Merge branch 'legacy-nn-2' into legacy

generic-github-user · Sep 1, 2022 · d16c8b1 · d16c8b1
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diff --git a/legacy/hurricane/.gitattributes b/legacy/hurricane/.gitattributes
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+# Auto detect text files and perform LF normalization
+* text=auto
diff --git a/legacy/hurricane/Documentation.txt b/legacy/hurricane/Documentation.txt
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+To use Hurricane:
+
+
+Place the "Hurricane.js" file in the same folder/directory as your HTML file.
+Then place this code in the "<body></body>" section of your HTML: <script src="Hurricane.js"></script>. This references the JavaScript file that contains Hurricane. See the "example.html" file for an example usage of Hurricane.
+
+
+There are two functions that can be used with Hurricane: train( ) and run( ). 
+
+train( ) accepts 5 arguments: inputCount, hiddenNodesCount, mutationSize, accuracy, and trainingData. trainingData is an array filled with integers or floating point numbers, and the other 4 parameters are integers (mutationSize and accuracy can be a float). This is what the function looks like with blank parameters: train(inputCount,hiddenNodesCount,mutationSize,accuracy,trainingData). With values filled in the function arguments/in actual code, it looks like this: train(9,5,1000,0.1,[1,2,3,4,5,6,7,8,9,10,34,35,36,37,38,39,40,41,42,43,20,19,18,17,16,15,14,13,12,11,10]);. This function must be called before run( ). This function trains the neural network for use based on a given set of training data. It takes much longer than run( ), but it only needs to be run once.
+
+run( ) accepts only one argument: inputValues. This is an array filled with integers or floating point numbers. This is what the function looks like with blank parameters: run(inputValues). With values filled in the function argument/in actual code, it looks like this: run([20,21,22,23,24,25,26,27,28]);. This function can be called whenever the neural network has to be reevaluated with new inputs. It runs much faster than train( ).
diff --git a/legacy/hurricane/Example.html b/legacy/hurricane/Example.html
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+<html>
+	<head>
+		<title>
+			Example Usage of Hurricane
+		</title>
+	</head>
+
+	<body>
+
+		<script src="Hurricane.js"></script> <!--Import Hurricane.js-->
+		<script>
+
+			train(9,5,1,0.1,[1,2,3,4,5,6,7,8,9,10,34,35,36,37,38,39,40,41,42,43,14,15,16,17,18,19,20,21,22,23,101,102,103,104,105,106,107,108,109,110,10,9,8,7,6,5,4,3,2,1,46,47,48,49,50,51,52,53,54,55,-10,-9,-8,-7,-6,-5,-4,-3,-2,-1]);  //Run training function for neural network (this must be done before the neural network can be run)
+
+			run([20,21,22,23,24,25,26,27,28]); //Run the neural network, return the output, and log the data
+			document.write(Math.round(output[0]));
+
+		</script>
+
+	</body>
+</html>
diff --git a/legacy/hurricane/Hurricane.js b/legacy/hurricane/Hurricane.js
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+
+//Hurricane, a simple feedforward neural network in JavaScript.
+
+
+
+{  //Defines variables
+
+	var inputCount_ = 20;
+	var hiddenNodesCount_ = 10;
+	var stepSize_ = 10000;
+	var trainingData_ = [];
+
+	var trainingDataSetLength = inputCount_ + 1; //Length of a set of training data
+	var input = []; //Creates array to store values for the input layer
+	for(u=0;u<inputCount_;u++){
+		input.push(0);
+	}
+	var synapse1 = []; //Creates array to store values for the first layer of synapses
+	for(k=0;k<input.length*hiddenNodesCount_;k++){ //Adds synapses for each input
+		synapse1.push(0); //Adds synapse
+	}
+	var hidden = []; //Creates array to store values for hidden layer
+	for(j=0;j<hiddenNodesCount_;j++){
+		hidden.push(0);
+	}
+	var synapse2 = []; //Creates array to store values for the second layer of synapses
+	for(d=0;d<hiddenNodesCount_;d++){
+		synapse2.push(0);
+	}
+	var output = [0]; //Creates array to store output
+	var randSynapse = Math.floor(Math.random()*synapse1.length); //Picks a random synapse from synapse layer 1 to mutate
+	var trainingDataSet = Math.floor(Math.random()*(trainingData_.length/trainingDataSetLength)); //Picks a random set of training data to use for synapse layer 1
+	var oldDifference = 0; //Error of previous mutation
+	var cost = 0; //Error of current mutation
+	var randMutation = (Math.random()/10000)*stepSize_ - (Math.random()/10000)*stepSize_;
+	var done = 0;
+	var outputChange = output[0];
+
+}
+
+function runNeuralNet(){
+
+		for(d=0;d<hidden.length;d++){
+			hidden[d] = 0;
+		}
+		for(j=0;j<input.length;j++){
+			for(w=0;w<hidden.length;w++){
+				hidden[w] = hidden[w] + input[j] * synapse1[j*hiddenNodesCount_+w];
+			}
+		}
+		output[0] = 0;
+		for(e=0;e<hidden.length;e++){
+			output[0] = output[0] + (hidden[e] * synapse2[e]);
+		}
+
+	}
+
+function logData(){ //Logs neural network data for reference
+
+	console.log("Hurricane:"); //Clears console
+	console.log("");
+	console.log("");
+
+	console.log(input); //Logs input
+	console.log(synapse1); //Logs synapse layer 1
+	console.log(hidden); //Logs hidden layer
+	console.log(synapse2); //Logs synapse layer 2
+	console.log(output); //Logs output
+
+	console.log("");
+	console.log("");
+
+	console.log("Output = " + output) //Logs output
+
+}
+
+
+
+function train(inputCount,hiddenNodesCount,stepSize,accuracy,trainingData){ //Trains neural network
+
+	inputCount_ = inputCount;
+	hiddenNodesCount_ = hiddenNodesCount;
+	stepSize_ = stepSize;
+	trainingData_ = trainingData;
+
+	trainingDataSetLength = inputCount_ + 1; //Length of a set of training data
+	input = []; //Creates array to store values for the input layer
+	for(u=0;u<inputCount_;u++){
+		input.push(0);
+	}
+	synapse1 = []; //Creates array to store values for the first layer of synapses
+	for(k=0;k<input.length*hiddenNodesCount_;k++){ //Adds synapses for each input
+		synapse1.push(0); //Adds synapse
+	}
+	hidden = []; //Creates array to store values for hidden layer
+	for(j=0;j<hiddenNodesCount_;j++){
+		hidden.push(0);
+	}
+	synapse2 = []; //Creates array to store values for the second layer of synapses
+	for(d=0;d<hiddenNodesCount_;d++){
+		synapse2.push(0);
+	}
+	output = [0]; //Creates array to store output
+	randSynapse = Math.floor(Math.random()*synapse1.length); //Picks a random synapse from synapse layer 1 to mutate
+	trainingDataSet = Math.floor(Math.random()*(trainingData_.length/trainingDataSetLength)); //Picks a random set of training data to use for synapse layer 1
+	oldDifference = 0; //Error of previous mutation
+	cost = 0; //Error of current mutation
+	randMutation = (Math.random()/10000)*stepSize_ - (Math.random()/10000)*stepSize_;
+	done = 0;
+
+
+	do{ //Mutates and tests synapses
+
+
+
+
+			randSynapse = Math.floor(Math.random()*synapse1.length); //Picks random synapse from layer 1 of synapses to mutate
+
+			oldDifference = 0;
+			trainingDataSet = 0; //Picks random set of training data to use in training
+			for(qv=0;qv<(trainingData_.length/trainingDataSetLength);qv++){
+				for(g=0;g<input.length;g++){
+					input[g] = trainingData_[(trainingDataSet*trainingDataSetLength)+g];
+				}
+				runNeuralNet();
+				oldDifference += Math.abs(output[0] - trainingData_[(trainingDataSet*trainingDataSetLength)+input.length]);
+				trainingDataSet++;
+
+			}
+
+
+			randMutation = (Math.random()/10000)*stepSize_ - (Math.random()/10000)*stepSize_;
+			synapse1[randSynapse] += randMutation;
+
+
+			cost = 0;
+			trainingDataSet = 0; //Picks random set of training data to use in training
+				for(qw=0;qw<(trainingData_.length/trainingDataSetLength);qw++){
+					for(g=0;g<input.length;g++){
+						input[g] = trainingData_[(trainingDataSet*trainingDataSetLength)+g];
+					}
+					runNeuralNet();
+					cost += Math.abs(output[0] - trainingData_[(trainingDataSet*trainingDataSetLength)+input.length]);
+					trainingDataSet++;
+
+				}
+
+			if(cost > oldDifference){
+				synapse1[randSynapse] -= 2*randMutation;
+			}
+			if(cost / (trainingData_.length/trainingDataSetLength) < accuracy){
+				done = 1;
+			}
+
+
+
+
+
+			randSynapse = Math.floor(Math.random()*synapse2.length); //Picks random synapse from layer 1 of synapses to mutate
+
+			oldDifference = 0;
+			trainingDataSet = 0; //Picks random set of training data to use in training
+			for(qv=0;qv<(trainingData_.length/trainingDataSetLength);qv++){
+				for(g=0;g<input.length;g++){
+					input[g] = trainingData_[(trainingDataSet*trainingDataSetLength)+g];
+				}
+				runNeuralNet();
+				oldDifference += Math.abs(output[0] - trainingData_[(trainingDataSet*trainingDataSetLength)+input.length]);
+				trainingDataSet++;
+
+			}
+
+
+
+			cost = 0;
+			trainingDataSet = 0; //Picks random set of training data to use in training
+				for(qw=0;qw<(trainingData_.length/trainingDataSetLength);qw++){
+					for(g=0;g<input.length;g++){
+						input[g] = trainingData_[(trainingDataSet*trainingDataSetLength)+g];
+					}
+					runNeuralNet();
+					cost += Math.abs(output[0] - trainingData_[(trainingDataSet*trainingDataSetLength)+input.length]);
+					trainingDataSet++;
+
+				}
+
+
+				synapse2[randSynapse] += stepSize_;
+				runNeuralNet();
+				outputChange = output[0];
+				synapse2[randSynapse] -= stepSize_;
+				runNeuralNet();
+				outputChange -= output[0];
+
+				if(outputChange > 0){
+					synapse2[randSynapse] -= stepSize_;
+				}
+				else{
+					synapse2[randSynapse] += stepSize_;
+				}
+
+
+			if(cost / (trainingData_.length/trainingDataSetLength) < accuracy){
+				done = 1;
+			}
+
+
+
+	}while(done == 0)
+
+}
+
+function run(inputValues){ //Activates neural network use
+
+	input = [];
+	for(i=0;i<inputValues.length;i++){ //Fills array with a "0" for every input
+		input.push(inputValues[i]);
+	}
+	runNeuralNet();
+	logData();
+	return(output[0]);
+
+}
diff --git a/legacy/hurricane/LICENSE b/legacy/hurricane/LICENSE
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+MIT License
+
+Copyright (c) 2018 Hurricane Neural Network and Machine Learning Library
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
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		# Auto detect text files and perform LF normalization
		* text=auto