1 2 // 3 // This file is auto-generated. Please don't modify it! 4 // 5 package org.opencv.ml; 6 7 import org.opencv.core.Mat; 8 import org.opencv.core.TermCriteria; 9 10 // C++: class EM 11 //javadoc: EM 12 public class EM extends StatModel { 13 14 protected EM(long addr) { super(addr); } 15 16 17 public static final int 18 COV_MAT_SPHERICAL = 0, 19 COV_MAT_DIAGONAL = 1, 20 COV_MAT_GENERIC = 2, 21 COV_MAT_DEFAULT = COV_MAT_DIAGONAL, 22 DEFAULT_NCLUSTERS = 5, 23 DEFAULT_MAX_ITERS = 100, 24 START_E_STEP = 1, 25 START_M_STEP = 2, 26 START_AUTO_STEP = 0; 27 28 29 // 30 // C++: int getClustersNumber() 31 // 32 33 //javadoc: EM::getClustersNumber() 34 public int getClustersNumber() 35 { 36 37 int retVal = getClustersNumber_0(nativeObj); 38 39 return retVal; 40 } 41 42 43 // 44 // C++: void setClustersNumber(int val) 45 // 46 47 //javadoc: EM::setClustersNumber(val) 48 public void setClustersNumber(int val) 49 { 50 51 setClustersNumber_0(nativeObj, val); 52 53 return; 54 } 55 56 57 // 58 // C++: int getCovarianceMatrixType() 59 // 60 61 //javadoc: EM::getCovarianceMatrixType() 62 public int getCovarianceMatrixType() 63 { 64 65 int retVal = getCovarianceMatrixType_0(nativeObj); 66 67 return retVal; 68 } 69 70 71 // 72 // C++: void setCovarianceMatrixType(int val) 73 // 74 75 //javadoc: EM::setCovarianceMatrixType(val) 76 public void setCovarianceMatrixType(int val) 77 { 78 79 setCovarianceMatrixType_0(nativeObj, val); 80 81 return; 82 } 83 84 85 // 86 // C++: TermCriteria getTermCriteria() 87 // 88 89 //javadoc: EM::getTermCriteria() 90 public TermCriteria getTermCriteria() 91 { 92 93 TermCriteria retVal = new TermCriteria(getTermCriteria_0(nativeObj)); 94 95 return retVal; 96 } 97 98 99 // 100 // C++: void setTermCriteria(TermCriteria val) 101 // 102 103 //javadoc: EM::setTermCriteria(val) 104 public void setTermCriteria(TermCriteria val) 105 { 106 107 setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon); 108 109 return; 110 } 111 112 113 // 114 // C++: Mat getWeights() 115 // 116 117 //javadoc: EM::getWeights() 118 public Mat getWeights() 119 { 120 121 Mat retVal = new Mat(getWeights_0(nativeObj)); 122 123 return retVal; 124 } 125 126 127 // 128 // C++: Mat getMeans() 129 // 130 131 //javadoc: EM::getMeans() 132 public Mat getMeans() 133 { 134 135 Mat retVal = new Mat(getMeans_0(nativeObj)); 136 137 return retVal; 138 } 139 140 141 // 142 // C++: Vec2d predict2(Mat sample, Mat& probs) 143 // 144 145 //javadoc: EM::predict2(sample, probs) 146 public double[] predict2(Mat sample, Mat probs) 147 { 148 149 double[] retVal = predict2_0(nativeObj, sample.nativeObj, probs.nativeObj); 150 151 return retVal; 152 } 153 154 155 // 156 // C++: bool trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) 157 // 158 159 //javadoc: EM::trainEM(samples, logLikelihoods, labels, probs) 160 public boolean trainEM(Mat samples, Mat logLikelihoods, Mat labels, Mat probs) 161 { 162 163 boolean retVal = trainEM_0(nativeObj, samples.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj); 164 165 return retVal; 166 } 167 168 //javadoc: EM::trainEM(samples) 169 public boolean trainEM(Mat samples) 170 { 171 172 boolean retVal = trainEM_1(nativeObj, samples.nativeObj); 173 174 return retVal; 175 } 176 177 178 // 179 // C++: bool trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) 180 // 181 182 //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) 184 { 185 186 boolean retVal = trainE_0(nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj, weights0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj); 187 188 return retVal; 189 } 190 191 //javadoc: EM::trainE(samples, means0) 192 public boolean trainE(Mat samples, Mat means0) 193 { 194 195 boolean retVal = trainE_1(nativeObj, samples.nativeObj, means0.nativeObj); 196 197 return retVal; 198 } 199 200 201 // 202 // C++: bool trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) 203 // 204 205 //javadoc: EM::trainM(samples, probs0, logLikelihoods, labels, probs) 206 public boolean trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs) 207 { 208 209 boolean retVal = trainM_0(nativeObj, samples.nativeObj, probs0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj); 210 211 return retVal; 212 } 213 214 //javadoc: EM::trainM(samples, probs0) 215 public boolean trainM(Mat samples, Mat probs0) 216 { 217 218 boolean retVal = trainM_1(nativeObj, samples.nativeObj, probs0.nativeObj); 219 220 return retVal; 221 } 222 223 224 // 225 // C++: static Ptr_EM create() 226 // 227 228 //javadoc: EM::create() 229 public static EM create() 230 { 231 232 EM retVal = new EM(create_0()); 233 234 return retVal; 235 } 236 237 238 @Override 239 protected void finalize() throws Throwable { 240 delete(nativeObj); 241 } 242 243 244 245 // C++: int getClustersNumber() 246 private static native int getClustersNumber_0(long nativeObj); 247 248 // C++: void setClustersNumber(int val) 249 private static native void setClustersNumber_0(long nativeObj, int val); 250 251 // C++: int getCovarianceMatrixType() 252 private static native int getCovarianceMatrixType_0(long nativeObj); 253 254 // C++: void setCovarianceMatrixType(int val) 255 private static native void setCovarianceMatrixType_0(long nativeObj, int val); 256 257 // C++: TermCriteria getTermCriteria() 258 private static native double[] getTermCriteria_0(long nativeObj); 259 260 // C++: void setTermCriteria(TermCriteria val) 261 private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon); 262 263 // C++: Mat getWeights() 264 private static native long getWeights_0(long nativeObj); 265 266 // C++: Mat getMeans() 267 private static native long getMeans_0(long nativeObj); 268 269 // C++: Vec2d predict2(Mat sample, Mat& probs) 270 private static native double[] predict2_0(long nativeObj, long sample_nativeObj, long probs_nativeObj); 271 272 // C++: bool trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) 273 private static native boolean trainEM_0(long nativeObj, long samples_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj); 274 private static native boolean trainEM_1(long nativeObj, long samples_nativeObj); 275 276 // C++: bool trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) 277 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); 278 private static native boolean trainE_1(long nativeObj, long samples_nativeObj, long means0_nativeObj); 279 280 // C++: bool trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat()) 281 private static native boolean trainM_0(long nativeObj, long samples_nativeObj, long probs0_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj); 282 private static native boolean trainM_1(long nativeObj, long samples_nativeObj, long probs0_nativeObj); 283 284 // C++: static Ptr_EM create() 285 private static native long create_0(); 286 287 // native support for java finalize() 288 private static native void delete(long nativeObj); 289 290 } 291
