BinomialHuffman Bitstream Encoder, lets gooo

bitstream.py

@@ -1,4 +1,5 @@
-import bz2
+import bz2, math
+import heapq
 from abc import ABC, abstractmethod
 from arithmetic_compressor import AECompressor
 from arithmetic_compressor.models import StaticModel
@@ -56,3 +57,65 @@ class Bzip2Encoder(BaseEncoder):
 
     def build_model(self, data):
         pass
+
+class BinomialHuffmanEncoder(BaseEncoder):
+    def encode(self, data):
+        return ''.join(self.codebook[int(value)+512] for value in data)
+
+    def decode(self, encoded_data):
+        decoded_output = []
+        current_node = self.root
+        for bit in encoded_data:
+            if bit == '0':
+                current_node = current_node.left
+            else:
+                current_node = current_node.right
+            
+            if current_node.left is None and current_node.right is None:
+                decoded_output.append(current_node.value)
+                current_node = self.root
+        
+        return decoded_output
+
+    def _generate_codes(self, node, prefix="", codebook={}):
+        if node is not None:
+            if node.value is not None:
+                codebook[node.value] = prefix
+            self._generate_codes(node.left, prefix + "0", codebook)
+            self._generate_codes(node.right, prefix + "1", codebook)
+        return codebook
+
+    def build_model(self, data):
+        # Number of possible 10-bit integers (0 to 1023)
+        num_symbols = 1024
+
+        # Mean and standard deviation for a binomial distribution centered around 0
+        mean = (num_symbols - 1) / 2
+        std_dev = math.sqrt(num_symbols / 4)
+
+        class Node:
+            def __init__(self, value, freq):
+                self.value = value
+                self.freq = freq
+                self.left = None
+                self.right = None
+
+            def __lt__(self, other):
+                return self.freq < other.freq
+
+        # Build a min-heap
+        heap = [Node(x, (1 / (std_dev * math.sqrt(2 * math.pi))) * math.exp(-0.5 * ((x - mean) / std_dev) ** 2)) for x in range(num_symbols)]
+        heapq.heapify(heap)
+
+        # Merge nodes to build the Huffman tree
+        while len(heap) > 1:
+            left = heapq.heappop(heap)
+            right = heapq.heappop(heap)
+            merged = Node(None, left.freq + right.freq)
+            merged.left = left
+            merged.right = right
+            heapq.heappush(heap, merged)
+
+        # The root of the Huffman tree
+        self.root = heapq.heappop(heap)
+        self.codebook = self._generate_codes(self.root)