//
//  TrainAndTest.c
//  MLCoursework
//
//  This is a fairly inefficient implentation that does not use any dynamic memory allocation
// because that wouldnot be safe on the DEWIS marking system
//
//  Created by Jim Smith on 06/02/2017.
//  Copyright © 2017 Jim SmithJim Smith. All rights reserved.
//

#include "TrainAndTest.h"
#include "math.h"
#include <stdio.h>
#include <stdlib.h>



//declare this array as static but make it available to any function in this file
//in case we want to store the training examples  and use them later
static    double myModel[NUM_TRAINING_SAMPLES][NUM_FEATURES];
//even if each item in the training set is from a diffferent class we know how many there are
static char myModelLabels[NUM_TRAINING_SAMPLES];

static int trainingSetSize = 0;


//Swap function for Bubble Sort
void swap(double *a, double *b) {
	double placeholder = *a;
	*a = *b;
	*b = placeholder;
}

//Bubble sort
void bubbles(double array[], int n) {
	int i, j;
	for (i = 0; i < n - 1; i++)

		// Last i elements are already in place
		for (j = 0; j < n - i - 1; j++)
			if (array[j] > array[j + 1])
				swap(&array[j], &array[j + 1]);
}//end of bubble sort


int  train(double **trainingSamples, char *trainingLabels, int numSamples, int numFeatures) {

	int returnval = 1;
	int sample, feature;

	//clean the model because C leaves whatever is in the memory |JIM|
	for (sample = 0; sample < NUM_TRAINING_SAMPLES; sample++) {
		for (feature = 0; feature<NUM_FEATURES; feature++) {
			myModel[sample][feature] = 0.0;
		}
	}

	//sanity checking  |JIM|
	if (numFeatures > NUM_FEATURES || numSamples > NUM_TRAINING_SAMPLES) {
		fprintf(stdout, "error: called train with data set larger than spaced allocated to store it");
		returnval = 0;
	}


	if (returnval == 1) {
		//store the labels and the feature values
		trainingSetSize = numSamples;
		int index, feature;
		for (index = 0; index < numSamples; index++) {
			myModelLabels[index] = trainingLabels[index];
			for (feature = 0; feature < numFeatures; feature++) {
				myModel[index][feature] = trainingSamples[index][feature];
			}
		}
		fprintf(stdout, "data stored locally \n");
	}//end else


	return returnval;
}



char  predictLabel(double *testSample, int numFeatures) {

	double duplicateArray[NUM_TRAINING_SAMPLES]; //Array to hold neighbourDistances before sorting
	double neighbourDistances[NUM_TRAINING_SAMPLES]; //Array to hold distances to neighbours
	int catA = 0, catB = 0, catC = 0; // Variables for classifying and holding the different categories of data
	int k = 9; //Number of neighbours checked
	int prediction[9];


	// square root of the sum of the squared differences between the two arrays of numbers
	for (int i = 0; i < NUM_TRAINING_SAMPLES; i++)
		neighbourDistances[i] =
		sqrt((myModel[i][0] - testSample[0]) * (myModel[i][0] - testSample[0]) +
			(myModel[i][1] - testSample[1]) * (myModel[i][1] - testSample[1]) +
			(myModel[i][2] - testSample[2]) * (myModel[i][2] - testSample[2]) +
			(myModel[i][3] - testSample[3]) * (myModel[i][3] - testSample[3]));

	//puts original array into new duplicateArray
	for (int i = 0; i < NUM_TRAINING_SAMPLES; i++)
		duplicateArray[i] = neighbourDistances[i];


	//sorts original array
	bubbles(neighbourDistances, NUM_TRAINING_SAMPLES);


	//compares original array with the sorted array
	for (int i = 0; i < k; i++) {
		for (int j = 0; j < NUM_TRAINING_SAMPLES; j++) {
			if (neighbourDistances[i] == duplicateArray[j]) {
				prediction[i] = j;
			}
		}
	}


	//prints sorted neighbours array
	printf("Sorted array: \n");
	for (int i = 0; i < k; i++) {
		printf("%lf\n", neighbourDistances[i]);
	}


	for (int i = 0; i < k; i++)
	{
		if (myModelLabels[prediction[i]] == myModelLabels[0])
			catA++;
		else if (myModelLabels[prediction[i]] > myModelLabels[0] && myModelLabels[prediction[i]] <= myModelLabels[49])
			catB++;
		else  if (myModelLabels[prediction[i]] >= myModelLabels[99])
			catC++;
	}

	//Check likelihood of category and return most prominent one
	if ((catA > catB) && (catA > catC)) return myModelLabels[0];
	else if ((catB > catA) && (catB > catC)) return myModelLabels[49];
	else return myModelLabels[99];

}