Add learning module

Cargo.toml

@@ -7,9 +7,11 @@ edition = "2021"
 
 [dependencies]
 bincode = "1.3.3"
+chrono = "0.4.33"
 dirs = "5.0.1"
 env_logger = "0.10.1"
 hex = "0.4.3"
+lazy_static = "1.4.0"
 log = "0.4.20"
 rand = "0.8.5"
 rocket = { version = "=0.5.0-rc.4", features = ["json"] }

learning.local.yml

@@ -0,0 +1,8 @@
+decay_rate_factor_correct: 0.5 # 0.0 no decay 0.1 - 0.9 (positive effect fades faster, requiring more frequent review)
+decay_rate_factor_incorrect: 0.3 # 0.1 to 0.5 - up to 0.5 increases the penalty for incorrect answers
+consecutive_hours_threshold: 3 # 2 to 6 hours
+progress_threshold: 40 # higher means easier to recall 20 to 60
+streak_bonus: 5
+min_score: 2
+max_score: 120
+decimals: 0

learning.production.yml

@@ -0,0 +1,3 @@
+decay_rate_factor_correct: 0.0 # 0.0 no decay
+decay_rate_factor_incorrect: 0.0
+consecutive_hours_threshold: 0

learning.staging.yml

@@ -0,0 +1,3 @@
+decay_rate_factor_correct: 0.0 # 0.0 no decay
+decay_rate_factor_incorrect: 0.0
+consecutive_hours_threshold: 0

src/api/mod.rs

@@ -3,4 +3,5 @@ pub mod get_chapters;
 pub mod get_labels;
 pub mod verse_by_chapter;
 pub mod verse_similar_by_chapter;
-pub mod ping;
+pub mod ping;
+pub mod user_stats_analytics;

src/api/user_stats_analytics.rs

@@ -0,0 +1,17 @@
+use rocket::post;
+use rocket::{serde::json::Json, State};
+
+use crate::learning::models::{
+    user_stat::UserStat,
+    analytic::Analytic,
+    learning_config::LearningConfig
+};
+use crate::learning::compute_user_stats_analytics::compute_user_stats_analytics;
+
+#[post("/user-stats-analytics", format = "json", data = "<user_stats>")]
+pub fn user_stats_analytics(config: &State<LearningConfig>, user_stats: Json<Vec<UserStat>>) 
+-> Json<Vec<Analytic>> {
+    let item_progress = compute_user_stats_analytics(&**config, &user_stats);
+
+    Json(item_progress)
+}

src/learning/calculate_decay_rate.rs

@@ -0,0 +1,14 @@
+pub fn calculate_decay_rate(elapsed_days: i64, repetitions: Option<i32>) -> f32 {
+    // Adjust decay rate based on the number of repetitions (if available)
+    let repetitions_factor = match repetitions {
+        Some(rep_count) if rep_count > 0 => 1.0 / (rep_count as f32),
+        _ => 1.0, // Default factor if no repetitions or repetitions is 0
+    };
+
+    // Calculate the decay rate based on elapsed days and repetitions factor
+    // Adjust these parameters as needed for your specific application
+    let decay_rate = 1.0 - (elapsed_days as f32 * repetitions_factor * 0.01); 
+
+    // Ensure the decay rate is within bounds (between 0 and 1)
+    decay_rate.clamp(0.0, 1.0)
+}