tiny.profiling.experiment.design

1const std = @import("std");23const model = @import("model.zig");45pub fn samplePlan(6    calibration_log_ratios: []const f64,7    practical_effect_percent: f64,8    design: model.Design,9) !model.SamplePlan {10    try design.validate();11    if (calibration_log_ratios.len != design.calibration_pairs) {12        return error.InvalidCalibrationSamples;13    }14    const standard_deviation = sampleStandardDeviation(calibration_log_ratios);15    const effect_log = std.math.log1p(practical_effect_percent / 100.0);16    if (!std.math.isFinite(standard_deviation) or17        !std.math.isFinite(effect_log) or effect_log <= 0)18    {19        return error.InvalidExperimentStatistics;20    }21    const raw = std.math.pow(22        f64,23        model.normal_critical_sum * standard_deviation / effect_log,24        2,25    );26    const calculated = if (raw >= @as(f64, @floatFromInt(model.maximum_pairs)))27        model.maximum_pairs + 128    else29        @as(usize, @intFromFloat(@ceil(raw)));30    const evaluation_pairs = @max(31        calculated,32        design.minimum_evaluation_pairs,33    );34    return .{35        .calibration_standard_deviation_log_ratio = standard_deviation,36        .calculated_evaluation_pairs = calculated,37        .evaluation_pairs = evaluation_pairs,38        .within_budget = evaluation_pairs <= design.maximum_evaluation_pairs,39    };40}4142fn sampleStandardDeviation(values: []const f64) f64 {43    std.debug.assert(values.len >= 2);44    var total: f64 = 0;45    for (values) |value| total += value;46    const mean = total / @as(f64, @floatFromInt(values.len));47    var squared: f64 = 0;48    for (values) |value| {49        const delta = value - mean;50        squared += delta * delta;51    }52    return @sqrt(squared / @as(f64, @floatFromInt(values.len - 1)));53}5455pub fn classify(56    interval: model.Interval,57    practical_effect_percent: f64,58) model.Verdict {59    std.debug.assert(practical_effect_percent > 0);60    if (interval.high_percent < -practical_effect_percent) return .faster;61    if (interval.low_percent > practical_effect_percent) return .slower;62    if (interval.low_percent >= -practical_effect_percent and63        interval.high_percent <= practical_effect_percent)64    {65        return .equivalent;66    }67    return .inconclusive;68}6970test "profiling experiment derives evaluation N from discarded calibration" {71    const low_noise = [_]f64{72        -0.012,73        0.008,74        -0.004,75        0.010,76        -0.006,77        0.004,78        -0.009,79        0.009,80    };81    const plan = try samplePlan(&low_noise, 5, .{});82    try std.testing.expect(plan.within_budget);83    try std.testing.expectEqual(84        model.default_minimum_evaluation_pairs,85        plan.evaluation_pairs,86    );87    const noisy = [_]f64{88        -0.40,89        0.35,90        -0.30,91        0.25,92        -0.20,93        0.15,94        -0.10,95        0.05,96    };97    const unsupported = try samplePlan(&noisy, 1, .{});98    try std.testing.expect(!unsupported.within_budget);99}100101test "profiling experiment verdict requires practical support" {102    try std.testing.expectEqual(103        model.Verdict.faster,104        classify(.{ .low_percent = -9, .high_percent = -6 }, 5),105    );106    try std.testing.expectEqual(107        model.Verdict.slower,108        classify(.{ .low_percent = 6, .high_percent = 9 }, 5),109    );110    try std.testing.expectEqual(111        model.Verdict.equivalent,112        classify(.{ .low_percent = -2, .high_percent = 3 }, 5),113    );114    try std.testing.expectEqual(115        model.Verdict.inconclusive,116        classify(.{ .low_percent = -7, .high_percent = 2 }, 5),117    );118}

1pub const design = @import("design.zig");