FEAT: Added support for float coefficients (#13)
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Reviewed-on: #13 Co-authored-by: Jonathan Rampersad <rampersad.jonathan@gmail.com> Co-committed-by: Jonathan Rampersad <rampersad.jonathan@gmail.com>
This commit was merged in pull request #13.
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@@ -12,8 +12,32 @@ try:
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except ImportError:
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_CUPY_AVAILABLE = False
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# The CUDA kernel for the fitness function
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_FITNESS_KERNEL = """
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# The CUDA kernels for the fitness function
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_FITNESS_KERNEL_FLOAT = """
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extern "C" __global__ void fitness_kernel(
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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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int size,
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double y_val)
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{
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int idx = threadIdx.x + blockIdx.x * blockDim.x;
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if (idx < size)
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{
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double ans = 0;
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int lrgst_expo = num_coefficients - 1;
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for (int i = 0; i < num_coefficients; ++i)
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{
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ans += coefficients[i] * pow(x_vals[idx], (double)(lrgst_expo - i));
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}
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ans -= y_val;
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ranks[idx] = (ans == 0) ? 1.7976931348623157e+308 : fabs(1.0 / ans);
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}
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}
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"""
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_FITNESS_KERNEL_INT = """
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extern "C" __global__ void fitness_kernel(
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const long long* coefficients,
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int num_coefficients,
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@@ -38,6 +62,7 @@ extern "C" __global__ void fitness_kernel(
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}
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"""
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@dataclass
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class GA_Options:
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"""
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@@ -76,13 +101,14 @@ 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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@@ -95,8 +121,14 @@ class Function:
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)
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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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# Check if any coefficient is a float
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is_float = any(isinstance(c, float) for c in coefficients)
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self.coefficients = np.array(coefficients, dtype=np.int64)
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# Choose the dtype based on the input
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target_dtype = np.float64 if is_float else np.int64
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self.coefficients = np.array(coefficients, dtype=target_dtype)
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self._initialized = True
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def _check_initialized(self):
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@@ -275,11 +307,16 @@ class Function:
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def _solve_x_cuda(self, y_val: float, options: GA_Options) -> np.ndarray:
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"""Genetic algorithm implementation using CuPy (GPU/CUDA)."""
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# Load the raw CUDA kernel
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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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# Check the dtype of our coefficients array
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if self.coefficients.dtype == np.float64:
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fitness_gpu = cupy.RawKernel(_FITNESS_KERNEL_FLOAT, 'fitness_kernel')
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d_coefficients = cupy.array(self.coefficients, dtype=cupy.float64)
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elif self.coefficients.dtype == np.int64:
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fitness_gpu = cupy.RawKernel(_FITNESS_KERNEL_INT, 'fitness_kernel')
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d_coefficients = cupy.array(self.coefficients, dtype=cupy.int64)
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else:
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raise TypeError(f"Unsupported dtype for CUDA solver: {self.coefficients.dtype}")
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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 +374,16 @@ 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 +397,7 @@ 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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