-- P9ML Integration - Main module for neural-membrane integration

-- Provides convenient wrappers and initialization for the P9ML system

local P9MLUtils = require('P9MLUtils')

local P9MLMembrane = require('P9MLMembrane')

local P9MLNamespace = require('P9MLNamespace')

local P9MLCognitiveKernel = require('P9MLCognitiveKernel')

local P9ML = {}

-- Global system state

P9ML.initialized = false

P9ML.namespace = nil

P9ML.cognitive_kernel = nil

-- Initialize the P9ML system

function P9ML.init(config)

config = config or {}

print("Initializing P9ML Membrane Computing System...")

-- Initialize namespace

P9ML.namespace = P9MLNamespace:init()

-- Initialize cognitive kernel

P9ML.cognitive_kernel = P9MLCognitiveKernel:init()

-- Set global references for easy access

_G.P9ML = P9ML

_G.P9MLNamespace = P9ML.namespace

_G.P9MLCognitiveKernel = P9ML.cognitive_kernel

P9ML.initialized = true

print("✓ P9ML System initialized successfully")

print("✓ Namespace ready for membrane registration")

print("✓ Cognitive kernel ready for tensor encoding")

return P9ML

end

-- Wrap a neural module with P9ML membrane computing

function P9ML.wrapModule(module, config)

if not P9ML.initialized then

P9ML.init()

end

config = config or {}

-- Create membrane wrapper

local membrane = P9MLMembrane.new(module, config)

-- The membrane will auto-register with the namespace

print("Wrapped module with membrane ID:", membrane:getId())

return membrane

end

-- Convenience function to create a linear membrane

function P9ML.Linear(inputSize, outputSize, config)

local module = {}

-- Mock linear module (in real implementation would use nn.Linear)

module.weight = {} -- Simplified for demo

module.bias = {}

function module:forward(input)

-- Simple linear transformation (simplified for demonstration)

if torch and torch.isTensor and torch.isTensor(input) then

-- In real implementation: return input * weight + bias

return input:clone() -- Simplified

else

return input -- Pass through for non-tensors

end

end

function module:backward(gradOutput)

return gradOutput -- Simplified

end

return P9ML.wrapModule(module, config)

end

-- Convenience function to create a convolutional membrane

function P9ML.Conv2d(inputChannels, outputChannels, kernelSize, config)

local module = {}

-- Mock conv module (in real implementation would use nn.SpatialConvolution)

module.weight = {} -- Simplified for demo

module.bias = {}

function module:forward(input)

-- Simple convolution (simplified for demonstration)

if torch and torch.isTensor and torch.isTensor(input) then

return input:clone() -- Simplified

else

return input -- Pass through for non-tensors

end

end

function module:backward(gradOutput)

return gradOutput -- Simplified

end

return P9ML.wrapModule(module, config)

end

-- Get system status

function P9ML.status()

if not P9ML.initialized then

return {

initialized = false,

message = "P9ML system not initialized"

}

end

local namespace_metadata = P9ML.namespace:getMetadata()

local gestalt_state = P9ML.namespace:getGestaltState()

local hypergraph = P9ML.cognitive_kernel:getHypergraphTopology()

return {

initialized = true,

membranes = {

total = namespace_metadata.total_membranes,

active = namespace_metadata.active_membranes,

types = namespace_metadata.membrane_types

},

gestalt = {

coherence = gestalt_state.coherence_metric,

last_synthesis = gestalt_state.last_synthesis,

dimensional_harmony = gestalt_state.dimensional_harmony

},

cognitive = {

nodes = 0,

hyperedges = 0,

clusters = 0

}

}

end

-- Update cognitive status counts

function P9ML._updateCognitiveStatus(status)

if P9ML.cognitive_kernel then

local hypergraph = P9ML.cognitive_kernel:getHypergraphTopology()

-- Count nodes

for _ in pairs(hypergraph.nodes) do

status.cognitive.nodes = status.cognitive.nodes + 1

end

-- Count hyperedges

for _ in pairs(hypergraph.hyperedges) do

status.cognitive.hyperedges = status.cognitive.hyperedges + 1

end

-- Count clusters

for _ in pairs(hypergraph.cognitive_clusters) do

status.cognitive.clusters = status.cognitive.clusters + 1

end

end

return status

end

-- Synthesize gestalt field and update cognitive state

function P9ML.synthesize()

if not P9ML.initialized then

error("P9ML system not initialized")

end

print("Synthesizing gestalt tensor field...")

-- Synthesize gestalt field

local gestalt_field = P9ML.namespace:synthesizeGestaltField()

-- Update cognitive clusters

if P9ML.cognitive_kernel then

-- The kernel automatically updates when encodings are added

local clusters = P9ML.cognitive_kernel:getCognitiveClusters()

print("Found", tableLength(clusters), "cognitive clusters")

end

local gestalt_state = P9ML.namespace:getGestaltState()

print("Gestalt coherence:", gestalt_state.coherence_metric)

return gestalt_field

end

-- Helper function to count table length

function tableLength(t)

local count = 0

for _ in pairs(t) do

count = count + 1

end

return count

end

-- Get all membranes in the system

function P9ML.getMembranes()

if not P9ML.initialized then

return {}

end

return P9ML.namespace:getAllMembranes()

end

-- Find membranes by type

function P9ML.getMembranesByType(membrane_type)

if not P9ML.initialized then

return {}

end

return P9ML.namespace:getMembranesByType(membrane_type)

end

-- Get cognitive similarity between membranes

function P9ML.getCognitiveSimilarity(membrane1, membrane2)

if not P9ML.initialized or not membrane1 or not membrane2 then

return 0.0

end

local lexeme1 = membrane1:getLexeme()

local lexeme2 = membrane2:getLexeme()

if not lexeme1 or not lexeme2 then

return 0.0

end

return 1.0 - P9MLUtils.cognitiveDistance(lexeme1, lexeme2)

end

-- Create a sequence of connected membranes

function P9ML.createSequence(modules, configs)

configs = configs or {}

local membranes = {}

for i, module in ipairs(modules) do

local config = configs[i] or {}

local membrane = P9ML.wrapModule(module, config)

table.insert(membranes, membrane)

-- Connect to previous membrane

if i > 1 then

membranes[i-1]:connectTo(membrane)

end

end

return membranes

end

-- Process a tensor through a sequence of membranes

function P9ML.forwardSequence(membranes, input)

local current_input = input

for i, membrane in ipairs(membranes) do

current_input = membrane:forward(current_input)

-- Encode tensor shape in cognitive kernel

if torch and torch.isTensor and torch.isTensor(current_input) then

P9ML.cognitive_kernel:encodeTensorShape(

current_input:size():totable(),

membrane:getId()

)

end

end

return current_input

end

-- Reset the P9ML system

function P9ML.reset()

print("Resetting P9ML system...")

P9ML.namespace = nil

P9ML.cognitive_kernel = nil

P9ML.initialized = false

_G.P9MLNamespace = nil

_G.P9MLCognitiveKernel = nil

print("✓ P9ML system reset")

end

return P9ML