// ABOUTME: Unit tests for statistical analysis functionality
// ABOUTME: Validates statistical analysis behavior, edge cases, and error handling
//
// SPDX-License-Identifier: MIT OR Apache-2.0
// Copyright (c) 2025 Pierre Fitness Intelligence
// Statistical analysis module tests
#![allow(clippy::unwrap_used, clippy::expect_used, clippy::panic)]
#![allow(missing_docs)]
#![allow(clippy::cast_possible_wrap)] // Test helper uses small indices
use chrono::Utc;
use pierre_mcp_server::intelligence::{StatisticalAnalyzer, TrendDataPoint};
fn create_test_data_points(values: Vec<f64>) -> Vec<TrendDataPoint> {
values
.into_iter()
.enumerate()
.map(|(i, value)| TrendDataPoint {
date: Utc::now() + chrono::Duration::days(i as i64),
value,
smoothed_value: None,
})
.collect()
}
#[test]
fn test_linear_regression_perfect_positive_correlation() {
let data_points = create_test_data_points(vec![1.0, 2.0, 3.0, 4.0, 5.0]);
let result = StatisticalAnalyzer::linear_regression(&data_points).unwrap();
assert!((result.slope - 1.0).abs() < 0.001);
assert!((result.correlation - 1.0).abs() < 0.001);
assert!((result.r_squared - 1.0).abs() < 0.001);
}
#[test]
fn test_linear_regression_perfect_negative_correlation() {
let data_points = create_test_data_points(vec![5.0, 4.0, 3.0, 2.0, 1.0]);
let result = StatisticalAnalyzer::linear_regression(&data_points).unwrap();
assert!((result.slope - (-1.0)).abs() < 0.001);
assert!((result.correlation - (-1.0)).abs() < 0.001);
assert!((result.r_squared - 1.0).abs() < 0.001);
}
#[test]
fn test_trend_strength_calculation() {
let data_points = create_test_data_points(vec![1.0, 2.0, 3.0, 4.0, 5.0]);
let trend_strength = StatisticalAnalyzer::calculate_trend_strength(&data_points).unwrap();
assert!((trend_strength - 1.0).abs() < 0.001);
}
#[test]
fn test_insufficient_data_points() {
let data_points = create_test_data_points(vec![1.0]);
let result = StatisticalAnalyzer::linear_regression(&data_points);
assert!(result.is_err());
}
