module aid.nn.multilevel.backprop;

import aid.nn.outputFunctions;
import aid.misc;
import std.random;
import std.stream;
import std.stdio;

class Backprop {
	private uint       numInputs;
	private double[][][] units;     // Includes the output units. units[layer][unit][inputWeight]
	private OutputFunctionPtr[] functions;
	public double learningRate;
	
	///Constructor
	public this(uint numInputs,uint[] numUnits,OutputFunctionPtr[] functions,double learningRate=0.03,double value=0.1,bool randomize=true){
		if(numUnits.length == 0) throw new InputException("numUnits must be greater than 0");
		if(numUnits.length != functions.length) throw new InputException("numUnits and functions must be the same length");
		this.numInputs = numInputs;
		this.functions = functions;
		this.learningRate = learningRate;
		units.length = numUnits.length;
		initUnitLayer(0,numUnits[0],numInputs,value,randomize);
		for(int i=1; i<numUnits.length; i++){
			initUnitLayer(i,numUnits[i],numUnits[i-1],value,randomize);
		}
	}
	
	// Helper function to initialize a certain layer.
	private void initUnitLayer(uint layer,uint num,uint numPrev,double value,bool randomize){
		units[layer].length = num;
		for(int i=0; i<num; i++){
			units[layer][i].length = numPrev+1; // include the bias weight
			for(int j=0; j<numPrev+1; j++){
				if(randomize) units[layer][i][j] = rnd() * value * 2 - value; // between -value and value
				else units[layer][i][j] = value;
			}
		}
	}
	
	////////////////////////////////////////////////////// Evaluation //////////////////////////////////////////////////////
	/// Evaluates the neural network.
	public double[] evaluate(double[] inputs){
		return evaluateFull(inputs)[$-1]; // the last item (outputs) of the return value
	}
	
	/// Evaluates the neural network and returns the output from all units.
	public double[][] evaluateFull(double[] inputs){
		if(inputs.length != numInputs) throw new InputException("Wrong length of inputs.");
		double[][] outputs;
		outputs.length = units.length;
		outputs[0] = evaluateLayer(0,inputs);
		for(int i=1; i<units.length; i++){
			outputs[i] = this.evaluateLayer(i,outputs[i-1]);
		}
		return outputs;
	}
	
	// Helper function to evaluate the outputs of a single layer.
	private double[] evaluateLayer(uint layer,double[] layerInputs){
		double[] output;
		output.length = units[layer].length;
		//printArray(layerInputs);
		for(int i=0; i<units[layer].length; i++){
			output[i] = evaluateUnit(layer,i,layerInputs);
		}
		return output;
	}
	
	// Helper function to evaluate the output of a single unit.
	private double evaluateUnit(uint layer, uint unit, double[] layerInputs){
		//writef("(%d,%d)=",layer,unit);
		//printArray(layerInputs);
		double total = units[layer][unit][0]; //bias
		for(int i=1; i<layerInputs.length+1; i++){
			total += layerInputs[i-1] * units[layer][unit][i]; // wi * xi
			//writef("@");
		}
		//writefln("        ! %f",total);
		if(functions[layer] != null) return functions[layer](total); // apply the function (if there is one)
		writefln("no function");
		return total; // just return the result instead
	}
	
	
	////////////////////////////////////////////////////// Training //////////////////////////////////////////////////////
	/// Trains the neural network.
	/// TODO:
	///   Pull error calculation into a separate function.
	public void train(double[][] trainingInputs, double[][] trainingOutputs){
		if(trainingInputs.length != trainingOutputs.length) throw new InputException("trainingInputs and trainingOutputs must be the same size");
		double[][][] weightUpdate;
		double[][] outputsError;
		double[][] outputs;
		double total; //temp variable
		
		// Initialize the weightUpdate and outputsError variables
		weightUpdate.length = units.length;
		outputsError.length = units.length;
		//writefln("#%d,%d",weightUpdate.length,outputsError.length);
		for(int i=0; i<units.length; i++){
			weightUpdate[i].length = units[i].length;
			outputsError[i].length = units[i].length;
			//writefln("##(%d)%d,%d",i,weightUpdate[i].length,outputsError[i].length);
			for(int j=0; j<weightUpdate[i].length; j++){
				weightUpdate[i][j].length = units[i][j].length;
				for(int k=0; k<weightUpdate[i][j].length; k++) weightUpdate[i][j][k] = 0.0f;
				//writefln("###(%d)%d",j,weightUpdate[i][j].length);
			}
		}
		
		
		// Loop through each of the training examples
		for(int example=0; example < trainingInputs.length; example++){
			outputs = evaluateFull(trainingInputs[example]);
			
			// Computing error of output layer
			for(int i=0; i<outputs[$-1].length; i++){ // units of last layer
				//writefln("{%d,%d,%d,%d}",example,i,outputs.length,outputsError[$-1].length);
				outputsError[$-1][i] = outputs[$-1][i] * (1 - outputs[$-1][i]) * (trainingOutputs[example][i] - outputs[$-1][i]);
			} // o(1-o)(t-o)
			
			//printArray(outputsError[$-1]);
			//printArray(units[length-1]);
			
			//*
			// Loop through each of the hidden layers (backwards - BACKpropagation!)
			for(int layer=units.length-2; layer >= 0; layer--){ // -2 to skip the output layer
				//writef("|");
				// loop through the units in each hidden layer
				for(int unit=0; unit<units[layer].length; unit++){
					//writef("*");
					total=0;
					// total up w * e for the units the output of this unit goes into
					for(int k=0; k<units[layer+1].length; k++){
						//writef("{weight=%f,error=%f}", units[layer+1][k][unit+1/* +1 for bias*/], outputsError[layer+1][k]);
						total += units[layer+1][k][unit+1/* +1 for bias*/] * outputsError[layer+1][k];
					}
					//writefln("=%f(total)",total);
					// multiply total by o(1-o), store in outputsError
					outputsError[layer][unit] = outputs[layer][unit] * (1 - outputs[layer][unit]) * total;
				}
			} //writefln();
			
			//writef("outputError="); printArray(outputsError);
			
			// special case for the units that receive the input values
			for(int unit=0; unit<units[0].length; unit++){ // unit
				//writefln(":%d,%d,%d,%d",j,weightUpdate.length,weightUpdate[0].length,weightUpdate[0][j].length);
				weightUpdate[0][unit][0] += outputsError[0][unit]; //bias
				for(int input=1; input<units[0][unit].length; input++){ // input
					weightUpdate[0][unit][input] += outputsError[0][unit] * trainingInputs[example][input-1]; // account for bias
				}
			}
			
			// Update the weightUpdate array
			for(int i=1; i<units.length; i++){ // layer
				for(int j=0; j<units[i].length; j++){ // unit
					weightUpdate[i][j][0] += outputsError[i][j]; //bias
					for(int k=1; k<units[i][j].length; k++){ // input
						//writefln("[%d,%d,%d]=%f; %f; %f",i,j,k,weightUpdate[i][j][k],outputsError[i][j],outputs[i-1][k-1]);
						weightUpdate[i][j][k] += outputsError[i][j] * outputs[i-1][k-1]; // previous layer, account for bias
					}
				}
			}
		}
		
		// Apply the weightUpdate array to the weights
		for(int i=0; i<units.length; i++){ // layer
			for(int j=0; j<units[i].length; j++){ // unit
				for(int k=0; k<units[i][j].length; k++){ // input
					//writefln("[%d,%d,%d]=%f; %f",i,j,k,units[i][j][k],weightUpdate[i][j][k]);
					units[i][j][k] += this.learningRate * weightUpdate[i][j][k];
				}
			}
		}
	}
	
	/// Calculate the output error
	double calculateError(double[][] trainingInputs, double[][] trainingOutputs){
		if(trainingInputs.length != trainingOutputs.length) throw new InputException("trainingInputs and trainingOutputs must be the same size");
		double[] outputs;
		double total=0,temp;
		for(int i=0; i<trainingInputs.length; i++){
			outputs = evaluate(trainingInputs[i]);
			if(outputs.length != trainingOutputs[i].length) throw new InputException("Wrong output length");
			for(int j=0; j<outputs.length; j++){
				temp = trainingOutputs[i][j] - outputs[j];
				//writefln("&%f,%f",temp*temp,total);
				total += temp * temp;
			}
		}
		return 0.5 * total;
	}
	
	double[][][] getWeights(){
		return units.dup;
	}
}

void printArray(double[] array){
	writef("[");
	for(int i=0; i<array.length-1; i++){
		writef("%f, ",array[i]);
	}
	writefln("%f]",array[$-1]);
}

void printArray(double[][] array){
	writef("[");
	for(int i=0; i<array.length; i++){
		printArray(array[i]);
	}
	writefln("]");
}

void printArray(double[][][] array){
	writef("[");
	for(int i=0; i<array.length; i++){
		printArray(array[i]);
	}
	writefln("]");
}