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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,136 @@ | ||
| # DiceForge | ||
| A C++ library with the same functionality as the *random* library in python. | ||
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| Go to the **Learning_For_Contributors** folder and then view the **Instructions.md** file to get started on the basics of LaTeX and GitHub. | ||
| ## Overview | ||
| This is a simple C++ library for *Pseudo Random Number Generation (PRNG)*. It provides a functionality similar to that of the python std library *random*. | ||
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| ## Features | ||
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| - **Ease of Use**: Straightforward API for quick integration into your C++ projects. | ||
| - **Versatility**: Supports different PRNG algorithms, allowing you to choose based on your requirements. | ||
| - **Seed Control**: Easily set and manipulate the seed for reproducibility. | ||
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| ### Upcoming Features | ||
| - Applying distributions to uniform random generators to make biased generators | ||
| - Detailed documentation on distrbutions | ||
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| ## Supported PRNG Algorithms | ||
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| 1. Mersenne Twister (MT) | ||
| 2. Linear Congruential Generator (LCG) | ||
| 3. XOR-Shift (XOR) | ||
| 4. Blum Blum Shub (B.B.S.) | ||
| 5. Naor-Reingold (NR) | ||
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| ## Supported Distrbutions | ||
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| 1. Cauchy | ||
| 2. Exponential | ||
| 3. Gaussian | ||
| 4. Maxwell | ||
| 5. Weibull | ||
| 6. Bernoulli | ||
| 7. Binomial | ||
| 8. Gibbs | ||
| 9. Hypergeometric | ||
| 10. Negative-Hypergeometric | ||
| 11. Poisson | ||
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| ## Benchmarks | ||
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| ### Statistical Analysis | ||
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| | Generator | mean | variance | | ||
| | --------- | ---- | -------- | | ||
| | BBS32 | 0.500189 | 0.0834384 | | ||
| | BBS64 | 0.499066 | 0.0834061 | | ||
| | XOR32 | 0.499978 | 0.08334 | | ||
| | XOR64 | 0.499931 | 0.0833327 | | ||
| | MT32 | 0.499956 | 0.0833425 | | ||
| | MT64 | 0.500034 | 0.0833241 | | ||
| | LFSR32 | 0.500033 | 0.0833329 | | ||
| | LFSR64 | 0.49994, | 0.0833352 | | ||
| | NR | 0.341687 | 0.0498607 | | ||
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| ### Time Performance | ||
| Generating *100000000* random numbers: | ||
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| #### DiceForge: | ||
| | Generator | Time Taken | | ||
| | --------- | ---------- | | ||
| | XorShift32 | 435.497ms | | ||
| | XorShift64 | 410.498ms | | ||
| | BBS32 | 14780ms | | ||
| | BBS64 | 24568.6ms | | ||
| | MT32 | 1738.91ms | | ||
| | MT64 | 1811.27ms | | ||
| | LFSR | 18540.7ms | | ||
| | Naor Reingold | 7143ms| | ||
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| For comparison, benchmarking other existing standard libraries for the same test. | ||
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| | Generator | Time Taken | | ||
| | --------- | ---------- | | ||
| | C++ MT| 4032.7ms | | ||
| | C++ rand()| 1388.03ms | | ||
| | python randint | 51171.51641845703 ms | | ||
| | numpy randint | 194015.43641090393 ms | | ||
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| ## Documentation | ||
| Check out the [Documentation](https://www.overleaf.com/project/65d9eea60dbb4690fe6ff8be) for detailed information on library usage, supported algorithms, and more! | ||
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| ## Getting Started | ||
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| ### Prerequisites | ||
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| - C++ compiler (supporting C++11 or later) | ||
| - CMake (>=3.28) [If building library] | ||
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| ### Installation | ||
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| #### Using a prebuilt library | ||
| 1. Download the library file from [out](out) folder and add to PATH | ||
| 2. Make sure the [include](include) folder is PATH | ||
| 3. Start generating pseudo-random numbers! | ||
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| #### Building it yourself | ||
| 1. Clone the repository: `git clone https://github.com/yourusername/prng-library.git` | ||
| 2. Create the build folder by using the CMakeLists.txt | ||
| 2. Build library using the given CMake configurations (```cmake --build <build-folder>```) | ||
| 3. After the build is sucessful, follow the steps mentioned in [Using a prebuilt library](#using-a-prebuilt-library) | ||
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| ## Usage | ||
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| Here's a quick example to get you started: | ||
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| ```cpp | ||
| #include "diceforge.h" | ||
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| int main() { | ||
| // Create a PRNG object | ||
| DiceForge::XORShift32 prng = DiceForge::XORShift32(123); | ||
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| // Generate and print a random number | ||
| std::cout << "Random Number: " << prng.next() << std::endl; | ||
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| return 0; | ||
| } | ||
| ``` | ||
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| Feel free to explore the library and experiment with different algorithms! | ||
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| ## Contributing | ||
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| Currently, this is an IIT-M Maths CLub exclusive project and we aren't accepting outside contributions. | ||
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| ### For Project Members | ||
| - View the [Tasks folder](Contributors_Only/Tasks) to view the tasks to be done. | ||
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| - Reference materials are provided in the [Contributers_Only](Contributors_Only) folder. | ||
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| Current TODO | ||
| - [ ] Custom pdf | ||
| - [ ] Exception handling | ||
| - [ ] 2d rv | ||
| - [ ] Documentation | ||
| - [ ] Testing the RNGs |
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| Original file line number | Diff line number | Diff line change |
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| #include "basicfxn.h" | ||
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| namespace DiceForge | ||
| { | ||
| matrix_t::matrix_t(int r, int c) | ||
| : r(r), c(c) | ||
| { | ||
| m = new real_t*[r]; | ||
| for (size_t i = 0; i < r; i++) | ||
| { | ||
| m[i] = new real_t[c]; | ||
| for (size_t j = 0; j < c; j++) | ||
| { | ||
| m[i][j] = 0; | ||
| } | ||
| } | ||
| } | ||
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| matrix_t::matrix_t(matrix_t& other) | ||
| : r(other.r), c(other.c) | ||
| { | ||
| m = new real_t*[r]; | ||
| for (size_t i = 0; i < r; i++) | ||
| { | ||
| m[i] = new real_t[c]; | ||
| for (size_t j = 0; j < c; j++) | ||
| { | ||
| m[i][j] = other.m[i][j]; | ||
| } | ||
| } | ||
| } | ||
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| matrix_t::~matrix_t() | ||
| { | ||
| for (size_t i = 0; i < r; i++) | ||
| { | ||
| delete[] m[i]; | ||
| } | ||
| delete[] m; | ||
| } | ||
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| const real_t* matrix_t::operator[](int i) const | ||
| { | ||
| return m[i]; | ||
| } | ||
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| real_t* matrix_t::operator[](int i) | ||
| { | ||
| return m[i]; | ||
| } | ||
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| matrix_t matrix_t::operator*(const matrix_t& other) const | ||
| { | ||
| matrix_t pdt = matrix_t(r, other.c); | ||
| for (size_t i = 0; i < r; i++) | ||
| { | ||
| for (size_t j = 0; j < other.c; j++) | ||
| { | ||
| pdt[i][j] = 0; | ||
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| for (size_t k = 0; k < c; k++) | ||
| { | ||
| pdt[i][j] += m[i][k] * other.m[k][j]; | ||
| } | ||
| } | ||
| } | ||
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| return pdt; | ||
| } | ||
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| matrix_t matrix_t::operator+(const matrix_t& other) const | ||
| { | ||
| matrix_t sum = matrix_t(r, c); | ||
| for (size_t i = 0; i < r; i++) | ||
| { | ||
| for (size_t j = 0; j < c; j++) | ||
| { | ||
| sum.m[i][j] = m[i][j] + other.m[j][i]; | ||
| } | ||
| } | ||
| return sum; | ||
| } | ||
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| matrix_t matrix_t::operator-(const matrix_t& other) const | ||
| { | ||
| matrix_t diff = matrix_t(r, c); | ||
| for (size_t i = 0; i < r; i++) | ||
| { | ||
| for (size_t j = 0; j < c; j++) | ||
| { | ||
| diff.m[i][j] = m[i][j] - other.m[j][i]; | ||
| } | ||
| } | ||
| return diff; | ||
| } | ||
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| matrix_t matrix_t::operator-() const | ||
| { | ||
| matrix_t neg = matrix_t(r, c); | ||
| for (size_t i = 0; i < r; i++) | ||
| { | ||
| for (size_t j = 0; j < c; j++) | ||
| { | ||
| neg.m[i][j] = -m[i][j]; | ||
| } | ||
| } | ||
| return neg; | ||
| } | ||
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| matrix_t matrix_t::transpose() const | ||
| { | ||
| matrix_t t = matrix_t(c, r); | ||
| for (size_t i = 0; i < r; i++) | ||
| { | ||
| for (size_t j = 0; j < c; j++) | ||
| { | ||
| t.m[j][i] = m[i][j]; | ||
| } | ||
| } | ||
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| return t; | ||
| } | ||
| } |
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