Remove old extension
This commit is contained in:
sBubshait
parent
15c8482dd3
commit
fd1fcbf7f7
@ -1,18 +0,0 @@
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CC = gcc
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CFLAGS = -std=c17 -g\
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-D_POSIX_SOURCE -D_DEFAULT_SOURCE\
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-Wall -Werror -pedantic\
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LDLIBS = -lm
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.SUFFIXES: .c .o
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.PHONY: all clean
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all: test/test_math test/test_ann
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test/test_math: test/test_math.o mymath.o
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test/test_ann: test/test_ann.o ann.o mymath.o
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clean:
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$(RM) *.o test/*.o
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@ -1,81 +0,0 @@
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#include <stdlib.h>
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#include "ann.h"
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// Helper function that simulate 'bias' term by setting a neuron that always outputs 1.0
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static void network_fillbias(Network network, unsigned int layerIndex) {
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for (int j = 0; j < network->trainingWidth; j++) {
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*(network->outputs[layerIndex]->data + network->layers[layerIndex].neuronCount * network->trainingWidth + j) = 1.0;
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}
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}
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// Defines a sequence of random numbers sampled from a standard normal distribution
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double normalseq(unsigned int i) {
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return stdnormal();
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}
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// Creates a new network instance given an array of layers and the size of the array
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Network network_create(Layer *layers, unsigned int noLayers, unsigned int trainingWidth) {
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// Initialize network struct and its fields
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Network network = malloc(sizeof(struct Network));
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if (network == NULL) {
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fprintf(stderr, "ERROR: Couldn't allocate sufficient memory for Network struct!\n");
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abort();
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}
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network->layers = layers;
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network->noLayers = noLayers;
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network->trainingWidth = trainingWidth;
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network->outputs = malloc(sizeof(struct Matrix) * noLayers);
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if (network->outputs == NULL) {
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fprintf(stderr, "ERROR: Couldn't allocate sufficient memory for Network output matrix array!\n");
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abort();
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}
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network->weights = malloc(sizeof(struct Matrix) * (noLayers - 1));
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if (network->weights == NULL) {
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fprintf(stderr, "ERROR: Couldn't allocate sufficient memory for Network weight matrix array!\n");
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abort();
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}
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// Initialize output and weight matrix array data
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for (unsigned int i = 0; i < noLayers; i++) {
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network->outputs[i] = matrix_create(layers[i].neuronCount + 1, trainingWidth);
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network_fillbias(network, i);
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if (i < noLayers - 1) {
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network->weights[i] = matrix_seqcreate(layers[i + 1].neuronCount + 1, layers[i].neuronCount + 1, &normalseq);
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}
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}
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return network;
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}
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// Returns the output of the ANN given the specified input
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Matrix network_pass(Network network, Matrix features) {
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Matrix currentOut = features;
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for (int i = 0; i < network->noLayers; i++) {
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// Update current layer's outputs
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matrix_free(network->outputs[i]);
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network->outputs[i] = currentOut;
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network_fillbias(network, i);
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// Calculate outputs of next layer if not in last layer
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if (i < network->noLayers - 1) {
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currentOut = matrix_apply(matrix_multiply(network->weights[i], currentOut), network->layers[i].activation);
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}
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}
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return currentOut;
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}
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Matrix log_loss(Matrix result, Matrix expected) {
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}
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@ -1,41 +0,0 @@
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/* @file ann.h
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@brief Data structures and functions for feedforward neural networks.
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@author Themis Demetriades */
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#ifndef __ANN__
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#define __ANN__
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#include "mymath.h"
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// Defines function pointer type for activation functions
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typedef double (*activationFunc)(double);
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// Specifies structure of a given layer within a network
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typedef struct {
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unsigned int neuronCount;
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activationFunc activation;
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} Layer;
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// Defines a specific instance of an active network
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struct Network {
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Layer *layers; // Array of layers specifying network structure
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unsigned int noLayers;
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Matrix *weights; // Array of matrices specifying weights of *outputs* to each layer
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Matrix *outputs;
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unsigned int trainingWidth; // Number of training examples to train per gradient descent step
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};
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typedef struct Network *Network;
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// Creates a new network instance given an array of layers and the size of the array
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Network network_create(Layer *layers, unsigned int noLayers, unsigned int trainingWidth);
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// Returns the output of the ANN given the specified input
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Matrix network_pass(Network network, Matrix features);
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// Calculates the log loss of a result wrt to the expected result
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Matrix log_loss(Matrix result, Matrix expected);
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#endif
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@ -1,166 +0,0 @@
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#include <stddef.h>
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#include <stdlib.h>
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#include <string.h>
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#include <math.h>
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#include "mymath.h"
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#define LRELU_SFACTOR 0.001
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// Maximum number of characters that would ever be required to represent a valid matrix entry
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#define MAX_ENTRY_LENGTH 100
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// Defines the format used to print matrix entries
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#define VALUE_PRINT_FORMAT "%.2lf"
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// Pretty print matrix data to the given output stream
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void matrix_print(Matrix m, FILE *stream) {
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double (*data)[m->cols] = (double (*)[m->cols]) m->data;
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char valstr[MAX_ENTRY_LENGTH];
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unsigned int maxlen = 1;
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// Get maximum length of a matrix entry
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for (int i = 0; i < m->rows; i++) {
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for (int j = 0; j < m->cols; j++) {
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sprintf(valstr, VALUE_PRINT_FORMAT, data[i][j]);
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unsigned int curlen = strlen(valstr);
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if (curlen > maxlen) maxlen = curlen;
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}
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}
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// Print matrix entries
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for (int i = 0; i < m->rows; i++) {
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fputc('[', stream);
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for (int j = 0; j < m->cols; j++) {
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sprintf(valstr, VALUE_PRINT_FORMAT, data[i][j]);
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fprintf(stream, "%s", valstr);
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for (int k = 0; k < maxlen - strlen(valstr); k++) fputc(' ', stream);
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if (j < m->cols - 1) fputc(' ', stream);
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}
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fprintf(stream, "]\n");
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}
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}
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// Return matrix with specified dimensions and random values as its entries
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Matrix matrix_create(unsigned int rows, unsigned int cols) {
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// Allocate memory for matrix struct, aborting if couldn't allocate memory
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Matrix m = malloc(sizeof(struct Matrix));
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if (m == NULL) {
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fprintf(stderr, "ERROR: Couldn't allocate sufficient memory for matrix struct!\n");
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abort();
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}
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// Allocate memory for matrix data (values), aborting if couldn't allocate memory
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m->data = malloc(rows * cols * sizeof(double));
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if (m->data == NULL) {
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fprintf(stderr, "ERROR: Couldn't allocate sufficient memory for matrix data!\n");
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abort();
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}
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// Initialize matrix struct data
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m->rows = rows;
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m->cols = cols;
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return m;
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}
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// Free data associated with the specified matrix
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void matrix_free(Matrix m) {
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free(m->data);
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free(m);
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}
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// Return matrix with specified dimensions and entries whose values are the first values of the specified matrix sequence
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Matrix matrix_seqcreate(unsigned int rows, unsigned int cols, matrix_sequence seq) {
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// Create matrix with specified dimensions containing entries with random values
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Matrix m = matrix_create(rows, cols);
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double (*data)[cols] = (double (*)[cols]) m->data;
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// Fill matrix with correct entries
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for (int i = 0; i < rows; i++) {
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for (int j = 0; j < cols; j++) {
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data[i][j] = seq(i * cols + j);
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}
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}
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return m;
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}
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// Multiply left matrix (l) with right matrix (r), returning a new instance representing the result
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Matrix matrix_multiply(Matrix l, Matrix r) {
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// Ensure that dimensions of matrices are compatible for multiplication
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if (l->cols != r->rows) {
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fprintf(stderr, "ERROR: Attempting to multiply matrices with incompatible dimensions!\n");
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abort();
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}
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// Create matrix instance to store result of product
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Matrix prod = matrix_create(l->rows, r->cols);
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double (*prod_data)[prod->cols] = (double (*)[prod->cols]) prod->data;
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double (*l_data)[l->cols] = (double (*)[l->cols]) l->data;
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double (*r_data)[r->cols] = (double (*)[r->cols]) r->data;
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// Perform matrix multiplication, storing result in prod
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for (int i = 0; i < l->rows; i++) {
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for (int j = 0; j < r->cols; j++) {
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prod_data[i][j] = 0.0;
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for (int k = 0; k < l->cols; k++) {
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prod_data[i][j] += l_data[i][k] * r_data[k][j];
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}
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}
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}
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// Return result
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return prod;
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}
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// Return result of applying given function to each entry in the matrix independently
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Matrix matrix_apply(Matrix m, matrix_entrytrans f) {
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Matrix result = matrix_create(m->rows, m->cols);
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double (*result_data)[result->cols] = (double (*)[result->cols])result->data;
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double (*m_data)[m->cols] = (double (*)[m->cols])m->data;
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for (int i = 0; i < m->rows; i++) {
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for (int j = 0; j < m->cols; j++) {
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result_data[i][j] = f(m_data[i][j]);
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}
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}
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return result;
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}
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// Draws a value from the standard normal distribution
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double stdnormal() {
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// Generate 2 random numbers in the interval [0, 1)
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double s1 = ((double)rand() / RAND_MAX);
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double s2 = ((double)rand() / RAND_MAX);
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// Peform box-muller transform
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return sqrt(-2 * log(s1)) * cos(2 * PI * s2);
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}
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// Defines the leaky rectified linear unit activation function
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double lrelu(double x) {
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if (x > 0) {
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return x;
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} else {
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return LRELU_SFACTOR * x;
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}
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}
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// Defines the identity activation function f(x) = x
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double identity(double x) {
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return x;
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}
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// Defines a simple counting sequence
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double countseq(unsigned int i) {
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return i + 1;
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}
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@ -1,58 +0,0 @@
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/* @file math.h
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@brief Math functions and types for feedforward ANNs.
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@author Themis Demetriades */
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#ifndef __MATH__
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#define __MATH__
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#include <stdio.h>
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#define PI 3.1415926535
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// Definition of matrix types
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struct Matrix {
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unsigned int rows;
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unsigned int cols;
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double *data;
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};
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typedef struct Matrix *Matrix;
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// Pretty print matrix data to the given output stream
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void matrix_print(Matrix m, FILE *stream);
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// Defines a sequence that maps matrix entry indices to floating point values
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typedef double (*matrix_sequence)(unsigned int);
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// Defines a function that transforms matrix entries
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typedef double (*matrix_entrytrans)(double);
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// Return matrix with specified dimensions and random values as its entries
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Matrix matrix_create(unsigned int rows, unsigned int cols);
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// Free data associated with the specified matrix
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void matrix_free(Matrix m);
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// Return matrix with specified dimensions and entries whose values are the first values of the specified matrix sequence
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Matrix matrix_seqcreate(unsigned int rows, unsigned int cols, matrix_sequence seq);
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// Multiply left matrix (l) with right matrix (r), returning a new instance representing the result
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Matrix matrix_multiply(Matrix l, Matrix r);
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// Return result of applying given function to each entry in the matrix independently
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Matrix matrix_apply(Matrix m, matrix_entrytrans f);
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// Draws a value from the standard normal distribution
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double stdnormal();
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// Defines the identity activation function f(x) = x
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double identity(double x);
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// Defines the rectified linear unit activation function
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double lrelu(double x);
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// Defines a simple counting sequence
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double countseq(unsigned int i);
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#endif
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#include "../ann.h"
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#define TEST1_LAYERS 4
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int main(int argc, char **argv) {
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Layer layers[] = {{1, &lrelu}, {3, &lrelu}, {3, &lrelu}, {2, &identity}};
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Network network = network_create(layers, TEST1_LAYERS, 1);
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for (int i = 0; i < TEST1_LAYERS; i++) {
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matrix_print(network->outputs[i], stdout);
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printf("\n");
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if (i < TEST1_LAYERS - 1) {
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matrix_print(network->weights[i], stdout);
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printf("\n ------------- \n");
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}
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}
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printf("===========================\n");
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Matrix features = matrix_create(2, 1);
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*(features->data + 0) = 1;
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Matrix out = network_pass(network, features);
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matrix_print(out, stdout);
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return 0;
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}
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#include "../mymath.h"
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int main(int argc, char **argv) {
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Matrix l = matrix_seqcreate(4, 3, &countseq);
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Matrix r = matrix_seqcreate(3, 2, &countseq);
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matrix_print(l, stdout);
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printf("\n");
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matrix_print(r, stdout);
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printf("\n");
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Matrix p = matrix_multiply(l, r);
|
|
||||||
matrix_print(p, stdout);
|
|
||||||
|
|
||||||
matrix_free(l);
|
|
||||||
matrix_free(r);
|
|
||||||
matrix_free(p);
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
Loading…
Reference in New Issue
Block a user