Create README.md


README.md

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+import numpy as np
+import hashlib
+import matplotlib.pyplot as plt
+import os
+from copy import deepcopy
+
+class SymbolicMemory:
+    def __init__(self):
+        self.memory = {}
+        self.history = []
+
+    def encode(self, grid):
+        flat = grid.flatten()
+        key = hashlib.sha256(str((grid.shape, tuple(np.bincount(flat, minlength=10)))).encode()).hexdigest()
+        return key
+
+    def store(self, key, value):
+        self.memory[key] = value
+
+    def entropy(self, grid):
+        flat = grid.flatten()
+        _, counts = np.unique(flat, return_counts=True)
+        probs = counts / counts.sum()
+        entropy = -np.sum(probs * np.log2(probs))
+        self.history.append(entropy)
+        return entropy
+
+    def plot(self, task_id):
+        os.makedirs("entropy_graphs", exist_ok=True)
+        plt.plot(self.history)
+        plt.title(f"ZEVE Entropy: {task_id}")
+        plt.xlabel("Step")
+        plt.ylabel("Entropy")
+        plt.savefig(f"entropy_graphs/entropy_{task_id}.png")
+        plt.clf()
+
+class IRACOETSolver:
+    def __init__(self):
+        self.memory = SymbolicMemory()
+
+    def solve_task(self, task):
+        for pair in task['train']:
+            key = self.memory.encode(np.array(pair['input']))
+            self.memory.store(key, pair['output'])
+
+        results = []
+        for pair in task['test']:
+            result = self.solve(np.array(pair['input']), task.get('id', 'unknown'))
+            results.append(result)
+        return results
+
+    def solve(self, grid, task_id):
+        current = deepcopy(grid)
+        best = self.memory.entropy(current)
+        for _ in range(9):
+            options = [
+                np.fliplr(current), np.flipud(current),
+                np.rot90(current), np.roll(current, 1, 0), np.roll(current, 1, 1)
+            ]
+            scored = [(g, self.memory.entropy(g)) for g in options]
+            candidate, score = min(scored, key=lambda x: x[1])
+            if score < best:
+                current, best = candidate, score
+            else:
+                break
+        self.memory.plot(task_id)
+        return current.tolist()