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2131 lines
104 KiB
C++
2131 lines
104 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Copyright (C) 2014, Itseez Inc, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//M*/
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#ifndef __OPENCV_ML_HPP__
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#define __OPENCV_ML_HPP__
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#ifdef __cplusplus
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# include "opencv2/core.hpp"
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#endif
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#ifdef __cplusplus
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#include <float.h>
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#include <map>
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#include <iostream>
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/**
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@defgroup ml Machine Learning
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@{
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@defgroup ml_stat Statistical Models
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@defgroup ml_bayes Normal Bayes Classifier
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This simple classification model assumes that feature vectors from each class are normally
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distributed (though, not necessarily independently distributed). So, the whole data distribution
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function is assumed to be a Gaussian mixture, one component per class. Using the training data the
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algorithm estimates mean vectors and covariance matrices for every class, and then it uses them for
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prediction.
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@defgroup ml_knearest K-Nearest Neighbors
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The algorithm caches all training samples and predicts the response for a new sample by analyzing a
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certain number (**K**) of the nearest neighbors of the sample using voting, calculating weighted
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sum, and so on. The method is sometimes referred to as "learning by example" because for prediction
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it looks for the feature vector with a known response that is closest to the given vector.
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@defgroup ml_svm Support Vector Machines
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Originally, support vector machines (SVM) was a technique for building an optimal binary (2-class)
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classifier. Later the technique was extended to regression and clustering problems. SVM is a partial
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case of kernel-based methods. It maps feature vectors into a higher-dimensional space using a kernel
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function and builds an optimal linear discriminating function in this space or an optimal
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hyper-plane that fits into the training data. In case of SVM, the kernel is not defined explicitly.
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Instead, a distance between any 2 points in the hyper-space needs to be defined.
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The solution is optimal, which means that the margin between the separating hyper-plane and the
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nearest feature vectors from both classes (in case of 2-class classifier) is maximal. The feature
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vectors that are the closest to the hyper-plane are called *support vectors*, which means that the
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position of other vectors does not affect the hyper-plane (the decision function).
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SVM implementation in OpenCV is based on @cite LibSVM .
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Prediction with SVM
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-------------------
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StatModel::predict(samples, results, flags) should be used. Pass flags=StatModel::RAW_OUTPUT to get
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the raw response from SVM (in the case of regression, 1-class or 2-class classification problem).
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@defgroup ml_decsiontrees Decision Trees
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The ML classes discussed in this section implement Classification and Regression Tree algorithms
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described in @cite Breiman84 .
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The class cv::ml::DTrees represents a single decision tree or a collection of decision trees. It's
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also a base class for RTrees and Boost.
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A decision tree is a binary tree (tree where each non-leaf node has two child nodes). It can be used
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either for classification or for regression. For classification, each tree leaf is marked with a
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class label; multiple leaves may have the same label. For regression, a constant is also assigned to
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each tree leaf, so the approximation function is piecewise constant.
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Predicting with Decision Trees
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------------------------------
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To reach a leaf node and to obtain a response for the input feature vector, the prediction procedure
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starts with the root node. From each non-leaf node the procedure goes to the left (selects the left
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child node as the next observed node) or to the right based on the value of a certain variable whose
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index is stored in the observed node. The following variables are possible:
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- **Ordered variables.** The variable value is compared with a threshold that is also stored in
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the node. If the value is less than the threshold, the procedure goes to the left. Otherwise, it
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goes to the right. For example, if the weight is less than 1 kilogram, the procedure goes to the
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left, else to the right.
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- **Categorical variables.** A discrete variable value is tested to see whether it belongs to a
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certain subset of values (also stored in the node) from a limited set of values the variable
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could take. If it does, the procedure goes to the left. Otherwise, it goes to the right. For
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example, if the color is green or red, go to the left, else to the right.
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So, in each node, a pair of entities (variable_index , `decision_rule (threshold/subset)` ) is
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used. This pair is called a *split* (split on the variable variable_index ). Once a leaf node is
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reached, the value assigned to this node is used as the output of the prediction procedure.
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Sometimes, certain features of the input vector are missed (for example, in the darkness it is
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difficult to determine the object color), and the prediction procedure may get stuck in the certain
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node (in the mentioned example, if the node is split by color). To avoid such situations, decision
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trees use so-called *surrogate splits*. That is, in addition to the best "primary" split, every tree
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node may also be split to one or more other variables with nearly the same results.
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Training Decision Trees
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-----------------------
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The tree is built recursively, starting from the root node. All training data (feature vectors and
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responses) is used to split the root node. In each node the optimum decision rule (the best
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"primary" split) is found based on some criteria. In machine learning, gini "purity" criteria are
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used for classification, and sum of squared errors is used for regression. Then, if necessary, the
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surrogate splits are found. They resemble the results of the primary split on the training data. All
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the data is divided using the primary and the surrogate splits (like it is done in the prediction
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procedure) between the left and the right child node. Then, the procedure recursively splits both
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left and right nodes. At each node the recursive procedure may stop (that is, stop splitting the
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node further) in one of the following cases:
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- Depth of the constructed tree branch has reached the specified maximum value.
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- Number of training samples in the node is less than the specified threshold when it is not
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statistically representative to split the node further.
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- All the samples in the node belong to the same class or, in case of regression, the variation is
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too small.
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- The best found split does not give any noticeable improvement compared to a random choice.
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When the tree is built, it may be pruned using a cross-validation procedure, if necessary. That is,
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some branches of the tree that may lead to the model overfitting are cut off. Normally, this
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procedure is only applied to standalone decision trees. Usually tree ensembles build trees that are
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small enough and use their own protection schemes against overfitting.
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Variable Importance
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-------------------
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Besides the prediction that is an obvious use of decision trees, the tree can be also used for
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various data analyses. One of the key properties of the constructed decision tree algorithms is an
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ability to compute the importance (relative decisive power) of each variable. For example, in a spam
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filter that uses a set of words occurred in the message as a feature vector, the variable importance
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rating can be used to determine the most "spam-indicating" words and thus help keep the dictionary
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size reasonable.
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Importance of each variable is computed over all the splits on this variable in the tree, primary
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and surrogate ones. Thus, to compute variable importance correctly, the surrogate splits must be
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enabled in the training parameters, even if there is no missing data.
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@defgroup ml_boost Boosting
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A common machine learning task is supervised learning. In supervised learning, the goal is to learn
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the functional relationship \f$F: y = F(x)\f$ between the input \f$x\f$ and the output \f$y\f$ . Predicting the
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qualitative output is called *classification*, while predicting the quantitative output is called
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*regression*.
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Boosting is a powerful learning concept that provides a solution to the supervised classification
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learning task. It combines the performance of many "weak" classifiers to produce a powerful
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committee @cite HTF01 . A weak classifier is only required to be better than chance, and thus can be
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very simple and computationally inexpensive. However, many of them smartly combine results to a
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strong classifier that often outperforms most "monolithic" strong classifiers such as SVMs and
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Neural Networks.
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Decision trees are the most popular weak classifiers used in boosting schemes. Often the simplest
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decision trees with only a single split node per tree (called stumps ) are sufficient.
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The boosted model is based on \f$N\f$ training examples \f${(x_i,y_i)}1N\f$ with \f$x_i \in{R^K}\f$ and
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\f$y_i \in{-1, +1}\f$ . \f$x_i\f$ is a \f$K\f$ -component vector. Each component encodes a feature relevant to
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the learning task at hand. The desired two-class output is encoded as -1 and +1.
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Different variants of boosting are known as Discrete Adaboost, Real AdaBoost, LogitBoost, and Gentle
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AdaBoost @cite FHT98 . All of them are very similar in their overall structure. Therefore, this chapter
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focuses only on the standard two-class Discrete AdaBoost algorithm, outlined below. Initially the
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same weight is assigned to each sample (step 2). Then, a weak classifier \f$f_{m(x)}\f$ is trained on
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the weighted training data (step 3a). Its weighted training error and scaling factor \f$c_m\f$ is
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computed (step 3b). The weights are increased for training samples that have been misclassified
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(step 3c). All weights are then normalized, and the process of finding the next weak classifier
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continues for another \f$M\f$ -1 times. The final classifier \f$F(x)\f$ is the sign of the weighted sum over
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the individual weak classifiers (step 4).
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**Two-class Discrete AdaBoost Algorithm**
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- Set \f$N\f$ examples \f${(x_i,y_i)}1N\f$ with \f$x_i \in{R^K}, y_i \in{-1, +1}\f$ .
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- Assign weights as \f$w_i = 1/N, i = 1,...,N\f$ .
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- Repeat for \f$m = 1,2,...,M\f$ :
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3.1. Fit the classifier \f$f_m(x) \in{-1,1}\f$, using weights \f$w_i\f$ on the training data.
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3.2. Compute \f$err_m = E_w [1_{(y \neq f_m(x))}], c_m = log((1 - err_m)/err_m)\f$ .
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3.3. Set \f$w_i \Leftarrow w_i exp[c_m 1_{(y_i \neq f_m(x_i))}], i = 1,2,...,N,\f$ and renormalize
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so that \f$\Sigma i w_i = 1\f$ .
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1. Classify new samples *x* using the formula: \f$\textrm{sign} (\Sigma m = 1M c_m f_m(x))\f$ .
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@note Similar to the classical boosting methods, the current implementation supports two-class
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classifiers only. For M \> 2 classes, there is the **AdaBoost.MH** algorithm (described in
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@cite FHT98) that reduces the problem to the two-class problem, yet with a much larger training set.
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To reduce computation time for boosted models without substantially losing accuracy, the influence
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trimming technique can be employed. As the training algorithm proceeds and the number of trees in
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the ensemble is increased, a larger number of the training samples are classified correctly and with
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increasing confidence, thereby those samples receive smaller weights on the subsequent iterations.
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Examples with a very low relative weight have a small impact on the weak classifier training. Thus,
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such examples may be excluded during the weak classifier training without having much effect on the
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induced classifier. This process is controlled with the weight_trim_rate parameter. Only examples
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with the summary fraction weight_trim_rate of the total weight mass are used in the weak
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classifier training. Note that the weights for **all** training examples are recomputed at each
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training iteration. Examples deleted at a particular iteration may be used again for learning some
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of the weak classifiers further @cite FHT98 .
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Prediction with Boost
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---------------------
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StatModel::predict(samples, results, flags) should be used. Pass flags=StatModel::RAW_OUTPUT to get
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the raw sum from Boost classifier.
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@defgroup ml_randomtrees Random Trees
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Random trees have been introduced by Leo Breiman and Adele Cutler:
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<http://www.stat.berkeley.edu/users/breiman/RandomForests/> . The algorithm can deal with both
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classification and regression problems. Random trees is a collection (ensemble) of tree predictors
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that is called *forest* further in this section (the term has been also introduced by L. Breiman).
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The classification works as follows: the random trees classifier takes the input feature vector,
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classifies it with every tree in the forest, and outputs the class label that received the majority
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of "votes". In case of a regression, the classifier response is the average of the responses over
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all the trees in the forest.
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All the trees are trained with the same parameters but on different training sets. These sets are
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generated from the original training set using the bootstrap procedure: for each training set, you
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randomly select the same number of vectors as in the original set ( =N ). The vectors are chosen
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with replacement. That is, some vectors will occur more than once and some will be absent. At each
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node of each trained tree, not all the variables are used to find the best split, but a random
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subset of them. With each node a new subset is generated. However, its size is fixed for all the
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nodes and all the trees. It is a training parameter set to \f$\sqrt{number_of_variables}\f$ by
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default. None of the built trees are pruned.
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In random trees there is no need for any accuracy estimation procedures, such as cross-validation or
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bootstrap, or a separate test set to get an estimate of the training error. The error is estimated
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internally during the training. When the training set for the current tree is drawn by sampling with
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replacement, some vectors are left out (so-called *oob (out-of-bag) data* ). The size of oob data is
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about N/3 . The classification error is estimated by using this oob-data as follows:
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- Get a prediction for each vector, which is oob relative to the i-th tree, using the very i-th
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tree.
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- After all the trees have been trained, for each vector that has ever been oob, find the
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class-*winner* for it (the class that has got the majority of votes in the trees where the
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vector was oob) and compare it to the ground-truth response.
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- Compute the classification error estimate as a ratio of the number of misclassified oob vectors
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to all the vectors in the original data. In case of regression, the oob-error is computed as the
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squared error for oob vectors difference divided by the total number of vectors.
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For the random trees usage example, please, see letter_recog.cpp sample in OpenCV distribution.
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**References:**
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- *Machine Learning*, Wald I, July 2002.
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<http://stat-www.berkeley.edu/users/breiman/wald2002-1.pdf>
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- *Looking Inside the Black Box*, Wald II, July 2002.
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<http://stat-www.berkeley.edu/users/breiman/wald2002-2.pdf>
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- *Software for the Masses*, Wald III, July 2002.
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<http://stat-www.berkeley.edu/users/breiman/wald2002-3.pdf>
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- And other articles from the web site
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<http://www.stat.berkeley.edu/users/breiman/RandomForests/cc_home.htm>
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@defgroup ml_em Expectation Maximization
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The Expectation Maximization(EM) algorithm estimates the parameters of the multivariate probability
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density function in the form of a Gaussian mixture distribution with a specified number of mixtures.
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Consider the set of the N feature vectors { \f$x_1, x_2,...,x_{N}\f$ } from a d-dimensional Euclidean
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space drawn from a Gaussian mixture:
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\f[p(x;a_k,S_k, \pi _k) = \sum _{k=1}^{m} \pi _kp_k(x), \quad \pi _k \geq 0, \quad \sum _{k=1}^{m} \pi _k=1,\f]
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\f[p_k(x)= \varphi (x;a_k,S_k)= \frac{1}{(2\pi)^{d/2}\mid{S_k}\mid^{1/2}} exp \left \{ - \frac{1}{2} (x-a_k)^TS_k^{-1}(x-a_k) \right \} ,\f]
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where \f$m\f$ is the number of mixtures, \f$p_k\f$ is the normal distribution density with the mean \f$a_k\f$
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and covariance matrix \f$S_k\f$, \f$\pi_k\f$ is the weight of the k-th mixture. Given the number of mixtures
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\f$M\f$ and the samples \f$x_i\f$, \f$i=1..N\f$ the algorithm finds the maximum-likelihood estimates (MLE) of
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all the mixture parameters, that is, \f$a_k\f$, \f$S_k\f$ and \f$\pi_k\f$ :
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\f[L(x, \theta )=logp(x, \theta )= \sum _{i=1}^{N}log \left ( \sum _{k=1}^{m} \pi _kp_k(x) \right ) \to \max _{ \theta \in \Theta },\f]
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\f[\Theta = \left \{ (a_k,S_k, \pi _k): a_k \in \mathbbm{R} ^d,S_k=S_k^T>0,S_k \in \mathbbm{R} ^{d \times d}, \pi _k \geq 0, \sum _{k=1}^{m} \pi _k=1 \right \} .\f]
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The EM algorithm is an iterative procedure. Each iteration includes two steps. At the first step
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(Expectation step or E-step), you find a probability \f$p_{i,k}\f$ (denoted \f$\alpha_{i,k}\f$ in the
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formula below) of sample i to belong to mixture k using the currently available mixture parameter
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estimates:
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\f[\alpha _{ki} = \frac{\pi_k\varphi(x;a_k,S_k)}{\sum\limits_{j=1}^{m}\pi_j\varphi(x;a_j,S_j)} .\f]
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At the second step (Maximization step or M-step), the mixture parameter estimates are refined using
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the computed probabilities:
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\f[\pi _k= \frac{1}{N} \sum _{i=1}^{N} \alpha _{ki}, \quad a_k= \frac{\sum\limits_{i=1}^{N}\alpha_{ki}x_i}{\sum\limits_{i=1}^{N}\alpha_{ki}} , \quad S_k= \frac{\sum\limits_{i=1}^{N}\alpha_{ki}(x_i-a_k)(x_i-a_k)^T}{\sum\limits_{i=1}^{N}\alpha_{ki}}\f]
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Alternatively, the algorithm may start with the M-step when the initial values for \f$p_{i,k}\f$ can be
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provided. Another alternative when \f$p_{i,k}\f$ are unknown is to use a simpler clustering algorithm to
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pre-cluster the input samples and thus obtain initial \f$p_{i,k}\f$ . Often (including machine learning)
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the k-means algorithm is used for that purpose.
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One of the main problems of the EM algorithm is a large number of parameters to estimate. The
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majority of the parameters reside in covariance matrices, which are \f$d \times d\f$ elements each where
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\f$d\f$ is the feature space dimensionality. However, in many practical problems, the covariance
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matrices are close to diagonal or even to \f$\mu_k*I\f$ , where \f$I\f$ is an identity matrix and \f$\mu_k\f$ is
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a mixture-dependent "scale" parameter. So, a robust computation scheme could start with harder
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constraints on the covariance matrices and then use the estimated parameters as an input for a less
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constrained optimization problem (often a diagonal covariance matrix is already a good enough
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approximation).
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References:
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- Bilmes98 J. A. Bilmes. *A Gentle Tutorial of the EM Algorithm and its Application to Parameter
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Estimation for Gaussian Mixture and Hidden Markov Models*. Technical Report TR-97-021,
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International Computer Science Institute and Computer Science Division, University of California
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at Berkeley, April 1998.
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@defgroup ml_neural Neural Networks
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ML implements feed-forward artificial neural networks or, more particularly, multi-layer perceptrons
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(MLP), the most commonly used type of neural networks. MLP consists of the input layer, output
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layer, and one or more hidden layers. Each layer of MLP includes one or more neurons directionally
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linked with the neurons from the previous and the next layer. The example below represents a 3-layer
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perceptron with three inputs, two outputs, and the hidden layer including five neurons:
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All the neurons in MLP are similar. Each of them has several input links (it takes the output values
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from several neurons in the previous layer as input) and several output links (it passes the
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response to several neurons in the next layer). The values retrieved from the previous layer are
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summed up with certain weights, individual for each neuron, plus the bias term. The sum is
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transformed using the activation function \f$f\f$ that may be also different for different neurons.
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In other words, given the outputs \f$x_j\f$ of the layer \f$n\f$ , the outputs \f$y_i\f$ of the layer \f$n+1\f$ are
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computed as:
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\f[u_i = \sum _j (w^{n+1}_{i,j}*x_j) + w^{n+1}_{i,bias}\f]
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\f[y_i = f(u_i)\f]
|
|
|
|
Different activation functions may be used. ML implements three standard functions:
|
|
|
|
- Identity function ( ANN_MLP::IDENTITY ): \f$f(x)=x\f$
|
|
|
|
- Symmetrical sigmoid ( ANN_MLP::SIGMOID_SYM ): \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x}\f$
|
|
), which is the default choice for MLP. The standard sigmoid with \f$\beta =1, \alpha =1\f$ is shown
|
|
below:
|
|
|
|
|
|
|
|
- Gaussian function ( ANN_MLP::GAUSSIAN ): \f$f(x)=\beta e^{-\alpha x*x}\f$ , which is not completely
|
|
supported at the moment.
|
|
|
|
In ML, all the neurons have the same activation functions, with the same free parameters (
|
|
\f$\alpha, \beta\f$ ) that are specified by user and are not altered by the training algorithms.
|
|
|
|
So, the whole trained network works as follows:
|
|
|
|
1. Take the feature vector as input. The vector size is equal to the size of the input layer.
|
|
2. Pass values as input to the first hidden layer.
|
|
3. Compute outputs of the hidden layer using the weights and the activation functions.
|
|
4. Pass outputs further downstream until you compute the output layer.
|
|
|
|
So, to compute the network, you need to know all the weights \f$w^{n+1)}_{i,j}\f$ . The weights are
|
|
computed by the training algorithm. The algorithm takes a training set, multiple input vectors with
|
|
the corresponding output vectors, and iteratively adjusts the weights to enable the network to give
|
|
the desired response to the provided input vectors.
|
|
|
|
The larger the network size (the number of hidden layers and their sizes) is, the more the potential
|
|
network flexibility is. The error on the training set could be made arbitrarily small. But at the
|
|
same time the learned network also "learns" the noise present in the training set, so the error on
|
|
the test set usually starts increasing after the network size reaches a limit. Besides, the larger
|
|
networks are trained much longer than the smaller ones, so it is reasonable to pre-process the data,
|
|
using PCA::operator() or similar technique, and train a smaller network on only essential features.
|
|
|
|
Another MLP feature is an inability to handle categorical data as is. However, there is a
|
|
workaround. If a certain feature in the input or output (in case of n -class classifier for \f$n>2\f$ )
|
|
layer is categorical and can take \f$M>2\f$ different values, it makes sense to represent it as a binary
|
|
tuple of M elements, where the i -th element is 1 if and only if the feature is equal to the i -th
|
|
value out of M possible. It increases the size of the input/output layer but speeds up the training
|
|
algorithm convergence and at the same time enables "fuzzy" values of such variables, that is, a
|
|
tuple of probabilities instead of a fixed value.
|
|
|
|
ML implements two algorithms for training MLP's. The first algorithm is a classical random
|
|
sequential back-propagation algorithm. The second (default) one is a batch RPROP algorithm.
|
|
|
|
@defgroup ml_lr Logistic Regression
|
|
|
|
ML implements logistic regression, which is a probabilistic classification technique. Logistic
|
|
Regression is a binary classification algorithm which is closely related to Support Vector Machines
|
|
(SVM). Like SVM, Logistic Regression can be extended to work on multi-class classification problems
|
|
like digit recognition (i.e. recognizing digitis like 0,1 2, 3,... from the given images). This
|
|
version of Logistic Regression supports both binary and multi-class classifications (for multi-class
|
|
it creates a multiple 2-class classifiers). In order to train the logistic regression classifier,
|
|
Batch Gradient Descent and Mini-Batch Gradient Descent algorithms are used (see <http://en.wikipedia.org/wiki/Gradient_descent_optimization>).
|
|
Logistic Regression is a discriminative classifier (see <http://www.cs.cmu.edu/~tom/NewChapters.html> for more details).
|
|
Logistic Regression is implemented as a C++ class in LogisticRegression.
|
|
|
|
In Logistic Regression, we try to optimize the training paramater \f$\theta\f$ such that the hypothesis
|
|
\f$0 \leq h_\theta(x) \leq 1\f$ is acheived. We have \f$h_\theta(x) = g(h_\theta(x))\f$ and
|
|
\f$g(z) = \frac{1}{1+e^{-z}}\f$ as the logistic or sigmoid function. The term "Logistic" in Logistic
|
|
Regression refers to this function. For given data of a binary classification problem of classes 0
|
|
and 1, one can determine that the given data instance belongs to class 1 if \f$h_\theta(x) \geq 0.5\f$
|
|
or class 0 if \f$h_\theta(x) < 0.5\f$ .
|
|
|
|
In Logistic Regression, choosing the right parameters is of utmost importance for reducing the
|
|
training error and ensuring high training accuracy. LogisticRegression::Params is the structure that
|
|
defines parameters that are required to train a Logistic Regression classifier. The learning rate is
|
|
determined by LogisticRegression::Params.alpha. It determines how faster we approach the solution.
|
|
It is a positive real number. Optimization algorithms like Batch Gradient Descent and Mini-Batch
|
|
Gradient Descent are supported in LogisticRegression. It is important that we mention the number of
|
|
iterations these optimization algorithms have to run. The number of iterations are mentioned by
|
|
LogisticRegression::Params.num_iters. The number of iterations can be thought as number of steps
|
|
taken and learning rate specifies if it is a long step or a short step. These two parameters define
|
|
how fast we arrive at a possible solution. In order to compensate for overfitting regularization is
|
|
performed, which can be enabled by setting LogisticRegression::Params.regularized to a positive
|
|
integer (greater than zero). One can specify what kind of regularization has to be performed by
|
|
setting LogisticRegression::Params.norm to LogisticRegression::REG_L1 or
|
|
LogisticRegression::REG_L2 values. LogisticRegression provides a choice of 2 training methods with
|
|
Batch Gradient Descent or the Mini-Batch Gradient Descent. To specify this, set
|
|
LogisticRegression::Params.train_method to either LogisticRegression::BATCH or
|
|
LogisticRegression::MINI_BATCH. If LogisticRegression::Params is set to
|
|
LogisticRegression::MINI_BATCH, the size of the mini batch has to be to a postive integer using
|
|
LogisticRegression::Params.mini_batch_size.
|
|
|
|
A sample set of training parameters for the Logistic Regression classifier can be initialized as
|
|
follows:
|
|
@code
|
|
LogisticRegression::Params params;
|
|
params.alpha = 0.5;
|
|
params.num_iters = 10000;
|
|
params.norm = LogisticRegression::REG_L2;
|
|
params.regularized = 1;
|
|
params.train_method = LogisticRegression::MINI_BATCH;
|
|
params.mini_batch_size = 10;
|
|
@endcode
|
|
|
|
@defgroup ml_data Training Data
|
|
|
|
In machine learning algorithms there is notion of training data. Training data includes several
|
|
components:
|
|
|
|
- A set of training samples. Each training sample is a vector of values (in Computer Vision it's
|
|
sometimes referred to as feature vector). Usually all the vectors have the same number of
|
|
components (features); OpenCV ml module assumes that. Each feature can be ordered (i.e. its
|
|
values are floating-point numbers that can be compared with each other and strictly ordered,
|
|
i.e. sorted) or categorical (i.e. its value belongs to a fixed set of values that can be
|
|
integers, strings etc.).
|
|
- Optional set of responses corresponding to the samples. Training data with no responses is used
|
|
in unsupervised learning algorithms that learn structure of the supplied data based on distances
|
|
between different samples. Training data with responses is used in supervised learning
|
|
algorithms, which learn the function mapping samples to responses. Usually the responses are
|
|
scalar values, ordered (when we deal with regression problem) or categorical (when we deal with
|
|
classification problem; in this case the responses are often called "labels"). Some algorithms,
|
|
most noticeably Neural networks, can handle not only scalar, but also multi-dimensional or
|
|
vector responses.
|
|
- Another optional component is the mask of missing measurements. Most algorithms require all the
|
|
components in all the training samples be valid, but some other algorithms, such as decision
|
|
tress, can handle the cases of missing measurements.
|
|
- In the case of classification problem user may want to give different weights to different
|
|
classes. This is useful, for example, when
|
|
- user wants to shift prediction accuracy towards lower false-alarm rate or higher hit-rate.
|
|
- user wants to compensate for significantly different amounts of training samples from
|
|
different classes.
|
|
- In addition to that, each training sample may be given a weight, if user wants the algorithm to
|
|
pay special attention to certain training samples and adjust the training model accordingly.
|
|
- Also, user may wish not to use the whole training data at once, but rather use parts of it, e.g.
|
|
to do parameter optimization via cross-validation procedure.
|
|
|
|
As you can see, training data can have rather complex structure; besides, it may be very big and/or
|
|
not entirely available, so there is need to make abstraction for this concept. In OpenCV ml there is
|
|
cv::ml::TrainData class for that.
|
|
|
|
@}
|
|
*/
|
|
|
|
namespace cv
|
|
{
|
|
|
|
namespace ml
|
|
{
|
|
|
|
//! @addtogroup ml
|
|
//! @{
|
|
|
|
/* Variable type */
|
|
enum
|
|
{
|
|
VAR_NUMERICAL =0,
|
|
VAR_ORDERED =0,
|
|
VAR_CATEGORICAL =1
|
|
};
|
|
|
|
enum
|
|
{
|
|
TEST_ERROR = 0,
|
|
TRAIN_ERROR = 1
|
|
};
|
|
|
|
enum
|
|
{
|
|
ROW_SAMPLE = 0,
|
|
COL_SAMPLE = 1
|
|
};
|
|
|
|
//! @addtogroup ml_svm
|
|
//! @{
|
|
|
|
/** @brief The structure represents the logarithmic grid range of statmodel parameters.
|
|
|
|
It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate
|
|
being computed by cross-validation.
|
|
- member double ParamGrid::minVal
|
|
Minimum value of the statmodel parameter.
|
|
- member double ParamGrid::maxVal
|
|
Maximum value of the statmodel parameter.
|
|
- member double ParamGrid::logStep
|
|
Logarithmic step for iterating the statmodel parameter.
|
|
The grid determines the following iteration sequence of the statmodel parameter values:
|
|
|
|
\f[(minVal, minVal*step, minVal*{step}^2, \dots, minVal*{logStep}^n),\f]
|
|
|
|
where \f$n\f$ is the maximal index satisfying
|
|
|
|
\f[\texttt{minVal} * \texttt{logStep} ^n < \texttt{maxVal}\f]
|
|
|
|
The grid is logarithmic, so logStep must always be greater then 1.
|
|
*/
|
|
class CV_EXPORTS_W_MAP ParamGrid
|
|
{
|
|
public:
|
|
/** @brief The constructors.
|
|
|
|
The full constructor initializes corresponding members. The default constructor creates a dummy
|
|
grid:
|
|
@code
|
|
ParamGrid::ParamGrid()
|
|
{
|
|
minVal = maxVal = 0;
|
|
logStep = 1;
|
|
}
|
|
@endcode
|
|
*/
|
|
ParamGrid();
|
|
ParamGrid(double _minVal, double _maxVal, double _logStep);
|
|
|
|
CV_PROP_RW double minVal;
|
|
CV_PROP_RW double maxVal;
|
|
CV_PROP_RW double logStep;
|
|
};
|
|
|
|
//! @} ml_svm
|
|
|
|
//! @addtogroup ml_data
|
|
//! @{
|
|
|
|
/** @brief Class encapsulating training data.
|
|
|
|
Please note that the class only specifies the interface of training data, but not implementation.
|
|
All the statistical model classes in ml take Ptr\<TrainData\>. In other words, you can create your
|
|
own class derived from TrainData and supply smart pointer to the instance of this class into
|
|
StatModel::train.
|
|
*/
|
|
class CV_EXPORTS TrainData
|
|
{
|
|
public:
|
|
static inline float missingValue() { return FLT_MAX; }
|
|
virtual ~TrainData();
|
|
|
|
virtual int getLayout() const = 0;
|
|
virtual int getNTrainSamples() const = 0;
|
|
virtual int getNTestSamples() const = 0;
|
|
virtual int getNSamples() const = 0;
|
|
virtual int getNVars() const = 0;
|
|
virtual int getNAllVars() const = 0;
|
|
|
|
virtual void getSample(InputArray varIdx, int sidx, float* buf) const = 0;
|
|
virtual Mat getSamples() const = 0;
|
|
virtual Mat getMissing() const = 0;
|
|
|
|
/** @brief Returns matrix of train samples
|
|
|
|
@param layout The requested layout. If it's different from the initial one, the matrix is
|
|
transposed.
|
|
@param compressSamples if true, the function returns only the training samples (specified by
|
|
sampleIdx)
|
|
@param compressVars if true, the function returns the shorter training samples, containing only
|
|
the active variables.
|
|
|
|
In current implementation the function tries to avoid physical data copying and returns the matrix
|
|
stored inside TrainData (unless the transposition or compression is needed).
|
|
*/
|
|
virtual Mat getTrainSamples(int layout=ROW_SAMPLE,
|
|
bool compressSamples=true,
|
|
bool compressVars=true) const = 0;
|
|
|
|
/** @brief Returns the vector of responses
|
|
|
|
The function returns ordered or the original categorical responses. Usually it's used in regression
|
|
algorithms.
|
|
*/
|
|
virtual Mat getTrainResponses() const = 0;
|
|
|
|
/** @brief Returns the vector of normalized categorical responses
|
|
|
|
The function returns vector of responses. Each response is integer from 0 to \<number of
|
|
classes\>-1. The actual label value can be retrieved then from the class label vector, see
|
|
TrainData::getClassLabels.
|
|
*/
|
|
virtual Mat getTrainNormCatResponses() const = 0;
|
|
virtual Mat getTestResponses() const = 0;
|
|
virtual Mat getTestNormCatResponses() const = 0;
|
|
virtual Mat getResponses() const = 0;
|
|
virtual Mat getNormCatResponses() const = 0;
|
|
virtual Mat getSampleWeights() const = 0;
|
|
virtual Mat getTrainSampleWeights() const = 0;
|
|
virtual Mat getTestSampleWeights() const = 0;
|
|
virtual Mat getVarIdx() const = 0;
|
|
virtual Mat getVarType() const = 0;
|
|
virtual int getResponseType() const = 0;
|
|
virtual Mat getTrainSampleIdx() const = 0;
|
|
virtual Mat getTestSampleIdx() const = 0;
|
|
virtual void getValues(int vi, InputArray sidx, float* values) const = 0;
|
|
virtual void getNormCatValues(int vi, InputArray sidx, int* values) const = 0;
|
|
virtual Mat getDefaultSubstValues() const = 0;
|
|
|
|
virtual int getCatCount(int vi) const = 0;
|
|
|
|
/** @brief Returns the vector of class labels
|
|
|
|
The function returns vector of unique labels occurred in the responses.
|
|
*/
|
|
virtual Mat getClassLabels() const = 0;
|
|
|
|
virtual Mat getCatOfs() const = 0;
|
|
virtual Mat getCatMap() const = 0;
|
|
|
|
virtual void setTrainTestSplit(int count, bool shuffle=true) = 0;
|
|
|
|
/** @brief Splits the training data into the training and test parts
|
|
|
|
The function selects a subset of specified relative size and then returns it as the training set. If
|
|
the function is not called, all the data is used for training. Please, note that for each of
|
|
TrainData::getTrain\* there is corresponding TrainData::getTest\*, so that the test subset can be
|
|
retrieved and processed as well.
|
|
*/
|
|
virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0;
|
|
virtual void shuffleTrainTest() = 0;
|
|
|
|
static Mat getSubVector(const Mat& vec, const Mat& idx);
|
|
|
|
/** @brief Reads the dataset from a .csv file and returns the ready-to-use training data.
|
|
|
|
@param filename The input file name
|
|
@param headerLineCount The number of lines in the beginning to skip; besides the header, the
|
|
function also skips empty lines and lines staring with '\#'
|
|
@param responseStartIdx Index of the first output variable. If -1, the function considers the last
|
|
variable as the response
|
|
@param responseEndIdx Index of the last output variable + 1. If -1, then there is single response
|
|
variable at responseStartIdx.
|
|
@param varTypeSpec The optional text string that specifies the variables' types. It has the format ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]. That is, variables from n1 to n2 (inclusive range), n3, n4 to n5 ... are considered ordered and n6, n7 to n8 ... are considered as categorical. The range [n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8] should cover all the variables. If varTypeSpec is not specified, then algorithm uses the following rules:
|
|
# all input variables are considered ordered by default. If some column contains has
|
|
non-numerical values, e.g. 'apple', 'pear', 'apple', 'apple', 'mango', the corresponding
|
|
variable is considered categorical.
|
|
# if there are several output variables, they are all considered as ordered. Error is
|
|
reported when non-numerical values are used.
|
|
# if there is a single output variable, then if its values are non-numerical or are all
|
|
integers, then it's considered categorical. Otherwise, it's considered ordered.
|
|
@param delimiter The character used to separate values in each line.
|
|
@param missch The character used to specify missing measurements. It should not be a digit.
|
|
Although it's a non-numerical value, it surely does not affect the decision of whether the
|
|
variable ordered or categorical.
|
|
*/
|
|
static Ptr<TrainData> loadFromCSV(const String& filename,
|
|
int headerLineCount,
|
|
int responseStartIdx=-1,
|
|
int responseEndIdx=-1,
|
|
const String& varTypeSpec=String(),
|
|
char delimiter=',',
|
|
char missch='?');
|
|
/** @brief Creates training data from in-memory arrays.
|
|
|
|
@param samples matrix of samples. It should have CV_32F type.
|
|
@param layout it's either ROW_SAMPLE, which means that each training sample is a row of samples,
|
|
or COL_SAMPLE, which means that each training sample occupies a column of samples.
|
|
@param responses matrix of responses. If the responses are scalar, they should be stored as a
|
|
single row or as a single column. The matrix should have type CV_32F or CV_32S (in the former
|
|
case the responses are considered as ordered by default; in the latter case - as categorical)
|
|
@param varIdx vector specifying which variables to use for training. It can be an integer vector
|
|
(CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active
|
|
variables.
|
|
@param sampleIdx vector specifying which samples to use for training. It can be an integer vector
|
|
(CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training
|
|
samples.
|
|
@param sampleWeights optional vector with weights for each sample. It should have CV_32F type.
|
|
@param varType optional vector of type CV_8U and size \<number_of_variables_in_samples\> +
|
|
\<number_of_variables_in_responses\>, containing types of each input and output variable. The
|
|
ordered variables are denoted by value VAR_ORDERED, and categorical - by VAR_CATEGORICAL.
|
|
*/
|
|
static Ptr<TrainData> create(InputArray samples, int layout, InputArray responses,
|
|
InputArray varIdx=noArray(), InputArray sampleIdx=noArray(),
|
|
InputArray sampleWeights=noArray(), InputArray varType=noArray());
|
|
};
|
|
|
|
//! @} ml_data
|
|
|
|
//! @addtogroup ml_stat
|
|
//! @{
|
|
|
|
/** @brief Base class for statistical models in OpenCV ML.
|
|
*/
|
|
class CV_EXPORTS_W StatModel : public Algorithm
|
|
{
|
|
public:
|
|
enum { UPDATE_MODEL = 1, RAW_OUTPUT=1, COMPRESSED_INPUT=2, PREPROCESSED_INPUT=4 };
|
|
virtual void clear();
|
|
|
|
/** @brief Returns the number of variables in training samples
|
|
|
|
The method must be overwritten in the derived classes.
|
|
*/
|
|
virtual int getVarCount() const = 0;
|
|
|
|
/** @brief Returns true if the model is trained
|
|
|
|
The method must be overwritten in the derived classes.
|
|
*/
|
|
virtual bool isTrained() const = 0;
|
|
/** @brief Returns true if the model is classifier
|
|
|
|
The method must be overwritten in the derived classes.
|
|
*/
|
|
virtual bool isClassifier() const = 0;
|
|
|
|
/** @brief Trains the statistical model
|
|
|
|
@param trainData training data that can be loaded from file using TrainData::loadFromCSV or
|
|
created with TrainData::create.
|
|
@param flags optional flags, depending on the model. Some of the models can be updated with the
|
|
new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
|
|
|
|
There are 2 instance methods and 2 static (class) template methods. The first two train the already
|
|
created model (the very first method must be overwritten in the derived classes). And the latter two
|
|
variants are convenience methods that construct empty model and then call its train method.
|
|
*/
|
|
virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
|
|
/** @overload
|
|
@param samples training samples
|
|
@param layout ROW_SAMPLE (training samples are the matrix rows) or COL_SAMPLE (training samples
|
|
are the matrix columns)
|
|
@param responses vector of responses associated with the training samples.
|
|
*/
|
|
virtual bool train( InputArray samples, int layout, InputArray responses );
|
|
|
|
/** @brief Computes error on the training or test dataset
|
|
|
|
@param data the training data
|
|
@param test if true, the error is computed over the test subset of the data, otherwise it's
|
|
computed over the training subset of the data. Please note that if you loaded a completely
|
|
different dataset to evaluate already trained classifier, you will probably want not to set the
|
|
test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the
|
|
error is computed for the whole new set. Yes, this sounds a bit confusing.
|
|
@param resp the optional output responses.
|
|
|
|
The method uses StatModel::predict to compute the error. For regression models the error is computed
|
|
as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
|
|
*/
|
|
virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
|
|
|
|
/** @brief Predicts response(s) for the provided sample(s)
|
|
|
|
@param samples The input samples, floating-point matrix
|
|
@param results The optional output matrix of results.
|
|
@param flags The optional flags, model-dependent. Some models, such as Boost, SVM recognize
|
|
StatModel::RAW_OUTPUT flag, which makes the method return the raw results (the sum), not the
|
|
class label.
|
|
*/
|
|
virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
|
|
|
|
/** @brief Loads model from the file
|
|
|
|
This is static template method of StatModel. It's usage is following (in the case of SVM): :
|
|
|
|
Ptr<SVM> svm = StatModel::load<SVM>("my_svm_model.xml");
|
|
|
|
In order to make this method work, the derived class must overwrite
|
|
Algorithm::read(const FileNode& fn).
|
|
*/
|
|
template<typename _Tp> static Ptr<_Tp> load(const String& filename)
|
|
{
|
|
FileStorage fs(filename, FileStorage::READ);
|
|
Ptr<_Tp> model = _Tp::create();
|
|
model->read(fs.getFirstTopLevelNode());
|
|
return model->isTrained() ? model : Ptr<_Tp>();
|
|
}
|
|
|
|
/** @brief Loads model from an XML String
|
|
|
|
@param strModel The string variable containing the model (in an XML format) you want to load.
|
|
|
|
This is static template method of StatModel. It's usage is following (in the case of SVM):
|
|
Ptr<SVM> svm = StatModel::loadFromString<SVM>(myXMLStringModel);
|
|
*/
|
|
template<typename _Tp> static Ptr<_Tp> loadFromString(const String& strModel)
|
|
{
|
|
FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY + FileStorage::FORMAT_XML);
|
|
Ptr<_Tp> model = _Tp::create();
|
|
model->read(fs.getFirstTopLevelNode());
|
|
return model->isTrained() ? model : Ptr<_Tp>();
|
|
}
|
|
|
|
template<typename _Tp> static Ptr<_Tp> train(const Ptr<TrainData>& data, const typename _Tp::Params& p, int flags=0)
|
|
{
|
|
Ptr<_Tp> model = _Tp::create(p);
|
|
return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
|
|
}
|
|
|
|
template<typename _Tp> static Ptr<_Tp> train(InputArray samples, int layout, InputArray responses,
|
|
const typename _Tp::Params& p, int flags=0)
|
|
{
|
|
Ptr<_Tp> model = _Tp::create(p);
|
|
return !model.empty() && model->train(TrainData::create(samples, layout, responses), flags) ? model : Ptr<_Tp>();
|
|
}
|
|
|
|
/** @brief Saves the model to a file.
|
|
|
|
In order to make this method work, the derived class must overwrite
|
|
Algorithm::write(FileStorage& fs).
|
|
*/
|
|
virtual void save(const String& filename) const;
|
|
virtual String getDefaultModelName() const = 0;
|
|
};
|
|
|
|
//! @} ml_stat
|
|
|
|
/****************************************************************************************\
|
|
* Normal Bayes Classifier *
|
|
\****************************************************************************************/
|
|
|
|
//! @addtogroup ml_bayes
|
|
//! @{
|
|
|
|
/** @brief Bayes classifier for normally distributed data.
|
|
*/
|
|
class CV_EXPORTS_W NormalBayesClassifier : public StatModel
|
|
{
|
|
public:
|
|
class CV_EXPORTS_W Params
|
|
{
|
|
public:
|
|
Params();
|
|
};
|
|
/** @brief Predicts the response for sample(s).
|
|
|
|
The method estimates the most probable classes for input vectors. Input vectors (one or more) are
|
|
stored as rows of the matrix inputs. In case of multiple input vectors, there should be one output
|
|
vector outputs. The predicted class for a single input vector is returned by the method. The vector
|
|
outputProbs contains the output probabilities corresponding to each element of result.
|
|
*/
|
|
virtual float predictProb( InputArray inputs, OutputArray outputs,
|
|
OutputArray outputProbs, int flags=0 ) const = 0;
|
|
virtual void setParams(const Params& params) = 0;
|
|
virtual Params getParams() const = 0;
|
|
|
|
/** @brief Creates empty model
|
|
|
|
@param params The model parameters. There is none so far, the structure is used as a placeholder
|
|
for possible extensions.
|
|
|
|
Use StatModel::train to train the model,
|
|
StatModel::train\<NormalBayesClassifier\>(traindata, params) to create and train the model,
|
|
StatModel::load\<NormalBayesClassifier\>(filename) to load the pre-trained model.
|
|
*/
|
|
static Ptr<NormalBayesClassifier> create(const Params& params=Params());
|
|
};
|
|
|
|
//! @} ml_bayes
|
|
|
|
/****************************************************************************************\
|
|
* K-Nearest Neighbour Classifier *
|
|
\****************************************************************************************/
|
|
|
|
//! @addtogroup ml_knearest
|
|
//! @{
|
|
|
|
/** @brief The class implements K-Nearest Neighbors model as described in the beginning of this section.
|
|
|
|
@note
|
|
- (Python) An example of digit recognition using KNearest can be found at
|
|
opencv_source/samples/python2/digits.py
|
|
- (Python) An example of grid search digit recognition using KNearest can be found at
|
|
opencv_source/samples/python2/digits_adjust.py
|
|
- (Python) An example of video digit recognition using KNearest can be found at
|
|
opencv_source/samples/python2/digits_video.py
|
|
*/
|
|
class CV_EXPORTS_W KNearest : public StatModel
|
|
{
|
|
public:
|
|
class CV_EXPORTS_W_MAP Params
|
|
{
|
|
public:
|
|
Params(int defaultK=10, bool isclassifier_=true, int Emax_=INT_MAX, int algorithmType_=BRUTE_FORCE);
|
|
|
|
CV_PROP_RW int defaultK;
|
|
CV_PROP_RW bool isclassifier;
|
|
CV_PROP_RW int Emax; // for implementation with KDTree
|
|
CV_PROP_RW int algorithmType;
|
|
};
|
|
virtual void setParams(const Params& p) = 0;
|
|
virtual Params getParams() const = 0;
|
|
|
|
/** @brief Finds the neighbors and predicts responses for input vectors.
|
|
|
|
@param samples Input samples stored by rows. It is a single-precision floating-point matrix of
|
|
\<number_of_samples\> \* k size.
|
|
@param k Number of used nearest neighbors. Should be greater than 1.
|
|
@param results Vector with results of prediction (regression or classification) for each input
|
|
sample. It is a single-precision floating-point vector with \<number_of_samples\> elements.
|
|
@param neighborResponses Optional output values for corresponding neighbors. It is a
|
|
single-precision floating-point matrix of \<number_of_samples\> \* k size.
|
|
@param dist Optional output distances from the input vectors to the corresponding neighbors. It is
|
|
a single-precision floating-point matrix of \<number_of_samples\> \* k size.
|
|
|
|
For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. In
|
|
case of regression, the predicted result is a mean value of the particular vector's neighbor
|
|
responses. In case of classification, the class is determined by voting.
|
|
|
|
For each input vector, the neighbors are sorted by their distances to the vector.
|
|
|
|
In case of C++ interface you can use output pointers to empty matrices and the function will
|
|
allocate memory itself.
|
|
|
|
If only a single input vector is passed, all output matrices are optional and the predicted value is
|
|
returned by the method.
|
|
|
|
The function is parallelized with the TBB library.
|
|
*/
|
|
virtual float findNearest( InputArray samples, int k,
|
|
OutputArray results,
|
|
OutputArray neighborResponses=noArray(),
|
|
OutputArray dist=noArray() ) const = 0;
|
|
|
|
enum { BRUTE_FORCE=1, KDTREE=2 };
|
|
|
|
/** @brief Creates the empty model
|
|
|
|
@param params The model parameters: default number of neighbors to use in predict method (in
|
|
KNearest::findNearest this number must be passed explicitly) and the flag on whether
|
|
classification or regression model should be trained.
|
|
|
|
The static method creates empty KNearest classifier. It should be then trained using train method
|
|
(see StatModel::train). Alternatively, you can load boost model from file using
|
|
StatModel::load\<KNearest\>(filename).
|
|
*/
|
|
static Ptr<KNearest> create(const Params& params=Params());
|
|
};
|
|
|
|
//! @} ml_knearest
|
|
|
|
/****************************************************************************************\
|
|
* Support Vector Machines *
|
|
\****************************************************************************************/
|
|
|
|
//! @addtogroup ml_svm
|
|
//! @{
|
|
|
|
/** @brief Support Vector Machines.
|
|
|
|
@note
|
|
- (Python) An example of digit recognition using SVM can be found at
|
|
opencv_source/samples/python2/digits.py
|
|
- (Python) An example of grid search digit recognition using SVM can be found at
|
|
opencv_source/samples/python2/digits_adjust.py
|
|
- (Python) An example of video digit recognition using SVM can be found at
|
|
opencv_source/samples/python2/digits_video.py
|
|
*/
|
|
class CV_EXPORTS_W SVM : public StatModel
|
|
{
|
|
public:
|
|
/** @brief SVM training parameters.
|
|
|
|
The structure must be initialized and passed to the training method of SVM.
|
|
*/
|
|
class CV_EXPORTS_W_MAP Params
|
|
{
|
|
public:
|
|
Params();
|
|
/** @brief The constructors
|
|
|
|
@param svm_type Type of a SVM formulation. Possible values are:
|
|
- **SVM::C_SVC** C-Support Vector Classification. n-class classification (n \f$\geq\f$ 2), allows
|
|
imperfect separation of classes with penalty multiplier C for outliers.
|
|
- **SVM::NU_SVC** \f$\nu\f$-Support Vector Classification. n-class classification with possible
|
|
imperfect separation. Parameter \f$\nu\f$ (in the range 0..1, the larger the value, the smoother
|
|
the decision boundary) is used instead of C.
|
|
- **SVM::ONE_CLASS** Distribution Estimation (One-class SVM). All the training data are from
|
|
the same class, SVM builds a boundary that separates the class from the rest of the feature
|
|
space.
|
|
- **SVM::EPS_SVR** \f$\epsilon\f$-Support Vector Regression. The distance between feature vectors
|
|
from the training set and the fitting hyper-plane must be less than p. For outliers the
|
|
penalty multiplier C is used.
|
|
- **SVM::NU_SVR** \f$\nu\f$-Support Vector Regression. \f$\nu\f$ is used instead of p.
|
|
See @cite LibSVM for details.
|
|
@param kernel_type Type of a SVM kernel. Possible values are:
|
|
- **SVM::LINEAR** Linear kernel. No mapping is done, linear discrimination (or regression) is
|
|
done in the original feature space. It is the fastest option. \f$K(x_i, x_j) = x_i^T x_j\f$.
|
|
- **SVM::POLY** Polynomial kernel:
|
|
\f$K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\f$.
|
|
- **SVM::RBF** Radial basis function (RBF), a good choice in most cases.
|
|
\f$K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\f$.
|
|
- **SVM::SIGMOID** Sigmoid kernel: \f$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$.
|
|
- **SVM::CHI2** Exponential Chi2 kernel, similar to the RBF kernel:
|
|
\f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$.
|
|
- **SVM::INTER** Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$.
|
|
@param degree Parameter degree of a kernel function (POLY).
|
|
@param gamma Parameter \f$\gamma\f$ of a kernel function (POLY / RBF / SIGMOID / CHI2).
|
|
@param coef0 Parameter coef0 of a kernel function (POLY / SIGMOID).
|
|
@param Cvalue Parameter C of a SVM optimization problem (C_SVC / EPS_SVR / NU_SVR).
|
|
@param nu Parameter \f$\nu\f$ of a SVM optimization problem (NU_SVC / ONE_CLASS / NU_SVR).
|
|
@param p Parameter \f$\epsilon\f$ of a SVM optimization problem (EPS_SVR).
|
|
@param classWeights Optional weights in the C_SVC problem , assigned to particular classes. They
|
|
are multiplied by C so the parameter C of class \#i becomes classWeights(i) \* C. Thus these
|
|
weights affect the misclassification penalty for different classes. The larger weight, the larger
|
|
penalty on misclassification of data from the corresponding class.
|
|
@param termCrit Termination criteria of the iterative SVM training procedure which solves a
|
|
partial case of constrained quadratic optimization problem. You can specify tolerance and/or the
|
|
maximum number of iterations.
|
|
|
|
The default constructor initialize the structure with following values:
|
|
@code
|
|
SVMParams::SVMParams() :
|
|
svmType(SVM::C_SVC), kernelType(SVM::RBF), degree(0),
|
|
gamma(1), coef0(0), C(1), nu(0), p(0), classWeights(0)
|
|
{
|
|
termCrit = TermCriteria( TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, FLT_EPSILON );
|
|
}
|
|
@endcode
|
|
A comparison of different kernels on the following 2D test case with four classes. Four C_SVC SVMs
|
|
have been trained (one against rest) with auto_train. Evaluation on three different kernels (CHI2,
|
|
INTER, RBF). The color depicts the class with max score. Bright means max-score \> 0, dark means
|
|
max-score \< 0.
|
|
|
|
|
|
*/
|
|
Params( int svm_type, int kernel_type,
|
|
double degree, double gamma, double coef0,
|
|
double Cvalue, double nu, double p,
|
|
const Mat& classWeights, TermCriteria termCrit );
|
|
|
|
CV_PROP_RW int svmType;
|
|
CV_PROP_RW int kernelType;
|
|
CV_PROP_RW double gamma, coef0, degree;
|
|
|
|
CV_PROP_RW double C; // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
|
|
CV_PROP_RW double nu; // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
|
|
CV_PROP_RW double p; // for CV_SVM_EPS_SVR
|
|
CV_PROP_RW Mat classWeights; // for CV_SVM_C_SVC
|
|
CV_PROP_RW TermCriteria termCrit; // termination criteria
|
|
};
|
|
|
|
class CV_EXPORTS Kernel : public Algorithm
|
|
{
|
|
public:
|
|
virtual int getType() const = 0;
|
|
virtual void calc( int vcount, int n, const float* vecs, const float* another, float* results ) = 0;
|
|
};
|
|
|
|
// SVM type
|
|
enum { C_SVC=100, NU_SVC=101, ONE_CLASS=102, EPS_SVR=103, NU_SVR=104 };
|
|
|
|
// SVM kernel type
|
|
enum { CUSTOM=-1, LINEAR=0, POLY=1, RBF=2, SIGMOID=3, CHI2=4, INTER=5 };
|
|
|
|
// SVM params type
|
|
enum { C=0, GAMMA=1, P=2, NU=3, COEF=4, DEGREE=5 };
|
|
|
|
/** @brief Trains an SVM with optimal parameters.
|
|
|
|
@param data the training data that can be constructed using TrainData::create or
|
|
TrainData::loadFromCSV.
|
|
@param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
|
|
subset is used to test the model, the others form the train set. So, the SVM algorithm is executed
|
|
kFold times.
|
|
@param Cgrid
|
|
@param gammaGrid
|
|
@param pGrid
|
|
@param nuGrid
|
|
@param coeffGrid
|
|
@param degreeGrid Iteration grid for the corresponding SVM parameter.
|
|
@param balanced If true and the problem is 2-class classification then the method creates more
|
|
balanced cross-validation subsets that is proportions between classes in subsets are close to such
|
|
proportion in the whole train dataset.
|
|
|
|
The method trains the SVM model automatically by choosing the optimal parameters C, gamma, p, nu,
|
|
coef0, degree from SVM::Params. Parameters are considered optimal when the cross-validation estimate
|
|
of the test set error is minimal.
|
|
|
|
If there is no need to optimize a parameter, the corresponding grid step should be set to any value
|
|
less than or equal to 1. For example, to avoid optimization in gamma, set gammaGrid.step = 0,
|
|
gammaGrid.minVal, gamma_grid.maxVal as arbitrary numbers. In this case, the value params.gamma is
|
|
taken for gamma.
|
|
|
|
And, finally, if the optimization in a parameter is required but the corresponding grid is unknown,
|
|
you may call the function SVM::getDefaulltGrid. To generate a grid, for example, for gamma, call
|
|
SVM::getDefaulltGrid(SVM::GAMMA).
|
|
|
|
This function works for the classification (params.svmType=SVM::C_SVC or
|
|
params.svmType=SVM::NU_SVC) as well as for the regression (params.svmType=SVM::EPS_SVR or
|
|
params.svmType=SVM::NU_SVR). If params.svmType=SVM::ONE_CLASS, no optimization is made and the
|
|
usual SVM with parameters specified in params is executed.
|
|
*/
|
|
virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10,
|
|
ParamGrid Cgrid = SVM::getDefaultGrid(SVM::C),
|
|
ParamGrid gammaGrid = SVM::getDefaultGrid(SVM::GAMMA),
|
|
ParamGrid pGrid = SVM::getDefaultGrid(SVM::P),
|
|
ParamGrid nuGrid = SVM::getDefaultGrid(SVM::NU),
|
|
ParamGrid coeffGrid = SVM::getDefaultGrid(SVM::COEF),
|
|
ParamGrid degreeGrid = SVM::getDefaultGrid(SVM::DEGREE),
|
|
bool balanced=false) = 0;
|
|
|
|
/** @brief Retrieves all the support vectors
|
|
|
|
The method returns all the support vector as floating-point matrix, where support vectors are stored
|
|
as matrix rows.
|
|
*/
|
|
CV_WRAP virtual Mat getSupportVectors() const = 0;
|
|
|
|
virtual void setParams(const Params& p, const Ptr<Kernel>& customKernel=Ptr<Kernel>()) = 0;
|
|
|
|
/** @brief Returns the current SVM parameters.
|
|
|
|
This function may be used to get the optimal parameters obtained while automatically training
|
|
SVM::trainAuto.
|
|
*/
|
|
virtual Params getParams() const = 0;
|
|
virtual Ptr<Kernel> getKernel() const = 0;
|
|
|
|
/** @brief Retrieves the decision function
|
|
|
|
@param i the index of the decision function. If the problem solved is regression, 1-class or
|
|
2-class classification, then there will be just one decision function and the index should always
|
|
be 0. Otherwise, in the case of N-class classification, there will be N\*(N-1)/2 decision
|
|
functions.
|
|
@param alpha the optional output vector for weights, corresponding to different support vectors.
|
|
In the case of linear SVM all the alpha's will be 1's.
|
|
@param svidx the optional output vector of indices of support vectors within the matrix of support
|
|
vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear SVM each
|
|
decision function consists of a single "compressed" support vector.
|
|
|
|
The method returns rho parameter of the decision function, a scalar subtracted from the weighted sum
|
|
of kernel responses.
|
|
*/
|
|
virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0;
|
|
|
|
/** @brief Generates a grid for SVM parameters.
|
|
|
|
@param param_id SVM parameters IDs that must be one of the following:
|
|
- **SVM::C**
|
|
- **SVM::GAMMA**
|
|
- **SVM::P**
|
|
- **SVM::NU**
|
|
- **SVM::COEF**
|
|
- **SVM::DEGREE**
|
|
The grid is generated for the parameter with this ID.
|
|
|
|
The function generates a grid for the specified parameter of the SVM algorithm. The grid may be
|
|
passed to the function SVM::trainAuto.
|
|
*/
|
|
static ParamGrid getDefaultGrid( int param_id );
|
|
|
|
/** @brief Creates empty model
|
|
|
|
@param p SVM parameters
|
|
@param customKernel the optional custom kernel to use. It must implement SVM::Kernel interface.
|
|
|
|
Use StatModel::train to train the model, StatModel::train\<RTrees\>(traindata, params) to create and
|
|
train the model, StatModel::load\<RTrees\>(filename) to load the pre-trained model. Since SVM has
|
|
several parameters, you may want to find the best parameters for your problem. It can be done with
|
|
SVM::trainAuto.
|
|
*/
|
|
static Ptr<SVM> create(const Params& p=Params(), const Ptr<Kernel>& customKernel=Ptr<Kernel>());
|
|
};
|
|
|
|
//! @} ml_svm
|
|
|
|
/****************************************************************************************\
|
|
* Expectation - Maximization *
|
|
\****************************************************************************************/
|
|
|
|
//! @addtogroup ml_em
|
|
//! @{
|
|
|
|
/** @brief The class implements the EM algorithm as described in the beginning of this section.
|
|
*/
|
|
class CV_EXPORTS_W EM : public StatModel
|
|
{
|
|
public:
|
|
// Type of covariation matrices
|
|
enum {COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2, COV_MAT_DEFAULT=COV_MAT_DIAGONAL};
|
|
|
|
// Default parameters
|
|
enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
|
|
|
|
// The initial step
|
|
enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
|
|
|
|
/** @brief The class describes EM training parameters.
|
|
*/
|
|
class CV_EXPORTS_W_MAP Params
|
|
{
|
|
public:
|
|
/** @brief The constructor
|
|
|
|
@param nclusters The number of mixture components in the Gaussian mixture model. Default value of
|
|
the parameter is EM::DEFAULT_NCLUSTERS=5. Some of EM implementation could determine the optimal
|
|
number of mixtures within a specified value range, but that is not the case in ML yet.
|
|
@param covMatType Constraint on covariance matrices which defines type of matrices. Possible
|
|
values are:
|
|
- **EM::COV_MAT_SPHERICAL** A scaled identity matrix \f$\mu_k * I\f$. There is the only
|
|
parameter \f$\mu_k\f$ to be estimated for each matrix. The option may be used in special cases,
|
|
when the constraint is relevant, or as a first step in the optimization (for example in case
|
|
when the data is preprocessed with PCA). The results of such preliminary estimation may be
|
|
passed again to the optimization procedure, this time with
|
|
covMatType=EM::COV_MAT_DIAGONAL.
|
|
- **EM::COV_MAT_DIAGONAL** A diagonal matrix with positive diagonal elements. The number of
|
|
free parameters is d for each matrix. This is most commonly used option yielding good
|
|
estimation results.
|
|
- **EM::COV_MAT_GENERIC** A symmetric positively defined matrix. The number of free
|
|
parameters in each matrix is about \f$d^2/2\f$. It is not recommended to use this option, unless
|
|
there is pretty accurate initial estimation of the parameters and/or a huge number of
|
|
training samples.
|
|
@param termCrit The termination criteria of the EM algorithm. The EM algorithm can be terminated
|
|
by the number of iterations termCrit.maxCount (number of M-steps) or when relative change of
|
|
likelihood logarithm is less than termCrit.epsilon. Default maximum number of iterations is
|
|
EM::DEFAULT_MAX_ITERS=100.
|
|
*/
|
|
explicit Params(int nclusters=DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL,
|
|
const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,
|
|
EM::DEFAULT_MAX_ITERS, 1e-6));
|
|
CV_PROP_RW int nclusters;
|
|
CV_PROP_RW int covMatType;
|
|
CV_PROP_RW TermCriteria termCrit;
|
|
};
|
|
|
|
virtual void setParams(const Params& p) = 0;
|
|
virtual Params getParams() const = 0;
|
|
/** @brief Returns weights of the mixtures
|
|
|
|
Returns vector with the number of elements equal to the number of mixtures.
|
|
*/
|
|
virtual Mat getWeights() const = 0;
|
|
/** @brief Returns the cluster centers (means of the Gaussian mixture)
|
|
|
|
Returns matrix with the number of rows equal to the number of mixtures and number of columns equal
|
|
to the space dimensionality.
|
|
*/
|
|
virtual Mat getMeans() const = 0;
|
|
/** @brief Returns covariation matrices
|
|
|
|
Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures, each
|
|
matrix is a square floating-point matrix NxN, where N is the space dimensionality.
|
|
*/
|
|
virtual void getCovs(std::vector<Mat>& covs) const = 0;
|
|
|
|
/** @brief Returns a likelihood logarithm value and an index of the most probable mixture component for the
|
|
given sample.
|
|
|
|
@param sample A sample for classification. It should be a one-channel matrix of \f$1 \times dims\f$ or
|
|
\f$dims \times 1\f$ size.
|
|
@param probs Optional output matrix that contains posterior probabilities of each component given
|
|
the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type.
|
|
|
|
The method returns a two-element double vector. Zero element is a likelihood logarithm value for the
|
|
sample. First element is an index of the most probable mixture component for the given sample.
|
|
*/
|
|
CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0;
|
|
|
|
virtual bool train( const Ptr<TrainData>& trainData, int flags=0 ) = 0;
|
|
|
|
/** @brief Static methods that estimate the Gaussian mixture parameters from a samples set
|
|
|
|
@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
|
|
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it
|
|
will be converted to the inner matrix of such type for the further computing.
|
|
@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
|
|
each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
|
|
@param labels The optional output "class label" for each sample:
|
|
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable mixture
|
|
component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
|
|
@param probs The optional output matrix that contains posterior probabilities of each Gaussian
|
|
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and CV_64FC1
|
|
type.
|
|
@param params The Gaussian mixture params, see EM::Params description
|
|
@return true if the Gaussian mixture model was trained successfully, otherwise it returns
|
|
false.
|
|
|
|
Starts with Expectation step. Initial values of the model parameters will be estimated by the
|
|
k-means algorithm.
|
|
|
|
Unlike many of the ML models, EM is an unsupervised learning algorithm and it does not take
|
|
responses (class labels or function values) as input. Instead, it computes the *Maximum Likelihood
|
|
Estimate* of the Gaussian mixture parameters from an input sample set, stores all the parameters
|
|
inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in covs[k], \f$\pi_k\f$ in weights ,
|
|
and optionally computes the output "class label" for each sample:
|
|
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable mixture
|
|
component for each sample).
|
|
|
|
The trained model can be used further for prediction, just like any other classifier. The trained
|
|
model is similar to the NormalBayesClassifier.
|
|
*/
|
|
static Ptr<EM> train(InputArray samples,
|
|
OutputArray logLikelihoods=noArray(),
|
|
OutputArray labels=noArray(),
|
|
OutputArray probs=noArray(),
|
|
const Params& params=Params());
|
|
|
|
/** Starts with Expectation step. You need to provide initial means \f$a_k\f$ of mixture
|
|
components. Optionally you can pass initial weights \f$\pi_k\f$ and covariance matrices
|
|
\f$S_k\f$ of mixture components.
|
|
|
|
@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
|
|
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it
|
|
will be converted to the inner matrix of such type for the further computing.
|
|
@param means0 Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of
|
|
\f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be converted to the
|
|
inner matrix of such type for the further computing.
|
|
@param covs0 The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of
|
|
covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices do not
|
|
have CV_64F type they will be converted to the inner matrices of such type for the further
|
|
computing.
|
|
@param weights0 Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel
|
|
floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size.
|
|
@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
|
|
each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
|
|
@param labels The optional output "class label" for each sample:
|
|
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable mixture
|
|
component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
|
|
@param probs The optional output matrix that contains posterior probabilities of each Gaussian
|
|
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and CV_64FC1
|
|
type.
|
|
@param params The Gaussian mixture params, see EM::Params description
|
|
*/
|
|
static Ptr<EM> train_startWithE(InputArray samples, InputArray means0,
|
|
InputArray covs0=noArray(),
|
|
InputArray weights0=noArray(),
|
|
OutputArray logLikelihoods=noArray(),
|
|
OutputArray labels=noArray(),
|
|
OutputArray probs=noArray(),
|
|
const Params& params=Params());
|
|
|
|
/** Starts with Maximization step. You need to provide initial probabilities \f$p_{i,k}\f$ to
|
|
use this option.
|
|
|
|
@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
|
|
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it
|
|
will be converted to the inner matrix of such type for the further computing.
|
|
@param probs0
|
|
@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
|
|
each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
|
|
@param labels The optional output "class label" for each sample:
|
|
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable mixture
|
|
component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
|
|
@param probs The optional output matrix that contains posterior probabilities of each Gaussian
|
|
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and CV_64FC1
|
|
type.
|
|
@param params The Gaussian mixture params, see EM::Params description
|
|
*/
|
|
static Ptr<EM> train_startWithM(InputArray samples, InputArray probs0,
|
|
OutputArray logLikelihoods=noArray(),
|
|
OutputArray labels=noArray(),
|
|
OutputArray probs=noArray(),
|
|
const Params& params=Params());
|
|
|
|
/** @brief Creates empty EM model
|
|
|
|
@param params EM parameters
|
|
|
|
The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you
|
|
can use one of the EM::train\* methods or load it from file using StatModel::load\<EM\>(filename).
|
|
*/
|
|
static Ptr<EM> create(const Params& params=Params());
|
|
};
|
|
|
|
//! @} ml_em
|
|
|
|
/****************************************************************************************\
|
|
* Decision Tree *
|
|
\****************************************************************************************/
|
|
|
|
//! @addtogroup ml_decsiontrees
|
|
//! @{
|
|
|
|
/** @brief The class represents a single decision tree or a collection of decision trees. The current public
|
|
interface of the class allows user to train only a single decision tree, however the class is
|
|
capable of storing multiple decision trees and using them for prediction (by summing responses or
|
|
using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost) use this
|
|
capability to implement decision tree ensembles.
|
|
*/
|
|
class CV_EXPORTS_W DTrees : public StatModel
|
|
{
|
|
public:
|
|
enum { PREDICT_AUTO=0, PREDICT_SUM=(1<<8), PREDICT_MAX_VOTE=(2<<8), PREDICT_MASK=(3<<8) };
|
|
|
|
/** @brief The structure contains all the decision tree training parameters. You can initialize it by default
|
|
constructor and then override any parameters directly before training, or the structure may be fully
|
|
initialized using the advanced variant of the constructor.
|
|
*/
|
|
class CV_EXPORTS_W_MAP Params
|
|
{
|
|
public:
|
|
Params();
|
|
/** @brief The constructors
|
|
|
|
@param maxDepth The maximum possible depth of the tree. That is the training algorithms attempts
|
|
to split a node while its depth is less than maxDepth. The root node has zero depth. The actual
|
|
depth may be smaller if the other termination criteria are met (see the outline of the training
|
|
procedure in the beginning of the section), and/or if the tree is pruned.
|
|
@param minSampleCount If the number of samples in a node is less than this parameter then the node
|
|
will not be split.
|
|
@param regressionAccuracy Termination criteria for regression trees. If all absolute differences
|
|
between an estimated value in a node and values of train samples in this node are less than this
|
|
parameter then the node will not be split further.
|
|
@param useSurrogates If true then surrogate splits will be built. These splits allow to work with
|
|
missing data and compute variable importance correctly.
|
|
|
|
@note currently it's not implemented.
|
|
|
|
@param maxCategories Cluster possible values of a categorical variable into K\<=maxCategories
|
|
clusters to find a suboptimal split. If a discrete variable, on which the training procedure
|
|
tries to make a split, takes more than maxCategories values, the precise best subset estimation
|
|
may take a very long time because the algorithm is exponential. Instead, many decision trees
|
|
engines (including our implementation) try to find sub-optimal split in this case by clustering
|
|
all the samples into maxCategories clusters that is some categories are merged together. The
|
|
clustering is applied only in n \> 2-class classification problems for categorical variables
|
|
with N \> max_categories possible values. In case of regression and 2-class classification the
|
|
optimal split can be found efficiently without employing clustering, thus the parameter is not
|
|
used in these cases.
|
|
|
|
@param CVFolds If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold
|
|
cross-validation procedure where K is equal to CVFolds.
|
|
|
|
@param use1SERule If true then a pruning will be harsher. This will make a tree more compact and
|
|
more resistant to the training data noise but a bit less accurate.
|
|
|
|
@param truncatePrunedTree If true then pruned branches are physically removed from the tree.
|
|
Otherwise they are retained and it is possible to get results from the original unpruned (or
|
|
pruned less aggressively) tree.
|
|
|
|
@param priors The array of a priori class probabilities, sorted by the class label value. The
|
|
parameter can be used to tune the decision tree preferences toward a certain class. For example,
|
|
if you want to detect some rare anomaly occurrence, the training base will likely contain much
|
|
more normal cases than anomalies, so a very good classification performance will be achieved
|
|
just by considering every case as normal. To avoid this, the priors can be specified, where the
|
|
anomaly probability is artificially increased (up to 0.5 or even greater), so the weight of the
|
|
misclassified anomalies becomes much bigger, and the tree is adjusted properly. You can also
|
|
think about this parameter as weights of prediction categories which determine relative weights
|
|
that you give to misclassification. That is, if the weight of the first category is 1 and the
|
|
weight of the second category is 10, then each mistake in predicting the second category is
|
|
equivalent to making 10 mistakes in predicting the first category.
|
|
|
|
The default constructor initializes all the parameters with the default values tuned for the
|
|
standalone classification tree:
|
|
@code
|
|
DTrees::Params::Params()
|
|
{
|
|
maxDepth = INT_MAX;
|
|
minSampleCount = 10;
|
|
regressionAccuracy = 0.01f;
|
|
useSurrogates = false;
|
|
maxCategories = 10;
|
|
CVFolds = 10;
|
|
use1SERule = true;
|
|
truncatePrunedTree = true;
|
|
priors = Mat();
|
|
}
|
|
@endcode
|
|
*/
|
|
Params( int maxDepth, int minSampleCount,
|
|
double regressionAccuracy, bool useSurrogates,
|
|
int maxCategories, int CVFolds,
|
|
bool use1SERule, bool truncatePrunedTree,
|
|
const Mat& priors );
|
|
|
|
CV_PROP_RW int maxCategories;
|
|
CV_PROP_RW int maxDepth;
|
|
CV_PROP_RW int minSampleCount;
|
|
CV_PROP_RW int CVFolds;
|
|
CV_PROP_RW bool useSurrogates;
|
|
CV_PROP_RW bool use1SERule;
|
|
CV_PROP_RW bool truncatePrunedTree;
|
|
CV_PROP_RW float regressionAccuracy;
|
|
CV_PROP_RW Mat priors;
|
|
};
|
|
|
|
/** @brief The class represents a decision tree node. It has public members:
|
|
|
|
- member double value
|
|
Value at the node: a class label in case of classification or estimated function value in case
|
|
of regression.
|
|
- member int classIdx
|
|
Class index normalized to 0..class_count-1 range and assigned to the node. It is used
|
|
internally in classification trees and tree ensembles.
|
|
- member int parent
|
|
Index of the parent node
|
|
- member int left
|
|
Index of the left child node
|
|
- member int right
|
|
Index of right child node.
|
|
- member int defaultDir
|
|
Default direction where to go (-1: left or +1: right). It helps in the case of missing values.
|
|
- member int split
|
|
Index of the first split
|
|
*/
|
|
class CV_EXPORTS Node
|
|
{
|
|
public:
|
|
Node();
|
|
double value;
|
|
int classIdx;
|
|
|
|
int parent;
|
|
int left;
|
|
int right;
|
|
int defaultDir;
|
|
|
|
int split;
|
|
};
|
|
|
|
/** @brief The class represents split in a decision tree. It has public members:
|
|
- member int varIdx
|
|
Index of variable on which the split is created.
|
|
- member bool inversed
|
|
If true, then the inverse split rule is used (i.e. left and right branches are exchanged in
|
|
the rule expressions below).
|
|
- member float quality
|
|
The split quality, a positive number. It is used to choose the best split.
|
|
- member int next
|
|
Index of the next split in the list of splits for the node
|
|
- member float c
|
|
The threshold value in case of split on an ordered variable. The rule is: :
|
|
if var_value < c
|
|
then next_node<-left
|
|
else next_node<-right
|
|
- member int subsetOfs
|
|
Offset of the bitset used by the split on a categorical variable. The rule is: :
|
|
if bitset[var_value] == 1
|
|
then next_node <- left
|
|
else next_node <- right
|
|
*/
|
|
class CV_EXPORTS Split
|
|
{
|
|
public:
|
|
Split();
|
|
int varIdx;
|
|
bool inversed;
|
|
float quality;
|
|
int next;
|
|
float c;
|
|
int subsetOfs;
|
|
};
|
|
|
|
/** @brief Sets the training parameters
|
|
|
|
@param p Training parameters of type DTrees::Params.
|
|
|
|
The method sets the training parameters.
|
|
*/
|
|
virtual void setDParams(const Params& p);
|
|
/** @brief Returns the training parameters
|
|
|
|
The method returns the training parameters.
|
|
*/
|
|
virtual Params getDParams() const;
|
|
|
|
/** @brief Returns indices of root nodes
|
|
*/
|
|
virtual const std::vector<int>& getRoots() const = 0;
|
|
/** @brief Returns all the nodes
|
|
|
|
all the node indices, mentioned above (left, right, parent, root indices) are indices in the
|
|
returned vector
|
|
*/
|
|
virtual const std::vector<Node>& getNodes() const = 0;
|
|
/** @brief Returns all the splits
|
|
|
|
all the split indices, mentioned above (split, next etc.) are indices in the returned vector
|
|
*/
|
|
virtual const std::vector<Split>& getSplits() const = 0;
|
|
/** @brief Returns all the bitsets for categorical splits
|
|
|
|
Split::subsetOfs is an offset in the returned vector
|
|
*/
|
|
virtual const std::vector<int>& getSubsets() const = 0;
|
|
|
|
/** @brief Creates the empty model
|
|
|
|
The static method creates empty decision tree with the specified parameters. It should be then
|
|
trained using train method (see StatModel::train). Alternatively, you can load the model from file
|
|
using StatModel::load\<DTrees\>(filename).
|
|
*/
|
|
static Ptr<DTrees> create(const Params& params=Params());
|
|
};
|
|
|
|
//! @} ml_decsiontrees
|
|
|
|
/****************************************************************************************\
|
|
* Random Trees Classifier *
|
|
\****************************************************************************************/
|
|
|
|
//! @addtogroup ml_randomtrees
|
|
//! @{
|
|
|
|
/** @brief The class implements the random forest predictor as described in the beginning of this section.
|
|
*/
|
|
class CV_EXPORTS_W RTrees : public DTrees
|
|
{
|
|
public:
|
|
/** @brief The set of training parameters for the forest is a superset of the training
|
|
parameters for a single tree.
|
|
|
|
However, random trees do not need all the functionality/features of decision trees. Most
|
|
noticeably, the trees are not pruned, so the cross-validation parameters are not used.
|
|
*/
|
|
class CV_EXPORTS_W_MAP Params : public DTrees::Params
|
|
{
|
|
public:
|
|
Params();
|
|
/** @brief The constructors
|
|
|
|
@param maxDepth the depth of the tree. A low value will likely underfit and conversely a high
|
|
value will likely overfit. The optimal value can be obtained using cross validation or other
|
|
suitable methods.
|
|
@param minSampleCount minimum samples required at a leaf node for it to be split. A reasonable
|
|
value is a small percentage of the total data e.g. 1%.
|
|
@param regressionAccuracy
|
|
@param useSurrogates
|
|
@param maxCategories Cluster possible values of a categorical variable into K \<= maxCategories
|
|
clusters to find a suboptimal split. If a discrete variable, on which the training procedure tries
|
|
to make a split, takes more than max_categories values, the precise best subset estimation may
|
|
take a very long time because the algorithm is exponential. Instead, many decision trees engines
|
|
(including ML) try to find sub-optimal split in this case by clustering all the samples into
|
|
maxCategories clusters that is some categories are merged together. The clustering is applied only
|
|
in n\>2-class classification problems for categorical variables with N \> max_categories possible
|
|
values. In case of regression and 2-class classification the optimal split can be found
|
|
efficiently without employing clustering, thus the parameter is not used in these cases.
|
|
@param priors
|
|
@param calcVarImportance If true then variable importance will be calculated and then it can be
|
|
retrieved by RTrees::getVarImportance.
|
|
@param nactiveVars The size of the randomly selected subset of features at each tree node and that
|
|
are used to find the best split(s). If you set it to 0 then the size will be set to the square
|
|
root of the total number of features.
|
|
@param termCrit The termination criteria that specifies when the training algorithm stops - either
|
|
when the specified number of trees is trained and added to the ensemble or when sufficient
|
|
accuracy (measured as OOB error) is achieved. Typically the more trees you have the better the
|
|
accuracy. However, the improvement in accuracy generally diminishes and asymptotes pass a certain
|
|
number of trees. Also to keep in mind, the number of tree increases the prediction time linearly.
|
|
|
|
The default constructor sets all parameters to default values which are different from default
|
|
values of `DTrees::Params`:
|
|
@code
|
|
RTrees::Params::Params() : DTrees::Params( 5, 10, 0, false, 10, 0, false, false, Mat() ),
|
|
calcVarImportance(false), nactiveVars(0)
|
|
{
|
|
termCrit = cvTermCriteria( TermCriteria::MAX_ITERS + TermCriteria::EPS, 50, 0.1 );
|
|
}
|
|
@endcode
|
|
*/
|
|
Params( int maxDepth, int minSampleCount,
|
|
double regressionAccuracy, bool useSurrogates,
|
|
int maxCategories, const Mat& priors,
|
|
bool calcVarImportance, int nactiveVars,
|
|
TermCriteria termCrit );
|
|
|
|
CV_PROP_RW bool calcVarImportance; // true <=> RF processes variable importance
|
|
CV_PROP_RW int nactiveVars;
|
|
CV_PROP_RW TermCriteria termCrit;
|
|
};
|
|
|
|
virtual void setRParams(const Params& p) = 0;
|
|
virtual Params getRParams() const = 0;
|
|
|
|
/** @brief Returns the variable importance array.
|
|
|
|
The method returns the variable importance vector, computed at the training stage when
|
|
RTParams::calcVarImportance is set to true. If this flag was set to false, the empty matrix is
|
|
returned.
|
|
*/
|
|
virtual Mat getVarImportance() const = 0;
|
|
|
|
/** @brief Creates the empty model
|
|
|
|
Use StatModel::train to train the model, StatModel::train to create and
|
|
train the model, StatModel::load to load the pre-trained model.
|
|
*/
|
|
static Ptr<RTrees> create(const Params& params=Params());
|
|
};
|
|
|
|
//! @} ml_randomtrees
|
|
|
|
/****************************************************************************************\
|
|
* Boosted tree classifier *
|
|
\****************************************************************************************/
|
|
|
|
//! @addtogroup ml_boost
|
|
//! @{
|
|
|
|
/** @brief Boosted tree classifier derived from DTrees
|
|
*/
|
|
class CV_EXPORTS_W Boost : public DTrees
|
|
{
|
|
public:
|
|
/** @brief The structure is derived from DTrees::Params but not all of the decision tree parameters are
|
|
supported. In particular, cross-validation is not supported.
|
|
|
|
All parameters are public. You can initialize them by a constructor and then override some of them
|
|
directly if you want.
|
|
*/
|
|
class CV_EXPORTS_W_MAP Params : public DTrees::Params
|
|
{
|
|
public:
|
|
CV_PROP_RW int boostType;
|
|
CV_PROP_RW int weakCount;
|
|
CV_PROP_RW double weightTrimRate;
|
|
|
|
Params();
|
|
/** @brief The constructors.
|
|
|
|
@param boostType Type of the boosting algorithm. Possible values are:
|
|
- **Boost::DISCRETE** Discrete AdaBoost.
|
|
- **Boost::REAL** Real AdaBoost. It is a technique that utilizes confidence-rated predictions
|
|
and works well with categorical data.
|
|
- **Boost::LOGIT** LogitBoost. It can produce good regression fits.
|
|
- **Boost::GENTLE** Gentle AdaBoost. It puts less weight on outlier data points and for that
|
|
reason is often good with regression data.
|
|
Gentle AdaBoost and Real AdaBoost are often the preferable choices.
|
|
@param weakCount The number of weak classifiers.
|
|
@param weightTrimRate A threshold between 0 and 1 used to save computational time. Samples
|
|
with summary weight \f$\leq 1 - weight_trim_rate\f$ do not participate in the *next* iteration of
|
|
training. Set this parameter to 0 to turn off this functionality.
|
|
@param maxDepth
|
|
@param useSurrogates
|
|
@param priors
|
|
|
|
See DTrees::Params for description of other parameters.
|
|
|
|
Default parameters are:
|
|
@code
|
|
Boost::Params::Params()
|
|
{
|
|
boostType = Boost::REAL;
|
|
weakCount = 100;
|
|
weightTrimRate = 0.95;
|
|
CVFolds = 0;
|
|
maxDepth = 1;
|
|
}
|
|
@endcode
|
|
*/
|
|
Params( int boostType, int weakCount, double weightTrimRate,
|
|
int maxDepth, bool useSurrogates, const Mat& priors );
|
|
};
|
|
|
|
// Boosting type
|
|
enum { DISCRETE=0, REAL=1, LOGIT=2, GENTLE=3 };
|
|
|
|
/** @brief Returns the boosting parameters
|
|
|
|
The method returns the training parameters.
|
|
*/
|
|
virtual Params getBParams() const = 0;
|
|
/** @brief Sets the boosting parameters
|
|
|
|
@param p Training parameters of type Boost::Params.
|
|
|
|
The method sets the training parameters.
|
|
*/
|
|
virtual void setBParams(const Params& p) = 0;
|
|
|
|
/** @brief Creates the empty model
|
|
|
|
Use StatModel::train to train the model, StatModel::train\<Boost\>(traindata, params) to create and
|
|
train the model, StatModel::load\<Boost\>(filename) to load the pre-trained model.
|
|
*/
|
|
static Ptr<Boost> create(const Params& params=Params());
|
|
};
|
|
|
|
//! @} ml_boost
|
|
|
|
/****************************************************************************************\
|
|
* Gradient Boosted Trees *
|
|
\****************************************************************************************/
|
|
|
|
/*class CV_EXPORTS_W GBTrees : public DTrees
|
|
{
|
|
public:
|
|
struct CV_EXPORTS_W_MAP Params : public DTrees::Params
|
|
{
|
|
CV_PROP_RW int weakCount;
|
|
CV_PROP_RW int lossFunctionType;
|
|
CV_PROP_RW float subsamplePortion;
|
|
CV_PROP_RW float shrinkage;
|
|
|
|
Params();
|
|
Params( int lossFunctionType, int weakCount, float shrinkage,
|
|
float subsamplePortion, int maxDepth, bool useSurrogates );
|
|
};
|
|
|
|
enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
|
|
|
|
virtual void setK(int k) = 0;
|
|
|
|
virtual float predictSerial( InputArray samples,
|
|
OutputArray weakResponses, int flags) const = 0;
|
|
|
|
static Ptr<GBTrees> create(const Params& p);
|
|
};*/
|
|
|
|
/****************************************************************************************\
|
|
* Artificial Neural Networks (ANN) *
|
|
\****************************************************************************************/
|
|
|
|
/////////////////////////////////// Multi-Layer Perceptrons //////////////////////////////
|
|
|
|
//! @addtogroup ml_neural
|
|
//! @{
|
|
|
|
/** @brief MLP model.
|
|
|
|
Unlike many other models in ML that are constructed and trained at once, in the MLP model these
|
|
steps are separated. First, a network with the specified topology is created using the non-default
|
|
constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is
|
|
trained using a set of input and output vectors. The training procedure can be repeated more than
|
|
once, that is, the weights can be adjusted based on the new training data.
|
|
*/
|
|
class CV_EXPORTS_W ANN_MLP : public StatModel
|
|
{
|
|
public:
|
|
/** @brief Parameters of the MLP and of the training algorithm.
|
|
|
|
You can initialize the structure by a constructor or the individual parameters can be adjusted
|
|
after the structure is created.
|
|
The network structure:
|
|
- member Mat layerSizes
|
|
The number of elements in each layer of network. The very first element specifies the number
|
|
of elements in the input layer. The last element - number of elements in the output layer.
|
|
- member int activateFunc
|
|
The activation function. Currently the only fully supported activation function is
|
|
ANN_MLP::SIGMOID_SYM.
|
|
- member double fparam1
|
|
The first parameter of activation function, 0 by default.
|
|
- member double fparam2
|
|
The second parameter of the activation function, 0 by default.
|
|
@note
|
|
If you are using the default ANN_MLP::SIGMOID_SYM activation function with the default
|
|
parameter values fparam1=0 and fparam2=0 then the function used is y = 1.7159\*tanh(2/3 \* x),
|
|
so the output will range from [-1.7159, 1.7159], instead of [0,1].
|
|
|
|
The back-propagation algorithm parameters:
|
|
- member double bpDWScale
|
|
Strength of the weight gradient term. The recommended value is about 0.1.
|
|
- member double bpMomentScale
|
|
Strength of the momentum term (the difference between weights on the 2 previous iterations).
|
|
This parameter provides some inertia to smooth the random fluctuations of the weights. It
|
|
can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good
|
|
enough
|
|
The RPROP algorithm parameters (see @cite RPROP93 for details):
|
|
- member double prDW0
|
|
Initial value \f$\Delta_0\f$ of update-values \f$\Delta_{ij}\f$.
|
|
- member double rpDWPlus
|
|
Increase factor \f$\eta^+\f$. It must be \>1.
|
|
- member double rpDWMinus
|
|
Decrease factor \f$\eta^-\f$. It must be \<1.
|
|
- member double rpDWMin
|
|
Update-values lower limit \f$\Delta_{min}\f$. It must be positive.
|
|
- member double rpDWMax
|
|
Update-values upper limit \f$\Delta_{max}\f$. It must be \>1.
|
|
*/
|
|
struct CV_EXPORTS_W_MAP Params
|
|
{
|
|
Params();
|
|
/** @brief Construct the parameter structure
|
|
|
|
@param layerSizes Integer vector specifying the number of neurons in each layer including the
|
|
input and output layers.
|
|
@param activateFunc Parameter specifying the activation function for each neuron: one of
|
|
ANN_MLP::IDENTITY, ANN_MLP::SIGMOID_SYM, and ANN_MLP::GAUSSIAN.
|
|
@param fparam1 The first parameter of the activation function, \f$\alpha\f$. See the formulas in the
|
|
introduction section.
|
|
@param fparam2 The second parameter of the activation function, \f$\beta\f$. See the formulas in the
|
|
introduction section.
|
|
@param termCrit Termination criteria of the training algorithm. You can specify the maximum number
|
|
of iterations (maxCount) and/or how much the error could change between the iterations to make the
|
|
algorithm continue (epsilon).
|
|
@param trainMethod Training method of the MLP. Possible values are:
|
|
- **ANN_MLP_TrainParams::BACKPROP** The back-propagation algorithm.
|
|
- **ANN_MLP_TrainParams::RPROP** The RPROP algorithm.
|
|
@param param1 Parameter of the training method. It is rp_dw0 for RPROP and bp_dw_scale for
|
|
BACKPROP.
|
|
@param param2 Parameter of the training method. It is rp_dw_min for RPROP and bp_moment_scale
|
|
for BACKPROP.
|
|
|
|
By default the RPROP algorithm is used:
|
|
@code
|
|
ANN_MLP_TrainParams::ANN_MLP_TrainParams()
|
|
{
|
|
layerSizes = Mat();
|
|
activateFun = SIGMOID_SYM;
|
|
fparam1 = fparam2 = 0;
|
|
term_crit = TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01 );
|
|
train_method = RPROP;
|
|
bpDWScale = bpMomentScale = 0.1;
|
|
rpDW0 = 0.1; rpDWPlus = 1.2; rpDWMinus = 0.5;
|
|
rpDWMin = FLT_EPSILON; rpDWMax = 50.;
|
|
}
|
|
@endcode
|
|
*/
|
|
Params( const Mat& layerSizes, int activateFunc, double fparam1, double fparam2,
|
|
TermCriteria termCrit, int trainMethod, double param1, double param2=0 );
|
|
|
|
enum { BACKPROP=0, RPROP=1 };
|
|
|
|
CV_PROP_RW Mat layerSizes;
|
|
CV_PROP_RW int activateFunc;
|
|
CV_PROP_RW double fparam1;
|
|
CV_PROP_RW double fparam2;
|
|
|
|
CV_PROP_RW TermCriteria termCrit;
|
|
CV_PROP_RW int trainMethod;
|
|
|
|
// backpropagation parameters
|
|
CV_PROP_RW double bpDWScale, bpMomentScale;
|
|
|
|
// rprop parameters
|
|
CV_PROP_RW double rpDW0, rpDWPlus, rpDWMinus, rpDWMin, rpDWMax;
|
|
};
|
|
|
|
// possible activation functions
|
|
enum { IDENTITY = 0, SIGMOID_SYM = 1, GAUSSIAN = 2 };
|
|
|
|
// available training flags
|
|
enum { UPDATE_WEIGHTS = 1, NO_INPUT_SCALE = 2, NO_OUTPUT_SCALE = 4 };
|
|
|
|
virtual Mat getWeights(int layerIdx) const = 0;
|
|
|
|
/** @brief Sets the new network parameters
|
|
|
|
@param p The new parameters
|
|
|
|
The existing network, if any, will be destroyed and new empty one will be created. It should be
|
|
re-trained after that.
|
|
*/
|
|
virtual void setParams(const Params& p) = 0;
|
|
|
|
/** @brief Retrieves the current network parameters
|
|
*/
|
|
virtual Params getParams() const = 0;
|
|
|
|
/** @brief Creates empty model
|
|
|
|
Use StatModel::train to train the model, StatModel::train\<ANN_MLP\>(traindata, params) to create
|
|
and train the model, StatModel::load\<ANN_MLP\>(filename) to load the pre-trained model. Note that
|
|
the train method has optional flags, and the following flags are handled by \`ANN_MLP\`:
|
|
|
|
- **UPDATE_WEIGHTS** Algorithm updates the network weights, rather than computes them from
|
|
scratch. In the latter case the weights are initialized using the Nguyen-Widrow algorithm.
|
|
- **NO_INPUT_SCALE** Algorithm does not normalize the input vectors. If this flag is not set,
|
|
the training algorithm normalizes each input feature independently, shifting its mean value to
|
|
0 and making the standard deviation equal to 1. If the network is assumed to be updated
|
|
frequently, the new training data could be much different from original one. In this case, you
|
|
should take care of proper normalization.
|
|
- **NO_OUTPUT_SCALE** Algorithm does not normalize the output vectors. If the flag is not set,
|
|
the training algorithm normalizes each output feature independently, by transforming it to the
|
|
certain range depending on the used activation function.
|
|
*/
|
|
static Ptr<ANN_MLP> create(const Params& params=Params());
|
|
};
|
|
|
|
//! @} ml_neural
|
|
|
|
/****************************************************************************************\
|
|
* Logistic Regression *
|
|
\****************************************************************************************/
|
|
|
|
//! @addtogroup ml_lr
|
|
//! @{
|
|
|
|
/** @brief Implements Logistic Regression classifier.
|
|
*/
|
|
class CV_EXPORTS LogisticRegression : public StatModel
|
|
{
|
|
public:
|
|
class CV_EXPORTS Params
|
|
{
|
|
public:
|
|
/** @brief The constructors
|
|
|
|
@param learning_rate Specifies the learning rate.
|
|
@param iters Specifies the number of iterations.
|
|
@param method Specifies the kind of training method used. It should be set to either
|
|
LogisticRegression::BATCH or LogisticRegression::MINI_BATCH. If using
|
|
LogisticRegression::MINI_BATCH, set LogisticRegression::Params.mini_batch_size to a positive
|
|
integer.
|
|
@param normalization Specifies the kind of regularization to be applied.
|
|
LogisticRegression::REG_L1 or LogisticRegression::REG_L2 (L1 norm or L2 norm). To use this, set
|
|
LogisticRegression::Params.regularized to a integer greater than zero.
|
|
@param reg To enable or disable regularization. Set to positive integer (greater than zero) to
|
|
enable and to 0 to disable.
|
|
@param batch_size Specifies the number of training samples taken in each step of Mini-Batch
|
|
Gradient Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm.
|
|
It has to take values less than the total number of training samples.
|
|
|
|
By initializing this structure, one can set all the parameters required for Logistic Regression
|
|
classifier.
|
|
*/
|
|
Params(double learning_rate = 0.001,
|
|
int iters = 1000,
|
|
int method = LogisticRegression::BATCH,
|
|
int normalization = LogisticRegression::REG_L2,
|
|
int reg = 1,
|
|
int batch_size = 1);
|
|
double alpha;
|
|
int num_iters;
|
|
int norm;
|
|
int regularized;
|
|
int train_method;
|
|
int mini_batch_size;
|
|
TermCriteria term_crit;
|
|
};
|
|
|
|
enum { REG_L1 = 0, REG_L2 = 1};
|
|
enum { BATCH = 0, MINI_BATCH = 1};
|
|
|
|
/** @brief This function writes the trained LogisticRegression clasifier to disk.
|
|
*/
|
|
virtual void write( FileStorage &fs ) const = 0;
|
|
/** @brief This function reads the trained LogisticRegression clasifier from disk.
|
|
*/
|
|
virtual void read( const FileNode &fn ) = 0;
|
|
|
|
/** @brief Trains the Logistic Regression classifier and returns true if successful.
|
|
|
|
@param trainData Instance of ml::TrainData class holding learning data.
|
|
@param flags Not used.
|
|
*/
|
|
virtual bool train( const Ptr<TrainData>& trainData, int flags=0 ) = 0;
|
|
/** @brief Predicts responses for input samples and returns a float type.
|
|
|
|
@param samples The input data for the prediction algorithm. Matrix [m x n], where each row
|
|
contains variables (features) of one object being classified. Should have data type CV_32F.
|
|
@param results Predicted labels as a column matrix of type CV_32S.
|
|
@param flags Not used.
|
|
*/
|
|
virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
|
|
virtual void clear() = 0;
|
|
|
|
/** @brief This function returns the trained paramters arranged across rows.
|
|
|
|
For a two class classifcation problem, it returns a row matrix.
|
|
It returns learnt paramters of the Logistic Regression as a matrix of type CV_32F.
|
|
*/
|
|
virtual Mat get_learnt_thetas() const = 0;
|
|
|
|
/** @brief Creates empty model.
|
|
|
|
@param params The training parameters for the classifier of type LogisticRegression::Params.
|
|
|
|
Creates Logistic Regression model with parameters given.
|
|
*/
|
|
static Ptr<LogisticRegression> create( const Params& params = Params() );
|
|
};
|
|
|
|
//! @} ml_lr
|
|
|
|
/****************************************************************************************\
|
|
* Auxilary functions declarations *
|
|
\****************************************************************************************/
|
|
|
|
/** Generates `sample` from multivariate normal distribution, where `mean` - is an
|
|
average row vector, `cov` - symmetric covariation matrix */
|
|
CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, OutputArray samples);
|
|
|
|
/** Generates sample from gaussian mixture distribution */
|
|
CV_EXPORTS void randGaussMixture( InputArray means, InputArray covs, InputArray weights,
|
|
int nsamples, OutputArray samples, OutputArray sampClasses );
|
|
|
|
/** creates test set */
|
|
CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses,
|
|
OutputArray samples, OutputArray responses);
|
|
|
|
//! @} ml
|
|
|
|
}
|
|
}
|
|
|
|
#endif // __cplusplus
|
|
#endif // __OPENCV_ML_HPP__
|
|
|
|
/* End of file. */
|