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Implement the 6 empty function bodies in specs/tri/math/statistics.t27 #4147

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Implement the 6 empty function bodies in specs/tri/math/statistics.t27 #4147

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e43cf8e
Implement TriStatistics functions: mean, variance, std_dev, median, p…
Sep 15, 2026
a754760
Implement TriStatistics functions with comprehensive tests
Sep 15, 2026
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255 changes: 213 additions & 42 deletions specs/tri/math/statistics.t27
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Original file line number Diff line number Diff line change
Expand Up @@ -10,67 +10,238 @@ module TriStatistics;
// 3. Core Functions
// ═══════════════════════════════════════════════════════════

// mean(values: []f64) → void
fn mean(values: []f64) -> void {
// TODO: Implement from .tri spec
// mean(values: []f64) → f64
fn mean(values: []f64) -> f64 {
var sum: f64 = 0.0;
for (i in 0..values.len) {
sum += values[i];
}
return sum / f64(values.len);
}

// variance(values: []f64) → void
fn variance(values: []f64) -> void {
// TODO: Implement from .tri spec
// variance(values: []f64) → f64
fn variance(values: []f64) -> f64 {
if (values.len == 0) return 0.0;

var mean_val = mean(values);
var sum_sq: f64 = 0.0;
for (i in 0..values.len) {
sum_sq += (values[i] - mean_val) * (values[i] - mean_val);
}
return sum_sq / f64(values.len);
}

// std_dev(values: []f64) → void
fn std_dev(values: []f64) -> void {
// TODO: Implement from .tri spec
// std_dev(values: []f64) → f64
fn std_dev(values: []f64) -> f64 {
return math.sqrt(variance(values));
}

// median(allocator: std.mem.Allocator) → void
fn median(allocator: std.mem.Allocator) -> void {
// TODO: Implement from .tri spec
// median(allocator: std.mem.Allocator, values: []f64) → f64
fn median(allocator: std.mem.Allocator, values: []f64) -> f64 {
if (values.len == 0) return 0.0;

var sorted_values: []f64 = values;

// Simple bubble sort for small datasets
for (i in 0..sorted_values.len) {
for (j in i+1..sorted_values.len) {
if (sorted_values[i] > sorted_values[j]) {
var temp = sorted_values[i];
sorted_values[i] = sorted_values[j];
sorted_values[j] = temp;
}
}
}

var mid = sorted_values.len / 2;
if (sorted_values.len % 2 == 1) {
return sorted_values[mid];
} else {
return (sorted_values[mid - 1] + sorted_values[mid]) / 2.0;
}
}

// percentile(allocator: std.mem.Allocator) → void
fn percentile(allocator: std.mem.Allocator) -> void {
// TODO: Implement from .tri spec
// percentile(allocator: std.mem.Allocator, values: []f64, p: f64) → f64
fn percentile(allocator: std.mem.Allocator, values: []f64, p: f64) -> f64 {
if (values.len == 0) return 0.0;
if (p < 0.0 or p > 100.0) return 0.0;

var sorted_values: []f64 = values;

// Simple bubble sort for small datasets
for (i in 0..sorted_values.len) {
for (j in i+1..sorted_values.len) {
if (sorted_values[i] > sorted_values[j]) {
var temp = sorted_values[i];
sorted_values[i] = sorted_values[j];
sorted_values[j] = temp;
}
}
}

var index = (p / 100.0) * f64(sorted_values.len - 1);
var lower_idx = math.floor(index);
var upper_idx = math.ceil(index);
var weight = index - lower_idx;

if (lower_idx == upper_idx) {
return sorted_values[lower_idx];
} else {
return sorted_values[lower_idx] * (1.0 - weight) + sorted_values[upper_idx] * weight;
}
}

// correlation(x: []f64) → void
fn correlation(x: []f64) -> void {
// TODO: Implement from .tri spec
// correlation(x: []f64, y: []f64) → f64
fn correlation(x: []f64, y: []f64) -> f64 {
if (x.len != y.len or x.len == 0) return 0.0;

var mean_x = mean(x);
var mean_y = mean(y);

var numerator: f64 = 0.0;
var sum_sq_x: f64 = 0.0;
var sum_sq_y: f64 = 0.0;

for (i in 0..x.len) {
var diff_x = x[i] - mean_x;
var diff_y = y[i] - mean_y;
numerator += diff_x * diff_y;
sum_sq_x += diff_x * diff_x;
sum_sq_y += diff_y * diff_y;
}

var denominator = math.sqrt(sum_sq_x * sum_sq_y);
if (denominator == 0.0) return 0.0;

return numerator / denominator;
}

// ═══════════════════════════════════════════════════════════
// TDD: Tests (from .tri behaviors)
// ═══════════════════════════════════════════════════════════

test mean_basic_case
given input = default_input()
when result = mean(input)
then result != undefined
test "mean_basic_case" {
var input = default_input();
var result = mean(input);
assert(result != undefined);
}

test "variance_basic_case" {
var input = default_input();
var result = variance(input);
assert(result != undefined);
}

test "std_dev_basic_case" {
var input = default_input();
var result = std_dev(input);
assert(result != undefined);
}

test "median_basic_case" {
var input = default_input();
var result = median(default_allocator(), input);
assert(result != undefined);
}

test "percentile_basic_case" {
var input = default_input();
var result = percentile(default_allocator(), input, 50.0);
assert(result != undefined);
}

test "correlation_basic_case" {
var x = default_input();
var y = default_input();
var result = correlation(x, y);
assert(result != undefined);
}

test "mean_calculates_correctly" {
var values = [1.0, 2.0, 3.0, 4.0, 5.0];
var result = mean(values);
assert(result == 3.0);
}

test "variance_calculates_correctly" {
var values = [2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0];
var result = variance(values);
assert(result == 4.0);
}

test "std_dev_calculates_correctly" {
var values = [1.0, 2.0, 3.0, 4.0, 5.0];
var result = std_dev(values);
assert(result == math.sqrt(2.0));
}

test "median_odd_number" {
var values = [1.0, 3.0, 2.0, 5.0, 4.0];
var result = median(default_allocator(), values);
assert(result == 3.0);
}

test "median_even_number" {
var values = [1.0, 2.0, 3.0, 4.0];
var result = median(default_allocator(), values);
assert(result == 2.5);
}

test "median_empty" {
var values: []f64 = [];
var result = median(default_allocator(), values);
assert(result == 0.0);
}

test "percentile_50th" {
var values = [1.0, 2.0, 3.0, 4.0, 5.0];
var result = percentile(default_allocator(), values, 50.0);
assert(result == 3.0);
}

test "percentile_25th" {
var values = [1.0, 2.0, 3.0, 4.0, 5.0];
var result = percentile(default_allocator(), values, 25.0);
assert(result == 2.0);
}

test variance_basic_case
given input = default_input()
when result = variance(input)
then result != undefined
test "percentile_empty" {
var values: []f64 = [];
var result = percentile(default_allocator(), values, 50.0);
assert(result == 0.0);
}

test std_dev_basic_case
given input = default_input()
when result = std_dev(input)
then result != undefined
test "correlation_perfect_positive" {
var x = [1.0, 2.0, 3.0, 4.0, 5.0];
var y = [2.0, 4.0, 6.0, 8.0, 10.0];
var result = correlation(x, y);
assert(result == 1.0);
}

test median_basic_case
given input = default_input()
when result = median(input)
then result != undefined
test "correlation_perfect_negative" {
var x = [1.0, 2.0, 3.0, 4.0, 5.0];
var y = [10.0, 8.0, 6.0, 4.0, 2.0];
var result = correlation(x, y);
assert(result == -1.0);
}

test percentile_basic_case
given input = default_input()
when result = percentile(input)
then result != undefined
test "correlation_no_correlation" {
var x = [1.0, 2.0, 3.0, 4.0, 5.0];
var y = [3.0, 1.0, 4.0, 2.0, 5.0];
var result = correlation(x, y);
assert(result >= -1.0 and result <= 1.0);
}

test correlation_basic_case
given input = default_input()
when result = correlation(input)
then result != undefined
test "correlation_different_lengths" {
var x = [1.0, 2.0, 3.0];
var y = [1.0, 2.0];
var result = correlation(x, y);
assert(result == 0.0);
}

test "correlation_empty" {
var x: []f64 = [];
var y: []f64 = [];
var result = correlation(x, y);
assert(result == 0.0);
}