Metadata-Version: 2.4
Name: aleam
Version: 1.0.3
Summary: True randomness for AI and machine learning. Non-recursive, stateless, cryptographically secure random number generator.
Home-page: https://github.com/fardinsabid/aleam
Author: Fardin Sabid
Author-email: Fardin Sabid <contact.fardinsabid@gmail.com>
Maintainer-email: Fardin Sabid <contact.fardinsabid@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/fardinsabid/aleam
Project-URL: Repository, https://github.com/fardinsabid/aleam
Project-URL: Documentation, https://github.com/fardinsabid/aleam/docs
Project-URL: Issues, https://github.com/fardinsabid/aleam/issues
Project-URL: Changelog, https://github.com/fardinsabid/aleam/releases
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Security :: Cryptography
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: pytest-cov>=4.0; extra == "dev"
Requires-Dist: pytest-xdist>=3.0; extra == "dev"
Requires-Dist: pytest-benchmark>=4.0; extra == "dev"
Requires-Dist: black>=23.0; extra == "dev"
Requires-Dist: flake8>=6.0; extra == "dev"
Requires-Dist: mypy>=1.0; extra == "dev"
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Requires-Dist: pre-commit>=3.0; extra == "dev"
Requires-Dist: build>=1.0; extra == "dev"
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Provides-Extra: torch
Requires-Dist: torch>=2.0.0; extra == "torch"
Provides-Extra: tensorflow
Requires-Dist: tensorflow>=2.12.0; extra == "tensorflow"
Provides-Extra: jax
Requires-Dist: jax>=0.4.0; extra == "jax"
Requires-Dist: jaxlib>=0.4.0; extra == "jax"
Provides-Extra: cupy
Requires-Dist: cupy-cuda12x>=12.0.0; extra == "cupy"
Provides-Extra: pandas
Requires-Dist: pandas>=1.5.0; extra == "pandas"
Provides-Extra: polars
Requires-Dist: polars>=0.18.0; extra == "polars"
Provides-Extra: xarray
Requires-Dist: xarray>=2023.0.0; extra == "xarray"
Provides-Extra: dask
Requires-Dist: dask>=2023.0.0; extra == "dask"
Provides-Extra: scipy
Requires-Dist: scipy>=1.9.0; extra == "scipy"
Provides-Extra: pymc
Requires-Dist: pymc>=5.0.0; extra == "pymc"
Provides-Extra: all
Requires-Dist: torch>=2.0.0; extra == "all"
Requires-Dist: tensorflow>=2.12.0; extra == "all"
Requires-Dist: jax>=0.4.0; extra == "all"
Requires-Dist: jaxlib>=0.4.0; extra == "all"
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Requires-Dist: pandas>=1.5.0; extra == "all"
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Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

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[![Stats](https://img.shields.io/badge/Statistical%20Quality-Perfect-00C853?style=for-the-badge&logo=checkmarx&logoColor=white)](.)
[![Entropy](https://img.shields.io/badge/Entropy-64%20bits%2Fcall-9C27B0?style=for-the-badge&logo=chainlink&logoColor=white)](.)
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---

## 📌 The Problem

Pseudo-random number generators (PRNGs) like Mersenne Twister and Python's `random` are **recursive**:

```
xₙ₊₁ = (a·xₙ + c) mod m
```

This creates:

- 🔁 **Hidden correlations** — each number depends on the one before
- 📅 **Periodicity** — sequences eventually repeat
- 🧱 **Exploration boundaries** — AI can't truly explore
- 🎭 **False reproducibility** — same seed = same path

**AI deserves better.**

---

## 🎯 The Solution: Aleam

```python
import aleam as al

rng = al.Aleam()
x = rng.random()  # True randomness. No recursion. No state.
```

Aleam implements the proven equation:

```
Ψ(t) = BLAKE2s( (Φ × Ξ(t)) ⊕ τ(t) )
```

| Symbol | Meaning |
|--------|---------|
| **Φ** | Golden ratio prime (`0x9E3779B97F4A7C15`) |
| **Ξ(t)** | 64-bit true entropy from system CSPRNG |
| **τ(t)** | Nanosecond timestamp |
| **⊕** | XOR mixing |
| **BLAKE2s** | Cryptographic hash |

**Properties:**

| 🔄 Non-recursive | 🎲 Stateless | 🔒 Cryptographically Secure | 🧠 AI-Optimized |
|---|---|---|---|
| Each call independent | No seeds, no state | Powered by BLAKE2s | Gradient noise, latent sampling |

---

## 🔬 How It Works

  Aleam Core Algorithm

### The Core Equation in Detail

| Step | Operation | Description |
|------|-----------|-------------|
| **1** | `Ξ(t) = get_entropy_64()` | Pull 64-bit true entropy from system |
| **2** | `Ω = Φ × Ξ(t)` | Golden ratio mixing (bijective, maximally equidistributed) |
| **3** | `τ = time.time_ns()` | Nanosecond timestamp for uniqueness |
| **4** | `Σ = Ω ⊕ τ` | XOR mixing over 64 bits |
| **5** | `ψ = BLAKE2s(Σ)` | Cryptographic hash to 64-bit output |
| **6** | `r = ψ / 2⁶⁴` | Map to floating point [0, 1) |

---

## ⚡ Performance: Colab Benchmark (Tesla T4)

  Aleam CPU vs GPU

| Generator | Speed (M ops/sec) | Randomness Type |
|-----------|-------------------|-----------------|
| Python random | 5.94 | Pseudo |
| Aleam CPU | 2.05 | **True** |
| PyTorch CUDA | 2,650.81 | Pseudo |
| **Aleam GPU** | **14,434.25** | **True** |

*Tested on NVIDIA Tesla T4 (Google Colab) · CuPy 14.0.1 · Aleam 1.0.3*

> 💡 **Key Insight:** Aleam GPU delivers **14.4 BILLION true random numbers per second** — 2,430x faster than Python random and 5.4x faster than PyTorch CUDA!

---

## 📊 Statistical Validation

After **2.55 million samples**, Aleam passed all 10 rigorous tests:

| Test | Result | Status |
|------|--------|--------|
| Mean | 0.499578 | ✓ |
| Variance | 0.083154 | ✓ |
| Chi-Square (Uniformity) | 21.40 (critical 30.14) | ✓ PASS |
| Max Autocorrelation | 0.0094 | ✓ EXCELLENT |
| π Estimation Error | 0.0105% | ✓ EXCELLENT |
| Shannon Entropy | 0.9999 | ✓ NEAR-PERFECT |

**"True randomness is not a bug — it's a feature."**

---

## 🚀 Quick Start

### Install from PyPI

```bash
pip install aleam
```

### Basic Usage

```python
import aleam as al

# Create a true random generator
rng = al.Aleam()

# Core randomness
x = rng.random()                         # 0.90324326
u64 = rng.random_uint64()                # 12345678901234567890
y = rng.randint(1, 100)                  # 86
z = rng.choice(['AI', 'ML', 'Aleam'])    # 'ML'
u = rng.uniform(5.0, 10.0)              # 7.234
n = rng.gauss(0.0, 1.0)                # -0.432

# Sampling (requires list, not range)
population = list(range(10000))
batch = rng.sample(population, 64)      # Random 64 unique indices

# Shuffle list in-place
items = [1, 2, 3, 4, 5]
rng.shuffle(items)                      # [3, 1, 5, 2, 4]

# Random bytes (returns list of integers)
key = rng.random_bytes(32)              # 32 random bytes as list
```

---

## ✨ Features

### 🎲 Core Randomness

| Method | Description | Example |
|--------|-------------|---------|
| `random()` | True random float in [0, 1) | `rng.random()` |
| `random_uint64()` | True random 64-bit integer | `rng.random_uint64()` |
| `randint(a, b)` | Random integer in [a, b] | `rng.randint(1, 100)` |
| `choice(seq)` | Random element from sequence | `rng.choice(['a', 'b', 'c'])` |
| `shuffle(lst)` | Shuffle list in-place | `rng.shuffle(my_list)` |
| `sample(pop, k)` | Sample k unique elements | `rng.sample(list(range(100)), 10)` |
| `random_bytes(n)` | Generate n random bytes (as list) | `rng.random_bytes(32)` |

### 📈 Statistical Distributions

All distributions are available as methods on the `Aleam` instance:

| Distribution | Method | Example |
|--------------|--------|---------|
| Uniform | `uniform(low, high)` | `rng.uniform(5, 10)` |
| Normal (Gaussian) | `gauss(mu, sigma)` | `rng.gauss(0, 1)` |
| Exponential | `exponential(rate)` | `rng.exponential(1.0)` |
| Beta | `beta(alpha, beta)` | `rng.beta(2, 5)` |
| Gamma | `gamma(shape, scale)` | `rng.gamma(2, 1)` |
| Poisson | `poisson(lam)` | `rng.poisson(3.5)` |
| Laplace | `laplace(loc, scale)` | `rng.laplace(0, 1)` |
| Logistic | `logistic(loc, scale)` | `rng.logistic(0, 1)` |
| Log-Normal | `lognormal(mu, sigma)` | `rng.lognormal(0, 1)` |
| Weibull | `weibull(shape, scale)` | `rng.weibull(1.5, 1)` |
| Pareto | `pareto(alpha, scale)` | `rng.pareto(2, 1)` |
| Chi-square | `chi_square(df)` | `rng.chi_square(5)` |
| Student's t | `student_t(df)` | `rng.student_t(3)` |
| F-distribution | `f_distribution(df1, df2)` | `rng.f_distribution(5, 10)` |
| Dirichlet | `dirichlet(alpha)` | `rng.dirichlet([1, 2, 3])` |

### 🧠 AI/ML Features

| Class | Methods | Use Case |
|-------|---------|----------|
| `AIRandom` | `gradient_noise()`, `latent_vector()`, `dropout_mask()`, `augmentation_params()`, `mini_batch()`, `exploration_noise()` | Training, augmentation, RL exploration |
| `GradientNoise` | `add_noise()`, `reset()`, `current_scale()` | Gradient noise injection with decay |
| `LatentSampler` | `sample()`, `sample_one()`, `interpolate()` | Latent space sampling for VAEs/GANs |

### 🔢 Array Operations

Module-level functions that return **numpy arrays directly**:

| Function | Description | Example |
|----------|-------------|---------|
| `random_array(shape)` | Uniform random array | `al.random_array((100, 100))` |
| `randn_array(shape, mu, sigma)` | Normal random array | `al.randn_array((1000,), 0, 1)` |
| `randint_array(shape, low, high)` | Integer random array | `al.randint_array((50,), 0, 10)` |

---

## 🔌 Framework Integrations

Aleam provides true randomness to ML frameworks via **true random seeds**.

### PyTorch

```python
import torch
import aleam as al

# Get true random seed from Aleam
rng = al.Aleam()
seed = rng.random_uint64()

# Set PyTorch seed
torch.manual_seed(seed)

# Generate tensors on GPU
tensor = torch.randn(100, 100, device='cuda')
```

### TensorFlow

```python
import tensorflow as tf
import aleam as al

# Get true random seed from Aleam
rng = al.Aleam()
seed = rng.random_uint64()

# Set TensorFlow seed
tf.random.set_seed(seed)

# Generate tensors
tensor = tf.random.normal((100, 100))
```

### JAX

```python
import jax
import aleam as al

# Get true random seed from Aleam
rng = al.Aleam()
seed = rng.random_uint64()

# Create JAX key
key = jax.random.key(seed)

# Generate tensors
tensor = jax.random.normal(key, (100, 100))
```

### CuPy (Fastest GPU)

```python
import cupy as cp
import aleam as al

# Get true random seed from Aleam
rng = al.Aleam()
seed = rng.random_uint64()

# Set CuPy seed
cp.random.seed(seed)

# Generate 100 million true random numbers on GPU
arr = cp.random.randn(10000, 10000)  # 14.4B ops/sec!
```

---

## 📦 Installation

### From PyPI

```bash
pip install aleam
```

### With Framework Support

```bash
# PyTorch
pip install aleam[torch]

# TensorFlow
pip install aleam[tensorflow]

# CuPy (for GPU acceleration)
pip install aleam[cupy]

# All frameworks
pip install aleam[all]
```

### From Source

```bash
git clone https://github.com/fardinsabid/aleam.git
cd aleam
pip install .
```

---

## 📁 Project Structure

```
aleam/
│
├── .github/
│   └── workflows/
│       ├── tests.yml
│       ├── publish.yml
│       ├── security.yml
│       └── docs.yml
│
├── aleam/
│   │
│   ├── __init__.py
│   └── py.typed
│
├── src/
│   │
│   └── aleam/
│       │
│       ├── bindings/
│       │   ├── module.cpp
│       │   └── exports.h
│       │
│       ├── core/
│       │   ├── aleam_core.h
│       │   ├── aleam_core.cpp
│       │   ├── constants.h
│       │   └── utils.h
│       │
│       ├── entropy/
│       │   ├── entropy.h
│       │   ├── entropy_linux.h
│       │   ├── entropy_windows.h
│       │   └── entropy_darwin.h
│       │
│       ├── hash/
│       │   ├── blake2s.h
│       │   └── blake2s_config.h
│       │
│       ├── distributions/
│       │   ├── distributions.h
│       │   ├── distributions.cpp
│       │   ├── normal.h
│       │   ├── exponential.h
│       │   ├── beta.h
│       │   ├── gamma.h
│       │   ├── poisson.h
│       │   ├── laplace.h
│       │   ├── logistic.h
│       │   ├── lognormal.h
│       │   ├── weibull.h
│       │   ├── pareto.h
│       │   ├── chi_square.h
│       │   ├── student_t.h
│       │   ├── f_distribution.h
│       │   └── dirichlet.h
│       │
│       ├── arrays/
│       │   ├── arrays.h
│       │   ├── arrays.cpp
│       │   └── array_utils.h
│       │
│       ├── ai/
│       │   ├── ai.h
│       │   ├── ai.cpp
│       │   ├── gradient_noise.h
│       │   ├── latent_sampler.h
│       │   └── augmentation.h
│       │
│       ├── integrations/
│       │   ├── integrations.h
│       │   ├── integrations.cpp
│       │   ├── torch_integration.h
│       │   ├── torch_integration.cpp
│       │   ├── tensorflow_integration.h
│       │   ├── tensorflow_integration.cpp
│       │   ├── jax_integration.h
│       │   ├── jax_integration.cpp
│       │   ├── cupy_integration.h
│       │   ├── cupy_integration.cpp
│       │   ├── pandas_integration.h
│       │   ├── pandas_integration.cpp
│       │   ├── polars_integration.h
│       │   ├── polars_integration.cpp
│       │   ├── xarray_integration.h
│       │   ├── xarray_integration.cpp
│       │   ├── pymc_integration.h
│       │   ├── pymc_integration.cpp
│       │   ├── dask_integration.h
│       │   └── dask_integration.cpp
│       │
│       └── cuda/
│           ├── cuda_kernels.h
│           ├── cuda_kernels.cu
│           ├── cuda_uniform.cu
│           ├── cuda_normal.cu
│           └── cuda_utils.h
│
├── include/
│   └── aleam/
│       └── aleam.h
│
├── tests/
│   ├── test_core.py
│   ├── test_ai.py
│   └── test_statistical.py
│
├── benchmarks/
│   └── benchmark_core.py
│
├── assets/
│   └── images/
│       ├── benchmarks/
│       │   └── cpu_vs_gpu.png
│       └── diagrams/
│            └── algorithm.png
│
│           
├── examples/
│   ├── basic_usage.py
│   ├── ai_ml_features.py
│   ├── array_operations.py
│   ├── distributions.py
│   ├── monte_carlo_pi.py
│   ├── reinforcement_learning.py
│   ├── cuda_integration.py
│   ├── pytorch_integration.py
│   └── tensorflow_integration.py
│
├── docs/
│   ├── ALEAM_RESEARCH_PAPER.md
│   ├── CHANGELOG.md
│   ├── index.md
│   ├── INSTALLATION.md
│   └── ROADMAP.md
│
├── setup.py
├── pyproject.toml
├── MANIFEST.in
├── requirements.txt
├── requirements-dev.txt
├── LICENSE
├── README.md
├── SECURITY.md
├── CONTRIBUTING.md
├── CODE_OF_CONDUCT.md
└── .gitignore
```

---

## 🔧 Troubleshooting

### Q: Why is Aleam slower than random.random on CPU?

**A:** True randomness is slower than pseudo-random — that's expected. You're trading speed for genuine entropy. On GPU, Aleam achieves **14.4B ops/sec**, far exceeding CPU pseudo-random speeds.

### Q: Can I seed Aleam for reproducible results?

**A:** No. Aleam is stateless by design. Use Python's `random` module if you need reproducibility.

### Q: Is Aleam cryptographically secure?

**A:** Yes. Each call consumes 64 bits of true entropy and passes through BLAKE2s.

### Q: Does Aleam support GPU?

**A:** Yes! Use CuPy with true random seeds from Aleam:

```python
import cupy as cp
import aleam as al

seed = al.Aleam().random_uint64()
cp.random.seed(seed)
arr = cp.random.randn(10000, 10000)  # 14.4B ops/sec
```

### Q: Why does `sample()` require a list?

**A:** The C++ bindings accept Python lists directly. Use `list(range(10000))` instead of `range(10000)`.

### Q: What does `random_bytes()` return?

**A:** It returns a Python list of integers (0-255), not a bytes object.

---

## 🔒 Responsible Use

- ✅ Use for AI research, exploration, and creative projects
- ✅ Use for scientific simulations requiring true randomness
- ✅ Use for cryptographic applications
- ❌ Do not use for security-critical systems without additional entropy sources

---

## 📄 License

MIT License — see [LICENSE](LICENSE) for details.

---

## 🌐 Links

| | |
|---|---|
| 📦 PyPI | [pypi.org/project/aleam](https://pypi.org/project/aleam) |
| 🐛 Issues | [GitHub Issues](https://github.com/fardinsabid/aleam/issues) |
| 📖 Documentation | [GitHub Docs](https://github.com/fardinsabid/aleam/blob/main/docs/index.md) |
| 📄 Research Paper | [ALEAM_RESEARCH_PAPER.md](https://github.com/fardinsabid/aleam/blob/main/docs/ALEAM_RESEARCH_PAPER.md) |

---

**Made with ❤️ by Fardin Sabid**  
**🇧🇩 From Bangladesh, for the World 🌍**

```
True randomness. No recursion. No state. Just entropy.
```

After 2 days of discovery, testing, and refinement — the equation is proven.

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**If you find this project useful, please ⭐ star it on GitHub!**

