initial github commit

Implementation of backprop in C using Grand Central Dispatch and Blocks

nn.h

@@ -0,0 +1,171 @@
+#ifndef NNMODEL
+#define NNMODEL
+
+#define DEFAULT_LAYERS 2
+#define DEFAULT_HIDDEN_NODES_PER_LAYER 20
+#define DEFAULT_LEARNING_RATE 0.1
+#define DEFAULT_MOMENTUM 0.9
+#define DEFAULT_TEST_ERROR_MIN 0.3
+#define DEFAULT_MAX_EPOCHS_SINCE_MIN 500
+#define EPOCH_MAX_ERROR_INCREASE 20
+
+/*
+	A struct containing a 2 dimensional array storing connection weights
+*/
+typedef struct matrix {
+	double ** weight_matrix;
+	int rows;
+	int columns;
+} matrix;
+
+/*
+	A struct representing the network and parameters
+*/
+typedef struct nn_model {
+	double learning_rate;
+	double momentum;
+	int layers;
+	int * nodes_per_layer;
+	int outputs;
+	matrix ** layer_weights;
+	matrix ** layer_input_vectors;
+	matrix ** layer_output_vectors;
+	matrix * output;
+	matrix ** previous_weight_updates;
+} nn_model;
+
+/*
+	A function that will free all the memory allocated for a matrix struct
+	@param to_free The data struct that should be free'd
+	@return 0 if it is free'd successfully
+*/
+int free_matrix(matrix * to_free);
+
+/*
+	A function that will free all the memory allocated for a model struct
+	@param to_free The data struct that should be free'd
+	@return 0 if it is free'd successfully
+*/
+int free_model(nn_model * to_free);
+
+/*
+	A function that allocates memory for a matrix
+	@param r The number of rows
+	@param c The number of columns
+*/
+matrix * create_matrix(int r, int c);
+
+/*
+	A function that creates a new neural network model.
+	@param rate The network learning rate
+	@param layers The number of layers of nodes (inclusive of input layer)
+	@param layernodes An array of size layers, containing the number of nodes per layer
+	@param outputs the number of nodes in the output layer
+	@return A new network with random weights
+*/
+struct nn_model * create_model(const double rate, const int layers, const int layer_nodes[], const int outputs);
+
+/*
+	A method that creates a copy of a model
+	@param model The model to be copied
+	@return A copy of the model that was passed.
+*/
+struct nn_model * copy_model(const struct nn_model * model);
+
+/*
+	A function that writes a well performing and trained model to txt file
+	@param m The model to be saved
+	@param file The file name for the saved model
+	@return 0 if it wrote correctly
+*/
+int save_model(const nn_model * m, const char * file);
+
+/*
+	A method that reads a saved model in from a file
+	@param file The file name for the saved model
+	@return A trained nn_model with the weights from the saved file.
+*/
+struct nn_model * load_model(char * file);
+
+/*
+	A function that takes two matricies and multiplies them together, returning a new matrix
+	@param a Matrix 1 to be considered
+	@param a Matrix 2 to be considered
+	@result the product of matrix multiplication
+*/
+matrix * multiply_matricies(const matrix * a, const matrix * b);
+
+/*
+	Add the contents of matrix b to matrix a.
+	@param a A matrix to be modified
+	@param b A matrix of values to add to the first matrix
+	@return 0 if successfully modified the first matrix
+*/
+int add_matricies(matrix * a, const matrix * b);
+/*
+	A function that takes an input vector representing a layer of nodes and uses the Sigmoid
+	activation functoin to create values that will be used for the next layer
+	@param input_vector the input vector to be activated for the next later
+	@return a matrix containing an 1xn vector of sigmoid values of the input vector
+*/
+matrix * activation_function(const matrix * input_vector);
+
+/*
+	Classify a provided message using the currently learned model.
+	@param current The model to be used to classify
+	@param input The input vector to be classified by the model
+	@return 0 if vector has been classified
+*/
+int classify_instance(nn_model * current, message * input, const int size);
+
+/*
+	Free the memory used by layer value vectors.
+	This function is for use after classifying.
+	@param m The model whose input and output vectors should be freed
+	@return 0 if the models vectors are reset for another run
+*/
+int reset_model_vectors(nn_model * m);
+
+/*
+	Propagate error back through the network
+	@param model the model whose weights need to be updated
+	@param outputs the desired outputs given the current values
+	@return 0 if model weights are updated correctly
+*/
+int backprop_update(nn_model * update, matrix * output);
+
+/*
+	Train the network using backpropagation using the training_data and validating
+	against the test_data
+	@param m The model to traing
+	@param training_data The dataset of training instances
+	@param test_data The dataset of the test instances
+	@param error_rate The minimum test data error rate accepted
+	@param epoch_max The maximum number of times to continue training without finding a minimum
+	@return 0 when an acceptible threshold of training has been reached.
+*/
+nn_model * train_model(nn_model * m, data * training_data, data * test_data, double error_rate, int epoch_max);
+
+/*
+	A function that classifies all the instances in the dataset
+	@param m The model that is to be used for classification
+	@param set The dataset whose instances are to be classified
+	@return 0 If the set is successfully classified
+*/
+int classify_dataset(nn_model * m, data * set);
+
+/*
+	A function that prints a confusion matrix for a dataset of how many of each instance
+	are classifed as what, as well as some stats
+	@param set The dataset whose confusion matrix is to be printed
+*/
+void print_confusion_matrix(data * set);
+
+/*
+	A function that prints a matrix with optional column labels
+	@param m The matrix that is to be printed
+	@param labels The string labels to be output in the case that there are some
+*/
+void print_matrix(matrix * m, char ** labels);
+
+#endif