typo fix

Signed-off-by: Jonathan Rampersad <jonathan@jono-rams.work>

CONTRIBUTORS.md

@@ -1,10 +0,0 @@
-## Contributors
-
-<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
-<!-- prettier-ignore-start -->
-<!-- markdownlint-disable -->
-[![All Contributors](https://img.shields.io/github/all-contributors/jono-rams/PolySolve?color=ee8449&style=flat-square)](#contributors)
-<!-- markdownlint-restore -->
-<!-- prettier-ignore-end -->
-
-<!-- ALL-CONTRIBUTORS-LIST:END -->

README.md

@@ -1,4 +1,6 @@
-# polysolve
+<p align="center">
+  <img src="https://i.ibb.co/N22Gx6xq/Poly-Solve-Logo.png" alt="polysolve Logo" width="256">
+</p>
 
 [![PyPI version](https://img.shields.io/pypi/v/polysolve.svg)](https://pypi.org/project/polysolve/)
 [![PyPI pyversions](https://img.shields.io/pypi/pyversions/polysolve.svg)](https://pypi.org/project/polysolve/)
@@ -9,7 +11,7 @@ A Python library for representing, manipulating, and solving polynomial equation
 
 ## Key Features
 
-* **Create and Manipulate Polynomials**: Easily define polynomials of any degree and perform arithmetic operations like addition, subtraction, and scaling.
+* **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.
 * **Genetic Algorithm Solver**: Find approximate real roots for complex polynomials where analytical solutions are difficult or impossible.
 * **CUDA Accelerated**: Leverage NVIDIA GPUs for a massive performance boost when finding roots in large solution spaces.
 * **Analytical Solvers**: Includes standard, exact solvers for simple cases (e.g., `quadratic_solve`).
@@ -44,6 +46,7 @@ Here is a simple example of how to define a quadratic function, find its propert
 from polysolve import Function, GA_Options, quadratic_solve
 
 # 1. Define the function f(x) = 2x^2 - 3x - 5
+#    Coefficients can be integers or floats.
 f1 = Function(largest_exponent=2)
 f1.set_constants([2, -3, -5])
 
@@ -56,17 +59,22 @@ print(f"Value of f1 at x=5 is: {y_val}")
 # > Value of f1 at x=5 is: 30.0
 
 # 3. Get the derivative: 4x - 3
-df1 = f1.differential()
+df1 = f1.derivative()
 print(f"Derivative of f1: {df1}")
 # > Derivative of f1: 4x - 3
 
-# 4. Find roots analytically using the quadratic formula
+# 4. Get the 2nd derivative: 4
+ddf1 = f1.nth_derivative(2)
+print(f"2nd Derivative of f1: {ddf1}")
+# > Derivative of f1: 4
+
+# 5. Find roots analytically using the quadratic formula
 #    This is exact and fast for degree-2 polynomials.
 roots_analytic = quadratic_solve(f1)
 print(f"Analytic roots: {sorted(roots_analytic)}")
 # > Analytic roots: [-1.0, 2.5]
 
-# 5. Find roots with the genetic algorithm (CPU)
+# 6. Find roots with the genetic algorithm (CPU)
 #    This can solve polynomials of any degree.
 ga_opts = GA_Options(num_of_generations=20)
 roots_ga = f1.get_real_roots(ga_opts, use_cuda=False)

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 # --- Core Metadata ---
 name = "polysolve"
-version = "0.1.1"
+version = "0.3.2"
 authors = [
   { name="Jonathan Rampersad", email="jonathan@jono-rams.work" },
 ]
@@ -42,6 +42,7 @@ cuda12 = ["cupy-cuda12x"]
 dev = ["pytest"]
 
 [project.urls]
-Homepage = "https://github.com/jono-rams/PolySolve"
-"Source Code" = "https://github.com/jono-rams/PolySolve"
+Homepage = "https://polysolve.jono-rams.work"
+Documentation = "https://polysolve.jono-rams.work/docs"
+Repository  = "https://github.com/jono-rams/PolySolve"
 "Bug Tracker" = "https://github.com/jono-rams/PolySolve/issues"

src/polysolve/__init__.py

@@ -1,7 +1,7 @@
 import math
 import numpy as np
 from dataclasses import dataclass
-from typing import List, Optional
+from typing import List, Optional, Union
 import warnings
 
 # Attempt to import CuPy for CUDA acceleration.
@@ -12,8 +12,32 @@ try:
 except ImportError:
     _CUPY_AVAILABLE = False
 
-# The CUDA kernel for the fitness function
-_FITNESS_KERNEL = """
+# The CUDA kernels for the fitness function
+_FITNESS_KERNEL_FLOAT = """
+extern "C" __global__ void fitness_kernel(
+    const double* coefficients, 
+    int num_coefficients, 
+    const double* x_vals, 
+    double* ranks, 
+    int size, 
+    double y_val)
+{
+    int idx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (idx < size)
+    {
+        double ans = 0;
+        int lrgst_expo = num_coefficients - 1;
+        for (int i = 0; i < num_coefficients; ++i)
+        {
+            ans += coefficients[i] * pow(x_vals[idx], (double)(lrgst_expo - i));
+        }
+
+        ans -= y_val;
+        ranks[idx] = (ans == 0) ? 1.7976931348623157e+308 : fabs(1.0 / ans);
+    }
+}
+"""
+_FITNESS_KERNEL_INT = """
 extern "C" __global__ void fitness_kernel(
     const long long* coefficients, 
     int num_coefficients, 
@@ -38,6 +62,7 @@ extern "C" __global__ void fitness_kernel(
 }
 """
 
+
 @dataclass
 class GA_Options:
     """
@@ -76,13 +101,14 @@ class Function:
         self.coefficients: Optional[np.ndarray] = None
         self._initialized = False
 
-    def set_coeffs(self, coefficients: List[int]):
+    def set_coeffs(self, coefficients: List[Union[int, float]]):
         """
         Sets the coefficients of the polynomial.
 
         Args:
-            coefficients (List[int]): A list of integer coefficients. The list size
-                                   must be largest_exponent + 1.
+            coefficients (List[Union[int, float]]): A list of integer or float
+                                                   coefficients. The list size
+                                                   must be largest_exponent + 1.
 
         Raises:
             ValueError: If the input is invalid.
@@ -95,8 +121,14 @@ class Function:
             )
         if coefficients[0] == 0 and self._largest_exponent > 0:
             raise ValueError("The first constant (for the largest exponent) cannot be 0.")
+        
+        # Check if any coefficient is a float
+        is_float = any(isinstance(c, float) for c in coefficients)
 
-        self.coefficients = np.array(coefficients, dtype=np.int64)
+        # Choose the dtype based on the input
+        target_dtype = np.float64 if is_float else np.int64
+
+        self.coefficients = np.array(coefficients, dtype=target_dtype)
         self._initialized = True
 
     def _check_initialized(self):
@@ -108,6 +140,11 @@ class Function:
     def largest_exponent(self) -> int:
         """Returns the largest exponent of the function."""
         return self._largest_exponent
+    
+    @property
+    def degree(self) -> int:
+        """Returns the largest exponent of the function."""
+        return self._largest_exponent
 
     def solve_y(self, x_val: float) -> float:
         """
@@ -129,15 +166,66 @@ class Function:
         Returns:
             Function: A new Function object representing the derivative.
         """
+        warnings.warn(
+            "The 'differential' function has been renamed. Please use 'derivative' instead.",
+            DeprecationWarning,
+            stacklevel=2
+        )
+
         self._check_initialized()
         if self._largest_exponent == 0:
             raise ValueError("Cannot differentiate a constant (Function of degree 0).")
 
+        return self.derivitive()
+        
+    
+    def derivative(self) -> 'Function':
+        """
+        Calculates the derivative of the function.
+
+        Returns:
+            Function: A new Function object representing the derivative.
+        """
+        self._check_initialized()
+        if self._largest_exponent == 0:
+            raise ValueError("Cannot differentiate a constant (Function of degree 0).")
+        
         derivative_coefficients = np.polyder(self.coefficients)
         
         diff_func = Function(self._largest_exponent - 1)
         diff_func.set_coeffs(derivative_coefficients.tolist())
         return diff_func
+    
+
+    def nth_derivative(self, n: int) -> 'Function':
+        """
+        Calculates the nth derivative of the function.
+
+        Args:
+            n (int): The order of the derivative to calculate.
+
+        Returns:
+           Function: A new Function object representing the nth derivative.
+        """
+        self._check_initialized()
+        
+        if not isinstance(n, int) or n < 1:
+            raise ValueError("Derivative order 'n' must be a positive integer.")
+
+        if n > self.largest_exponent:
+            function = Function(0)
+            function.set_coeffs([0])
+            return function
+
+        if n == 1:
+            return self.derivative()
+        
+        function = self
+        for _ in range(n):
+            function = function.derivative()
+
+        return function
+
 
     def get_real_roots(self, options: GA_Options = GA_Options(), use_cuda: bool = False) -> np.ndarray:
         """
@@ -219,11 +307,16 @@ class Function:
 
     def _solve_x_cuda(self, y_val: float, options: GA_Options) -> np.ndarray:
         """Genetic algorithm implementation using CuPy (GPU/CUDA)."""
-        # Load the raw CUDA kernel
-        fitness_gpu = cupy.RawKernel(_FITNESS_KERNEL, 'fitness_kernel')
         
-        # Move coefficients to GPU
-        d_coefficients = cupy.array(self.coefficients, dtype=cupy.int64)
+        # Check the dtype of our coefficients array
+        if self.coefficients.dtype == np.float64:
+            fitness_gpu = cupy.RawKernel(_FITNESS_KERNEL_FLOAT, 'fitness_kernel')
+            d_coefficients = cupy.array(self.coefficients, dtype=cupy.float64)
+        elif self.coefficients.dtype == np.int64:
+            fitness_gpu = cupy.RawKernel(_FITNESS_KERNEL_INT, 'fitness_kernel')
+            d_coefficients = cupy.array(self.coefficients, dtype=cupy.int64)
+        else:
+            raise TypeError(f"Unsupported dtype for CUDA solver: {self.coefficients.dtype}")
         
         # Create initial random solutions on the GPU
         d_solutions = cupy.random.uniform(
@@ -281,12 +374,16 @@ class Function:
             power = self._largest_exponent - i
             
             # Coefficient part
-            if c == 1 and power != 0:
+            coeff_val = c
+            if c == int(c):
+                coeff_val = int(c)
+
+            if coeff_val == 1 and power != 0:
                 coeff = ""
-            elif c == -1 and power != 0:
+            elif coeff_val == -1 and power != 0:
                 coeff = "-"
             else:
-                coeff = str(c)
+                coeff = str(coeff_val)
 
             # Variable part
             if power == 0:
@@ -300,7 +397,7 @@ class Function:
             sign = ""
             if i > 0:
                 sign = " + " if c > 0 else " - "
-                coeff = str(abs(c))
+                coeff = str(abs(coeff_val))
                 if abs(c) == 1 and power != 0:
                     coeff = "" # Don't show 1 for non-constant terms
 
@@ -336,34 +433,55 @@ class Function:
         result_func = Function(len(new_coefficients) - 1)
         result_func.set_coeffs(new_coefficients.tolist())
         return result_func
-        
-    def __mul__(self, scalar: int) -> 'Function':
-        """Multiplies the function by a scalar constant."""
-        self._check_initialized()
-        if not isinstance(scalar, (int, float)):
-            return NotImplemented
-        if scalar == 0:
-            raise ValueError("Cannot multiply a function by 0.")
+    
+    def _multiply_by_scalar(self, scalar: Union[int, float]) -> 'Function':
+        """Helper method to multiply the function by a scalar constant."""
+        self._check_initialized() # It's good practice to check here too
 
+        if scalar == 0:
+            result_func = Function(0)
+            result_func.set_coeffs([0])
+            return result_func
+    
         new_coefficients = self.coefficients * scalar
-        
+    
         result_func = Function(self._largest_exponent)
         result_func.set_coeffs(new_coefficients.tolist())
         return result_func
 
-    def __rmul__(self, scalar: int) -> 'Function':
+    def _multiply_by_function(self, other: 'Function') -> 'Function':
+        """Helper method for polynomial multiplication (Function * Function)."""
+        self._check_initialized()
+        other._check_initialized()
+
+        # np.polymul performs convolution of coefficients to multiply polynomials
+        new_coefficients = np.polymul(self.coefficients, other.coefficients)
+    
+        # The degree of the resulting polynomial is derived from the new coefficients
+        new_degree = len(new_coefficients) - 1
+    
+        result_func = Function(new_degree)
+        result_func.set_coeffs(new_coefficients.tolist())
+        return result_func
+        
+    def __mul__(self, other: Union['Function', int, float]) -> 'Function':
+        """Multiplies the function by a scalar constant."""
+        if isinstance(other, (int, float)):
+            return self._multiply_by_scalar(other)
+        elif isinstance(other, self.__class__):
+            return self._multiply_by_function(other)
+        else:
+            return NotImplemented
+
+    def __rmul__(self, scalar: Union[int, float]) -> 'Function':
         """Handles scalar multiplication from the right (e.g., 3 * func)."""
+
         return self.__mul__(scalar)
         
-    def __imul__(self, scalar: int) -> 'Function':
+    def __imul__(self, other: Union['Function', int, float]) -> 'Function':
         """Performs in-place multiplication by a scalar (func *= 3)."""
-        self._check_initialized()
-        if not isinstance(scalar, (int, float)):
-            return NotImplemented
-        if scalar == 0:
-            raise ValueError("Cannot multiply a function by 0.")
-        
-        self.coefficients *= scalar
+
+        self.coefficients *= other
         return self
 
 
@@ -408,9 +526,13 @@ if __name__ == '__main__':
     print(f"Value of f1 at x=5 is: {y}") # Expected: 2*(25) - 3*(5) - 5 = 50 - 15 - 5 = 30
 
     # Find the derivative: 4x - 3
-    df1 = f1.differential()
+    df1 = f1.derivative()
     print(f"Derivative of f1: {df1}")
 
+    # Find the second derivative: 4
+    ddf1 = f1.nth_derivative(2)
+    print(f"Second derivative of f1: {ddf1}")
+
     # --- Root Finding ---
     # 1. Analytical solution for quadratic
     roots_analytic = quadratic_solve(f1)
@@ -448,4 +570,18 @@ if __name__ == '__main__':
 
     # Multiplication: (x + 10) * 3 = 3x + 30
     f_mul = f2 * 3
-    print(f"f2 * 3 = {f_mul}")
+    print(f"f2 * 3 = {f_mul}")
+
+    # f3 represents 2x^2 + 3x + 1
+    f3 = Function(2)
+    f3.set_coeffs([2, 3, 1]) 
+    print(f"Function f3: {f3}")
+
+    # f4 represents 5x - 4
+    f4 = Function(1)
+    f4.set_coeffs([5, -4])
+    print(f"Function f4: {f4}")
+
+    # Multiply the two functions
+    product_func = f3 * f4
+    print(f"f3 * f4 = {product_func}")

tests/test_polysolve.py

@@ -24,6 +24,18 @@ def linear_func() -> Function:
     f.set_coeffs([1, 10])
     return f
 
+@pytest.fixture
+def m_func_1() -> Function:
+    f = Function(2)
+    f.set_coeffs([2, 3, 1])
+    return f
+
+@pytest.fixture
+def m_func_2() -> Function:
+    f = Function(1)
+    f.set_coeffs([5, -4])
+    return f
+
 # --- Core Functionality Tests ---
 
 def test_solve_y(quadratic_func):
@@ -32,13 +44,20 @@ def test_solve_y(quadratic_func):
     assert quadratic_func.solve_y(0) == -5.0
     assert quadratic_func.solve_y(-1) == 0.0
 
-def test_differential(quadratic_func):
+def test_derivative(quadratic_func):
     """Tests the calculation of the function's derivative."""
-    derivative = quadratic_func.differential()
+    derivative = quadratic_func.derivative()
     assert derivative.largest_exponent == 1
     # The derivative of 2x^2 - 3x - 5 is 4x - 3
     assert np.array_equal(derivative.coefficients, [4, -3])
 
+def test_nth_derivative(quadratic_func):
+    """Tests the calculation of the function's 2nd derivative."""
+    derivative = quadratic_func.nth_derivative(2)
+    assert derivative.largest_exponent == 0
+    # The derivative of 2x^2 - 3x - 5 is 4x - 3
+    assert np.array_equal(derivative.coefficients, [4])
+
 def test_quadratic_solve(quadratic_func):
     """Tests the analytical quadratic solver for exact roots."""
     roots = quadratic_solve(quadratic_func)
@@ -61,13 +80,20 @@ def test_subtraction(quadratic_func, linear_func):
     assert result.largest_exponent == 2
     assert np.array_equal(result.coefficients, [2, -4, -15])
 
-def test_multiplication(linear_func):
+def test_scalar_multiplication(linear_func):
     """Tests the multiplication of a Function object by a scalar."""
     # (x + 10) * 3 = 3x + 30
     result = linear_func * 3
     assert result.largest_exponent == 1
     assert np.array_equal(result.coefficients, [3, 30])
 
+def test_function_multiplication(m_func_1, m_func_2):
+    """Tests the multiplication of two Function objects."""
+    # (2x^2 + 3x + 1) * (5x -4) = 10x^3 + 7x^2 - 7x -4
+    result = m_func_1 * m_func_2
+    assert result.largest_exponent == 3
+    assert np.array_equal(result.coefficients, [10, 7, -7, -4])
+
 # --- Genetic Algorithm Root-Finding Tests ---
 
 def test_get_real_roots_numpy(quadratic_func):