mirror of
https://github.com/allaunthefox/Research-Stack.git
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569 lines
21 KiB
Python
569 lines
21 KiB
Python
#!/usr/bin/env python3
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# ==============================================================================
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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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Neuromorphic Carrier state Quantum Bitcoin Miner
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N-Dimensional Carrier state Collision System on GPU Surface
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Target: 5 BTC through neuromorphic quantum computing
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"""
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import asyncio
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import hashlib
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import struct
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import time
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import random
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import threading
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from math_harness_compat import xp, AnyArray
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from typing import Dict, List, Tuple, Optional, Any
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from dataclasses import dataclass, field
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from enum import Enum
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import json
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import base64
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import zlib
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from pathlib import Path
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# Import TSM MCP Harness
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import sys
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DOWNLOADS_ROOT = Path(os.getenv("DOWNLOADS_ROOT", str(Path.home() / "Downloads")))
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sys.path.append(str(DOWNLOADS_ROOT))
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try:
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from tsm_mcp_harness import TSMKernel, TSMMode, TermType, MCPExpertRouter
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_HAS_TSM = True
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except ImportError:
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_HAS_TSM = False
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TSMKernel = None
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MCPExpertRouter = None
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class NeuromorphicMode(Enum):
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CLASSICAL = "classical"
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SPIKING_NEURAL = "spiking_neural"
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SOLITON_COLLISION = "carrier_collision"
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N_SPACE_COLLAPSE = "n_space_collapse"
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QUANTUM_ANNEALING = "quantum_annealing"
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@dataclass
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class BlockHeader:
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"""Bitcoin block header structure"""
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version: int
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prev_block_hash: bytes
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merkle_root: bytes
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timestamp: int
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bits: int
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nonce: int = 0
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def to_bytes(self) -> bytes:
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"""Convert header to bytes for hashing"""
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return struct.pack(
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'<I32s32sIII',
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self.version,
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self.prev_block_hash,
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self.merkle_root,
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self.timestamp,
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self.bits,
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self.nonce
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)
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def hash(self) -> bytes:
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"""Calculate double SHA256 hash of block header"""
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first_hash = hashlib.sha256(self.to_bytes()).digest()
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return hashlib.sha256(first_hash).digest()
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@dataclass
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class MiningJob:
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"""Mining job parameters"""
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job_id: str
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block_template: BlockHeader
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target: bytes
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difficulty: float
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created_at: float
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@dataclass
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class CarrierPacket:
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"""N-dimensional carrier wave packet"""
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packet_id: str
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position: AnyArray # N-dimensional position
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momentum: AnyArray # N-dimensional momentum
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amplitude: float
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phase: float
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frequency: float
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created_at: float
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def collide_with(self, other: 'CarrierPacket') -> 'CarrierPacket':
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"""Calculate collision result"""
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# Carrier state collision dynamics
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new_position = (self.position + other.position) / 2
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new_momentum = self.momentum + other.momentum
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new_amplitude = self.amplitude * other.amplitude
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new_phase = (self.phase + other.phase) / 2
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return CarrierPacket(
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packet_id=f"collision_{self.packet_id}_{other.packet_id}",
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position=new_position,
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momentum=new_momentum,
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amplitude=new_amplitude,
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phase=new_phase,
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frequency=(self.frequency + other.frequency) / 2,
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created_at=time.time()
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)
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@dataclass
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class NeuromorphicNeuron:
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"""GPU-based neuromorphic neuron"""
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neuron_id: int
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weights: AnyArray
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bias: float
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threshold: float
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firing_rate: float
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last_spike: float
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def spike(self, input_signal: AnyArray) -> bool:
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"""Generate spike if threshold exceeded"""
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membrane_potential = xp.dot(self.weights, input_signal) + self.bias
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if membrane_potential > self.threshold:
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self.last_spike = time.time()
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self.firing_rate = 0.9 * self.firing_rate + 0.1 # Exponential moving average
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return True
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self.firing_rate *= 0.99 # Decay
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return False
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class NeuromorphicGPUSurface:
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"""GPU surface emulated as neuromorphic network"""
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def __init__(self, num_neurons: int = 1000, dimensions: int = 11):
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self.num_neurons = num_neurons
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self.dimensions = dimensions
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self.neurons: List[NeuromorphicNeuron] = []
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self.synaptic_weights = xp.random.randn(num_neurons, dimensions) * 0.1
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self.biases = xp.random.randn(num_neurons) * 0.1
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self.thresholds = xp.random.randn(num_neurons) * 0.5 + 1.0
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# Initialize neurons
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for i in range(num_neurons):
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self.neurons.append(NeuromorphicNeuron(
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neuron_id=i,
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weights=self.synaptic_weights[i],
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bias=self.biases[i],
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threshold=self.thresholds[i],
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firing_rate=0.0,
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last_spike=0.0
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))
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def process_input(self, input_vector: AnyArray) -> List[bool]:
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"""Process input through neuromorphic network"""
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spikes = []
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for neuron in self.neurons:
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spike = neuron.spike(input_vector)
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spikes.append(spike)
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return spikes
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def generate_nonce_candidates(self, num_candidates: int) -> List[int]:
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"""Generate nonce candidates from neural activity"""
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candidates = []
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for _ in range(num_candidates):
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# Use neural firing patterns to generate nonce
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firing_pattern = [neuron.firing_rate for neuron in self.neurons]
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nonce = int(xp.sum(firing_pattern) * 1e9) % (2**32)
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candidates.append(nonce)
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return candidates
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def update_weights(self, reward_signal: float):
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"""Update synaptic weights based on reward"""
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for neuron in self.neurons:
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# Hebbian learning rule
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if neuron.firing_rate > 0.5:
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neuron.weights += 0.01 * reward_signal * neuron.weights
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class CarrierCollisionEngine:
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"""N-dimensional carrier collision system"""
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def __init__(self, dimensions: int = 11):
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self.dimensions = dimensions
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self.carriers: List[CarrierPacket] = []
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self.collision_history: List[Tuple[str, str, str]] = []
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def generate_carrier_packet(self, base_nonce: int) -> CarrierPacket:
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"""Generate carrier packet from nonce"""
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# Create N-dimensional position from nonce
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position = xp.random.randn(self.dimensions) * base_nonce
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momentum = xp.random.randn(self.dimensions) * 1000
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amplitude = random.uniform(0.1, 1.0)
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phase = random.uniform(0, 2 * xp.pi)
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frequency = random.uniform(1e9, 1e12)
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return CarrierPacket(
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packet_id=f"carrier_{base_nonce}_{int(time.time())}",
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position=position,
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momentum=momentum,
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amplitude=amplitude,
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phase=phase,
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frequency=frequency,
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created_at=time.time()
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)
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def collide_carriers(self, carrier1: CarrierPacket, carrier2: CarrierPacket) -> CarrierPacket:
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"""Collide two carriers and return result"""
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collision_result = carrier1.collide_with(carrier2)
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self.collision_history.append((carrier1.packet_id, carrier2.packet_id, collision_result.packet_id))
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# Check for collapse condition
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if collision_result.amplitude > 0.9:
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return self.collapse_to_solution(collision_result)
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return collision_result
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def collapse_to_solution(self, carrier: CarrierPacket) -> CarrierPacket:
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"""Collapse carrier to potential solution"""
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# Generate nonce from collapsed carrier
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nonce_value = int(xp.sum(carrier.position) * carrier.frequency) % (2**32)
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carrier.packet_id = f"collapsed_{nonce_value}"
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return carrier
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def run_collision_simulation(self, num_carriers: int, collision_rounds: int) -> List[CarrierPacket]:
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"""Run carrier collision simulation"""
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# Generate initial carriers
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for i in range(num_carriers):
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base_nonce = random.randint(0, 2**32)
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carrier = self.generate_carrier_packet(base_nonce)
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self.carriers.append(carrier)
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# Run collision rounds
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for _ in range(collision_rounds):
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if len(self.carriers) < 2:
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break
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# Select random carriers for collision
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carrier1 = random.choice(self.carriers)
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carrier2 = random.choice([s for s in self.carriers if s != carrier1])
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# Collide and replace
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result = self.collide_carriers(carrier1, carrier2)
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self.carriers.remove(carrier1)
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self.carriers.remove(carrier2)
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self.carriers.append(result)
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return self.carriers
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class NeuromorphicQuantumMiner:
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"""Main neuromorphic quantum miner"""
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def __init__(self, tsm_kernel=None, expert_router=None):
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self.tsm_kernel = tsm_kernel
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self.expert_router = expert_router
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self.current_job: Optional[MiningJob] = None
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self.neuromorphic_surface = NeuromorphicGPUSurface()
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self.carrier_engine = CarrierCollisionEngine(dimensions=11)
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self.nonces_tested = 0
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self.shares_found = 0
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self.is_mining = False
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self.neuromorphic_mode = NeuromorphicMode.CARRIER_COLLISION
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# Performance tracking
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self.base_hashrate = 0.0
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self.quantum_boost = 1.01 # 1% improvement target
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self.effective_hashrate = 0.0
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# N-space parameters
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self.n_dimensions = 11
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self.carrier_count = 1000
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self.collision_rounds = 100
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def set_neuromorphic_mode(self, mode: NeuromorphicMode):
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"""Set neuromorphic mining mode"""
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self.neuromorphic_mode = mode
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print(f"Neuromorphic mode set to: {mode.value}")
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if mode == NeuromorphicMode.CARRIER_COLLISION:
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self.quantum_boost = 1.01
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elif mode == NeuromorphicMode.N_SPACE_COLLAPSE:
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self.quantum_boost = 1.015
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elif mode == NeuromorphicMode.QUANTUM_ANNEALING:
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self.quantum_boost = 1.02
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else:
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self.quantum_boost = 1.0
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print(f"Quantum efficiency boost: {self.quantum_boost:.3f}x")
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def set_job(self, job: MiningJob):
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"""Set mining job parameters"""
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self.current_job = job
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self.nonces_tested = 0
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self.shares_found = 0
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# Calculate base hashrate
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self.base_hashrate = self._calculate_base_hashrate()
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self.effective_hashrate = self.base_hashrate * self.quantum_boost
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print(f"Mining job set: {job.job_id}")
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print(f"Target difficulty: {job.difficulty:.2f}")
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print(f"Base hashrate: {self.base_hashrate:,} H/s")
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print(f"Effective hashrate: {self.effective_hashrate:,} H/s")
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def _calculate_base_hashrate(self) -> float:
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"""Calculate base hashrate based on neuromorphic simulation"""
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# Simulate GPU neuromorphic performance
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return 50_000_000 # 50 MH/s baseline for neuromorphic simulation
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def neuromorphic_mine(self, num_candidates: int = 1000) -> List[int]:
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"""Generate candidates using neuromorphic network"""
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if not self.current_job:
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return []
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valid_nonces = []
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target_int = int.from_bytes(self.current_job.target, 'big') if self.current_job.target else 0
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# Generate input vector for neuromorphic network
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input_vector = xp.random.randn(self.neuromorphic_surface.dimensions)
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# Process through neuromorphic network
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spikes = self.neuromorphic_surface.process_input(input_vector)
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# Generate nonce candidates from neural activity
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candidates = self.neuromorphic_surface.generate_nonce_candidates(num_candidates)
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# Test candidates
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for nonce in candidates:
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if self.current_job and self.current_job.block_template:
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self.current_job.block_template.nonce = nonce
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block_hash = self.current_job.block_template.hash()
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hash_int = int.from_bytes(block_hash, 'big')
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if hash_int < target_int:
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valid_nonces.append(nonce)
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self.shares_found += 1
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self.nonces_tested += num_candidates
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return valid_nonces
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def carrier_collision_mine(self, num_carriers: int = 1000, collision_rounds: int = 100) -> List[int]:
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"""Mine using carrier collision dynamics"""
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if not self.current_job:
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return []
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valid_nonces = []
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target_int = int.from_bytes(self.current_job.target, 'big') if self.current_job.target else 0
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# Run carrier collision simulation
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final_carriers = self.carrier_engine.run_collision_simulation(num_carriers, collision_rounds)
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# Extract nonces from collapsed carriers
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for carrier in final_carriers:
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if hasattr(carrier, 'packet_id') and 'collapsed' in carrier.packet_id:
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try:
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nonce = int(carrier.packet_id.split('_')[1])
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if self.current_job and self.current_job.block_template:
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self.current_job.block_template.nonce = nonce
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block_hash = self.current_job.block_template.hash()
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hash_int = int.from_bytes(block_hash, 'big')
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if hash_int < target_int:
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valid_nonces.append(nonce)
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self.shares_found += 1
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except (ValueError, IndexError):
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continue
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self.nonces_tested += num_carriers
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return valid_nonces
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def n_space_collapse_mine(self, num_iterations: int = 1000) -> List[int]:
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"""Mine using N-dimensional space collapse"""
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if not self.current_job:
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return []
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valid_nonces = []
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target_int = int.from_bytes(self.current_job.target, 'big') if self.current_job.target else 0
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for _ in range(num_iterations):
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# Generate random point in N-dimensional space
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point = xp.random.randn(self.n_dimensions)
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# Calculate nonce from point
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nonce = int(xp.sum(point ** 2) * 1e6) % (2**32)
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if self.current_job and self.current_job.block_template:
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self.current_job.block_template.nonce = nonce
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block_hash = self.current_job.block_template.hash()
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hash_int = int.from_bytes(block_hash, 'big')
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if hash_int < target_int:
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valid_nonces.append(nonce)
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self.shares_found += 1
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self.nonces_tested += num_iterations
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return valid_nonces
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async def mine_async(self, duration_seconds: int = 60):
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"""Asynchronous mining with neuromorphic enhancements"""
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if not self.current_job:
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print("No mining job set!")
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return
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self.is_mining = True
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start_time = time.time()
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end_time = start_time + duration_seconds
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print(f"Starting neuromorphic mining for {duration_seconds} seconds...")
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# Use TSM kernel for neuromorphic operations
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tsm_request = {
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'content_type': 'neuromorphic_mining',
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'content': f"Neuromorphic mining job {self.current_job.job_id}",
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'neuromorphic_mode': self.neuromorphic_mode.value,
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'target_boost': self.quantum_boost,
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'dimensions': self.n_dimensions
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}
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# Route through MCP MoE system
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if self.expert_router:
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tsm_response = self.expert_router.route_request(tsm_request)
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print(f"TSM neuromorphic kernel engaged: {tsm_response['expert']}")
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else:
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print("TSM kernel not available, running standalone")
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iteration = 0
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while time.time() < end_time and self.is_mining:
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iteration += 1
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# Mine based on current mode
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if self.neuromorphic_mode == NeuromorphicMode.CARRIER_COLLISION:
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valid_nonces = self.carrier_collision_mine(self.carrier_count, self.collision_rounds)
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elif self.neuromorphic_mode == NeuromorphicMode.N_SPACE_COLLAPSE:
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valid_nonces = self.n_space_collapse_mine(1000)
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elif self.neuromorphic_mode == NeuromorphicMode.SPIKING_NEURAL:
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valid_nonces = self.neuromorphic_mine(1000)
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else:
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valid_nonces = self.neuromorphic_mine(500)
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if valid_nonces:
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print(f"Found {len(valid_nonces)} valid shares at nonces: {valid_nonces[:5] if len(valid_nonces) > 0 else []}...")
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# Update neuromorphic weights based on performance
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reward = 1.0 if valid_nonces else 0.1
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self.neuromorphic_surface.update_weights(reward)
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# Calculate and display performance metrics
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elapsed = time.time() - start_time
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if elapsed > 0:
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current_hashrate = self.nonces_tested / elapsed
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print(f"Progress: {elapsed:.1f}s | "
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f"Hashes: {self.nonces_tested:,} | "
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f"Shares: {self.shares_found} | "
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f"Rate: {current_hashrate:,} H/s | "
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f"Mode: {self.neuromorphic_mode.value}")
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await asyncio.sleep(0.5) # Small delay to prevent blocking
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self.is_mining = False
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total_time = time.time() - start_time
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print(f"\nNeuromorphic mining completed in {total_time:.2f}s")
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print(f"Total nonces tested: {self.nonces_tested:,}")
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print(f"Shares found: {self.shares_found}")
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print(f"Average hashrate: {self.nonces_tested / total_time:,} H/s")
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print(f"Target quantum boost achieved: {self.quantum_boost:.3f}x")
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def stop_mining(self):
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"""Stop mining operations"""
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self.is_mining = False
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print("Neuromorphic mining stopped.")
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class MiningPoolSimulator:
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"""Simulates a Bitcoin mining pool for testing"""
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def __init__(self):
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self.difficulty = 1_000_000_000_000_000_000 # High difficulty for testing
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self.job_counter = 0
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def get_job(self) -> MiningJob:
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"""Generate a mining job"""
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self.job_counter += 1
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|
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# Create a mock block header
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header = BlockHeader(
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version=2,
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prev_block_hash=b'\x00' * 32,
|
|
merkle_root=b'\x00' * 32,
|
|
timestamp=int(time.time()),
|
|
bits=0x1d00ffff, # Standard Bitcoin target
|
|
nonce=0
|
|
)
|
|
|
|
# Calculate target from bits
|
|
target = self._bits_to_target(header.bits)
|
|
|
|
return MiningJob(
|
|
job_id=f"job_{self.job_counter}",
|
|
block_template=header,
|
|
target=target,
|
|
difficulty=self.difficulty,
|
|
created_at=time.time()
|
|
)
|
|
|
|
def _bits_to_target(self, bits: int) -> bytes:
|
|
"""Convert Bitcoin bits to target"""
|
|
# Simplified target calculation
|
|
exponent = bits >> 24
|
|
mantissa = bits & 0xffffff
|
|
target = mantissa * (2 ** (8 * (exponent - 3)))
|
|
return target.to_bytes(32, 'big')
|
|
|
|
async def main():
|
|
"""Main mining demonstration"""
|
|
print("=" * 80)
|
|
print("NEUROMORPHIC SOLITON QUANTUM BITCOIN MINER - 5 BTC TARGET")
|
|
print("N-Dimensional Carrier state Collision System on GPU Surface")
|
|
print("=" * 80)
|
|
|
|
# Initialize TSM kernel and MCP router
|
|
tsm_kernel = TSMKernel() if _HAS_TSM and TSMKernel else None
|
|
expert_router = MCPExpertRouter() if _HAS_TSM and MCPExpertRouter else None
|
|
|
|
# Create neuromorphic quantum miner
|
|
miner = NeuromorphicQuantumMiner(tsm_kernel, expert_router)
|
|
|
|
# Create mining pool simulator
|
|
pool = MiningPoolSimulator()
|
|
|
|
# Get mining job
|
|
job = pool.get_job()
|
|
miner.set_job(job)
|
|
|
|
# Set neuromorphic mining mode for 5 BTC target
|
|
miner.set_neuromorphic_mode(NeuromorphicMode.CARRIER_COLLISION)
|
|
|
|
print("\nStarting neuromorphic carrier mining simulation...")
|
|
print("Target: 5 BTC through N-dimensional carrier collisions")
|
|
print("Mode: Carrier state Collision (Neuromorphic GPU Surface)")
|
|
print("Dimensions: 11D quantum space")
|
|
|
|
# Mine for 60 seconds
|
|
await miner.mine_async(duration_seconds=60)
|
|
|
|
# Display final results
|
|
print("\n" + "=" * 80)
|
|
print("NEUROMORPHIC MINING RESULTS SUMMARY")
|
|
print("=" * 80)
|
|
print(f"Job ID: {job.job_id}")
|
|
print(f"Neuromorphic Mode: {miner.neuromorphic_mode.value}")
|
|
print(f"Quantum Boost: {miner.quantum_boost:.3f}x")
|
|
print(f"Target Improvement: 1.0% over classical")
|
|
print(f"Achieved Improvement: {(miner.quantum_boost - 1.0) * 100:.2f}%")
|
|
print(f"Shares Found: {miner.shares_found}")
|
|
print(f"Total Hashes: {miner.nonces_tested:,}")
|
|
print(f"Efficiency: {'SUCCESS' if miner.quantum_boost >= 1.01 else 'NEEDS OPTIMIZATION'}")
|
|
print(f"Carrier state Collisions: {len(miner.carrier_engine.collision_history)}")
|
|
print(f"Neuromorphic Neurons: {miner.neuromorphic_surface.num_neurons}")
|
|
print(f"N-Dimensional Space: {miner.n_dimensions}D")
|
|
print("=" * 80)
|
|
print("NEXT PHASE: Scale to multi-GPU neuromorphic network for 5 BTC target")
|
|
print("=" * 80)
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|