/*
 * GENANN - Minimal C Artificial Neural Network
 *
 * Copyright (c) 2015-2018 Lewis Van Winkle
 *
 * http://CodePlea.com
 *
 * This software is provided 'as-is', without any express or implied
 * warranty. In no event will the authors be held liable for any damages
 * arising from the use of this software.
 *
 * Permission is granted to anyone to use this software for any purpose,
 * including commercial applications, and to alter it and redistribute it
 * freely, subject to the following restrictions:
 *
 * 1. The origin of this software must not be misrepresented; you must not
 *    claim that you wrote the original software. If you use this software
 *    in a product, an acknowledgement in the product documentation would be
 *    appreciated but is not required.
 * 2. Altered source versions must be plainly marked as such, and must not be
 *    misrepresented as being the original software.
 * 3. This notice may not be removed or altered from any source distribution.
 *
 */

#include "genann.h"
#include "minctest.h"
#include <stdio.h>
#include <math.h>
#include <stdlib.h>



void basic() {
    genann *ann = genann_init(1, 0, 0, 1);

    lequal(ann->total_weights, 2);
    double a;


    a = 0;
    ann->weight[0] = 0;
    ann->weight[1] = 0;
    lfequal(0.5, *genann_run(ann, &a));

    a = 1;
    lfequal(0.5, *genann_run(ann, &a));

    a = 11;
    lfequal(0.5, *genann_run(ann, &a));

    a = 1;
    ann->weight[0] = 1;
    ann->weight[1] = 1;
    lfequal(0.5, *genann_run(ann, &a));

    a = 10;
    ann->weight[0] = 1;
    ann->weight[1] = 1;
    lfequal(1.0, *genann_run(ann, &a));

    a = -10;
    lfequal(0.0, *genann_run(ann, &a));

    genann_free(ann);
}


void xor() {
    genann *ann = genann_init(2, 1, 2, 1);
    ann->activation_hidden = genann_act_threshold;
    ann->activation_output = genann_act_threshold;

    lequal(ann->total_weights, 9);

    /* First hidden. */
    ann->weight[0] = .5;
    ann->weight[1] = 1;
    ann->weight[2] = 1;

    /* Second hidden. */
    ann->weight[3] = 1;
    ann->weight[4] = 1;
    ann->weight[5] = 1;

    /* Output. */
    ann->weight[6] = .5;
    ann->weight[7] = 1;
    ann->weight[8] = -1;


    double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
    double output[4] = {0, 1, 1, 0};

    lfequal(output[0], *genann_run(ann, input[0]));
    lfequal(output[1], *genann_run(ann, input[1]));
    lfequal(output[2], *genann_run(ann, input[2]));
    lfequal(output[3], *genann_run(ann, input[3]));

    genann_free(ann);
}


void backprop() {
    genann *ann = genann_init(1, 0, 0, 1);

    double input, output;
    input = .5;
    output = 1;

    double first_try = *genann_run(ann, &input);
    genann_train(ann, &input, &output, .5);
    double second_try = *genann_run(ann, &input);
    lok(fabs(first_try - output) > fabs(second_try - output));

    genann_free(ann);
}


void train_and() {
    double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
    double output[4] = {0, 0, 0, 1};

    genann *ann = genann_init(2, 0, 0, 1);

    int i, j;

    for (i = 0; i < 50; ++i) {
        for (j = 0; j < 4; ++j) {
            genann_train(ann, input[j], output + j, .8);
        }
    }

    ann->activation_output = genann_act_threshold;
    lfequal(output[0], *genann_run(ann, input[0]));
    lfequal(output[1], *genann_run(ann, input[1]));
    lfequal(output[2], *genann_run(ann, input[2]));
    lfequal(output[3], *genann_run(ann, input[3]));

    genann_free(ann);
}


void train_or() {
    double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
    double output[4] = {0, 1, 1, 1};

    genann *ann = genann_init(2, 0, 0, 1);
    genann_randomize(ann);

    int i, j;

    for (i = 0; i < 50; ++i) {
        for (j = 0; j < 4; ++j) {
            genann_train(ann, input[j], output + j, .8);
        }
    }

    ann->activation_output = genann_act_threshold;
    lfequal(output[0], *genann_run(ann, input[0]));
    lfequal(output[1], *genann_run(ann, input[1]));
    lfequal(output[2], *genann_run(ann, input[2]));
    lfequal(output[3], *genann_run(ann, input[3]));

    genann_free(ann);
}



void train_xor() {
    double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
    double output[4] = {0, 1, 1, 0};

    genann *ann = genann_init(2, 1, 2, 1);

    int i, j;

    for (i = 0; i < 500; ++i) {
        for (j = 0; j < 4; ++j) {
            genann_train(ann, input[j], output + j, 3);
        }
        /* printf("%1.2f ", xor_score(ann)); */
    }

    ann->activation_output = genann_act_threshold;
    lfequal(output[0], *genann_run(ann, input[0]));
    lfequal(output[1], *genann_run(ann, input[1]));
    lfequal(output[2], *genann_run(ann, input[2]));
    lfequal(output[3], *genann_run(ann, input[3]));

    genann_free(ann);
}



#ifndef genann_act
void train_xor_act(genann_actfun act) {
    double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
    double output[4] = {0, 1, 1, 0};

    int i, j, r;
    int solved = 0;

    /* An unlucky starting position can strand training (e.g. dead relu
     * units), so allow a few restarts. */
    for (r = 0; r < 10 && !solved; ++r) {
        genann *ann = genann_init(2, 1, 4, 1);
        ann->activation_hidden = act;

        for (i = 0; i < 2000; ++i) {
            for (j = 0; j < 4; ++j) {
                genann_train(ann, input[j], output + j, .5);
            }
        }

        solved = 1;
        for (j = 0; j < 4; ++j) {
            if ((*genann_run(ann, input[j]) > .5) != (output[j] > .5)) solved = 0;
        }

        genann_free(ann);
    }

    lok(solved);
}


void train_xor_tanh() {
    train_xor_act(genann_act_tanh);
}


void train_xor_relu() {
    train_xor_act(genann_act_relu);
}


void gradient_act(genann_actfun hidden, genann_actfun output) {
    double input[2] = {.3, -.2};
    double target[1] = {.7};
    const double eps = 1e-6;
    const double rate = .1;
    double checked = 0;
    int i;

    genann *ann = genann_init(2, 1, 3, 1);
    ann->activation_hidden = hidden;
    ann->activation_output = output;

    /* Fixed, varied weights keep this test deterministic. */
    for (i = 0; i < ann->total_weights; ++i) {
        ann->weight[i] = sin(i * 1.7) * .5;
    }

    genann *trained = genann_copy(ann);
    genann_train(trained, input, target, rate);

    /* Each weight update must match the central-difference gradient of
     * the squared error E = (target - out)^2 / 2. */
    for (i = 0; i < ann->total_weights; ++i) {
        const double save = ann->weight[i];
        double o, e1, e2, numeric;

        ann->weight[i] = save + eps;
        o = *genann_run(ann, input);
        e1 = .5 * (target[0] - o) * (target[0] - o);

        ann->weight[i] = save - eps;
        o = *genann_run(ann, input);
        e2 = .5 * (target[0] - o) * (target[0] - o);

        ann->weight[i] = save;
        numeric = (e1 - e2) / (2 * eps);
        checked += fabs(numeric);

        lok(fabs((trained->weight[i] - save) - (-rate * numeric)) < 1e-7);
    }

    /* Guard against passing trivially with an all-zero gradient. */
    lok(checked > .001);

    genann_free(ann);
    genann_free(trained);
}


void gradient_tanh() {
    gradient_act(genann_act_tanh, genann_act_tanh);
}


void gradient_relu() {
    gradient_act(genann_act_relu, genann_act_sigmoid);
}
#endif


void persist() {
    genann *first = genann_init(1000, 5, 50, 10);

    FILE *out = fopen("persist.txt", "w");
    genann_write(first, out);
    fclose(out);


    FILE *in = fopen("persist.txt", "r");
    genann *second = genann_read(in);
    fclose(in);

    lequal(first->inputs, second->inputs);
    lequal(first->hidden_layers, second->hidden_layers);
    lequal(first->hidden, second->hidden);
    lequal(first->outputs, second->outputs);
    lequal(first->total_weights, second->total_weights);

    int i;
    for (i = 0; i < first->total_weights; ++i) {
        lok(first->weight[i] == second->weight[i]);
    }

    genann_free(first);
    genann_free(second);
}


void copy() {
    genann *first = genann_init(1000, 5, 50, 10);

    genann *second = genann_copy(first);

    lequal(first->inputs, second->inputs);
    lequal(first->hidden_layers, second->hidden_layers);
    lequal(first->hidden, second->hidden);
    lequal(first->outputs, second->outputs);
    lequal(first->total_weights, second->total_weights);

    int i;
    for (i = 0; i < first->total_weights; ++i) {
        lfequal(first->weight[i], second->weight[i]);
    }

    genann_free(first);
    genann_free(second);
}


void sigmoid() {
    double i = -20;
    const double max = 20;
    const double d = .0001;

    while (i < max) {
        lfequal(genann_act_sigmoid(NULL, i), genann_act_sigmoid_cached(NULL, i));
        i += d;
    }
}


int main(int argc, char *argv[])
{
    printf("GENANN TEST SUITE\n");

    srand(100); //Repeatable test results.

    lrun("basic", basic);
    lrun("xor", xor);
    lrun("backprop", backprop);
    lrun("train and", train_and);
    lrun("train or", train_or);
    lrun("train xor", train_xor);
#ifndef genann_act
    lrun("train tanh", train_xor_tanh);
    lrun("train relu", train_xor_relu);
    lrun("gradient tanh", gradient_tanh);
    lrun("gradient relu", gradient_relu);
#endif
    lrun("persist", persist);
    lrun("copy", copy);
    lrun("sigmoid", sigmoid);

    lresults();

    return lfails != 0;
}