FEAT: Added support for float coefficients
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@ -9,7 +9,7 @@ A Python library for representing, manipulating, and solving polynomial equation
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## Key Features
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* **Create and Manipulate Polynomials**: Easily define polynomials of any degree and perform arithmetic operations like addition, subtraction, multiplication, and scaling.
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* **Create and Manipulate Polynomials**: Easily define polynomials of any degree using integer or float coefficients, and perform arithmetic operations like addition, subtraction, multiplication, and scaling.
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* **Genetic Algorithm Solver**: Find approximate real roots for complex polynomials where analytical solutions are difficult or impossible.
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* **CUDA Accelerated**: Leverage NVIDIA GPUs for a massive performance boost when finding roots in large solution spaces.
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* **Analytical Solvers**: Includes standard, exact solvers for simple cases (e.g., `quadratic_solve`).
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@ -44,6 +44,7 @@ Here is a simple example of how to define a quadratic function, find its propert
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from polysolve import Function, GA_Options, quadratic_solve
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# 1. Define the function f(x) = 2x^2 - 3x - 5
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# Coefficients can be integers or floats.
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f1 = Function(largest_exponent=2)
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f1.set_constants([2, -3, -5])
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@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
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[project]
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# --- Core Metadata ---
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name = "polysolve"
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version = "0.2.2"
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version = "0.3.0"
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authors = [
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{ name="Jonathan Rampersad", email="jonathan@jono-rams.work" },
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]
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@ -15,7 +15,7 @@ except ImportError:
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# The CUDA kernel for the fitness function
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_FITNESS_KERNEL = """
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extern "C" __global__ void fitness_kernel(
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const long long* coefficients,
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const double* coefficients,
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int num_coefficients,
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const double* x_vals,
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double* ranks,
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@ -76,13 +76,14 @@ class Function:
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self.coefficients: Optional[np.ndarray] = None
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self._initialized = False
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def set_coeffs(self, coefficients: List[int]):
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def set_coeffs(self, coefficients: List[Union[int, float]]):
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"""
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Sets the coefficients of the polynomial.
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Args:
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coefficients (List[int]): A list of integer coefficients. The list size
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must be largest_exponent + 1.
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coefficients (List[Union[int, float]]): A list of integer or float
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coefficients. The list size
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must be largest_exponent + 1.
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Raises:
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ValueError: If the input is invalid.
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@ -96,7 +97,7 @@ class Function:
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if coefficients[0] == 0 and self._largest_exponent > 0:
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raise ValueError("The first constant (for the largest exponent) cannot be 0.")
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self.coefficients = np.array(coefficients, dtype=np.int64)
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self.coefficients = np.array(coefficients, dtype=np.float64)
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self._initialized = True
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def _check_initialized(self):
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@ -279,7 +280,7 @@ class Function:
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fitness_gpu = cupy.RawKernel(_FITNESS_KERNEL, 'fitness_kernel')
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# Move coefficients to GPU
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d_coefficients = cupy.array(self.coefficients, dtype=cupy.int64)
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d_coefficients = cupy.array(self.coefficients, dtype=cupy.float64)
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# Create initial random solutions on the GPU
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d_solutions = cupy.random.uniform(
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@ -337,12 +338,16 @@ class Function:
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power = self._largest_exponent - i
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# Coefficient part
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if c == 1 and power != 0:
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coeff_val = c
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if c == int(c):
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coeff_val = int(c)
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if coeff_val == 1 and power != 0:
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coeff = ""
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elif c == -1 and power != 0:
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elif coeff_val == -1 and power != 0:
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coeff = "-"
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else:
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coeff = str(c)
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coeff = str(coeff_val)
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# Variable part
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if power == 0:
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@ -356,7 +361,7 @@ class Function:
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sign = ""
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if i > 0:
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sign = " + " if c > 0 else " - "
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coeff = str(abs(c))
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coeff = str(abs(coeff_val))
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if abs(c) == 1 and power != 0:
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coeff = "" # Don't show 1 for non-constant terms
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