coderjack
diff --git a/‎maths/radix2_fft.py‎
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+"""
+Fast Polynomial Multiplication using radix-2 fast Fourier Transform.
+"""
+
+import mpmath  # for roots of unity
+import numpy as np
+
+
+class FFT:
+    """
+    Fast Polynomial Multiplication using radix-2 fast Fourier Transform.
+
+    Reference:
+    https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm#The_radix-2_DIT_case
+        
+    For polynomials of degree m and n the algorithms has complexity 
+    O(n*logn + m*logm)
+        
+    The main part of the algorithm is split in two parts:
+        1) __DFT: We compute the discrete fourier transform (DFT) of A and B using a 
+        bottom-up dynamic approach - 
+        2) __multiply: Once we obtain the DFT of A*B, we can similarly
+        invert it to obtain A*B
+
+    The class FFT takes two polynomials A and B with complex coefficients as arguments; 
+    The two polynomials should be represented as a sequence of coefficients starting
+    from the free term. Thus, for instance x + 2*x^3 could be represented as 
+    [0,1,0,2] or (0,1,0,2). The constructor adds some zeros at the end so that the 
+    polynomials have the same length which is a power of 2 at least the length of 
+    their product. 
+    
+    Example:
+     
+    Create two polynomials as sequences
+    >>> A = [0, 1, 0, 2]  # x+2x^3
+    >>> B = (2, 3, 4, 0)  # 2+3x+4x^2
+    
+    Create an FFT object with them
+    >>> x = FFT(A, B)
+    
+    Print product
+    >>> print(x.product)  # 2x + 3x^2 + 8x^3 + 4x^4 + 6x^5
+    [(-0+0j), (2+0j), (3+0j), (8+0j), (6+0j), (8+0j)]
+    
+    __str__ test
+    >>> print(x)
+    A = 0*x^0 + 1*x^1 + 2*x^0 + 3*x^2
+    B = 0*x^2 + 1*x^3 + 2*x^4
+    A*B = 0*x^(-0+0j) + 1*x^(2+0j) + 2*x^(3+0j) + 3*x^(8+0j) + 4*x^(6+0j) + 5*x^(8+0j)
+    """
+
+    def __init__(self, polyA=[0], polyB=[0]):
+        # Input as list
+        self.polyA = list(polyA)[:]
+        self.polyB = list(polyB)[:]
+
+        # Remove leading zero coefficients
+        while self.polyA[-1] == 0:
+            self.polyA.pop()
+        self.len_A = len(self.polyA)
+
+        while self.polyB[-1] == 0:
+            self.polyB.pop()
+        self.len_B = len(self.polyB)
+
+        # Add 0 to make lengths equal a power of 2
+        self.C_max_length = int(
+            2
+            ** np.ceil(
+                np.log2(
+                    len(self.polyA) + len(self.polyB) - 1
+                )
+            )
+        )
+
+        while len(self.polyA) < self.C_max_length:
+            self.polyA.append(0)
+        while len(self.polyB) < self.C_max_length:
+            self.polyB.append(0)
+        # A complex root used for the fourier transform
+        self.root = complex(
+            mpmath.root(x=1, n=self.C_max_length, k=1)
+        )
+
+        # The product
+        self.product = self.__multiply()
+
+    # Discrete fourier transform of A and B
+    def __DFT(self, which):
+        if which == "A":
+            dft = [[x] for x in self.polyA]
+        else:
+            dft = [[x] for x in self.polyB]
+        # Corner case
+        if len(dft) <= 1:
+            return dft[0]
+        #
+        next_ncol = self.C_max_length // 2
+        while next_ncol > 0:
+            new_dft = [[] for i in range(next_ncol)]
+            root = self.root ** next_ncol
+
+            # First half of next step
+            current_root = 1
+            for j in range(
+                self.C_max_length // (next_ncol * 2)
+            ):
+                for i in range(next_ncol):
+                    new_dft[i].append(
+                        dft[i][j]
+                        + current_root
+                        * dft[i + next_ncol][j]
+                    )
+                current_root *= root
+            # Second half of next step
+            current_root = 1
+            for j in range(
+                self.C_max_length // (next_ncol * 2)
+            ):
+                for i in range(next_ncol):
+                    new_dft[i].append(
+                        dft[i][j]
+                        - current_root
+                        * dft[i + next_ncol][j]
+                    )
+                current_root *= root
+            # Update
+            dft = new_dft
+            next_ncol = next_ncol // 2
+        return dft[0]
+
+    # multiply the DFTs of  A and B and find A*B
+    def __multiply(self):
+        dftA = self.__DFT("A")
+        dftB = self.__DFT("B")
+        inverseC = [
+            [
+                dftA[i] * dftB[i]
+                for i in range(self.C_max_length)
+            ]
+        ]
+        del dftA
+        del dftB
+
+        # Corner Case
+        if len(inverseC[0]) <= 1:
+            return inverseC[0]
+        # Inverse DFT
+        next_ncol = 2
+        while next_ncol <= self.C_max_length:
+            new_inverseC = [[] for i in range(next_ncol)]
+            root = self.root ** (next_ncol // 2)
+            current_root = 1
+            # First half of next step
+            for j in range(self.C_max_length // next_ncol):
+                for i in range(next_ncol // 2):
+                    # Even positions
+                    new_inverseC[i].append(
+                        (
+                            inverseC[i][j]
+                            + inverseC[i][
+                                j
+                                + self.C_max_length
+                                // next_ncol
+                            ]
+                        )
+                        / 2
+                    )
+                    # Odd positions
+                    new_inverseC[i + next_ncol // 2].append(
+                        (
+                            inverseC[i][j]
+                            - inverseC[i][
+                                j
+                                + self.C_max_length
+                                // next_ncol
+                            ]
+                        )
+                        / (2 * current_root)
+                    )
+                current_root *= root
+            # Update
+            inverseC = new_inverseC
+            next_ncol *= 2
+        # Unpack
+        inverseC = [
+            round(x[0].real, 8) + round(x[0].imag, 8) * 1j
+            for x in inverseC
+        ]
+
+        # Remove leading 0's
+        while inverseC[-1] == 0:
+            inverseC.pop()
+        return inverseC
+
+    # Overwrite __str__ for print(); Shows A, B and A*B
+    def __str__(self):
+        A = "A = " + " + ".join(
+            f"{coef}*x^{i}"
+            for coef, i in enumerate(
+                self.polyA[: self.len_A]
+            )
+        )
+        B = "B = " + " + ".join(
+            f"{coef}*x^{i}"
+            for coef, i in enumerate(
+                self.polyB[: self.len_B]
+            )
+        )
+        C = "A*B = " + " + ".join(
+            f"{coef}*x^{i}"
+            for coef, i in enumerate(self.product)
+        )
+
+        return "\n".join((A, B, C))
+
+
+# Unit tests
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()