struct Feature {
+    #[serde(rename = "type")]
+    feature_type: String,
+    geometry: Geometry,
+    properties: Properties,
+}
+
+#[derive(Debug, Deserialize)]
+struct Geometry {
+    #[serde(rename = "type")]
+    geometry_type: String,
+    coordinates: Vec<f64>,
+}
+
+#[derive(Debug, Deserialize)]
+struct Properties {
+    name: String,
+    population: u64,
+    health_data: HealthData,
+}
+
+#[derive(Debug, Deserialize)]
+struct HealthData {
+    infectious_diseases: InfectiousDiseases,
+    vaccination_rates: VaccinationRates,
+    health_infrastructure: HealthInfrastructure,
+}
+
+#[derive(Debug, Deserialize)]
+struct InfectiousDiseases {
+    dengue: u32,
+    malaria: u32,
+}
+
+#[derive(Debug, Deserialize)]
+struct VaccinationRates {
+    measles: u32,
+    polio: u32,
+}
+
+#[derive(Debug, Deserialize)]
+struct HealthInfrastructure {
+    hospitals: u32,
+    clinics: u32,
+}
+
+#[no_mangle]
+pub extern "C" fn _start() {
+    let result = run();
+}
+
+#[no_mangle]
+pub extern "C" fn run() {
+    match greet_internal() {
+        Ok(predictions) => {
+            println!("Model predictions (first 10):");
+            for (i, pred) in predictions.iter().take(10).enumerate() {
+                println!("Prediction {}: {:.4}", i + 1, pred);
+            }
+        },
+        Err(e) => println!("Error: {}", e),
+    }
+}
+
+fn greet_internal() -> Result<Vec<f64>, Box<dyn Error>> {
+    // Load and parse the JSON file containing health data
+    let file = File::open("healthdata.json")?;
+    let reader = BufReader::new(file);
+    let health_data: Value = serde_json::from_reader(reader)?;
+    let features: Vec<Feature> = serde_json::from_value(health_data["features"].clone())?;
+
+    // Prepare data for the GradientBooster model
+    let mut data = Vec::new();
+    let mut y = Vec::new();
+
+    for feature in features.iter() {
+        // Example of using population and health data to create inputs for the model
+        // You should replace this with relevant features based on your use case
+        y.push(feature.properties.population as f64); // Using population as the target variable
+
+        // Extract features from the JSON data. For example:
+        data.extend_from_slice(&[
+            feature.geometry.coordinates[0], // longitude
+            feature.geometry.coordinates[1], // latitude
+            feature.properties.health_data.infectious_diseases.dengue as f64,
+            feature.properties.health_data.infectious_diseases.malaria as f64,
+            feature.properties.health_data.health_infrastructure.hospitals as f64,
+            feature.properties.health_data.health_infrastructure.clinics as f64,
+        ]);
+    }
+
+    // Assume the number of features per data point is 6
+    let matrix = Matrix::new(&data, y.len(), 6);
+
+    let mut model = GradientBooster::default().set_learning_rate(0.3);
+    model.fit_unweighted(&matrix, &y, None)?;
+
+    let predictions = model.predict(&matrix, true);
+    Ok(predictions)
+}
+
+fn main() -> Result<(), Box<dyn Error>> {
+    let features = greet_internal()?;
+    // Further processing of `features` as needed
+
+    Ok(())
+}