Joe Verbout
opencv on mbed
Machine Learning
The Machine Learning Library (MLL) is a set of classes and functions for statistical classification, regression, and clustering of data. More...
Data Structures | |
| class | ParamGrid |
| The structure represents the logarithmic grid range of statmodel parameters. More... | |
| class | TrainData |
| Class encapsulating training data. More... | |
| class | StatModel |
| Base class for statistical models in OpenCV ML. More... | |
| class | NormalBayesClassifier |
| Bayes classifier for normally distributed data. More... | |
| class | KNearest |
| The class implements K-Nearest Neighbors model. More... | |
| class | SVM |
| Support Vector Machines. More... | |
| class | EM |
| The class implements the Expectation Maximization algorithm. More... | |
| class | DTrees |
| The class represents a single decision tree or a collection of decision trees. More... | |
| class | RTrees |
| The class implements the random forest predictor. More... | |
| class | Boost |
| Boosted tree classifier derived from DTrees. More... | |
| class | ANN_MLP |
| Artificial Neural Networks - Multi-Layer Perceptrons. More... | |
| class | LogisticRegression |
| Implements Logistic Regression classifier. More... | |
Enumerations | |
| enum | VariableTypes { VAR_NUMERICAL = 0, VAR_ORDERED = 0, VAR_CATEGORICAL = 1 } |
Variable types. More... | |
| enum | ErrorTypes |
Error types More... | |
| enum | SampleTypes { ROW_SAMPLE = 0, COL_SAMPLE = 1 } |
Sample types. More... | |
Functions | |
| CV_EXPORTS void | randMVNormal (InputArray mean, InputArray cov, int nsamples, OutputArray samples) |
| Generates _sample_ from multivariate normal distribution. | |
| CV_EXPORTS void | createConcentricSpheresTestSet (int nsamples, int nfeatures, int nclasses, OutputArray samples, OutputArray responses) |
| Creates test set. | |
Detailed Description
The Machine Learning Library (MLL) is a set of classes and functions for statistical classification, regression, and clustering of data.
Most of the classification and regression algorithms are implemented as C++ classes. As the algorithms have different sets of features (like an ability to handle missing measurements or categorical input variables), there is a little common ground between the classes. This common ground is defined by the class cv::ml::StatModel that all the other ML classes are derived from.
See detailed overview here: ml_intro.
Enumeration Type Documentation
| enum SampleTypes |
| enum VariableTypes |
Function Documentation
| CV_EXPORTS void cv::ml::createConcentricSpheresTestSet | ( | int | nsamples, |
| int | nfeatures, | ||
| int | nclasses, | ||
| OutputArray | samples, | ||
| OutputArray | responses | ||
| ) |
Creates test set.
| CV_EXPORTS void cv::ml::randMVNormal | ( | InputArray | mean, |
| InputArray | cov, | ||
| int | nsamples, | ||
| OutputArray | samples | ||
| ) |
Generates _sample_ from multivariate normal distribution.
- Parameters:
-
mean an average row vector cov symmetric covariation matrix nsamples returned samples count samples returned samples array
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