// // main.cpp // mlperceptron // // Created by Sergei Bugrov on 7/1/17. // Copyright © 2017 Sergei Bugrov. All rights reserved. // #include #include #include using std::vector; using std::cout; using std::endl; vector X { 5.1, 3.5, 1.4, 0.2, 4.9, 3.0, 1.4, 0.2, 6.2, 3.4, 5.4, 2.3, 5.9, 3.0, 5.1, 1.8 }; vector y { 0, 0, 1, 1 }; vector W { 0.5, 0.5, 0.5, 0.5}; vector sigmoid_d (const vector & m1) { /* Returns the value of the sigmoid function derivative f'(x) = f(x)(1 - f(x)), where f(x) is sigmoid function. Input: m1, a vector. Output: x(1 - x) for every element of the input matrix m1. */ const unsigned long VECTOR_SIZE = m1.size(); vector output (VECTOR_SIZE); for( unsigned i = 0; i != VECTOR_SIZE; ++i ) { output[ i ] = m1[ i ] * (1 - m1[ i ]); } return output; } vector sigmoid (const vector & m1) { /* Returns the value of the sigmoid function f(x) = 1/(1 + e^-x). Input: m1, a vector. Output: 1/(1 + e^-x) for every element of the input matrix m1. */ const unsigned long VECTOR_SIZE = m1.size(); vector output (VECTOR_SIZE); for( unsigned i = 0; i != VECTOR_SIZE; ++i ) { output[ i ] = 1 / (1 + exp(-m1[ i ])); } return output; } vector operator+(const vector & m1, const vector & m2){ /* Returns the elementwise sum of two vectors. Inputs: m1: a vector m2: a vector Output: a vector, sum of the vectors m1 and m2. */ const unsigned long VECTOR_SIZE = m1.size(); vector sum (VECTOR_SIZE); for (unsigned i = 0; i != VECTOR_SIZE; ++i){ sum[i] = m1[i] + m2[i]; }; return sum; } vector operator-(const vector & m1, const vector & m2){ /* Returns the difference between two vectors. Inputs: m1: vector m2: vector Output: vector, m1 - m2, difference between two vectors m1 and m2. */ const unsigned long VECTOR_SIZE = m1.size(); vector difference (VECTOR_SIZE); for (unsigned i = 0; i != VECTOR_SIZE; ++i){ difference[i] = m1[i] - m2[i]; }; return difference; } vector operator*(const vector & m1, const vector & m2){ /* Returns the product of two vectors (elementwise multiplication). Inputs: m1: vector m2: vector Output: vector, m1 * m2, product of two vectors m1 and m2 */ const unsigned long VECTOR_SIZE = m1.size(); vector product (VECTOR_SIZE); for (unsigned i = 0; i != VECTOR_SIZE; ++i){ product[i] = m1[i] * m2[i]; }; return product; } vector transpose (float *m, const int C, const int R) { /* Returns a transpose matrix of input matrix. Inputs: m: vector, input matrix C: int, number of columns in the input matrix R: int, number of rows in the input matrix Output: vector, transpose matrix mT of input matrix m */ vector mT (C*R); for(unsigned n = 0; n != C*R; n++) { unsigned i = n/C; unsigned j = n%C; mT[n] = m[R*j + i]; } return mT; } vector dot (const vector & m1, const vector & m2, const int m1_rows, const int m1_columns, const int m2_columns) { /* Returns the product of two matrices: m1 x m2. Inputs: m1: vector, left matrix of size m1_rows x m1_columns m2: vector, right matrix of size m1_columns x m2_columns (the number of rows in the right matrix must be equal to the number of the columns in the left one) m1_rows: int, number of rows in the left matrix m1 m1_columns: int, number of columns in the left matrix m1 m2_columns: int, number of columns in the right matrix m2 Output: vector, m1 * m2, product of two vectors m1 and m2, a matrix of size m1_rows x m2_columns */ vector output (m1_rows*m2_columns); for( int row = 0; row != m1_rows; ++row ) { for( int col = 0; col != m2_columns; ++col ) { output[ row * m2_columns + col ] = 0.f; for( int k = 0; k != m1_columns; ++k ) { output[ row * m2_columns + col ] += m1[ row * m1_columns + k ] * m2[ k * m2_columns + col ]; } } } return output; } void print ( const vector & m, int n_rows, int n_columns ) { /* "Couts" the input vector as n_rows x n_columns matrix. Inputs: m: vector, matrix of size n_rows x n_columns n_rows: int, number of rows in the left matrix m1 n_columns: int, number of columns in the left matrix m1 */ for( int i = 0; i != n_rows; ++i ) { for( int j = 0; j != n_columns; ++j ) { cout << m[ i * n_columns + j ] << " "; } cout << '\n'; } cout << endl; } int main(int argc, const char * argv[]) { for (unsigned i = 0; i != 50; ++i) { vector pred = sigmoid(dot(X, W, 4, 4, 1 ) ); vector pred_error = y - pred; vector pred_delta = pred_error * sigmoid_d(pred); vector W_delta = dot(transpose( &X[0], 4, 4 ), pred_delta, 4, 4, 1); W = W + W_delta; if (i == 49){ print ( pred, 4, 1 ); }; }; return 0; }

neural network in C++