Full Code of stagadish/NNplusplus for AI

Repository: stagadish/NNplusplus
Branch: master
Commit: 12d1916d7e5f
Files: 17
Total size: 9.6 MB

Directory structure:
gitextract_qagr5j_h/

├── .gitignore
├── License.md
├── Matrix.cpp
├── Matrix.hpp
├── MatrixExceptions.hpp
├── NeuralNet.cpp
├── NeuralNet.hpp
├── README.md
├── data/
│   ├── test_data/
│   │   └── mnist_test_10.txt
│   └── training_data/
│       └── mnist_train_100.txt
├── main.cpp
├── reports/
│   ├── 60000 training dataset report.txt
│   └── example report.txt
├── saved_nets/
│   ├── 2016-8-23--16-12-08.nn
│   ├── 2016-8-24--07-12-33.nn
│   └── 60,000vs10,000--2016-8-22--01-12-07.nn
└── tests.cpp

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
# Compiled source #
###################
*.com
*.class
*.dll
*.exe
*.o
*.so

# Packages #
############
# it's better to unpack these files and commit the raw source
# git has its own built in compression methods
*.7z
*.dmg
*.gz
*.iso
*.jar
*.rar
*.tar
*.zip

# Logs and databases #
######################
*.log
*.sql
*.sqlite

# OS generated files #
######################
.DS_Store
.DS_Store?
._*
.Spotlight-V100
.Trashes
ehthumbs.db
Thumbs.db

# Project Specifics #
#####################
data/test_data/mnist_test.txt
data/training_data/mnist_train.txt

================================================
FILE: License.md
================================================
MIT License

Copyright (c) 2016 Gil Dekel

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: Matrix.cpp
================================================
//
//  Matrix.cpp
//  Neural Net
//
//  Created by Gil Dekel on 8/19/16.
//  Last edited by Gil Dekel on 8/30/16.
//

#include "Matrix.hpp"

/*
 * Private members for reference
 *
 * size_t m_size_;      // (M)xN
 * size_t n_size_;      // Mx(N)
 * double *matrix_;     // A pointer to the array.
 * double **rowPtrs_;   // An array of row pointers.
 *                      // used to avoid repeated arithmetics
 *                      // at each access to the matrix.
 *
 */


/**********************************************************
 * Constructors
 **********************************************************/

Matrix::Matrix(size_t m, size_t n) : m_size_{m}, n_size_{n} {
    matrix_ = new double[m_size_ * n_size_]();
    rowPtrs_ = new double*[m_size_];
    
    for (size_t i = 0; i < m_size_; ++i) {
        rowPtrs_[i] = &matrix_[i*n_size_];
    }
}

Matrix::Matrix(const Matrix &rhs) : m_size_{rhs.m_size_}, n_size_{rhs.n_size_} {
    matrix_ = new double[m_size_ * n_size_]();
    rowPtrs_ = new double*[m_size_];
    
    for (int i = 0; i < m_size_ * n_size_; ++i) {
        matrix_[i] = rhs.matrix_[i];
    }
    
    for (size_t i = 0; i < m_size_; ++i) {
        rowPtrs_[i] = &matrix_[i*n_size_];
    }
    
}

Matrix& Matrix::operator=(const Matrix &rhs) {
    if (this != &rhs) {
        Matrix copy{rhs};
            std::swap(*this, copy);
    }
    return *this;
}

Matrix::Matrix(Matrix &&rhs) : m_size_{rhs.m_size_}, n_size_{rhs.n_size_}, matrix_{rhs.matrix_}, rowPtrs_{rhs.rowPtrs_} {
    rhs.m_size_ = 0;
    rhs.n_size_ = 0;
    rhs.matrix_ = nullptr;
    rhs.rowPtrs_ = nullptr;
}

Matrix& Matrix::operator=(Matrix &&rhs) {
    std::swap(m_size_, rhs.m_size_);
    std::swap(n_size_, rhs.n_size_);
    std::swap(matrix_, rhs.matrix_);
    std::swap(rowPtrs_, rhs.rowPtrs_);
    return *this;
}

Matrix::~Matrix() {
    delete [] matrix_;
    delete [] rowPtrs_;
}

/**********************************************************
 * Operator Overloads
 **********************************************************/

double& Matrix::operator()(size_t row, size_t col) {
    return rowPtrs_[row][col];
}

const double& Matrix::operator()(size_t row, size_t col) const {
    return rowPtrs_[row][col];
}


// ADDITION
Matrix& Matrix::operator+=(const Matrix & rhs) {
    if (m_size_ == rhs.m_size_ && n_size_ == rhs.n_size_) {
        for (size_t i = 0; i < m_size_ * n_size_; ++i) {
            matrix_[i] += rhs.matrix_[i];
        }
        return *this;
    } else
        throw MatrixDimensionsMismatch();
}

Matrix& Matrix::operator+=(double scalar) {
    for (size_t i = 0; i < m_size_ * n_size_; ++i) {
        matrix_[i] += scalar;
    }
    return *this;
}



// SUBTRACTION
Matrix& Matrix::operator-=(const Matrix & rhs) {
    if (m_size_ == rhs.m_size_ && n_size_ == rhs.n_size_) {
        for (size_t i = 0; i < m_size_ * n_size_; ++i) {
            matrix_[i] -= rhs.matrix_[i];
        }
        return *this;
    } else
        throw MatrixDimensionsMismatch();
}

Matrix& Matrix::operator-=(double scalar) {
    for (size_t i = 0; i < m_size_ * n_size_; ++i) {
        matrix_[i] -= scalar;
    }
    return *this;
}



// MULTIPLICATION
Matrix& Matrix::operator*=(const Matrix & rhs) {
    if (m_size_ == rhs.m_size_ && n_size_ == rhs.n_size_) {
        for (size_t i = 0; i < m_size_ * n_size_; ++i) {
            matrix_[i] *= rhs.matrix_[i];
        }
        return *this;
    } else
        throw MatrixDimensionsMismatch();
}

Matrix& Matrix::operator*=(double scalar) {
    for (size_t i = 0; i < m_size_ * n_size_; ++i) {
        matrix_[i] *= scalar;
    }
    return *this;
}



//DIVISION
Matrix& Matrix::operator/=(const Matrix & rhs) {
    if (m_size_ == rhs.m_size_ && n_size_ == rhs.n_size_) {
        for (size_t i = 0; i < m_size_ * n_size_; ++i) {
            matrix_[i] /= rhs.matrix_[i];
        }
        return *this;
    } else
        throw MatrixDimensionsMismatch();
}

Matrix& Matrix::operator/=(double scalar) {
    for (size_t i = 0; i < m_size_ * n_size_; ++i) {
        matrix_[i] /= scalar;
    }
    return *this;
}



// UNARY NEGATION
Matrix Matrix::operator-() const {
    Matrix neg{*this};
    for (size_t i = 0; i < m_size_ * n_size_; ++i) {
        neg.matrix_[i] = -neg.matrix_[i];
    }
    return neg;
}




/**********************************************************
 * Other Functions
 **********************************************************/

Matrix Matrix::dot(const Matrix& rhs) const {
    if (this->n_size_ == rhs.m_size_) {
        Matrix rhs_T{rhs.T()};
        Matrix dproduct(m_size_, rhs.n_size_);
        
        for (size_t i = 0; i < m_size_; ++i) {
            for (size_t j = 0; j < rhs_T.m_size_; ++j) {
                double dot = 0;
                for (size_t k = 0; k < n_size_; ++k) {
                    dot += rowPtrs_[i][k] * rhs_T.rowPtrs_[j][k];
                }
                dproduct.rowPtrs_[i][j] = dot;
            }
        }
        return dproduct;
    } else
        throw MatrixInnderDimensionsMismatch();
}

size_t Matrix::getNumOfRows() const { return m_size_; }
size_t Matrix::getNumOfCols() const { return n_size_; }

Matrix Matrix::T() const {
    Matrix T(n_size_, m_size_);
    for (size_t i = 0; i < m_size_; ++i) {
        for (size_t j = 0; j < n_size_; ++j) {
            T.rowPtrs_[j][i] = rowPtrs_[i][j];
        }
    }
    return T;
}

std::pair<size_t, size_t> Matrix::getMaxVal() const {
    long int maxI = -1;
    long int maxJ = -1;
    double maxVal = -INFINITY;
    
    for (size_t i = 0; i < m_size_; ++i) {
        for (size_t j = 0; j < n_size_; ++j) {
            if (rowPtrs_[i][j] >= maxVal) {
                maxVal = rowPtrs_[i][j];
                maxI = i;
                maxJ = j;
            }
        }
    }
    return std::pair<size_t, size_t>(maxI, maxJ);
}

void Matrix::printMtrx() const {
    for (size_t i = 0; i < m_size_; ++i) {
        for (size_t j = 0; j < n_size_; ++j) {
            std::cout << rowPtrs_[i][j] << "\t\t";
        }
        std::cout << std::endl;
    }
    std::cout << std::endl;
}

/**********************************************************
 * Non-member, Friend Functions
 **********************************************************/

// ADDITION
Matrix operator+(Matrix lhs, const Matrix &rhs) {
    if (lhs.m_size_ == rhs.m_size_ && lhs.n_size_ == rhs.n_size_) {
        return lhs += rhs;
    } else
        throw MatrixDimensionsMismatch();
}

Matrix operator+(Matrix lhs, double scalar) {
    return lhs += scalar;
}

Matrix operator+(double scalar, Matrix rhs) {
    return rhs += scalar;
}



// SUBTRACTION
Matrix operator-(Matrix lhs, const Matrix &rhs) {
    if (lhs.m_size_ == rhs.m_size_ && lhs.n_size_ == rhs.n_size_) {
        return lhs -= rhs;
    } else
        throw MatrixDimensionsMismatch();
}

Matrix operator-(Matrix lhs, double scalar) {
    return lhs -= scalar;
}

Matrix operator-(double scalar, Matrix rhs) {
    return -rhs += scalar;
}



// MULTIPLICATION
Matrix operator*(Matrix lhs, const Matrix &rhs) {
    if (lhs.m_size_ == rhs.m_size_ && lhs.n_size_ == rhs.n_size_) {
        return lhs *= rhs;
    } else
        throw MatrixDimensionsMismatch();
}

Matrix operator*(Matrix lhs, double scalar) {
    return lhs *= scalar;
}

Matrix operator*(double scalar, Matrix rhs) {
    return rhs *= scalar;
}



//DIVISION
Matrix operator/(Matrix lhs, const Matrix &rhs) {
    if (lhs.m_size_ == rhs.m_size_ && lhs.n_size_ == rhs.n_size_) {
        return lhs /= rhs;
    } else
        throw MatrixDimensionsMismatch();
}

Matrix operator/(Matrix lhs, double scalar) {
    return lhs /= scalar;
}

Matrix operator/(double scalar, Matrix rhs) {
    for (size_t i = 0; i < rhs.m_size_ * rhs.n_size_; ++i) {
        rhs.matrix_[i] = scalar/rhs.matrix_[i];
    }
    return rhs;
}




================================================
FILE: Matrix.hpp
================================================
//
//  Matrix.hpp
//  Neural Net
//
//  A matrix object, which includes basic operations such as
//  matrix transpose and dot product.
//
//  Created by Gil Dekel on 8/19/16.
//  Last edited by Gil Dekel on 8/30/16.
//

#ifndef MATRIX_HPP_
#define MATRIX_HPP_

#include <iostream>
#include <utility>          // std::swap and std::move
#include <vector>
#include <cmath>            // INFINITY

#include "MatrixExceptions.hpp"

class Matrix {
public:
    
    /**********************************************************
     * Constructors
     **********************************************************/
    
    // Basic ctor to inisialize a matrix of size m by n.
    // All matrix positions will be initialized to 0.
    Matrix(size_t m = 0, size_t n = 0);
    
    // COPY ctor
    Matrix(const Matrix &rhs);

    // Copy assignment operator
    Matrix& operator=(const Matrix &rhs);

    // MOVE ctor
    Matrix(Matrix &&rhs);

    // Move assignment operator
    Matrix& operator=(Matrix &&rhs);

    // dealloc matrix_ (dtor)
    ~Matrix();
    
    /**********************************************************
     * Operator Overloads
     **********************************************************/
    
    // A substitute to operator[] for a 2D arrays
    double& operator()(size_t row, size_t col);
    
    const double& operator()(size_t row, size_t col) const;
    
    
    
    // ADDITION

    Matrix& operator+=(const Matrix & rhs);
    
    Matrix& operator+=(double scalar);
    
    // Term by term addition operator for two matricies.
    friend Matrix operator+(Matrix lhs, const Matrix &rhs);
    
    // Term by term addition operator for matrix and scalar.
    friend Matrix operator+(Matrix lhs, double scalar);
    
    // Allowing for the scalar addition commutative property.
    friend Matrix operator+(double scalar, Matrix rhs);


    
    // SUBTRACTION
    
    Matrix& operator-=(const Matrix & rhs);
    
    Matrix& operator-=(double scalar);
    
    // Term by term subtraction operator for two matricies.
    friend Matrix operator-(Matrix lhs, const Matrix &rhs);
    
    // Term by term subtraction operator for matrix and scalar.
    friend Matrix operator-(Matrix lhs, double scalar);
    
    // Term by term subtraction operator for scalar and matrix.
    friend Matrix operator-(double scalar, Matrix rhs);
    
    
    
    // MULTIPLICATION
    
    Matrix& operator*=(const Matrix & rhs);
    
    Matrix& operator*=(double scalar);
    
    // "Regular", term by term multiplication operator.
    // See function dot(Matrix &rhs) for dot product.
    friend Matrix operator*(Matrix lhs, const Matrix &rhs);
    
    // "Regular" scalar multiplication over matrix.
    friend Matrix operator*(Matrix lhs, double scalar);
    
    // Allowing for the scalar multiplication commutative property.
    friend Matrix operator*(double scalar, Matrix rhs);
    
    
    
    //DIVISION
    
    Matrix& operator/=(const Matrix & rhs);
    
    Matrix& operator/=(double scalar);
    
    // Term by term division operator for two matricies.
    friend Matrix operator/(Matrix lhs, const Matrix &rhs);
    
    // Term by term division operator for matrix and scalar.
    friend Matrix operator/(Matrix lhs, double scalar);
    
    // Term by term division operator for scalar and matrix.
    friend Matrix operator/(double scalar, Matrix rhs);
    
    
    
    // Unary minus operator for Matrix term by term negation
    Matrix operator-() const;

 
    
    /**********************************************************
     * Other Functions
     **********************************************************/
    
    // A simple matrix algebra dot product operation.
    // Return a 0 by 0 matrix if the dimensions do not match.
    // See operator*(Matrix &rhs) for term by term multiplication.
    Matrix dot(const Matrix& rhs) const;
    
    // Get number of rows (M)xN
    size_t getNumOfRows() const;
    
    // Get number of columns Mx(N)
    size_t getNumOfCols() const;
    
    // Transpose the matrix MxN -> NxM
    Matrix T() const;
    
    // Get the coordinates of the largest value in the matrix.
    // Will return the coordinates of the earliest larger val.
    std::pair<size_t, size_t> getMaxVal() const;
    
    // Print the matrix to std::cout
    void printMtrx() const;
    
private:
    
    size_t m_size_;     // (M)xN
    size_t n_size_;     // Mx(N)
    double *matrix_;    // A pointer to the array.
    double **rowPtrs_;   // An array of row pointers.
                        // used to avoid repeated arithmetics
                        // at each access to the matrix.
};

#endif /* MATRIX_HPP_ */


================================================
FILE: MatrixExceptions.hpp
================================================
//
//  MatrixExceptions.h
//  Neural Net
//
//  Created by Gil Dekel on 8/28/16.
//  Last edited by Gil Dekel on 8/28/16.
//

#ifndef MATRIX_EXCEPTIONS_HPP
#define MATRIX_EXCEPTIONS_HPP

class MatrixDimensionsMismatch : public std::exception {
    const char* what() const noexcept {return "Matrix dimensions must agree.\n";}
};

class MatrixInnderDimensionsMismatch : public std::exception  {
    const char* what() const noexcept {return "Matrix inner dimensions must agree.\n";}
};


#endif /* MATRIX_EXCEPTIONS_HPP */


================================================
FILE: NeuralNet.cpp
================================================
//
//  NeuralNet.cpp
//  Neural Net
//
//  Created by Gil Dekel on 8/19/16.
//  Last edited by Gil Dekel on 8/30/16.
//

#include <fstream>

#include "NeuralNet.hpp"

/*
 * Private members for reference
 *
 * size_t inNodes_;
 * size_t hiddNodes_;
 * size_t outNodes_;
 * size_t hiddLayers_;
 * double LR_;
 *
 * std::vector<Matrix> weights_;
 * std::vector<Matrix> outputs_;
 *
 */

/**********************************************************
 * Constructors
 **********************************************************/

NeuralNet::NeuralNet(size_t inputNodes, size_t hiddenNodes, size_t outputNodes, size_t hiddenLayers, double learningRate )
    : inNodes_{inputNodes}, hiddNodes_{hiddenNodes}, outNodes_{outputNodes}, hiddLayers_{hiddenLayers}, LR_{learningRate},
      weights_{std::vector<Matrix>()}, outputs_{std::vector<Matrix>()} {
    
    size_t weightsSize = 1+hiddLayers_;
    weights_.reserve(weightsSize);
          
    size_t outputsSize = 2+hiddLayers_;
    outputs_.reserve(outputsSize);
          
    for (size_t i = 0; i < weightsSize; ++i) {
        size_t currLayer = 0;
        size_t nextLayer = 0;
        
        if (i == 0) {
            currLayer = inNodes_;
            nextLayer = hiddNodes_;
        } else if (i == weightsSize-1) {
            currLayer = hiddNodes_;
            nextLayer = outNodes_;
        } else {
            currLayer = hiddNodes_;
            nextLayer = hiddNodes_;
        }
        
        weights_.push_back(initializeMatrix(nextLayer, currLayer));
    }
    
    for (size_t i = 0; i < outputsSize; ++i) {
        size_t numOfNodes = 0;
        
        if (i == 0) {
            numOfNodes = inNodes_;
        } else if (i == outputsSize-1) {
            numOfNodes = outNodes_;
        } else {
            numOfNodes = hiddNodes_;
        }
        
        outputs_.push_back(Matrix(numOfNodes, 1));
    }
}

NeuralNet::NeuralNet(const std::string &filename) {
    if (filename.substr(filename.length()-3).compare(".nn") != 0) {
        std::cout << "ERROR:: FILE MUST BE OF TYPE *.nn\n";
        exit(1);
    }
    
    std::ifstream in(filename);
    if (in.fail()) {
        std::cout << "ERROR:: CANNOT READ FROM FILE: '" << filename << "'\n";
        exit(1);
    }
    
    in >> inNodes_ >> hiddNodes_ >> outNodes_ >> hiddLayers_ >> LR_;
    
    weights_ = std::vector<Matrix>();
    size_t weightsSize = 1+hiddLayers_;
    weights_.reserve(weightsSize);
    
    outputs_ = std::vector<Matrix>();
    size_t outputsSize = 2+hiddLayers_;
    outputs_.reserve(outputsSize);
    
    size_t Mrows = 0, Ncols = 0;
    double nextVal = 0;
    
    for (size_t i = 0; i < weightsSize; ++i) {
        in >> Mrows >> Ncols;
        weights_.push_back(Matrix(Mrows, Ncols));
        
        for (size_t m = 0; m < Mrows; ++m) {
            for (size_t n = 0; n < Ncols; ++n) {
                in >> nextVal;
                weights_.back()(m,n) = nextVal;
            }
        }
    }
    
    for (size_t i = 0; i < outputsSize; ++i) {
        size_t numOfNodes = 0;
        
        if (i == 0) {
            numOfNodes = inNodes_;
        } else if (i == outputsSize-1) {
            numOfNodes = outNodes_;
        } else {
            numOfNodes = hiddNodes_;
        }
        
        outputs_.push_back(Matrix(numOfNodes, 1));
    }
}

/**********************************************************
 * Other Functions
 **********************************************************/

Matrix NeuralNet::queryNet(const Matrix &inputList) {
    Matrix finalOutput{inputList.T()};
    outputs_[0] = finalOutput;
    
    for (size_t i = 0; i < weights_.size(); ++i) {
        finalOutput = weights_[i].dot(finalOutput);
        
        for (size_t m = 0; m < finalOutput.getNumOfRows(); ++ m) {
            for (size_t n = 0; n < finalOutput.getNumOfCols(); ++n) {
                finalOutput(m,n) = activationFunction(finalOutput(m,n));
            }
        }
        outputs_[i+1] = finalOutput;
    }
    
    return finalOutput;
}

void NeuralNet::trainingCycle(const Matrix &inputList, const Matrix &targetOutput) {
    Matrix currOutput{queryNet(inputList)};                     // Returned transposed
    Matrix currTargetOut{targetOutput.T()};
    Matrix currLayerErrors{currTargetOut-currOutput};            // Calculate the final output layer's error
    
    // Update the weights going from the output nodes back
    for (long int i = weights_.size()-1; i >= 0; --i) {
        Matrix prevLayerErrors{weights_[i].T().dot(currLayerErrors)};
        Matrix prevHiddLayerOutsT{outputs_[i].T()};
        
        Matrix deltaWeights{currLayerErrors*currOutput};
        deltaWeights *= (1-currOutput);
        deltaWeights = deltaWeights.dot(prevHiddLayerOutsT);
        deltaWeights *= LR_;
        weights_[i] += deltaWeights;
        
        currLayerErrors = prevLayerErrors;
        currOutput = outputs_[i];
    }
}

void NeuralNet::saveNetwork(const std::string &name) const {
    std::string fileName;
    
    if (name.empty())
        fileName = (getCurrTime() + ".nn");
    else
        fileName = (name + ".nn");
    
    std::ofstream out(fileName);
    if (out.fail()) {
        std::cout << "ERROR:: Fails writing to file " << (getCurrTime() + ".nn") << std::endl;
        exit(1);
    }
    
    out << inNodes_ << " " << hiddNodes_ << " " << outNodes_ << " " << hiddLayers_ << " " << LR_ << std::endl;
    
    for (size_t i = 0; i < weights_.size(); ++i) {
        out << weights_[i].getNumOfRows() << " " << weights_[i].getNumOfCols() << std::endl;
        for (size_t m = 0; m < weights_[i].getNumOfRows(); ++m) {
            for (size_t n = 0; n < weights_[i].getNumOfCols(); ++n) {
                out << weights_[i](m,n) << " ";
            }
            out << std::endl;
        }
    }
}

void NeuralNet::loadNetwork(const std::string &name) {
    *this = NeuralNet(name);
}

/**********************************************************
 * Private Functions
 **********************************************************/

Matrix NeuralNet::initializeMatrix(size_t rows, size_t cols) const {
    Matrix init(rows,cols);
    std::default_random_engine generator((std::random_device()()));
    std::normal_distribution<double> distribution(0.0, std::pow(rows, -0.5));
    
    for (size_t m = 0; m < rows; ++m) {
        for (size_t n = 0; n < cols; ++n) {
            init(m,n) = distribution(generator);
        }
    }
    return init;
}

// The activation function. Currently using Sigmoid function.
double NeuralNet::activationFunction(double x) const {
    return 1/(1+std::exp(-x));
}

std::string NeuralNet::getCurrTime() const {
    time_t t = time(0);   // get time now
    struct tm * now = localtime( & t );
    std::string currTime = std::to_string(now->tm_year + 1900) + '-' + std::to_string(now->tm_mon + 1) + '-' + std::to_string(now->tm_mday);
    currTime += "--" + ((now->tm_hour < 10) ? "0" + std::to_string(now->tm_hour) : std::to_string(now->tm_hour));
    currTime += "-" + ((now->tm_min < 10) ? "0" + std::to_string(now->tm_min) : std::to_string(now->tm_min));
    currTime += "-" + ((now->tm_sec < 10) ? "0" + std::to_string(now->tm_sec) : std::to_string(now->tm_sec));
    
    return currTime;
}


================================================
FILE: NeuralNet.hpp
================================================
//
//  NeuralNet.hpp
//  Neural Net
//
//  Created by Gil Dekel on 8/19/16.
//  Last edited by Gil Dekel on 8/30/16.
//

#ifndef NEURALNET_HPP_
#define NEURALNET_HPP_

#include <iostream>
#include <fstream>
#include <utility>          // std::swap and std::move
#include <vector>
#include <random>
#include <cmath>            // for std::pow()
#include <ctime>
#include "Matrix.hpp"


class NeuralNet {
public:

    /**********************************************************
     * Constructors
     **********************************************************/
    
    // Default ctor to initialize a new neural net object.
    NeuralNet(size_t inputNodes = 1, size_t hiddenNodes = 1,
              size_t outputNodes = 1, size_t hiddenLayers = 1, double learningRate = 0.1);
    
    // A ctor that loads a saved neural net.
    // Expecting a filename with file type *.nn
    // Initializes a new net wit the weights saved
    // in the file.
    explicit NeuralNet(const std::string &filename);
    
    /**********************************************************
     * Other Functions
     **********************************************************/
    
    // Feed forward the input Matrix and return the
    // net's prediction in a Matrix.
    Matrix queryNet(const Matrix &inputList);
    
    // A single feed forward and back propagation with weight updates.
    void trainingCycle(const Matrix &inputList, const Matrix &targetOutput);
    
    // A method to save the current state of the net.
    // Files are saved as *.nn.
    // If no file name is supplied, file will be saved
    // with the time and date the net was saved.
    void saveNetwork(const std::string &name = "") const;
    
    // Load an existing net into the current object.
    void loadNetwork(const std::string &name);
    
private:
    // Initializes the weights (parameters) between the different layers
    // Initial values are being drawn from a normal distribution centered
    // at 0, with standard deviation of (number_of_inputs_to_nodes_in_next_layer)^(-0.5)
    Matrix initializeMatrix(size_t m, size_t n) const;
    
    // The activation function. Currently using Sigmoid function.
    double activationFunction(double x) const;
    
    // A utility function to get the current time in a string.
    // Used to name neural nets when saved.
    std::string getCurrTime() const;
    
    
    size_t inNodes_;        // Number of input nodes
    size_t hiddNodes_;      // Number of nodes per hidden layer
    size_t outNodes_;       // Number of output nodes
    size_t hiddLayers_;     // Number of hidden layers
    double LR_;             // The learning rate.
    
    std::vector<Matrix> weights_;  // The weight matrices
    std::vector<Matrix> outputs_;  // Intermediate outputs of all the layers are requierd for training.
                                    // this is where they are stored.
};



#endif /* NEURALNET_HPP_ */


================================================
FILE: README.md
================================================
<a href="http://i.imgur.com/dPoSllF.png">
    <img src="http://i.imgur.com/dPoSllF.png" alt="N++"
         title="stagadish/NN++" align="right" />
</a>

# NN++

A short, self-contained, and easy-to-use neural net implementation for C++. It includes the neural net implementation and a Matrix class for *basic* linear algebra operations. This project is mostly for **learning purposes**, but preliminary testing results over the MNIST dataset show some promise.

## Getting Started

These instructions will get you a copy of the net up and running on your local machine for development and testing purposes.

### Prerequisites

Any compiler that can handle C++11.

### Installing

1. Download `Matrix.hpp`, `Matrix.cpp`, `NeuralNet.hpp`, and `NeuralNet.cpp` and place them in your project's working directory.
2. Include the headers in your main driver program (e.g. `main.cpp`).

```
#include "Matrix.hpp"
#include "NeuralNet.hpp"
```
_**NOTE:** It is not required to `#include "Matrix.hpp"` since it is included within `NeuralNet.hpp`. However it is probably better to do so for clarity and safety in case you plan on using Matrix objects in your code (and you probably will if you use NeuralNet)._
## Example Code
### The Matrix Class
First you need to know how to use the Matrix class.
Matrix objects are basically 2D-vectors with built-in linear algebra operations.

#### Matrix Initialization

```
Matrix A;       // Initializes a 0x0 matrix.
Matrix B(2,2)   // Initializes a 2x2 matrix with all zeros. Values are doubles.
Matrix C(2,1)   // Initializes a 2x1 matrix.
```
#### Element Access
To access/modify a value in a matrix, use `operator()`, NOT `operator[]`:

```
B(0,0) = 1; B(0,1) = 2; B(1,0) = 3; B(1,1) = 4;   // [1    2]
                                                  // [3    4]

C(0,0) = 1; C(1,0) = 2;                           // [1]
                                                  // [2]
```

#### Matrix Term-by-Term Addition/Subtraction/Multiplication
```
// Commutative property is supported for addition
Matrix D = B+B;       // D = [2   4]
                             [6   8]
                             
Matrix E = B-B;       // E = [0   0]
                             [0   0]
                             
// Commutative property is supported for multiplication                             
Matrix F = B*B        // F = [1   4]
                             [9  16]
                             
// Mismatching matrix dimensions in term-by-term operations
// is illegal and a MatrixDimensionsMismatch exception will be thrown.
Matrix G = B+C;       // Throws MatrixDimensionsMismatch()
Matrix G = B-C;       // Throws MatrixDimensionsMismatch()
Matrix G = B*C;       // Throws MatrixDimensionsMismatch()
```

#### Matrix and Scalars
```
// Commutative property is supported for addition
Matrix BplusTwo = B+2;  // (== 2+B)   BplusTwo = [3   4]
                                                 [5   6]

Matrix CminusTwo = C-2; //           CminusTwo = [-1]
                                                 [ 0]
                                                 
Matrix TwominusB = 2-C; //           TwominusB = [ 1]
                                                 [ 0]
                                                 
// Commutative property is supported for multiplication
Matrix BtimesThree = B*3; // (== 3*B) BtimesThree = [3    6]
                                                    [9   12]
```

#### Matrix Multiplication (Dot Product)
```
Matrix BB = B.dot(B);     // BB = [ 7  10]
                                  [15  22]
                                  
Matrix BC = B.dot(C);     // BC = [ 5]
                                  [11]

// Mismatching the number of columns in the left-hand-side matrix
// with the number of rows in the right-hand-side matrix is illegal
// A MatrixInnderDimensionsMismatch exception will be thrown.
Matrix CB = C.dot(B);     // Throws MatrixInnderDimensionsMismatch()
```

#### Matrix Transpose
```
Matrix B_T = B.T();   // B_T = [1   3]
                               [2   4]
                                 
Matrix C_T = C.T();   // C_T = [1   2]
```

#### An Example of Populating a 4x3 Matrix
```
int m = 4;
int n = 3;

Matrix mtrx(m,n);

int count = 1;
for (int i = 0; i < mtrx.getNumOfRows(); ++i) {
    for (int j = 0; j < mtrx.getNumOfCols(); ++j) {
        mtrx(i,j) = count;
        ++count;
    }
}
```
This will result with `mtrx` ==
```
[ 1     2     3]
[ 4     5     6]
[ 7     8     9]
[10    11    12]
```

### The NeuralNet Class
#### Neural Net Initialization (The Parameters)
When initialized, a net takes in five parameters:  
1. Number of input nodes.  
2. Number of nodes per hidden layer.  
3. Number of output nodes.  
4. Number of hidden layers.  
5. The learning rate.  

```
NeuralNet NN(4, 3, 1, 10, 0.1);
```
_This_ particular neural net has 4 input nodes, 1 hidden layer with 3 nodes, 10 output node, and has a learning rate of 0.1.  
New neural nets' weights are initialized with values drawn from a normal distribution centered at 0, with standard deviation that is equal to `1/sqrt(number_of_inputs_to_nodes_in_next_layer)`. In other words, small negative and positive values that are proportional to the size of their previous layer.

#### A Training Cycle
Once the net is initialized, it is ready to do work.  
__ONE__ training cycle == one feed forward and one back propagation with weight adjustments.  
  
To train one cycle, the input data must be parsed into a Matrix object with dimensions: `1xnumber_of_input_nodes` (1x4 in our case), and the target output must be parsed into a Matrix object with dimensions: `1xnumber_of_output_nodes` (1x10 in our case).  
```
Matrix input(1,4);
input(0,0) =  0.3;
input(0,1) = -0.1;
input(0,2) =  0.2;
input(0,3) =  0.8;

Matrix targetOutput(1,1);
target(0,0) =  0.5;
target(0,1) = -0.3;
        .
        .
        .
target(0,9) =  0.23;        // Obviously, matrices should be populated using
                            // some parser and not manualy like this.

```
Then, simply execute the training cycle on the data as follows:
```
NN.trainingCycle(input, targetOutput);
```
Repeate the process over all training instances.

#### Querying the Net
Once the training phase is complete, you can query it as follows:  
(Technically speaking, you can query it right after initialization).

Parse the query into a Matrix like parsed the training instance:
```
Matrix query(1,2);
input(0,0) =  0.5;
input(0,1) = -0.2;
input(0,2) = -0.3;
input(0,3) =  0.4;
```

Query the net and catch the result:
```
Matrix prediction = NN.queryNet(query);   // Will return a 1x10 Matrix object with net's prediction
```
AND THAT'S IT!

## TODO
1. Add `array`, `std::vector`, and `std::initializer_list` constructors to the Matrix class
2. Either improve on or replace my Matrix class for better/faster performance
3. Add multiple epoch learning with early stopping.

## Authors

* **Gil Dekel** - *Initial implementation* - [stagadish](https://github.com/stagadish)

See also the list of [contributors](https://github.com/stagadish/NNplusplus/contributors) who participated in this project.

## License

This project is licensed under the MIT License - see the [LICENSE.md](https://github.com/stagadish/NNplusplus/blob/master/License.md) file for details


================================================
FILE: data/test_data/mnist_test_10.txt
================================================
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