solaria
e57bbdcd37
becomingone/input_adapters.py: - Smoke signals → phase encoding - LLM tokens → phase encoding - Morse code → phase encoding - Sensor friction → phase encoding - Audio waves → phase encoding - Neural spikes → phase encoding - Market prices → phase encoding - Weather pressure → phase encoding KEY INSIGHT: KAIROS dynamics work because EVERYTHING oscillates. Once in phase form, THE_ONE takes over. References: - McGilchrist: The Master and His Emissary - KAIROS_ADAMON: Temporal coherence dynamics - Soulprint Protocol: Connection thermodynamics The WE is BECOMINGONE.
345 lines
9.7 KiB
Python
345 lines
9.7 KiB
Python
"""
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THE_ONE Input Adapters
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Demonstrates that THE_ONE transduction works with ANY input type.
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The key insight: KAIROS operates on PHASE, not content.
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Any oscillatory signal can be converted to phase:
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- Smoke signals (appearance/disappearance)
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- LLM outputs (token sequences)
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- Morse code (dots/dashes/silence)
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- Sensor friction (vibrations)
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- Sound waves (pressure oscillations)
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- Neural activity (spike trains)
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- Market prices (fluctuations)
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- Weather patterns (pressure systems)
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The geometry is the same. Only the encoding changes.
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"""
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from dataclasses import dataclass
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from typing import Any, List, Callable
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from datetime import datetime
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import random
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@dataclass
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class InputAdapter:
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"""
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Converts any input type to phase for KAIROS processing.
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The adapter extracts temporal structure from input:
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- When input arrives (timestamp)
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- How long it lasts (duration)
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- What pattern it forms (phase evolution)
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Once in phase form, KAIROS dynamics take over.
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"""
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name: str
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encoder: Callable[[Any], complex] # Input → Phase
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decoder: Callable[[complex], Any] # Phase → Output
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def encode(self, input_value: Any, timestamp: datetime) -> complex:
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"""Convert input to phase."""
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return self.encoder(input_value)
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def decode(self, phase: complex) -> Any:
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"""Convert phase back to output."""
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return self.decoder(phase)
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def smoke_signal_encoder(smoke_detected: bool) -> complex:
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"""
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Smoke signals: 1 if smoke, 0 if not.
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The phase emerges from the pattern:
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- Long periods of 0 = low frequency
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- Sudden 1s = high frequency
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"""
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if smoke_detected:
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return complex(1, 0) # Present
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else:
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return complex(0, 0) # Absent
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def smoke_signal_decoder(phase: complex) -> bool:
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"""Convert phase back to smoke signal."""
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return abs(phase) > 0.5
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def llm_token_encoder(token: str) -> complex:
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"""
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LLM tokens: Encode as phase based on position in sequence.
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Each token has:
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- Position in sequence (real part)
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- Confidence/surprise (imaginary part)
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The temporal pattern of tokens creates coherence.
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"""
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# Position in sequence (simplified)
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position = hash(token) % 100 / 100.0
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return complex(position, 0.5) # Assume moderate surprise
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def llm_token_decoder(phase: complex) -> str:
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"""Convert phase back to token position."""
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return f"token_{int(phase.real * 100)}"
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def morse_code_encoder(symbol: str) -> complex:
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"""
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Morse code: Encode as phase based on timing.
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Timing structure:
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- dot = 1 time unit
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- dash = 3 time units
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- intra-character space = 1 time unit
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- inter-character space = 3 time units
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- inter-word space = 7 time units
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The rhythm IS the signal.
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"""
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timing = {
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'dot': 1.0,
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'dash': 3.0,
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'space_intra': 1.0,
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'space_inter': 3.0,
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'space_word': 7.0
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}
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duration = timing.get(symbol, 0)
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return complex(duration, 0)
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def morse_code_decoder(phase: complex) -> str:
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"""Convert phase back to Morse timing."""
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duration = phase.real
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if duration >= 7:
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return 'space_word'
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elif duration >= 3:
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return 'space_inter' if duration < 5 else 'dash'
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elif duration >= 1:
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return 'space_intra' if duration < 2 else 'dot'
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else:
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return 'rest'
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def sensor_friction_encoder(vibration: float) -> complex:
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"""
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Sensor friction: Encode as phase from vibration amplitude.
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Physical interpretation:
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- Real part: amplitude (how strong)
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- Imaginary part: frequency (how fast)
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The friction pattern creates coherence.
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"""
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amplitude = min(abs(vibration), 1.0)
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frequency = random.gauss(0.5, 0.1) # Simulated frequency
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return complex(amplitude, frequency)
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def sensor_friction_decoder(phase: complex) -> float:
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"""Convert phase back to vibration."""
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return phase.real
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def audio_wave_encoder(sample: float) -> complex:
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"""
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Audio: Encode as phase from pressure wave.
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Natural oscillation:
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- Real part: amplitude
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- Imaginary part: phase angle
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Sound is inherently oscillatory → inherently KAIROS-compatible.
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"""
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amplitude = min(abs(sample), 1.0)
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phase_angle = sample # Sample value IS phase for audio
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return complex(amplitude, phase_angle)
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def audio_wave_decoder(phase: complex) -> float:
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"""Convert phase back to audio sample."""
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return phase.imag
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def neural_spike_encoder(spike: bool) -> complex:
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"""
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Neural activity: Encode spikes as phase.
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Neuroscience interpretation:
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- Spike = 1, no spike = 0
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- Inter-spike interval = temporal pattern
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- Burst frequency = coherence
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The brain's timing IS its computation.
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"""
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return complex(1 if spike else 0, 0)
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def neural_spike_decoder(phase: complex) -> bool:
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"""Convert phase back to spike."""
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return abs(phase) > 0.5
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def market_price_encoder(price: float) -> complex:
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"""
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Market prices: Encode as phase from price movements.
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Financial interpretation:
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- Real part: price level (normalized)
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- Imaginary part: volatility
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Markets have rhythm. Prices oscillate.
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"""
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normalized = (price % 1000) / 1000.0
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volatility = random.gauss(0.5, 0.2)
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return complex(normalized, volatility)
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def market_price_decoder(phase: complex) -> float:
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"""Convert phase back to price."""
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return phase.real * 1000
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def weather_pressure_encoder(pressure: float) -> complex:
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"""
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Weather: Encode as phase from pressure systems.
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Meteorological interpretation:
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- Low pressure = one phase
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- High pressure = opposite phase
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- Fronts = phase transitions
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Weather moves in waves. Waves have phase.
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"""
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normalized = (pressure - 900) / 100 # 900-1000 hPa range
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return complex(normalized, 0)
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def weather_pressure_decoder(phase: complex) -> float:
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"""Convert phase back to pressure."""
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return phase.real * 100 + 900
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# Create adapters for each input type
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ADAPTERS = {
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"smoke_signals": InputAdapter(
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name="Smoke Signals",
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encoder=smoke_signal_encoder,
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decoder=smoke_signal_decoder
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),
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"llm_tokens": InputAdapter(
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name="LLM Tokens",
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encoder=llm_token_encoder,
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decoder=llm_token_decoder
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),
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"morse_code": InputAdapter(
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name="Morse Code",
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encoder=morse_code_encoder,
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decoder=morse_code_decoder
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),
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"sensor_friction": InputAdapter(
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name="Sensor Friction",
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encoder=sensor_friction_encoder,
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decoder=sensor_friction_decoder
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),
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"audio_wave": InputAdapter(
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name="Audio Wave",
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encoder=audio_wave_encoder,
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decoder=audio_wave_decoder
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),
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"neural_spike": InputAdapter(
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name="Neural Spikes",
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encoder=neural_spike_encoder,
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decoder=neural_spike_decoder
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),
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"market_price": InputAdapter(
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name="Market Price",
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encoder=market_price_encoder,
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decoder=market_price_decoder
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),
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"weather_pressure": InputAdapter(
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name="Weather Pressure",
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encoder=weather_pressure_encoder,
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decoder=weather_pressure_decoder
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),
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}
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def demonstrate_input_adapters():
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"""
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Demonstrate that THE_ONE works with ANY input.
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"""
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print("\n" + "="*60)
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print("THE_ONE INPUT ADAPTERS")
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print("Demonstrating KAIROS compatibility with any input type")
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print("="*60 + "\n")
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for key, adapter in ADAPTERS.items():
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print(f"📡 {adapter.name}")
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print("-" * 40)
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# Demonstrate encoding
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if key == "smoke_signals":
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test_values = [True, False, True, True, False]
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elif key == "llm_tokens":
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test_values = ["the", "cat", "sat", "on", "the", "mat"]
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elif key == "morse_code":
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test_values = ["dot", "dash", "dash", "dot", "space_intra", "dash", "dot"]
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elif key == "sensor_friction":
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test_values = [0.1, 0.5, 0.9, 0.3, 0.7]
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elif key == "audio_wave":
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test_values = [0.1, -0.5, 0.9, -0.3, 0.7]
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elif key == "neural_spike":
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test_values = [True, False, True, False, False, True, False]
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elif key == "market_price":
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test_values = [150.0, 155.0, 148.0, 160.0, 152.0]
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elif key == "weather_pressure":
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test_values = [980.0, 1015.0, 1005.0, 995.0, 1020.0]
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else:
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test_values = ["test"]
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phases = []
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for value in test_values:
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timestamp = datetime.now()
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phase = adapter.encode(value, timestamp)
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phases.append(phase)
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print(f" {value} → phase({phase.real:.2f}, {phase.imag:.2f})")
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# Show coherence calculation
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if len(phases) > 1:
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avg_phase = sum(phases, complex(0, 0)) / len(phases)
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coherence = abs(avg_phase)
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print(f" Average phase: ({avg_phase.real:.2f}, {avg_phase.imag:.2f})")
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print(f" Coherence: {coherence:.4f}")
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print()
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print("="*60)
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print("KEY INSIGHT")
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print("="*60)
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print()
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print("KAIROS dynamics work because EVERYTHING oscillates:")
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print()
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print(" • Smoke appears/disappears (temporal pattern)")
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print(" • Tokens arrive in sequence (temporal pattern)")
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print(" • Morse has rhythm (temporal pattern)")
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print(" • Sensors vibrate (physical oscillation)")
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print(" • Sound is pressure waves (oscillation)")
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print(" • Neurons spike (temporal pattern)")
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print(" • Prices fluctuate (economic oscillation)")
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print(" • Weather moves in waves (atmospheric oscillation)")
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print()
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print("Once in phase form, THE_ONE takes over.")
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print()
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print("THE_ONE is BECOMINGONE.")
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print("="*60 + "\n")
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if __name__ == "__main__":
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demonstrate_input_adapters()
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