/*
* Copyright (c) 2023, Martin Janiczek <martin@janiczek.cz>
*
* SPDX-License-Identifier: BSD-2-Clause
*/
#pragma once
#include <LibTest/Macros.h>
#include <LibTest/Randomized/RandomRun.h>
#include <AK/Function.h>
#include <AK/Random.h>
#include <AK/String.h>
#include <AK/StringView.h>
#include <math.h>
namespace Test {
namespace Randomized {
// Returns a random double value in range 0..1.
// This is not a generator. It is meant to be used inside RandomnessSource::draw_value().
// Based on: https://dotat.at/@/2023-06-23-random-double.html
inline f64 get_random_probability()
{
return static_cast<f64>(AK::get_random<u64>() >> 11) * 0x1.0p-53;
}
// Generators take random bits from the RandomnessSource and return a value
// back.
//
// Example:
// - Gen::number_u64(5,10) --> 9, 7, 5, 10, 8, ...
namespace Gen {
// An unsigned integer generator.
//
// The minimum value will always be 0.
// The maximum value is given by user in the argument.
//
// Gen::number_u64(10) -> value 5, RandomRun [5]
// -> value 8, RandomRun [8]
// etc.
//
// Shrinks towards 0.
inline u64 number_u64(u64 max)
{
if (max == 0)
return 0;
u64 random = Test::randomness_source().draw_value(max, [&]() {
// `clamp` to guard against integer overflow
u64 exclusive_bound = AK::clamp(max + 1, max, NumericLimits<u64>::max());
return AK::get_random_uniform_64(exclusive_bound);
});
return random;
}
// An unsigned integer generator in a particular range.
//
// Gen::number_u64(3,10) -> value 3, RandomRun [0]
// -> value 8, RandomRun [5]
// -> value 10, RandomRun [7]
// etc.
//
// In case `min == max`, the RandomRun footprint will be smaller: no randomness
// is needed.
//
// Gen::number_u64(3,3) -> value 3, RandomRun [] (always)
//
// Shrinks towards the minimum.
inline u64 number_u64(u64 min, u64 max)
{
VERIFY(max >= min);
return number_u64(max - min) + min;
}
// Randomly (uniformly) selects a value out of the given arguments.
//
// Gen::one_of(20,5,10) --> value 20, RandomRun [0]
// --> value 5, RandomRun [1]
// --> value 10, RandomRun [2]
//
// Shrinks towards the earlier arguments (above, towards 20).
template<typename... Ts>
requires(sizeof...(Ts) > 0)
CommonType<Ts...> one_of(Ts... choices)
{
Vector<CommonType<Ts...>> choices_vec { choices... };
constexpr size_t count = sizeof...(choices);
size_t i = number_u64(count - 1);
return choices_vec[i];
}
template<typename T>
struct Choice {
i32 weight;
T value;
};
// Workaround for clang bug fixed in clang 17
template<typename T>
Choice(i32, T) -> Choice<T>;
// Randomly (uniformly) selects a value out of the given weighted arguments.
//
// Gen::frequency(
// Gen::Choice {5,999},
// Gen::Choice {1,111},
// )
// --> value 999 (5 out of 6 times), RandomRun [0]
// --> value 111 (1 out of 6 times), RandomRun [1]
//
// Shrinks towards the earlier arguments (above, towards 'x').
template<typename... Ts>
requires(sizeof...(Ts) > 0)
CommonType<Ts...> frequency(Choice<Ts>... choices)
{
Vector<Choice<CommonType<Ts...>>> choices_vec { choices... };
u64 sum = 0;
for (auto const& choice : choices_vec) {
VERIFY(choice.weight > 0);
sum += static_cast<u64>(choice.weight);
}
u64 target = number_u64(sum);
size_t i = 0;
for (auto const& choice : choices_vec) {
u64 weight = static_cast<u64>(choice.weight);
if (weight >= target) {
return choice.value;
}
target -= weight;
++i;
}
return choices_vec[i - 1].value;
}
// An unsigned integer generator in the full u64 range.
//
// Prefers 8bit numbers, then 4bit, 16bit, 32bit and 64bit ones.
// Around 11% of the time it tries edge cases like 0 and various NumericLimits::max().
//
// Gen::number_u64() -> value 3, RandomRun [0,3]
// -> value 8, RandomRun [1,8]
// -> value 100, RandomRun [2,100]
// -> value 5, RandomRun [3,5]
// -> value 255, RandomRun [4,1]
// -> value 65535, RandomRun [4,2]
// etc.
//
// Shrinks towards 0.
inline u64 number_u64()
{
u64 bits = frequency(
// weight, bits
Choice { 4, 4 },
Choice { 8, 8 },
Choice { 2, 16 },
Choice { 1, 32 },
Choice { 1, 64 },
Choice { 2, 0 });
// The special cases go last as they can be the most extreme (large) values.
if (bits == 0) {
// Special cases, eg. max integers for u8, u16, u32, u64.
return one_of(
0U,
NumericLimits<u8>::max(),
NumericLimits<u16>::max(),
NumericLimits<u32>::max(),
NumericLimits<u64>::max());
}
u64 max = bits == 64
? NumericLimits<u64>::max()
: ((u64)1 << bits) - 1;
return number_u64(max);
}
// A generator returning `true` with the given `probability` (0..1).
//
// If probability <= 0, doesn't use any randomness and returns false.
// If probability >= 1, doesn't use any randomness and returns true.
//
// In general case:
// Gen::weighted_boolean(0.75)
// -> value false, RandomRun [0]
// -> value true, RandomRun [1]
//
// Shrinks towards false.
inline bool weighted_boolean(f64 probability)
{
if (probability <= 0)
return false;
if (probability >= 1)
return true;
u64 random_int = Test::randomness_source().draw_value(1, [&]() {
f64 drawn_probability = get_random_probability();
return drawn_probability <= probability ? 1 : 0;
});
bool random_bool = random_int == 1;
return random_bool;
}
// A (fair) boolean generator.
//
// Gen::boolean()
// -> value false, RandomRun [0]
// -> value true, RandomRun [1]
//
// Shrinks towards false.
inline bool boolean()
{
return weighted_boolean(0.5);
}
// A vector generator of a random length between the given limits.
//
// Gen::vector(2,3,[]() { return Gen::number_u64(5); })
// -> value [1,5], RandomRun [1,1,1,5,0]
// -> value [1,5,0], RandomRun [1,1,1,5,1,0,0]
// etc.
//
// In case `min == max`, the RandomRun footprint will be smaller, as there will
// be no randomness involved in figuring out the length:
//
// Gen::vector(3,3,[]() { return Gen::number_u64(5); })
// -> value [1,3], RandomRun [1,3]
// -> value [5,2], RandomRun [5,2]
// etc.
//
// Shrinks towards shorter vectors, with simpler elements inside.
template<typename Fn>
inline Vector<InvokeResult<Fn>> vector(size_t min, size_t max, Fn item_gen)
{
VERIFY(max >= min);
size_t size = 0;
Vector<InvokeResult<Fn>> acc;
// Special case: no randomness for the boolean
if (min == max) {
while (size < min) {
acc.append(item_gen());
++size;
}
return acc;
}
// General case: before each item we "flip a coin" to decide whether to
// generate another one.
//
// This algorithm is used instead of the more intuitive "generate length,
// then generate that many items" algorithm, because it produces RandomRun
// patterns that shrink more easily.
//
// See the Hypothesis paper [1], section 3.3, around the paragraph starting
// with "More commonly".
//
// [1]: https://drops.dagstuhl.de/opus/volltexte/2020/13170/pdf/LIPIcs-ECOOP-2020-13.pdf
while (size < min) {
acc.append(item_gen());
++size;
}
f64 average = static_cast<f64>(min + max) / 2.0;
VERIFY(average > 0);
// A geometric distribution: https://en.wikipedia.org/wiki/Geometric_distribution#Moments_and_cumulants
// The below derives from the E(X) = 1/p formula.
//
// We need to flip the `p` to `1-p` as our success ("another item!") is
// a "failure" in the geometric distribution's interpretation ("we fail X
// times before succeeding the first time").
//
// That gives us `1 - 1/p`. Then, E(X) also contains the final success, so we
// need to say `1 + average` instead of `average`, as it will mean "our X
// items + the final failure that stops the process".
f64 probability = 1.0 - 1.0 / (1.0 + average);
while (size < max) {
if (weighted_boolean(probability)) {
acc.append(item_gen());
++size;
} else {
break;
}
}
return acc;
}
// A vector generator of a given length.
//
// Gen::vector_of_length(3,[]() { return Gen::number_u64(5); })
// -> value [1,5,0], RandomRun [1,1,1,5,1,0,0]
// -> value [2,9,3], RandomRun [1,2,1,9,1,3,0]
// etc.
//
// Shrinks towards shorter vectors, with simpler elements inside.
template<typename Fn>
inline Vector<InvokeResult<Fn>> vector(size_t length, Fn item_gen)
{
return vector(length, length, item_gen);
}
// A vector generator of a random length between 0 and 32 elements.
//
// If you need a different length, use vector(max,item_gen) or
// vector(min,max,item_gen).
//
// Gen::vector([]() { return Gen::number_u64(5); })
// -> value [], RandomRun [0]
// -> value [1], RandomRun [1,1,0]
// -> value [1,5], RandomRun [1,1,1,5,0]
// -> value [1,5,0], RandomRun [1,1,1,5,1,0,0]
// -> value [1,5,0,2], RandomRun [1,1,1,5,1,0,1,2,0]
// etc.
//
// Shrinks towards shorter vectors, with simpler elements inside.
template<typename Fn>
inline Vector<InvokeResult<Fn>> vector(Fn item_gen)
{
return vector(0, 32, item_gen);
}
// A double generator in the [0,1) range.
//
// RandomRun footprint: a single number.
//
// Shrinks towards 0.
//
// Based on: https://dotat.at/@/2023-06-23-random-double.html
inline f64 percentage()
{
return static_cast<f64>(number_u64() >> 11) * 0x1.0p-53;
}
// An internal double generator. This one won't make any attempt to shrink nicely.
// Test writers should use number_f64(f64 min, f64 max) instead.
inline f64 number_f64_scaled(f64 min, f64 max)
{
VERIFY(max >= min);
if (min == max)
return min;
f64 p = percentage();
return min * (1.0 - p) + max * p;
}
inline f64 number_f64(f64 min, f64 max)
{
// FIXME: after we figure out how to use frequency() with lambdas,
// do edge cases and nicely shrinking float generators here
return number_f64_scaled(min, max);
}
inline f64 number_f64()
{
// FIXME: this could be much nicer to the user, at the expense of code complexity
// We could follow Hypothesis' lead and remap integers 0..MAXINT to _simple_
// floats rather than small floats. Meaning, we would like to prefer integers
// over floats with decimal digits, positive numbers over negative numbers etc.
// As a result, users would get failures with floats like 0, 1, or 0.5 instead of
// ones like 1.175494e-38.
// Check the doc comment in Hypothesis: https://github.com/HypothesisWorks/hypothesis/blob/master/hypothesis-python/src/hypothesis/internal/conjecture/floats.py
return number_f64(NumericLimits<f64>::lowest(), NumericLimits<f64>::max());
}
// A double generator.
//
// The minimum value will always be NumericLimits<f64>::lowest().
// The maximum value is given by user in the argument.
//
// Prefers positive numbers, then negative numbers, then edge cases.
//
// Shrinks towards 0.
inline f64 number_f64(f64 max)
{
// FIXME: after we figure out how to use frequency() with lambdas,
// do edge cases and nicely shrinking float generators here
return number_f64_scaled(NumericLimits<f64>::lowest(), max);
}
// TODO
inline u32 number_u32(u32 max)
{
if (max == 0)
return 0;
u32 random = Test::randomness_source().draw_value(max, [&]() {
// `clamp` to guard against integer overflow
u32 exclusive_bound = AK::clamp(max + 1, max, NumericLimits<u32>::max());
return AK::get_random_uniform(exclusive_bound);
});
return random;
}
// TODO
inline u32 number_u32(u32 min, u32 max)
{
VERIFY(max >= min);
return number_u32(max - min) + min;
}
// TODO
inline u32 number_u32()
{
u32 bits = frequency(
// weight, bits
Choice { 4, 4 },
Choice { 8, 8 },
Choice { 2, 16 },
Choice { 1, 32 },
Choice { 1, 64 },
Choice { 2, 0 });
// The special cases go last as they can be the most extreme (large) values.
if (bits == 0) {
// Special cases, eg. max integers for u8, u16, u32.
return one_of(
0U,
NumericLimits<u8>::max(),
NumericLimits<u16>::max(),
NumericLimits<u32>::max());
}
u32 max = bits == 32
? NumericLimits<u32>::max()
: ((u32)1 << bits) - 1;
return number_u32(max);
}
} // namespace Gen
} // namespace Randomized
} // namespace Test
