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115 lines
4.4 KiB
Python
115 lines
4.4 KiB
Python
# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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"""
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Generate TSM_COMPILER.py — a softmax-based routing compiler.
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The routing mechanism uses log-normalized inputs through a random weight
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matrix with softmax activation to produce a scalar adjustment factor.
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Judge, Builder, and Warden are real operational roles with translation metrics.
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"""
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text = """# TSM Compiler
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# Uses softmax-based expert routing with log-normalized inputs
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from math_harness_compat import xp, AnyArray
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import json
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class TSM_State:
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\"\"\"Single-frequency state tracker.\"\"\"
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def __init__(self, base_hz):
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self.base_hz = base_hz
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self.observed_hz = None
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def observe(self, observer_frequency):
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self.observed_hz = observer_frequency
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return self.observed_hz
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class MoE_Router:
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\"\"\"Softmax-based expert router with log-normalized inputs.\"\"\"
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def __init__(self):
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print("[MoE] Initializing expert routing matrix...")
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# Judge, Builder, Warden have real translation metrics.
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# The rest are functional routing buckets.
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self.experts = {
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"JUDGE": "Decision Quality Evaluator",
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"BUILDER": "Code Generation Specialist",
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"WARDEN": "Integrity / Metrics Monitor",
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"FAST": "Low-Latency Response",
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"DEEP": "Long-Form Reasoning",
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"SUMMARY": "Compression / Summarization",
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}
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self.routing_matrix = xp.random.uniform(0.9, 1.1, (len(self.experts), len(self.experts)))
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def _route(self, tensor_data) -> float:
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tensor_array = xp.array(tensor_data, dtype=float)
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# Normalize to prevent overflow on large inputs
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safe_tensor = xp.log1p(xp.abs(tensor_array)) * xp.sign(tensor_array)
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padded = xp.pad(safe_tensor, (0, max(0, len(self.experts) - len(safe_tensor))), 'constant')[:len(self.experts)]
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activation = xp.dot(self.routing_matrix, padded)
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entropy = xp.sum(activation * xp.log(xp.maximum(activation, 1e-9)))
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std_dev = xp.std(activation)
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exp_act = xp.exp(activation - xp.max(activation))
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weights = exp_act / xp.sum(exp_act)
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dominant_idx = xp.argmax(weights)
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dominant_expert = list(self.experts.values())[dominant_idx]
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print(f"[MoE] Routed across {len(self.experts)} expert domains.")
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print(f"[MoE] Dominant: {dominant_expert} (Confidence: {weights[dominant_idx]*100:.2f}%)")
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gated = 1.0 + (xp.mean(weights) * (std_dev / max(abs(entropy), 1)))
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final = max(0.1, min(10.0, gated))
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print(f"[MoE] Routing adjustment factor: {final:.6f}")
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return final
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def forward_pass(self, tensor: list) -> float:
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return self._route(tensor)
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class TSM_Compiler:
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def __init__(self):
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print("[TSM] Compiler initialized.")
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self.moe_router = MoE_Router()
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def compile(self, tensor: list) -> float:
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print("[TSM] Routing tensor through expert matrix...")
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adjustment = self.moe_router.forward_pass(tensor)
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result = sum([TSM_State(hz).observe(hz * adjustment) for hz in tensor])
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print(f"[TSM] Output: {result:.2e} Hz")
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return result
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if __name__ == "__main__":
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compiler = TSM_Compiler()
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print("\\n=== ROUTING MODELS THROUGH EXPERT MATRIX ===")
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models = {
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"Bikini Atoll (Radiation Scrub + Coral Templating)": [250000.0, 412000.0, 77000.0, 108000.0],
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"Abyssal Geothermal Drill (Mantle Tap)": [38000.0, 8500.0, 2400000.0],
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"Atmospheric CO2 Scrubbing Array": [420.0, 850.0, 1200.0, 4.0],
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}
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for name, tensor in models.items():
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print(f"\\n--- Model: {name} ---")
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compiler.compile(tensor)
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print("\\n=== ROUTING CONSTANTS THROUGH EXPERT MATRIX ===")
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constants = {
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"Golden ratio + mathematical constants": [1.618033, 2.718281, 3.141592, 0.000000],
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"Physical constants (c, h, hbar, me)": [299792458.0, 6.626e-34, 1.054e-34, 9.109e-31],
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}
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for name, tensor in constants.items():
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print(f"\\n--- {name} ---")
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compiler.compile(tensor)
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"""
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with open("TSM_COMPILER.py", "w") as f:
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f.write(text)
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