Lines Matching full:probs
142 // C++: Vec2d predict2(Mat sample, Mat& probs)
145 //javadoc: EM::predict2(sample, probs)
146 public double[] predict2(Mat sample, Mat probs)
149 double[] retVal = predict2_0(nativeObj, sample.nativeObj, probs.nativeObj);
156 // C++: bool trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
159 //javadoc: EM::trainEM(samples, logLikelihoods, labels, probs)
160 public boolean trainEM(Mat samples, Mat logLikelihoods, Mat labels, Mat probs)
163 boolean retVal = trainEM_0(nativeObj, samples.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
179 // C++: bool trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
182 //javadoc: EM::trainE(samples, means0, covs0, weights0, logLikelihoods, labels, probs)
183 public boolean trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels, Mat probs)
186 boolean retVal = trainE_0(nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj, weights0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
202 // C++: bool trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
205 //javadoc: EM::trainM(samples, probs0, logLikelihoods, labels, probs)
206 public boolean trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs)
209 boolean retVal = trainM_0(nativeObj, samples.nativeObj, probs0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
269 // C++: Vec2d predict2(Mat sample, Mat& probs)
272 // C++: bool trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
276 // C++: bool trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
280 // C++: bool trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
