#include "cranium.h"

/*
This basic example program is the skeleton of a classification problem.
The training data should be in matrix form, where each row is a data point, and
    each column is a feature. 
The training classes should be in matrix form, where the ith row corresponds to
    the ith training example, and each column is a 1 if it is of that class, and
    0 otherwise. Each example may only be of 1 class.
*/

// create training data and target values (data collection not shown)
int rows, features, classes;
float** training;
float** classes;

// create datasets to hold the data
DataSet* trainingData = createDataSet(rows, features, training);
DataSet* trainingClasses = createDataSet(rows, classes, classes);

// create network with 2 input neurons, 1 hidden layer with sigmoid
// activation function and 5 neurons, and 2 output neurons with softmax 
// activation function
srand(time(NULL));
size_t hiddenSize[] = {5};
Activation hiddenActivation[] = {sigmoid};
Network* net = createNetwork(2, 1, hiddenSize, hiddenActivation, 2, softmax);

// train network with cross-entropy loss using Mini-Batch SGD
ParameterSet params;
params.network = net;
params.data = trainingData;
params.classes = trainingClasses;
params.lossFunction = CROSS_ENTROPY_LOSS;
params.batchSize = 20;
params.learningRate = .01;
params.searchTime = 5000;
params.regularizationStrength = .001;
params.momentumFactor = .9;
params.maxIters = 10000;
params.shuffle = 1;
params.verbose = 1;
optimize(params);

// test accuracy of network after training
printf("Accuracy is %f\n", accuracy(net, trainingData, trainingClasses));

// get network's predictions on input data after training
forwardPass(net, trainingData);
int* predictions = predict(net);
free(predictions);

// save network to a file
saveNetwork(net, "network");

// free network and data
destroyNetwork(net);
destroyDataSet(trainingData);
destroyDataSet(trainingClasses);

// load previous network from file
Network* previousNet = readNetwork("network");
destroyNetwork(previousNet);