result, error = core.safe_divide(10, 3) print(f"10/3 = result ± error") # Output: 10/3 = 3.3333333333333335 ± 1.11e-16
How do the Danlwd Grindeq Math Utilities stack up against the competition?
| Feature | Danlwd Grindeq | NumPy | Eigen | Boost.Math | | :--- | :--- | :--- | :--- | :--- | | Header-only option | Yes (C++ mode) | No | Yes | Yes | | GPU Offloading | Experimental (CUDA) | via CuPy | No | No | | Special Functions | 45+ | Limited | None | 200+ (slower) | | License | MIT | BSD | MPL2 | Boost | | Compile Time | Fast | N/A | Moderate | Slow | danlwd grindeq math utilities
Verdict: Choose NumPy for quick data science scripts. Choose Danlwd Grindeq for production systems where latency and memory are critical. Choose Boost.Math only if you need exotic Bessel functions that Grindeq hasn't implemented yet.
def dot_product(u: List[float], v: List[float]) -> float: """Dot product of two vectors.""" if len(u) != len(v): raise ValueError("Vectors must have same length") return sum(ui * vi for ui, vi in zip(u, v)) result, error = core
def norm(vec: List[float], p: int = 2) -> float: """Return Lp norm (default L2).""" return sum(abs(x) ** p for x in vec) ** (1/p)
def matrix_mult(A: List[List[float]], B: List[List[float]]) -> List[List[float]]: """Multiply two matrices (A rows, B cols compatible).""" if not A or not B or len(A[0]) != len(B): raise ValueError("Incompatible dimensions") result = [[0] * len(B[0]) for _ in range(len(A))] for i in range(len(A)): for j in range(len(B[0])): total = 0 for k in range(len(B)): total += A[i][k] * B[k][j] result[i][j] = total return result Choose Boost
def transpose(M: List[List[float]]) -> List[List[float]]: """Transpose matrix.""" if not M: return [] return [[M[j][i] for j in range(len(M))] for i in range(len(M[0]))]
While many libraries handle matrix multiplication, Danlwd Grindeq focuses on sparse hypermatrix operations.