/**********************************************************************
Neural Network example. Predicts sunspot magnitudes over a period
of years and then predicts over later years and finds accuracy of
those predictions.

For latest version see: http://moonflare.com/code/nnetwork.php

Copyright (c) 2003, Derrick Coetzee
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:

- Redistributions of source code must retain the above copyright
  notice, this list of conditions and the following disclaimer.

- Redistributions in binary form must reproduce the above copyright
  notice, this list of conditions and the following disclaimer in
  the documentation and/or other materials provided with the
  distribution.

- The name of Derrick Coetzee may not be used to endorse or promote
  products derived from this software without specific prior
  written permission.

This software is provided by the copyright holders and contributors
"as is" and any express or implied warranties, including, but not
limited to, the implied warranties of merchantability and fitness
for a particular purpose are disclaimed. In no event shall the
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(including, but not limited to, procurement of substitute goods or
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**********************************************************************/

#include "Net.h"

#include <time.h>
#include <string.h>
#include <stdlib.h>

#include <fstream>
#include <iostream>

using namespace NeuralNetwork;

// Number of years we have sunspot data for
const int NUM_YEARS = 280;
// Number of previous years given to the network to help predict
const int WINDOW_SIZE = 30;

// Normalized sunspots per year for each of many years in order
real      Sunspots [NUM_YEARS] = {

            0.0262,  0.0575,  0.0837,  0.1203,  0.1883,  0.3033,  
            0.1517,  0.1046,  0.0523,  0.0418,  0.0157,  0.0000,  
            0.0000,  0.0105,  0.0575,  0.1412,  0.2458,  0.3295,  
            0.3138,  0.2040,  0.1464,  0.1360,  0.1151,  0.0575,  
            0.1098,  0.2092,  0.4079,  0.6381,  0.5387,  0.3818,  
            0.2458,  0.1831,  0.0575,  0.0262,  0.0837,  0.1778,  
            0.3661,  0.4236,  0.5805,  0.5282,  0.3818,  0.2092,  
            0.1046,  0.0837,  0.0262,  0.0575,  0.1151,  0.2092,  
            0.3138,  0.4231,  0.4362,  0.2495,  0.2500,  0.1606,  
            0.0638,  0.0502,  0.0534,  0.1700,  0.2489,  0.2824,  
            0.3290,  0.4493,  0.3201,  0.2359,  0.1904,  0.1093,  
            0.0596,  0.1977,  0.3651,  0.5549,  0.5272,  0.4268,  
            0.3478,  0.1820,  0.1600,  0.0366,  0.1036,  0.4838,  
            0.8075,  0.6585,  0.4435,  0.3562,  0.2014,  0.1192,  
            0.0534,  0.1260,  0.4336,  0.6904,  0.6846,  0.6177,  
            0.4702,  0.3483,  0.3138,  0.2453,  0.2144,  0.1114,  
            0.0837,  0.0335,  0.0214,  0.0356,  0.0758,  0.1778,  
            0.2354,  0.2254,  0.2484,  0.2207,  0.1470,  0.0528,  
            0.0424,  0.0131,  0.0000,  0.0073,  0.0262,  0.0638,  
            0.0727,  0.1851,  0.2395,  0.2150,  0.1574,  0.1250,  
            0.0816,  0.0345,  0.0209,  0.0094,  0.0445,  0.0868,  
            0.1898,  0.2594,  0.3358,  0.3504,  0.3708,  0.2500,  
            0.1438,  0.0445,  0.0690,  0.2976,  0.6354,  0.7233,  
            0.5397,  0.4482,  0.3379,  0.1919,  0.1266,  0.0560,  
            0.0785,  0.2097,  0.3216,  0.5152,  0.6522,  0.5036,  
            0.3483,  0.3373,  0.2829,  0.2040,  0.1077,  0.0350,  
            0.0225,  0.1187,  0.2866,  0.4906,  0.5010,  0.4038,  
            0.3091,  0.2301,  0.2458,  0.1595,  0.0853,  0.0382,  
            0.1966,  0.3870,  0.7270,  0.5816,  0.5314,  0.3462,  
            0.2338,  0.0889,  0.0591,  0.0649,  0.0178,  0.0314,  
            0.1689,  0.2840,  0.3122,  0.3332,  0.3321,  0.2730,  
            0.1328,  0.0685,  0.0356,  0.0330,  0.0371,  0.1862,  
            0.3818,  0.4451,  0.4079,  0.3347,  0.2186,  0.1370,  
            0.1396,  0.0633,  0.0497,  0.0141,  0.0262,  0.1276,  
            0.2197,  0.3321,  0.2814,  0.3243,  0.2537,  0.2296,  
            0.0973,  0.0298,  0.0188,  0.0073,  0.0502,  0.2479,  
            0.2986,  0.5434,  0.4215,  0.3326,  0.1966,  0.1365,  
            0.0743,  0.0303,  0.0873,  0.2317,  0.3342,  0.3609,  
            0.4069,  0.3394,  0.1867,  0.1109,  0.0581,  0.0298,  
            0.0455,  0.1888,  0.4168,  0.5983,  0.5732,  0.4644,  
            0.3546,  0.2484,  0.1600,  0.0853,  0.0502,  0.1736,  
            0.4843,  0.7929,  0.7128,  0.7045,  0.4388,  0.3630,  
            0.1647,  0.0727,  0.0230,  0.1987,  0.7411,  0.9947,  
            0.9665,  0.8316,  0.5873,  0.2819,  0.1961,  0.1459,  
            0.0534,  0.0790,  0.2458,  0.4906,  0.5539,  0.5518,  
            0.5465,  0.3483,  0.3603,  0.1987,  0.1804,  0.0811,  
            0.0659,  0.1428,  0.4838,  0.8127 };

// Returns a sequence of examples whose desired outputs run from a
// given lower index to a given upper index in the array above.
class ArrayRangeExampleFactory : public ExampleFactory {
public:
    ArrayRangeExampleFactory(int initLower, int initUpper)
	: currentExample(initLower), lower(initLower), upper(initUpper)
	{ }
    void getExample(int inputSize, real* input, int outputSize, real* output)
    {
	memcpy(input, &Sunspots[currentExample-WINDOW_SIZE],
	       WINDOW_SIZE*sizeof(real));
	output[0] = Sunspots[currentExample];
	currentExample++;
	if (currentExample > upper)
	    currentExample = lower;
    }
    int numExamples() { return upper-lower+1; }

private:
    int currentExample;
    int lower, upper;
};

// Ranges in the sunspots array of training set, test set, and eval set
const int TRAIN_LWB = WINDOW_SIZE;
const int TRAIN_UPB = 179;
const int TEST_LWB  = 180;
const int TEST_UPB  = 259;
const int EVAL_LWB  = 260;
const int EVAL_UPB  = NUM_YEARS - 1;

// Creates a neural network to predict sunspots for a year given the
// previous WINDOW_SIZE years of data, and demonstrates the results
// on the eval set.
int main(int argc, char* argv[])
{
    using std::cout;
    using std::endl;

    srand(35233);

    Net *net;

    // Create a new network with 10 hidden layers, WINDOW_SIZE inputs, 1 output
    int layerSizes[] = { WINDOW_SIZE, 10, 1 };
    net = new Net(3, layerSizes, 0.05, 0.5, 1.0);

    // Initialize to random weights, then autotrain with training and test sets
    net->randomizeWeights();
    ArrayRangeExampleFactory training(TRAIN_LWB, TRAIN_UPB);
    ArrayRangeExampleFactory test    (TEST_LWB,  TEST_UPB);
    real error = net->autotrain(training, test, 10, 1.05f);

    // Show final test set error, which should be virtually minimized
    cout << "Final test set error: " << error << endl;

    net->doneTraining();

    // Compare results on the eval set
    for (int i=EVAL_LWB; i < EVAL_UPB; i++)
    {
	real output[1];
	net->run(&Sunspots[i-WINDOW_SIZE], output);
	cout.precision(4);
	cout << "Predicted: " << output[0] << ", Actual: " << Sunspots[i] << endl;
    }

    std::ofstream out("backprop.nnw", std::ios::binary);
    net->save(out);

    return 0;
}