Upload model.py with huggingface_hub

model.py

@@ -0,0 +1,125 @@
+import json
+import numpy as np
+import copy
+import time
+
+class AlgebraicEquation:
+    def __init__(self):
+        self.strength = 1.0
+        self.usage_count = 0
+        self.last_used_step = 0
+
+    def fit(self, x, y):
+        pass
+
+    def update_parameters(self, x, y):
+        pass
+
+    def predict(self, x):
+        pass
+
+    def get_error(self, x, y):
+        prediction = self.predict(x)
+        return abs(prediction - y) if prediction is not None else float('inf')
+
+    def copy(self):
+        return copy.deepcopy(self)
+
+class LinearEquation(AlgebraicEquation):
+    def __init__(self):
+        super().__init__()
+        self.c = 0.0
+    def fit(self, x, y): self.c = y - x
+    def update_parameters(self, x, y): self.c = y - x
+    def predict(self, x): return x + self.c
+
+class MultiplicativeEquation(AlgebraicEquation):
+    def __init__(self):
+        super().__init__()
+        self.a = 1.0
+    def fit(self, x, y): self.a = y / x if x != 0 else 0.0
+    def update_parameters(self, x, y): self.a = y / x if x != 0 else 0.0
+    def predict(self, x): return self.a * x
+
+class QuadraticEquation(AlgebraicEquation):
+    def __init__(self):
+        super().__init__()
+        self.c = 0.0
+    def fit(self, x, y): self.c = y - (x ** 2)
+    def update_parameters(self, x, y): self.c = y - (x ** 2)
+    def predict(self, x): return (x ** 2) + self.c
+
+class NextTokenSystem:
+    def __init__(self, vocabulary):
+        self.vocabulary = vocabulary
+        self.token_to_score = {token: float(i + 1) for i, token in enumerate(vocabulary)}
+        self.score_to_token = {score: token for token, score in self.token_to_score.items()}
+        self.relationship_table = []
+        self.candidate_equations = []
+        self.timestep = 0
+
+    def get_score(self, token): return self.token_to_score.get(token)
+
+    def add_relationship(self, token_x, token_y):
+        sx, sy = self.get_score(token_x), self.get_score(token_y)
+        if sx is not None and sy is not None: self.relationship_table.append((sx, sy))
+
+    def generate_candidate_rules(self):
+        self.candidate_equations = []
+        for x, y in self.relationship_table:
+            for Cls in [LinearEquation, MultiplicativeEquation, QuadraticEquation]:
+                eq = Cls(); eq.fit(x, y); self.candidate_equations.append(eq)
+
+class ConflictResolutionEngine(NextTokenSystem):
+    def __init__(self, vocabulary, decay_factor=0.9, reuse_bonus=0.2, inhibitory_penalty=10.0):
+        super().__init__(vocabulary)
+        self.decay_factor, self.reuse_bonus, self.inhibitory_penalty = decay_factor, reuse_bonus, inhibitory_penalty
+        self.current_step, self.last_selected_eq, self.local_anchors = 0, None, {}
+        self.reinforcement_bonus = 5.0
+
+    def calculate_dynamic_threshold(self, percentage=0.0001): return max(1.0, len(self.vocabulary) * percentage)
+
+    def register_local_anchor(self, token, equation): self.local_anchors[token] = equation.copy()
+
+    def refined_back_search(self, input_score, target_score, threshold=None):
+        if threshold is None: threshold = self.calculate_dynamic_threshold()
+        best_eq, min_diff = None, float('inf')
+        for eq in self.candidate_equations:
+            diff = abs(eq.predict(input_score) - target_score)
+            if diff <= threshold and diff < min_diff:
+                min_diff, best_eq = diff, eq
+        return best_eq
+
+    def select_governing_equation(self, current_token=None):
+        if current_token in self.local_anchors: return self.local_anchors[current_token]
+        self.current_step += 1
+        weights = []
+        for i, eq in enumerate(self.candidate_equations):
+            if eq.last_used_step < self.current_step - 1: eq.strength *= self.decay_factor
+            w = eq.strength + (self.relationship_table[i//3][0] + self.relationship_table[i//3][1]) / (2 * len(self.vocabulary))
+            weights.append(max(0, w))
+        best_eq = self.candidate_equations[np.argmax(weights)]
+        self.last_selected_eq = best_eq
+        return best_eq
+
+    def predict_next_token(self, current_token):
+        score = self.get_score(current_token)
+        eq = self.select_governing_equation(current_token)
+        target_y = eq.predict(score)
+        best_token = self.score_to_token[min(self.score_to_token.keys(), key=lambda k: abs(k - target_y))]
+        return {'predicted_token': best_token, 'governing_equation': type(eq).__name__}
+
+    def penalized_supervised_train(self, data_path, iterations=25, threshold=None):
+        with open(data_path, 'r') as f: examples = json.load(f)['training_examples']
+        if threshold is None: threshold = self.calculate_dynamic_threshold()
+        for i in range(iterations):
+            for ex in examples:
+                res = self.predict_next_token(ex['input'])
+                if res['predicted_token'] != ex['target']:
+                    self.last_selected_eq.strength = max(0, self.last_selected_eq.strength - self.inhibitory_penalty)
+                    best = self.refined_back_search(self.get_score(ex['input']), self.get_score(ex['target']), threshold)
+                    if best:
+                        opt = best.copy(); opt.update_parameters(self.get_score(ex['input']), self.get_score(ex['target']))
+                        self.register_local_anchor(ex['input'], opt)
+
+print('model.py has been successfully written to the current directory.')