Back to project page Operation-Valkyrie.
The source code is released under:
Terms and conditions Preamble: This Agreement, signed on Jun 10, 2012 [hereinafter: Effective Date] governs the relationship between the Enduser, a private person, (hereinafter: Licensee) and Paul N...
If you think the Android project Operation-Valkyrie listed in this page is inappropriate, such as containing malicious code/tools or violating the copyright, please email info at java2s dot com, thanks.
// // This file is auto-generated. Please don't modify it! ////w w w .j a va 2 s. com package org.opencv.ml; import org.opencv.core.Algorithm; import org.opencv.core.Mat; import org.opencv.core.TermCriteria; // C++: class EM /** * <p>The class implements the EM algorithm as described in the beginning of this * section. It is inherited from "Algorithm".</p> * * @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em">org.opencv.ml.EM : public Algorithm</a> */ public class EM extends Algorithm { protected EM(long addr) { super(addr); } public static final int COV_MAT_SPHERICAL = 0, COV_MAT_DIAGONAL = 1, COV_MAT_GENERIC = 2, COV_MAT_DEFAULT = COV_MAT_DIAGONAL, DEFAULT_NCLUSTERS = 5, DEFAULT_MAX_ITERS = 100, START_E_STEP = 1, START_M_STEP = 2, START_AUTO_STEP = 0; // // C++: EM::EM(int nclusters = EM::DEFAULT_NCLUSTERS, int covMatType = EM::COV_MAT_DIAGONAL, TermCriteria termCrit = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, EM::DEFAULT_MAX_ITERS, FLT_EPSILON)) // /** * <p>The constructor of the class</p> * * @param nclusters The number of mixture components in the Gaussian mixture * model. Default value of the parameter is <code>EM.DEFAULT_NCLUSTERS=5</code>. * 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: * <ul> * <li> EM.COV_MAT_SPHERICAL A scaled identity matrix <em>mu_k * I</em>. * There is the only parameter <em>mu_k</em> 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 <code>covMatType=EM.COV_MAT_DIAGONAL</code>. * <li> EM.COV_MAT_DIAGONAL A diagonal matrix with positive diagonal * elements. The number of free parameters is <code>d</code> for each matrix. * This is most commonly used option yielding good estimation results. * <li> EM.COV_MAT_GENERIC A symmetric positively defined matrix. The number * of free parameters in each matrix is about <em>d^2/2</em>. 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. * </ul> * @param termCrit The termination criteria of the EM algorithm. The EM * algorithm can be terminated by the number of iterations <code>termCrit.maxCount</code> * (number of M-steps) or when relative change of likelihood logarithm is less * than <code>termCrit.epsilon</code>. Default maximum number of iterations is * <code>EM.DEFAULT_MAX_ITERS=100</code>. * * @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-em">org.opencv.ml.EM.EM</a> */ public EM(int nclusters, int covMatType, TermCriteria termCrit) { super( EM_0(nclusters, covMatType, termCrit.type, termCrit.maxCount, termCrit.epsilon) ); return; } /** * <p>The constructor of the class</p> * * @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-em">org.opencv.ml.EM.EM</a> */ public EM() { super( EM_1() ); return; } // // C++: void EM::clear() // public void clear() { clear_0(nativeObj); return; } // // C++: bool EM::isTrained() // public boolean isTrained() { boolean retVal = isTrained_0(nativeObj); return retVal; } // // C++: Vec2d EM::predict(Mat sample, Mat& probs = Mat()) // /** * <p>Returns a likelihood logarithm value and an index of the most probable * mixture component for the given sample.</p> * * <p>The method returns a two-element <code>double</code> 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.</p> * * @param sample A sample for classification. It should be a one-channel matrix * of <em>1 x dims</em> or <em>dims x 1</em> size. * @param probs Optional output matrix that contains posterior probabilities of * each component given the sample. It has <em>1 x nclusters</em> size and * <code>CV_64FC1</code> type. * * @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-predict">org.opencv.ml.EM.predict</a> */ public double[] predict(Mat sample, Mat probs) { double[] retVal = predict_0(nativeObj, sample.nativeObj, probs.nativeObj); return retVal; } /** * <p>Returns a likelihood logarithm value and an index of the most probable * mixture component for the given sample.</p> * * <p>The method returns a two-element <code>double</code> 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.</p> * * @param sample A sample for classification. It should be a one-channel matrix * of <em>1 x dims</em> or <em>dims x 1</em> size. * * @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-predict">org.opencv.ml.EM.predict</a> */ public double[] predict(Mat sample) { double[] retVal = predict_1(nativeObj, sample.nativeObj); return retVal; } // // C++: bool EM::train(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) // /** * <p>Estimates the Gaussian mixture parameters from a samples set.</p> * * <p>Three versions of training method differ in the initialization of Gaussian * mixture model parameters and start step:</p> * <ul> * <li> train - Starts with Expectation step. Initial values of the model * parameters will be estimated by the k-means algorithm. * <li> trainE - Starts with Expectation step. You need to provide initial * means <em>a_k</em> of mixture components. Optionally you can pass initial * weights <em>pi_k</em> and covariance matrices <em>S_k</em> of mixture * components. * <li> trainM - Starts with Maximization step. You need to provide initial * probabilities <em>p_(i,k)</em> to use this option. * </ul> * * <p>The methods return <code>true</code> if the Gaussian mixture model was * trained successfully, otherwise it returns <code>false</code>.</p> * * <p>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: <em>p_(i,k)</em> in <code>probs</code>, <em>a_k</em> in * <code>means</code>, <em>S_k</em> in <code>covs[k]</code>, <em>pi_k</em> in * <code>weights</code>, and optionally computes the output "class label" for * each sample: <em>labels_i=arg max_k(p_(i,k)), i=1..N</em> (indices of the * most probable mixture component for each sample).</p> * * <p>The trained model can be used further for prediction, just like any other * classifier. The trained model is similar to the "CvNormalBayesClassifier".</p> * * @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 <code>CV_64F</code> 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 <em>nsamples x 1</em> size and * <code>CV_64FC1</code> type. * @param labels The optional output "class label" for each sample: * <em>labels_i=arg max_k(p_(i,k)), i=1..N</em> (indices of the most probable * mixture component for each sample). It has <em>nsamples x 1</em> size and * <code>CV_32SC1</code> type. * @param probs The optional output matrix that contains posterior probabilities * of each Gaussian mixture component given the each sample. It has <em>nsamples * x nclusters</em> size and <code>CV_64FC1</code> type. * * @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-train">org.opencv.ml.EM.train</a> */ public boolean train(Mat samples, Mat logLikelihoods, Mat labels, Mat probs) { boolean retVal = train_0(nativeObj, samples.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj); return retVal; } /** * <p>Estimates the Gaussian mixture parameters from a samples set.</p> * * <p>Three versions of training method differ in the initialization of Gaussian * mixture model parameters and start step:</p> * <ul> * <li> train - Starts with Expectation step. Initial values of the model * parameters will be estimated by the k-means algorithm. * <li> trainE - Starts with Expectation step. You need to provide initial * means <em>a_k</em> of mixture components. Optionally you can pass initial * weights <em>pi_k</em> and covariance matrices <em>S_k</em> of mixture * components. * <li> trainM - Starts with Maximization step. You need to provide initial * probabilities <em>p_(i,k)</em> to use this option. * </ul> * * <p>The methods return <code>true</code> if the Gaussian mixture model was * trained successfully, otherwise it returns <code>false</code>.</p> * * <p>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: <em>p_(i,k)</em> in <code>probs</code>, <em>a_k</em> in * <code>means</code>, <em>S_k</em> in <code>covs[k]</code>, <em>pi_k</em> in * <code>weights</code>, and optionally computes the output "class label" for * each sample: <em>labels_i=arg max_k(p_(i,k)), i=1..N</em> (indices of the * most probable mixture component for each sample).</p> * * <p>The trained model can be used further for prediction, just like any other * classifier. The trained model is similar to the "CvNormalBayesClassifier".</p> * * @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 <code>CV_64F</code> type it will be converted to * the inner matrix of such type for the further computing. * * @see <a href="http://docs.opencv.org/modules/ml/doc/expectation_maximization.html#em-train">org.opencv.ml.EM.train</a> */ public boolean train(Mat samples) { boolean retVal = train_1(nativeObj, samples.nativeObj); return retVal; } // // C++: bool EM::trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) // public boolean trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels, Mat probs) { boolean retVal = trainE_0(nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj, weights0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj); return retVal; } public boolean trainE(Mat samples, Mat means0) { boolean retVal = trainE_1(nativeObj, samples.nativeObj, means0.nativeObj); return retVal; } // // C++: bool EM::trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) // public boolean trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs) { boolean retVal = trainM_0(nativeObj, samples.nativeObj, probs0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj); return retVal; } public boolean trainM(Mat samples, Mat probs0) { boolean retVal = trainM_1(nativeObj, samples.nativeObj, probs0.nativeObj); return retVal; } @Override protected void finalize() throws Throwable { delete(nativeObj); } // // native stuff // static { System.loadLibrary("opencv_java"); } // C++: EM::EM(int nclusters = EM::DEFAULT_NCLUSTERS, int covMatType = EM::COV_MAT_DIAGONAL, TermCriteria termCrit = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, EM::DEFAULT_MAX_ITERS, FLT_EPSILON)) private static native long EM_0(int nclusters, int covMatType, int termCrit_type, int termCrit_maxCount, double termCrit_epsilon); private static native long EM_1(); // C++: void EM::clear() private static native void clear_0(long nativeObj); // C++: bool EM::isTrained() private static native boolean isTrained_0(long nativeObj); // C++: Vec2d EM::predict(Mat sample, Mat& probs = Mat()) private static native double[] predict_0(long nativeObj, long sample_nativeObj, long probs_nativeObj); private static native double[] predict_1(long nativeObj, long sample_nativeObj); // C++: bool EM::train(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) private static native boolean train_0(long nativeObj, long samples_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj); private static native boolean train_1(long nativeObj, long samples_nativeObj); // C++: bool EM::trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) private static native boolean trainE_0(long nativeObj, long samples_nativeObj, long means0_nativeObj, long covs0_nativeObj, long weights0_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj); private static native boolean trainE_1(long nativeObj, long samples_nativeObj, long means0_nativeObj); // C++: bool EM::trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) private static native boolean trainM_0(long nativeObj, long samples_nativeObj, long probs0_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj); private static native boolean trainM_1(long nativeObj, long samples_nativeObj, long probs0_nativeObj); // native support for java finalize() private static native void delete(long nativeObj); }