NEW Machine Learning Vector Matrix Library V.2, Chat Module, Awareness/Emotion Modules Linked [Open Sourced]

Magus_ArtStudios · September 27, 2023, 8:16pm

It’s feed data through Artificial intelligence models, its awareness module that constructs worded observations of distance size orientation. (its surroundings) and prompts from the player. This system runs locally so it’s instant except for server AI API calls. Which yield the result. It’s basically a overpowered multi model AI. It’s off ROBLOX equivalent would be akin to a Chatbot that can use whatever AI model you ask it to something like Poe.AI

Doomcolp · September 27, 2023, 8:18pm

Ah I was a bit interesting. But couldn’t you scrape data from websites w a web scraper and feed them to the AI?

Magus_ArtStudios · September 27, 2023, 8:18pm

I was just testing it and talking to A character named Witch Hunter who was telling me about the Dark Witch

Magus_ArtStudios · September 27, 2023, 8:20pm

I do I scrape data from wikipedia and AI models! I also have been working on my API library but currently I’ve explored what I needed to if you are interested I can link you to my open source documentation of all the free APIS I found that you can use to create a more sophisticated chatbot that scrapes more data. But overall A GPT4 solution would be fine. But the main point of this is to reduce cost of utilizing AI APIs.

Doomcolp · September 27, 2023, 8:20pm

Ah okay cool. I was just asking that because I was planning to write a web scraper in JS.

Magus_ArtStudios · September 27, 2023, 8:21pm

I did hours of research and compiled all of the free API services on this link
Artificial Intelligence Model APIs FREE (Open Source) and Other Useful APIs - Resources / Community Resources - Developer Forum | Roblox

I was just thinking I could utilize the Chuck Norris jokes API to have my bot tell horrible flattering jokes about itself or the player

If you utilize the huggingface models create an account and click a random model click interference endpoint and deploy to get your free bearer key.

Magus_ArtStudios · September 27, 2023, 8:52pm

Their are some very good inference models on hugging face. for example you can use one model to generate an output like this from a name.

Then you can use a different model to creatively expand upon that idea.

Also this is how my chatmodule works by recognizing synoynms for the purpose of being a better conversational search algorithm for a bot.

Magus_ArtStudios · September 29, 2023, 5:38pm

Magus_ArtStudios:

I wrote this Vector Matrix Calculation library from scratch yesterday. No AI would help me write it because of their policy against creating adversarial agents or something .

local neu={}

local Operators = {
	["+"] = function(a, b) return (a + b) end, -- addition
	["-"] = function(a, b) return (a - b) end, -- subtraction
	["*"] = function(a, b) return (a * b) end, -- multiplication
	["/"] = function(a, b) return (a / b) end  -- division
}

local ContextBias = {
	["sam"]=function(a) return (a.X) end,
	["los"]=function(a) return (a.X-(a.Y*a.Z)) end,
	["gan"]=function(a) return (a.X+(a.Y*a.Z)) end,
	["div"]=function(a) return (a.X/(a.Y*a.Z)) end,
	["mul"]=function(a) return (a.X*(a.Y*a.Z)) end,
}

function neu.matrix(x,y,z,optionalparameters)
	--,operator)--VectorMatrix
	local tblformat={X=x,Y=y,Z=z,optionalparameters}--["ctbias"]=ContextBias[operator]}		
	return tblformat end

function neu.matrixabs(difx,dify,difz,abs)
	local res
	if abs then 
		return neu.matrix(math.abs(difx),math.abs(dify),math.abs(difz))	
	else 
	return neu.matrix(difx,dify,difz)end end

function neu.mathoper(mathstr,mat1,mat2,abs)	
	return Operators[mathstr](mat1.X,mat2.X),Operators[mathstr](mat1.Y,mat2.Y),Operators[mathstr](mat1.Z,mat2.Z) end

function neu.mathmean(mathstr,mat1,mat2,abs)
	local difx,dify,difz=neu.mathoper(mathstr,mat1,mat2,abs)
	return difx/2,dify/2,difz/2 end

function neu.calcmean(mathstr,mat1,mat2,abs) 
	local difx,dify,difz=neu.mathmean(mathstr,mat1,mat2,abs) return neu.writemath(difx,dify,difz,abs) end

function neu.mathmagnitude(mat1,mat2)
	return (Vector3.new(mat1.X,mat1.Y,mat1.Z)-Vector3.new(mat2.X,mat2.Y,mat2.Z)).Magnitude end--distance

function neu.adjtolerance(scale,convulutedmatrix)
	--,operator)--VectorMatrix
	for nx,dt in convulutedmatrix do
		if nx=="weights" then
			convulutedmatrix[nx]=neu.matrix(dt.X*scale,dt.Y*scale,dt.Z*scale)
		else
			for smk,dtm in nx["chain"] do
				convulutedmatrix["chain"][smk]=neu.matrix(dt.X*scale,dt.Y*scale,dt.Z*scale)--chain matrix weight 
			end	
		end
	end
	return 	convulutedmatrix end

function neu.mathentropy(scale,denom,convulutedmatrix)
	--,operator)--VectorMatrix
	if scale or denom==nil then return convulutedmatrix end
	for nx,dt in convulutedmatrix do--through index of labels
		local scaler=math.random(1,math.min(2,scale))/denom--
		if nx=="weights" then
			convulutedmatrix[nx]=neu.matrix(dt.X*scaler,dt.Y*scaler,dt.Z*scaler)
		else
			for smk,dtm in nx["chain"] do
				local scaler=math.random(1,math.min(2,scale))/denom
				convulutedmatrix["chain"][smk]=neu.matrix(dt.X*scaler,dt.Y*scaler,dt.Z*scaler)--chain matrix weight 
			end	
		end

	end
	return 	convulutedmatrix end


function neu.weighmean(key,model,sublabel,param)
	return	neu.calcmean(model[key],sublabel[key],true) end

function neu.writemean(key,model,sublabel,param)
	model[key]=neu.weighmean(key,model,sublabel,param)
	return model[key] end

function neu.writemath(difx,dify,difz,abs)
	local res
	if abs then 
		res=neu.matrix(math.abs(difx),math.abs(dify),math.abs(difz))	
	else 
		res=neu.matrix(difx,dify,difz)		
	end	
	return res end	

function neu.writedif(mathstr,mat1,mat2,abs)--return x,y,z dif
	local difx,dify,difz=neu.mathmean(mathstr,mat1,mat2,abs)

	return neu.writemath(difx,dify,difz,abs)end--return matrix

function neu.chainover(chain,param)--returns chain
	--parameters are additional sub-model entries cur chain position
	for i,v in param do chain[i]=param[i]end
	return chain end--return chain of matrix

function neu.writeaccuracyloss(chain,bias)--loss based on weight of x
	local c=0 local d=#chain
	for i,v in chain do c=v.X 
		v.Z=v.Z/(c/bias) end
	return chain end 
return neu

I’ve tested this Vector Matrix for utilizing adjustable weights and immediately wrote this library to utilize math to make deterministic decisions about the context database’s relevance in scoring and ordering. of the output strings. Making the bot yield more accurate results.
It does this by weighing the projected accuracy which is a fraction of the matching words/synonyms with the Y which is repetition and x is changed by a loss function. So for example if you greet the bot the greeting experiences context loss and weighs its subweights to given context derived weight adjustments for each weight. So it’s like 10 vector x10 Vectors which are all connected by their probability and repetition and weights are adjusted overtime by player interaction and repetition and each labels loss function.

local neur={}
local neu=require(script.VectorMath)
local ContextBias = {
	["sam"]=function(a) return (a.X) end,
	["los"]=function(a) return (a.X-(a.Y*a.Z)) end,
	["gan"]=function(a) return (a.X+(a.Y*a.Z)) end,
	["div"]=function(a) return (a.X/(a.Y*a.Z)) end,
	["mul"]=function(a) return (a.X*(a.Y*a.Z)) end,
}
local cwm
function neur.ContextClassificationModel()
cwm={--context weight matrix Y,Z are calculated and influence X position. 
	["Greetings"]={
		["weights"]=neu.matrix(10,1,1),--10 table 10 numbers ez
		["chain"]={
			["Emotions"]=neu.matrix(9,1,1),
		   ["Awareness"]=neu.matrix(8,1,1),
			 ["Empathy"]=neu.matrix(7,1,1),
			  ["Search"]=neu.matrix(5,1,.9),
			["Classify"]=neu.matrix(6,1,1),		
			 ["Support"]=neu.matrix(2,1,.8),
			["Database"]=neu.matrix(3,1,1),	
			["Bestiary"]=neu.matrix(6,1,1),	
		 	  ["Wisdom"]=neu.matrix(4,1,1),						
		  ["Philosophy"]=neu.matrix(1,1,1),
			--["Search"]=neu.matrix(1,1,1),				
			--search accuracy
		},
		["entropy"]=ContextBias["los"],--decrease weight of go on to other topics.
	},--weight is X is default weight y is reptition z is 
	["Emotions"]={
		["weights"]=neu.matrix(9,1,1),
		["chain"]={
				--["Emotions"]=neu.matrix(9,1,1),
				["Greetings"]=neu.matrix(1,1,1),
				["Awareness"]=neu.matrix(8,1,1),
				["Empathy"]=neu.matrix(10,1,1),
				["Search"]=neu.matrix(4,1,.9),
				["Classify"]=neu.matrix(7,1,1),		
				["Support"]=neu.matrix(3,1,.8),
				["Database"]=neu.matrix(6,1,1),	
				["Bestiary"]=neu.matrix(5,1,1),	
				["Wisdom"]=neu.matrix(4,1,1),					
				["Philosophy"]=neu.matrix(2,1,1),},
			
		["entropy"]=ContextBias["los"],},--Z is score/weight add to x
	["Empathy"]={["weights"]=neu.matrix(7,1,1),
			["chain"]={
			["Philosophy"]=neu.matrix(10,1,1),},
			["Wisdom"]=neu.matrix(9,1,1),					
		 	["Bestiary"]=neu.matrix(8,1,1),	
			["Classify"]=neu.matrix(3,1,1),		
			["Emotions"]=neu.matrix(1,1,1),
			["Database"]=neu.matrix(6,1,1),	
			["Search"]=neu.matrix(7,1,.9),
			["Awareness"]=neu.matrix(6,1,1),
			["Greetings"]=neu.matrix(2,1,1),
			["Support"]=neu.matrix(5,1,.8),
				--["Empathy"]=neu.matrix(,1,1),
			
				
	
		
		["entropy"]=ContextBias["gan"]},--z is loss/entropy weight/score/
	["Support"]={neu.matrix(4,1,1),--subneuron only
		["chain"]={
				["Emotions"]=neu.matrix(3,1,1),
				["Greetings"]=neu.matrix(1,1,1),
				["Awareness"]=neu.matrix(6,1,1),
				["Empathy"]=neu.matrix(4,1,1),
				["Search"]=neu.matrix(2,1,.9),
				["Classify"]=neu.matrix(3,1,1),		
				--["Support"]=neu.matrix(,1,.8),
				["Database"]=neu.matrix(8,1,1),	
				["Bestiary"]=neu.matrix(9,1,1),	
				["Wisdom"]=neu.matrix(6,1,1),					
				["Philosophy"]=neu.matrix(3,1,1),},
		["entropy"]=ContextBias["gan"]	
	},	
	["Wisdom"]={neu.matrix(3,1,1),
		["chain"]={
				["Emotions"]=neu.matrix(4,1,1),
				["Greetings"]=neu.matrix(4,1,1),
				["Awareness"]=neu.matrix(5,1,1),
				["Empathy"]=neu.matrix(7,1,1),
				["Search"]=neu.matrix(3,1,.9),
				["Classify"]=neu.matrix(1,1,1),		
				["Support"]=neu.matrix(5,1,.8),
				["Database"]=neu.matrix(8,1,1),	
				["Bestiary"]=neu.matrix(9,1,1),	
				--["Wisdom"]=neu.matrix(4,1,1),					
				["Philosophy"]=neu.matrix(10,1,1),},
		["entropy"]=ContextBias["los"]
	},	--subtract y from x 					
	["Philosophy"]=	{neu.matrix(2,1,1),
		["chain"]={
				["Emotions"]=neu.matrix(9,1,1),
				["Greetings"]=neu.matrix(4,1,1),
				["Awareness"]=neu.matrix(5,1,1),
				["Empathy"]=neu.matrix(7,1,1),
				["Search"]=neu.matrix(5,1,.9),
				["Classify"]=neu.matrix(1,1,1),		
				["Support"]=neu.matrix(6,1,.8),
				["Database"]=neu.matrix(8,1,1),	
				["Bestiary"]=neu.matrix(9,1,1),	
				["Wisdom"]=neu.matrix(10,1,1),					
				--["Philosophy"]=neu.matrix(4,1,1),
			},
		["entropy"]=ContextBias["gan"]
	},
	["Bestiary"]={neu.matrix(3,1,1),
		["chain"]={
				["Emotions"]=neu.matrix(9,1,1),
				["Greetings"]=neu.matrix(4,1,1),
				["Awareness"]=neu.matrix(9,1,1),
				["Empathy"]=neu.matrix(7,1,1),
				["Search"]=neu.matrix(6,1,.9),
				["Classify"]=neu.matrix(5,1,1),		
				["Support"]=neu.matrix(1,1,.8),
				["Database"]=neu.matrix(10,1,1),	
			--	["Bestiary"]=neu.matrix(3,1,1),	
				["Wisdom"]=neu.matrix(2,1,1),					
				["Philosophy"]=neu.matrix(1,1,1),},
		["entropy"]=ContextBias["gan"]},
	["Search"]={neu.matrix(2,1,1),
		["chain"]={
				["Emotions"]=neu.matrix(9,1,1),
				["Greetings"]=neu.matrix(1,1,1),
				["Awareness"]=neu.matrix(4,1,1),
				["Empathy"]=neu.matrix(2,1,1),
				--["Search"]=neu.matrix(4,1,.9),
				["Classify"]=neu.matrix(7,1,1),		
				["Support"]=neu.matrix(5,1,.8),
				["Database"]=neu.matrix(7,1,1),	
				["Bestiary"]=neu.matrix(5,1,1),	
				["Wisdom"]=neu.matrix(7,1,1),					
				["Philosophy"]=neu.matrix(6,1,1),},
		["entropy"]=ContextBias["gan"]},	--x,y,z is cumulative			
	["Classify"]={neu.matrix(1,1,1),
		["chain"]={
				--["Emotions"]=neu.matrix(9,1,1),
				["Greetings"]=neu.matrix(5,1,1),
				["Awareness"]=neu.matrix(4,1,1),
				["Empathy"]=neu.matrix(5,1,1),
				["Search"]=neu.matrix(4,1,.9),
				["Classify"]=neu.matrix(6,1,1),		
				["Support"]=neu.matrix(7,1,.8),
				["Database"]=neu.matrix(8,1,1),	
				["Bestiary"]=neu.matrix(2,1,1),	
				["Wisdom"]=neu.matrix(4,1,1),					
				["Philosophy"]=neu.matrix(3,1,1),},
		["entropy"]=ContextBias["los"]},
	--classify last						
	}
end

local defcwm=cwm

function neur.resettodefparams(cwm)
	return defcwm--reset model
end
function neur.resetlabelparams(Key)
	return defcwm[Key]
end
function neur.writevector(Key,X,Y,Z)--x is reptition, y is count,z weight
	if X==nil then X=0 end if Y==nil then Y=0 end if Z==nil then Z=0 end
	local state=neu.matrix(cwm[Key].x+X,cwm[Key].y+Y,cwm[Key].z+Z)
	cwm[Key]=state--write weight matrix
	return state
end
return neu

This is a work in progress and I’ll be releasing updates as I develop this library further I just wrote it yesterday after testing the working concept of the writevector function at the end. This model will utilize these 100 weights to maintain context in a conversation.

I’m excited for this new thing I started creating yesterday. It was surprisingly easy. I’m new to machine learning though what do you think of the library so far? I tried keeping it comprehensive and coherent.

Doomcolp · September 29, 2023, 5:42pm

I’m ngl, I’m not good at math at all so I don’t know what’s going on lol.

Seems real complicated, so I’ll let the pros (you) do all that.

[Why would need the mean of something?

Magus_ArtStudios · September 29, 2023, 5:46pm

The vector math library is emcompassed in its own module. To make it simple But basically it’s just like having a position and the x of the position determines its weight. the matrix math module is not explicity used for much yet I just wanted to write all this math to basically give different ways of calculating X which is the weight, Y is the repetition and X is the context loss or gain. The accuracy is determined by the chatmodule and rewards or takes weight away from x such as if you were to greet the bot greetings would be weighed less. But if you were searching wikipedia for something you would prioritize that submodel more. It’s basically just a Zero-classification model. If you were to use it it would be simple as having 10 labels. I will be simplifying it in the future! It would be useful for making combat AIs with predefined weights that adapt to combat scenarios.

Doomcolp · September 29, 2023, 5:49pm

Still don’t understand it, maybe I will in the future. Lol great job

Magus_ArtStudios · September 29, 2023, 5:50pm

Okay so Vector3 is a position and these weights exist in 3D space like your brain and their x is the probability for that action= Vector matrix
so like if you have a combat dumby whos actions are attack() defend() and etc you can reward those weights and make adaptive behavior for any function determined by how it recieve rewards.
In this example they are the order of databases queried so it may provide most relevant responses.

Doomcolp · September 29, 2023, 5:54pm

Wait oh okay this makes a bit more sense.

It’s getting a bit more clear. Thanks for the explanation

Magus_ArtStudios · September 29, 2023, 5:56pm

Yes so the weight is just a big table to use for convienence and functionality. Setting it up may be tricky but you could always start with a blank slate! I just wanted to start with predefined weights. But This will be improved in functionality in the future. But the concept was escaping me too before yesterday and it just clicked.

Doomcolp · September 29, 2023, 5:57pm

Mhm. Do you have a lot of experience with this type of stuff? It seems like you’ve been working on this typa stuff for a while

Magus_ArtStudios · September 29, 2023, 5:59pm

It’s just really simple stuff it’s a position in space and you adjust it. You can do it with blocks but I wanted to make this efficient so i used tables because I know that vectors3 are slower. The higher X is the most likely action. But a good model needs good input. My input for the model is generated by my chatmodule that outputs the accuracy of the response. this is used to adjust the weight of x along with the frequency of that database being accessed and each labels context function. So like you can set up los or gan for the context by being like hey if you get damaged you get los for attacking. or if you make successful hit you get gan.
I do everything! I make animations, photoshop, programming, prodeural world generation

Doomcolp · September 29, 2023, 6:02pm

Wow you’ve got a huge skill set. in the future ima try to recreate what you jst did

Magus_ArtStudios · September 29, 2023, 6:05pm

I’m glad to hear that! I’m really proud of the chatmodule I released it’s very useful and simple to use. All the function are set up to take the minimal variables but they look more complicated than they are.
They are also very efficient! I just updated it recently and the synonym and reflection retrieval is very useful.
I’ve seen other developers copying my source code not providing feedback and just making their own chatbots which is cool. the only thing I haven’t open source is the personalities or my text data and the API structure.

Doomcolp · September 29, 2023, 6:10pm

Ran out of likes sadly but I’m happy you didn’t and was able to release this!

Magus_ArtStudios · September 29, 2023, 6:16pm

if you’d be interested collaborating on something shoot me a DM on some ideas.
But yeah I have linked resources here that I released that you could use to create a sophisticated chatbot. You input A prompt and a context dataset to the completequery function (string,table)