linear_svm_main.cpp File Reference

#include <mlpack/prereqs.hpp>
#include <mlpack/core/util/io.hpp>
#include <mlpack/core.hpp>
#include <mlpack/core/util/mlpack_main.hpp>
#include "linear_svm.hpp"
#include <ensmallen.hpp>

Include dependency graph for linear_svm_main.cpp:

This graph shows which files directly or indirectly include this file:

Classes
class	LinearSVMModel

Functions
	BINDING_NAME ("Linear SVM is an L2-regularized support vector machine.")
	BINDING_SHORT_DESC ("An implementation of linear SVM for multiclass classification. " "Given labeled data, a model can be trained and saved for " "future use; or, a pre-trained model can be used to classify new points.")
	BINDING_LONG_DESC ("An implementation of linear SVMs that uses either L-BFGS or parallel SGD" " (stochastic gradient descent) to train the model." "\" "This program allows loading a linear SVM model (via the "+PRINT_PARAM_STRING("input_model")+" parameter) " "or training a linear SVM model given training data (specified " "with the "+PRINT_PARAM_STRING("training")+" parameter), or both " "those things at once. In addition, this program allows classification on " "a test dataset (specified with the "+PRINT_PARAM_STRING("test")+" " "parameter) and the classification results may be saved with the "+PRINT_PARAM_STRING("predictions")+" output parameter." " The trained linear SVM model may be saved using the "+PRINT_PARAM_STRING("output_model")+" output parameter." "\" "The training data, if specified, may have class labels as its last " "dimension. Alternately, the "+PRINT_PARAM_STRING("labels")+" " "parameter may be used to specify a separate vector of labels." "\" "When a model is being trained, there are many options. L2 regularization " "(to prevent overfitting) can be specified with the "+PRINT_PARAM_STRING("lambda")+" option, and the number of classes can be " "manually specified with the "+PRINT_PARAM_STRING("num_classes")+"and if an intercept term is not desired in the model, the "+PRINT_PARAM_STRING("no_intercept")+" parameter can be specified." "Margin of difference between correct class and other classes can " "be specified with the "+PRINT_PARAM_STRING("delta")+" option." "The optimizer used to train the model can be specified with the "+PRINT_PARAM_STRING("optimizer")+" parameter. Available options are " "'psgd' (parallel stochastic gradient descent) and 'lbfgs' (the L-BFGS" " optimizer). There are also various parameters for the optimizer; the "+PRINT_PARAM_STRING("max_iterations")+" parameter specifies the maximum " "number of allowed iterations, and the "+PRINT_PARAM_STRING("tolerance")+" parameter specifies the tolerance for " "convergence. For the parallel SGD optimizer, the "+PRINT_PARAM_STRING("step_size")+" parameter controls the step size taken " "at each iteration by the optimizer and the maximum number of epochs " "(specified with "+PRINT_PARAM_STRING("epochs")+"). If the " "objective function for your data is oscillating between Inf and 0, the " "step size is probably too large. There are more parameters for the " "optimizers, but the C++ interface must be used to access these." "\" "Optionally, the model can be used to predict the labels for another " "matrix of data points, if "+PRINT_PARAM_STRING("test")+" is " "specified. The "+PRINT_PARAM_STRING("test")+" parameter can be " "specified without the "+PRINT_PARAM_STRING("training")+" parameter, " "so long as an existing linear SVM model is given with the "+PRINT_PARAM_STRING("input_model")+" parameter. The output predictions " "from the linear SVM model may be saved with the "+PRINT_PARAM_STRING("predictions")+" parameter.")
	BINDING_EXAMPLE ("As an example, to train a LinaerSVM on the data '"+PRINT_DATASET("data")+"' with labels '"+PRINT_DATASET("labels")+"' " "with L2 regularization of 0.1, saving the model to '"+PRINT_MODEL("lsvm_model")+"', the following command may be used:" "\"+PRINT_CALL("linear_svm", "training", "data", "labels", "labels", "lambda", 0.1, "delta", 1.0, "num_classes", 0, "output_model", "lsvm_model")+"\" "Then, to use that model to predict classes for the dataset '"+PRINT_DATASET("test")+"', storing the output predictions in '"+PRINT_DATASET("predictions")+"', the following command may be used: " "\"+PRINT_CALL("linear_svm", "input_model", "lsvm_model", "test", "test", "predictions", "predictions"))
	BINDING_SEE_ALSO ("@random_forest", "#random_forest")
	BINDING_SEE_ALSO ("@logistic_regression", "#logistic_regression")
	BINDING_SEE_ALSO ("LinearSVM on Wikipedia", "https://en.wikipedia.org/wiki/Support-vector_machine")
	BINDING_SEE_ALSO ("mlpack::svm::LinearSVM C++ class documentation", "@doxygen/classmlpack_1_1svm_1_1LinearSVM.html")
	PARAM_MATRIX_IN ("training", "A matrix containing the training set (the matrix " "of predictors, X).", "t")
	PARAM_UROW_IN ("labels", "A matrix containing labels (0 or 1) for the points " "in the training set (y).", "l")
	PARAM_DOUBLE_IN ("lambda", "L2-regularization parameter for training.", "r", 0.0001)
	PARAM_DOUBLE_IN ("delta", "Margin of difference between correct class and other " "classes.", "d", 1.0)
	PARAM_INT_IN ("num_classes", "Number of classes for classification; if " "unspecified (or 0), the number of classes found in the labels will be " "used.", "c", 0)
	PARAM_FLAG ("no_intercept", "Do not add the intercept term to the model.", "N")
	PARAM_STRING_IN ("optimizer", "Optimizer to use for training ('lbfgs' or " "'psgd').", "O", "lbfgs")
	PARAM_DOUBLE_IN ("tolerance", "Convergence tolerance for optimizer.", "e", 1e-10)
	PARAM_INT_IN ("max_iterations", "Maximum iterations for optimizer (0 indicates " "no limit).", "n", 10000)
	PARAM_DOUBLE_IN ("step_size", "Step size for parallel SGD optimizer.", "a", 0.01)
	PARAM_FLAG ("shuffle", "Don't shuffle the order in which data points are " "visited for parallel SGD.", "S")
	PARAM_INT_IN ("epochs", "Maximum number of full epochs over dataset for " "psgd", "E", 50)
	PARAM_INT_IN ("seed", "Random seed. If 0, 'std::time(NULL)' is used.", "s", 0)
	PARAM_MODEL_IN (LinearSVMModel, "input_model", "Existing model " "(parameters).", "m")
	PARAM_MODEL_OUT (LinearSVMModel, "output_model", "Output for trained " "linear svm model.", "M")
	PARAM_MATRIX_IN ("test", "Matrix containing test dataset.", "T")
	PARAM_UROW_IN ("test_labels", "Matrix containing test labels.", "L")
	PARAM_UROW_OUT ("predictions", "If test data is specified, this matrix is where " "the predictions for the test set will be saved.", "P")
	PARAM_MATRIX_OUT ("probabilities", "If test data is specified, this " "matrix is where the class probabilities for the test set will be saved.", "p")

Detailed Description

Author

Yashwant Singh Parihar

Main executable for linear svm.

mlpack is free software; you may redistribute it and/or modify it under the terms of the 3-clause BSD license. You should have received a copy of the 3-clause BSD license along with mlpack. If not, see http://www.opensource.org/licenses/BSD-3-Clause for more information.