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EM.html

EM.html 79 KB
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  104. <div class="subTitle">org.opencv.ml</div>
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  105. <h2 title="Class EM" class="title">Class EM</h2>
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  108. <ul class="inheritance">
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  109. <li>java.lang.Object</li>
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  115. <li><a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">org.opencv.ml.StatModel</a></li>
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  116. <li>
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  117. <ul class="inheritance">
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  118. <li>org.opencv.ml.EM</li>
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  131. <pre>public class <span class="typeNameLabel">EM</span>
    132. 

  132. extends <a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></pre>
    133. 

  133. <div class="block">The class implements the Expectation Maximization algorithm.
    134. 

  134. 

  135.  SEE: REF: ml_intro_em</div>
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  147. <h3>Field Summary</h3>
    148. 

  148. <table class="memberSummary" border="0" cellpadding="3" cellspacing="0" summary="Field Summary table, listing fields, and an explanation">
    149. 

  149. <caption><span>Fields</span><span class="tabEnd">&nbsp;</span></caption>
    150. 

  150. <tr>
    151. 

  151. <th class="colFirst" scope="col">Modifier and Type</th>
    152. 

  152. <th class="colLast" scope="col">Field and Description</th>
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  153. </tr>
    154. 

  154. <tr class="altColor">
    155. 

  155. <td class="colFirst"><code>static int</code></td>
    156. 

  156. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#COV_MAT_DEFAULT">COV_MAT_DEFAULT</a></span></code>&nbsp;</td>
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  157. </tr>
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  158. <tr class="rowColor">
    159. 

  159. <td class="colFirst"><code>static int</code></td>
    160. 

  160. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#COV_MAT_DIAGONAL">COV_MAT_DIAGONAL</a></span></code>&nbsp;</td>
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  161. </tr>
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  162. <tr class="altColor">
    163. 

  163. <td class="colFirst"><code>static int</code></td>
    164. 

  164. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#COV_MAT_GENERIC">COV_MAT_GENERIC</a></span></code>&nbsp;</td>
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  165. </tr>
    166. 

  166. <tr class="rowColor">
    167. 

  167. <td class="colFirst"><code>static int</code></td>
    168. 

  168. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#COV_MAT_SPHERICAL">COV_MAT_SPHERICAL</a></span></code>&nbsp;</td>
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  169. </tr>
    170. 

  170. <tr class="altColor">
    171. 

  171. <td class="colFirst"><code>static int</code></td>
    172. 

  172. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#DEFAULT_MAX_ITERS">DEFAULT_MAX_ITERS</a></span></code>&nbsp;</td>
    173. 

  173. </tr>
    174. 

  174. <tr class="rowColor">
    175. 

  175. <td class="colFirst"><code>static int</code></td>
    176. 

  176. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#DEFAULT_NCLUSTERS">DEFAULT_NCLUSTERS</a></span></code>&nbsp;</td>
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  177. </tr>
    178. 

  178. <tr class="altColor">
    179. 

  179. <td class="colFirst"><code>static int</code></td>
    180. 

  180. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#START_AUTO_STEP">START_AUTO_STEP</a></span></code>&nbsp;</td>
    181. 

  181. </tr>
    182. 

  182. <tr class="rowColor">
    183. 

  183. <td class="colFirst"><code>static int</code></td>
    184. 

  184. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#START_E_STEP">START_E_STEP</a></span></code>&nbsp;</td>
    185. 

  185. </tr>
    186. 

  186. <tr class="altColor">
    187. 

  187. <td class="colFirst"><code>static int</code></td>
    188. 

  188. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#START_M_STEP">START_M_STEP</a></span></code>&nbsp;</td>
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  195. <h3>Fields inherited from class&nbsp;org.opencv.ml.<a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></h3>
    196. 

  196. <code><a href="../../../org/opencv/ml/StatModel.html#COMPRESSED_INPUT">COMPRESSED_INPUT</a>, <a href="../../../org/opencv/ml/StatModel.html#PREPROCESSED_INPUT">PREPROCESSED_INPUT</a>, <a href="../../../org/opencv/ml/StatModel.html#RAW_OUTPUT">RAW_OUTPUT</a>, <a href="../../../org/opencv/ml/StatModel.html#UPDATE_MODEL">UPDATE_MODEL</a></code></li>
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  205. <h3>Method Summary</h3>
    206. 

  206. <table class="memberSummary" border="0" cellpadding="3" cellspacing="0" summary="Method Summary table, listing methods, and an explanation">
    207. 

  207. <caption><span id="t0" class="activeTableTab"><span>All Methods</span><span class="tabEnd">&nbsp;</span></span><span id="t1" class="tableTab"><span><a href="javascript:show(1);">Static Methods</a></span><span class="tabEnd">&nbsp;</span></span><span id="t2" class="tableTab"><span><a href="javascript:show(2);">Instance Methods</a></span><span class="tabEnd">&nbsp;</span></span><span id="t4" class="tableTab"><span><a href="javascript:show(8);">Concrete Methods</a></span><span class="tabEnd">&nbsp;</span></span></caption>
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  208. <tr>
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  209. <th class="colFirst" scope="col">Modifier and Type</th>
    210. 

  210. <th class="colLast" scope="col">Method and Description</th>
    211. 

  211. </tr>
    212. 

  212. <tr id="i0" class="altColor">
    213. 

  213. <td class="colFirst"><code>static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a></code></td>
    214. 

  214. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#Z:Z__fromPtr__-long-">__fromPtr__</a></span>(long&nbsp;addr)</code>&nbsp;</td>
    215. 

  215. </tr>
    216. 

  216. <tr id="i1" class="rowColor">
    217. 

  217. <td class="colFirst"><code>static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a></code></td>
    218. 

  218. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#create--">create</a></span>()</code>
    219. 

  219. <div class="block">Creates empty %EM model.</div>
    220. 

  220. </td>
    221. 

  221. </tr>
    222. 

  222. <tr id="i2" class="altColor">
    223. 

  223. <td class="colFirst"><code>int</code></td>
    224. 

  224. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getClustersNumber--">getClustersNumber</a></span>()</code>
    225. 

  225. <div class="block">SEE: setClustersNumber</div>
    226. 

  226. </td>
    227. 

  227. </tr>
    228. 

  228. <tr id="i3" class="rowColor">
    229. 

  229. <td class="colFirst"><code>int</code></td>
    230. 

  230. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getCovarianceMatrixType--">getCovarianceMatrixType</a></span>()</code>
    231. 

  231. <div class="block">SEE: setCovarianceMatrixType</div>
    232. 

  232. </td>
    233. 

  233. </tr>
    234. 

  234. <tr id="i4" class="altColor">
    235. 

  235. <td class="colFirst"><code>void</code></td>
    236. 

  236. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getCovs-java.util.List-">getCovs</a></span>(java.util.List&lt;<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&gt;&nbsp;covs)</code>
    237. 

  237. <div class="block">Returns covariation matrices
    238. 

  238. 

  239.      Returns vector of covariation matrices.</div>
    240. 

  240. </td>
    241. 

  241. </tr>
    242. 

  242. <tr id="i5" class="rowColor">
    243. 

  243. <td class="colFirst"><code><a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a></code></td>
    244. 

  244. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getMeans--">getMeans</a></span>()</code>
    245. 

  245. <div class="block">Returns the cluster centers (means of the Gaussian mixture)
    246. 

  246. 

  247.      Returns matrix with the number of rows equal to the number of mixtures and number of columns
    248. 

  248.      equal to the space dimensionality.</div>
    249. 

  249. </td>
    250. 

  250. </tr>
    251. 

  251. <tr id="i6" class="altColor">
    252. 

  252. <td class="colFirst"><code><a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a></code></td>
    253. 

  253. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getTermCriteria--">getTermCriteria</a></span>()</code>
    254. 

  254. <div class="block">SEE: setTermCriteria</div>
    255. 

  255. </td>
    256. 

  256. </tr>
    257. 

  257. <tr id="i7" class="rowColor">
    258. 

  258. <td class="colFirst"><code><a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a></code></td>
    259. 

  259. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#getWeights--">getWeights</a></span>()</code>
    260. 

  260. <div class="block">Returns weights of the mixtures
    261. 

  261. 

  262.      Returns vector with the number of elements equal to the number of mixtures.</div>
    263. 

  263. </td>
    264. 

  264. </tr>
    265. 

  265. <tr id="i8" class="altColor">
    266. 

  266. <td class="colFirst"><code>static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a></code></td>
    267. 

  267. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#load-java.lang.String-">load</a></span>(java.lang.String&nbsp;filepath)</code>
    268. 

  268. <div class="block">Loads and creates a serialized EM from a file
    269. 

  269. 

  270.  Use EM::save to serialize and store an EM to disk.</div>
    271. 

  271. </td>
    272. 

  272. </tr>
    273. 

  273. <tr id="i9" class="rowColor">
    274. 

  274. <td class="colFirst"><code>static <a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a></code></td>
    275. 

  275. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#load-java.lang.String-java.lang.String-">load</a></span>(java.lang.String&nbsp;filepath,
    276. 

  276.     java.lang.String&nbsp;nodeName)</code>
    277. 

  277. <div class="block">Loads and creates a serialized EM from a file
    278. 

  278. 

  279.  Use EM::save to serialize and store an EM to disk.</div>
    280. 

  280. </td>
    281. 

  281. </tr>
    282. 

  282. <tr id="i10" class="altColor">
    283. 

  283. <td class="colFirst"><code>float</code></td>
    284. 

  284. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#predict-org.opencv.core.Mat-">predict</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples)</code>
    285. 

  285. <div class="block">Returns posterior probabilities for the provided samples</div>
    286. 

  286. </td>
    287. 

  287. </tr>
    288. 

  288. <tr id="i11" class="rowColor">
    289. 

  289. <td class="colFirst"><code>float</code></td>
    290. 

  290. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#predict-org.opencv.core.Mat-org.opencv.core.Mat-">predict</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    291. 

  291.        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;results)</code>
    292. 

  292. <div class="block">Returns posterior probabilities for the provided samples</div>
    293. 

  293. </td>
    294. 

  294. </tr>
    295. 

  295. <tr id="i12" class="altColor">
    296. 

  296. <td class="colFirst"><code>float</code></td>
    297. 

  297. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#predict-org.opencv.core.Mat-org.opencv.core.Mat-int-">predict</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    298. 

  298.        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;results,
    299. 

  299.        int&nbsp;flags)</code>
    300. 

  300. <div class="block">Returns posterior probabilities for the provided samples</div>
    301. 

  301. </td>
    302. 

  302. </tr>
    303. 

  303. <tr id="i13" class="rowColor">
    304. 

  304. <td class="colFirst"><code>double[]</code></td>
    305. 

  305. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#predict2-org.opencv.core.Mat-org.opencv.core.Mat-">predict2</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;sample,
    306. 

  306.         <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs)</code>
    307. 

  307. <div class="block">Returns a likelihood logarithm value and an index of the most probable mixture component
    308. 

  308.      for the given sample.</div>
    309. 

  309. </td>
    310. 

  310. </tr>
    311. 

  311. <tr id="i14" class="altColor">
    312. 

  312. <td class="colFirst"><code>void</code></td>
    313. 

  313. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#setClustersNumber-int-">setClustersNumber</a></span>(int&nbsp;val)</code>
    314. 

  314. <div class="block">getClustersNumber SEE: getClustersNumber</div>
    315. 

  315. </td>
    316. 

  316. </tr>
    317. 

  317. <tr id="i15" class="rowColor">
    318. 

  318. <td class="colFirst"><code>void</code></td>
    319. 

  319. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#setCovarianceMatrixType-int-">setCovarianceMatrixType</a></span>(int&nbsp;val)</code>
    320. 

  320. <div class="block">getCovarianceMatrixType SEE: getCovarianceMatrixType</div>
    321. 

  321. </td>
    322. 

  322. </tr>
    323. 

  323. <tr id="i16" class="altColor">
    324. 

  324. <td class="colFirst"><code>void</code></td>
    325. 

  325. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#setTermCriteria-org.opencv.core.TermCriteria-">setTermCriteria</a></span>(<a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a>&nbsp;val)</code>
    326. 

  326. <div class="block">getTermCriteria SEE: getTermCriteria</div>
    327. 

  327. </td>
    328. 

  328. </tr>
    329. 

  329. <tr id="i17" class="rowColor">
    330. 

  330. <td class="colFirst"><code>boolean</code></td>
    331. 

  331. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    332. 

  332.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0)</code>
    333. 

  333. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    334. 

  334. </td>
    335. 

  335. </tr>
    336. 

  336. <tr id="i18" class="altColor">
    337. 

  337. <td class="colFirst"><code>boolean</code></td>
    338. 

  338. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    339. 

  339.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0,
    340. 

  340.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;covs0)</code>
    341. 

  341. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    342. 

  342. </td>
    343. 

  343. </tr>
    344. 

  344. <tr id="i19" class="rowColor">
    345. 

  345. <td class="colFirst"><code>boolean</code></td>
    346. 

  346. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    347. 

  347.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0,
    348. 

  348.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;covs0,
    349. 

  349.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;weights0)</code>
    350. 

  350. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    351. 

  351. </td>
    352. 

  352. </tr>
    353. 

  353. <tr id="i20" class="altColor">
    354. 

  354. <td class="colFirst"><code>boolean</code></td>
    355. 

  355. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    356. 

  356.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0,
    357. 

  357.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;covs0,
    358. 

  358.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;weights0,
    359. 

  359.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods)</code>
    360. 

  360. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    361. 

  361. </td>
    362. 

  362. </tr>
    363. 

  363. <tr id="i21" class="rowColor">
    364. 

  364. <td class="colFirst"><code>boolean</code></td>
    365. 

  365. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    366. 

  366.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0,
    367. 

  367.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;covs0,
    368. 

  368.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;weights0,
    369. 

  369.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    370. 

  370.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels)</code>
    371. 

  371. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    372. 

  372. </td>
    373. 

  373. </tr>
    374. 

  374. <tr id="i22" class="altColor">
    375. 

  375. <td class="colFirst"><code>boolean</code></td>
    376. 

  376. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainE</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    377. 

  377.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0,
    378. 

  378.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;covs0,
    379. 

  379.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;weights0,
    380. 

  380.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    381. 

  381.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels,
    382. 

  382.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs)</code>
    383. 

  383. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    384. 

  384. </td>
    385. 

  385. </tr>
    386. 

  386. <tr id="i23" class="rowColor">
    387. 

  387. <td class="colFirst"><code>boolean</code></td>
    388. 

  388. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainEM-org.opencv.core.Mat-">trainEM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples)</code>
    389. 

  389. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    390. 

  390. </td>
    391. 

  391. </tr>
    392. 

  392. <tr id="i24" class="altColor">
    393. 

  393. <td class="colFirst"><code>boolean</code></td>
    394. 

  394. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainEM-org.opencv.core.Mat-org.opencv.core.Mat-">trainEM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    395. 

  395.        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods)</code>
    396. 

  396. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    397. 

  397. </td>
    398. 

  398. </tr>
    399. 

  399. <tr id="i25" class="rowColor">
    400. 

  400. <td class="colFirst"><code>boolean</code></td>
    401. 

  401. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainEM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainEM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    402. 

  402.        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    403. 

  403.        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels)</code>
    404. 

  404. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    405. 

  405. </td>
    406. 

  406. </tr>
    407. 

  407. <tr id="i26" class="altColor">
    408. 

  408. <td class="colFirst"><code>boolean</code></td>
    409. 

  409. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainEM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainEM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    410. 

  410.        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    411. 

  411.        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels,
    412. 

  412.        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs)</code>
    413. 

  413. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    414. 

  414. </td>
    415. 

  415. </tr>
    416. 

  416. <tr id="i27" class="rowColor">
    417. 

  417. <td class="colFirst"><code>boolean</code></td>
    418. 

  418. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainM-org.opencv.core.Mat-org.opencv.core.Mat-">trainM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    419. 

  419.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs0)</code>
    420. 

  420. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    421. 

  421. </td>
    422. 

  422. </tr>
    423. 

  423. <tr id="i28" class="altColor">
    424. 

  424. <td class="colFirst"><code>boolean</code></td>
    425. 

  425. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    426. 

  426.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs0,
    427. 

  427.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods)</code>
    428. 

  428. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    429. 

  429. </td>
    430. 

  430. </tr>
    431. 

  431. <tr id="i29" class="rowColor">
    432. 

  432. <td class="colFirst"><code>boolean</code></td>
    433. 

  433. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    434. 

  434.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs0,
    435. 

  435.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    436. 

  436.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels)</code>
    437. 

  437. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    438. 

  438. </td>
    439. 

  439. </tr>
    440. 

  440. <tr id="i30" class="altColor">
    441. 

  441. <td class="colFirst"><code>boolean</code></td>
    442. 

  442. <td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/ml/EM.html#trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">trainM</a></span>(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    443. 

  443.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs0,
    444. 

  444.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    445. 

  445.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels,
    446. 

  446.       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs)</code>
    447. 

  447. <div class="block">Estimate the Gaussian mixture parameters from a samples set.</div>
    448. 

  448. </td>
    449. 

  449. </tr>
    450. 

  450. </table>
    451. 

  451. <ul class="blockList">
    452. 

  452. <li class="blockList"><a name="methods.inherited.from.class.org.opencv.ml.StatModel">
    453. 

  453. <!--   -->
    454. 

  454. </a>
    455. 

  455. <h3>Methods inherited from class&nbsp;org.opencv.ml.<a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></h3>
    456. 

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    463. 

  463. <code><a href="../../../org/opencv/core/Algorithm.html#clear--">clear</a>, <a href="../../../org/opencv/core/Algorithm.html#getDefaultName--">getDefaultName</a>, <a href="../../../org/opencv/core/Algorithm.html#getNativeObjAddr--">getNativeObjAddr</a>, <a href="../../../org/opencv/core/Algorithm.html#save-java.lang.String-">save</a></code></li>
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  494. <dt><span class="seeLabel">See Also:</span></dt>
    495. 

  495. <dd><a href="../../../constant-values.html#org.opencv.ml.EM.COV_MAT_DEFAULT">Constant Field Values</a></dd>
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  521. <dd><a href="../../../constant-values.html#org.opencv.ml.EM.COV_MAT_GENERIC">Constant Field Values</a></dd>
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  531. <pre>public static final&nbsp;int COV_MAT_SPHERICAL</pre>
    532. 

  532. <dl>
    533. 

  533. <dt><span class="seeLabel">See Also:</span></dt>
    534. 

  534. <dd><a href="../../../constant-values.html#org.opencv.ml.EM.COV_MAT_SPHERICAL">Constant Field Values</a></dd>
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  547. <dd><a href="../../../constant-values.html#org.opencv.ml.EM.DEFAULT_MAX_ITERS">Constant Field Values</a></dd>
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  560. <dd><a href="../../../constant-values.html#org.opencv.ml.EM.DEFAULT_NCLUSTERS">Constant Field Values</a></dd>
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  573. <dd><a href="../../../constant-values.html#org.opencv.ml.EM.START_AUTO_STEP">Constant Field Values</a></dd>
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    586. 

  586. <dd><a href="../../../constant-values.html#org.opencv.ml.EM.START_E_STEP">Constant Field Values</a></dd>
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  599. <dd><a href="../../../constant-values.html#org.opencv.ml.EM.START_M_STEP">Constant Field Values</a></dd>
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  617. <pre>public static&nbsp;<a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a>&nbsp;__fromPtr__(long&nbsp;addr)</pre>
    618. 

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  625. <h4>create</h4>
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  626. <pre>public static&nbsp;<a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a>&nbsp;create()</pre>
    627. 

  627. <div class="block">Creates empty %EM model.
    628. 

  628.      The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you
    629. 

  629.      can use one of the EM::train\* methods or load it from file using Algorithm::load&lt;EM&gt;(filename).</div>
    630. 

  630. <dl>
    631. 

  631. <dt><span class="returnLabel">Returns:</span></dt>
    632. 

  632. <dd>automatically generated</dd>
    633. 

  633. </dl>
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  634. </li>
    635. 

  635. </ul>
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  636. <a name="getClustersNumber--">
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  641. <h4>getClustersNumber</h4>
    642. 

  642. <pre>public&nbsp;int&nbsp;getClustersNumber()</pre>
    643. 

  643. <div class="block">SEE: setClustersNumber</div>
    644. 

  644. <dl>
    645. 

  645. <dt><span class="returnLabel">Returns:</span></dt>
    646. 

  646. <dd>automatically generated</dd>
    647. 

  647. </dl>
    648. 

  648. </li>
    649. 

  649. </ul>
    650. 

  650. <a name="getCovarianceMatrixType--">
    651. 

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  655. <h4>getCovarianceMatrixType</h4>
    656. 

  656. <pre>public&nbsp;int&nbsp;getCovarianceMatrixType()</pre>
    657. 

  657. <div class="block">SEE: setCovarianceMatrixType</div>
    658. 

  658. <dl>
    659. 

  659. <dt><span class="returnLabel">Returns:</span></dt>
    660. 

  660. <dd>automatically generated</dd>
    661. 

  661. </dl>
    662. 

  662. </li>
    663. 

  663. </ul>
    664. 

  664. <a name="getCovs-java.util.List-">
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  669. <h4>getCovs</h4>
    670. 

  670. <pre>public&nbsp;void&nbsp;getCovs(java.util.List&lt;<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&gt;&nbsp;covs)</pre>
    671. 

  671. <div class="block">Returns covariation matrices
    672. 

  672. 

  673.      Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures,
    674. 

  674.      each matrix is a square floating-point matrix NxN, where N is the space dimensionality.</div>
    675. 

  675. <dl>
    676. 

  676. <dt><span class="paramLabel">Parameters:</span></dt>
    677. 

  677. <dd><code>covs</code> - automatically generated</dd>
    678. 

  678. </dl>
    679. 

  679. </li>
    680. 

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    681. 

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    682. 

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    687. 

  687. <pre>public&nbsp;<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;getMeans()</pre>
    688. 

  688. <div class="block">Returns the cluster centers (means of the Gaussian mixture)
    689. 

  689. 

  690.      Returns matrix with the number of rows equal to the number of mixtures and number of columns
    691. 

  691.      equal to the space dimensionality.</div>
    692. 

  692. <dl>
    693. 

  693. <dt><span class="returnLabel">Returns:</span></dt>
    694. 

  694. <dd>automatically generated</dd>
    695. 

  695. </dl>
    696. 

  696. </li>
    697. 

  697. </ul>
    698. 

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    699. 

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    703. 

  703. <h4>getTermCriteria</h4>
    704. 

  704. <pre>public&nbsp;<a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a>&nbsp;getTermCriteria()</pre>
    705. 

  705. <div class="block">SEE: setTermCriteria</div>
    706. 

  706. <dl>
    707. 

  707. <dt><span class="returnLabel">Returns:</span></dt>
    708. 

  708. <dd>automatically generated</dd>
    709. 

  709. </dl>
    710. 

  710. </li>
    711. 

  711. </ul>
    712. 

  712. <a name="getWeights--">
    713. 

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  717. <h4>getWeights</h4>
    718. 

  718. <pre>public&nbsp;<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;getWeights()</pre>
    719. 

  719. <div class="block">Returns weights of the mixtures
    720. 

  720. 

  721.      Returns vector with the number of elements equal to the number of mixtures.</div>
    722. 

  722. <dl>
    723. 

  723. <dt><span class="returnLabel">Returns:</span></dt>
    724. 

  724. <dd>automatically generated</dd>
    725. 

  725. </dl>
    726. 

  726. </li>
    727. 

  727. </ul>
    728. 

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    729. 

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    733. 

  733. <h4>load</h4>
    734. 

  734. <pre>public static&nbsp;<a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a>&nbsp;load(java.lang.String&nbsp;filepath)</pre>
    735. 

  735. <div class="block">Loads and creates a serialized EM from a file
    736. 

  736. 

  737.  Use EM::save to serialize and store an EM to disk.
    738. 

  738.  Load the EM from this file again, by calling this function with the path to the file.
    739. 

  739.  Optionally specify the node for the file containing the classifier</div>
    740. 

  740. <dl>
    741. 

  741. <dt><span class="paramLabel">Parameters:</span></dt>
    742. 

  742. <dd><code>filepath</code> - path to serialized EM</dd>
    743. 

  743. <dt><span class="returnLabel">Returns:</span></dt>
    744. 

  744. <dd>automatically generated</dd>
    745. 

  745. </dl>
    746. 

  746. </li>
    747. 

  747. </ul>
    748. 

  748. <a name="load-java.lang.String-java.lang.String-">
    749. 

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    751. 

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    753. 

  753. <h4>load</h4>
    754. 

  754. <pre>public static&nbsp;<a href="../../../org/opencv/ml/EM.html" title="class in org.opencv.ml">EM</a>&nbsp;load(java.lang.String&nbsp;filepath,
    755. 

  755.                       java.lang.String&nbsp;nodeName)</pre>
    756. 

  756. <div class="block">Loads and creates a serialized EM from a file
    757. 

  757. 

  758.  Use EM::save to serialize and store an EM to disk.
    759. 

  759.  Load the EM from this file again, by calling this function with the path to the file.
    760. 

  760.  Optionally specify the node for the file containing the classifier</div>
    761. 

  761. <dl>
    762. 

  762. <dt><span class="paramLabel">Parameters:</span></dt>
    763. 

  763. <dd><code>filepath</code> - path to serialized EM</dd>
    764. 

  764. <dd><code>nodeName</code> - name of node containing the classifier</dd>
    765. 

  765. <dt><span class="returnLabel">Returns:</span></dt>
    766. 

  766. <dd>automatically generated</dd>
    767. 

  767. </dl>
    768. 

  768. </li>
    769. 

  769. </ul>
    770. 

  770. <a name="predict-org.opencv.core.Mat-">
    771. 

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    772. 

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    773. 

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    775. 

  775. <h4>predict</h4>
    776. 

  776. <pre>public&nbsp;float&nbsp;predict(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples)</pre>
    777. 

  777. <div class="block">Returns posterior probabilities for the provided samples</div>
    778. 

  778. <dl>
    779. 

  779. <dt><span class="overrideSpecifyLabel">Overrides:</span></dt>
    780. 

  780. <dd><code><a href="../../../org/opencv/ml/StatModel.html#predict-org.opencv.core.Mat-">predict</a></code>&nbsp;in class&nbsp;<code><a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></code></dd>
    781. 

  781. <dt><span class="paramLabel">Parameters:</span></dt>
    782. 

  782. <dd><code>samples</code> - The input samples, floating-point matrix
    783. 

  783.      posterior probabilities for each sample from the input</dd>
    784. 

  784. <dt><span class="returnLabel">Returns:</span></dt>
    785. 

  785. <dd>automatically generated</dd>
    786. 

  786. </dl>
    787. 

  787. </li>
    788. 

  788. </ul>
    789. 

  789. <a name="predict-org.opencv.core.Mat-org.opencv.core.Mat-">
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  794. <h4>predict</h4>
    795. 

  795. <pre>public&nbsp;float&nbsp;predict(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    796. 

  796.                      <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;results)</pre>
    797. 

  797. <div class="block">Returns posterior probabilities for the provided samples</div>
    798. 

  798. <dl>
    799. 

  799. <dt><span class="overrideSpecifyLabel">Overrides:</span></dt>
    800. 

  800. <dd><code><a href="../../../org/opencv/ml/StatModel.html#predict-org.opencv.core.Mat-org.opencv.core.Mat-">predict</a></code>&nbsp;in class&nbsp;<code><a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></code></dd>
    801. 

  801. <dt><span class="paramLabel">Parameters:</span></dt>
    802. 

  802. <dd><code>samples</code> - The input samples, floating-point matrix</dd>
    803. 

  803. <dd><code>results</code> - The optional output \( nSamples \times nClusters\) matrix of results. It contains
    804. 

  804.      posterior probabilities for each sample from the input</dd>
    805. 

  805. <dt><span class="returnLabel">Returns:</span></dt>
    806. 

  806. <dd>automatically generated</dd>
    807. 

  807. </dl>
    808. 

  808. </li>
    809. 

  809. </ul>
    810. 

  810. <a name="predict-org.opencv.core.Mat-org.opencv.core.Mat-int-">
    811. 

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    815. 

  815. <h4>predict</h4>
    816. 

  816. <pre>public&nbsp;float&nbsp;predict(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    817. 

  817.                      <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;results,
    818. 

  818.                      int&nbsp;flags)</pre>
    819. 

  819. <div class="block">Returns posterior probabilities for the provided samples</div>
    820. 

  820. <dl>
    821. 

  821. <dt><span class="overrideSpecifyLabel">Overrides:</span></dt>
    822. 

  822. <dd><code><a href="../../../org/opencv/ml/StatModel.html#predict-org.opencv.core.Mat-org.opencv.core.Mat-int-">predict</a></code>&nbsp;in class&nbsp;<code><a href="../../../org/opencv/ml/StatModel.html" title="class in org.opencv.ml">StatModel</a></code></dd>
    823. 

  823. <dt><span class="paramLabel">Parameters:</span></dt>
    824. 

  824. <dd><code>samples</code> - The input samples, floating-point matrix</dd>
    825. 

  825. <dd><code>results</code> - The optional output \( nSamples \times nClusters\) matrix of results. It contains
    826. 

  826.      posterior probabilities for each sample from the input</dd>
    827. 

  827. <dd><code>flags</code> - This parameter will be ignored</dd>
    828. 

  828. <dt><span class="returnLabel">Returns:</span></dt>
    829. 

  829. <dd>automatically generated</dd>
    830. 

  830. </dl>
    831. 

  831. </li>
    832. 

  832. </ul>
    833. 

  833. <a name="predict2-org.opencv.core.Mat-org.opencv.core.Mat-">
    834. 

  834. <!--   -->
    835. 

  835. </a>
    836. 

  836. <ul class="blockList">
    837. 

  837. <li class="blockList">
    838. 

  838. <h4>predict2</h4>
    839. 

  839. <pre>public&nbsp;double[]&nbsp;predict2(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;sample,
    840. 

  840.                          <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs)</pre>
    841. 

  841. <div class="block">Returns a likelihood logarithm value and an index of the most probable mixture component
    842. 

  842.      for the given sample.</div>
    843. 

  843. <dl>
    844. 

  844. <dt><span class="paramLabel">Parameters:</span></dt>
    845. 

  845. <dd><code>sample</code> - A sample for classification. It should be a one-channel matrix of
    846. 

  846.          \(1 \times dims\) or \(dims \times 1\) size.</dd>
    847. 

  847. <dd><code>probs</code> - Optional output matrix that contains posterior probabilities of each component
    848. 

  848.          given the sample. It has \(1 \times nclusters\) size and CV_64FC1 type.
    849. 

  849. 

  850.      The method returns a two-element double vector. Zero element is a likelihood logarithm value for
    851. 

  851.      the sample. First element is an index of the most probable mixture component for the given
    852. 

  852.      sample.</dd>
    853. 

  853. <dt><span class="returnLabel">Returns:</span></dt>
    854. 

  854. <dd>automatically generated</dd>
    855. 

  855. </dl>
    856. 

  856. </li>
    857. 

  857. </ul>
    858. 

  858. <a name="setClustersNumber-int-">
    859. 

  859. <!--   -->
    860. 

  860. </a>
    861. 

  861. <ul class="blockList">
    862. 

  862. <li class="blockList">
    863. 

  863. <h4>setClustersNumber</h4>
    864. 

  864. <pre>public&nbsp;void&nbsp;setClustersNumber(int&nbsp;val)</pre>
    865. 

  865. <div class="block">getClustersNumber SEE: getClustersNumber</div>
    866. 

  866. <dl>
    867. 

  867. <dt><span class="paramLabel">Parameters:</span></dt>
    868. 

  868. <dd><code>val</code> - automatically generated</dd>
    869. 

  869. </dl>
    870. 

  870. </li>
    871. 

  871. </ul>
    872. 

  872. <a name="setCovarianceMatrixType-int-">
    873. 

  873. <!--   -->
    874. 

  874. </a>
    875. 

  875. <ul class="blockList">
    876. 

  876. <li class="blockList">
    877. 

  877. <h4>setCovarianceMatrixType</h4>
    878. 

  878. <pre>public&nbsp;void&nbsp;setCovarianceMatrixType(int&nbsp;val)</pre>
    879. 

  879. <div class="block">getCovarianceMatrixType SEE: getCovarianceMatrixType</div>
    880. 

  880. <dl>
    881. 

  881. <dt><span class="paramLabel">Parameters:</span></dt>
    882. 

  882. <dd><code>val</code> - automatically generated</dd>
    883. 

  883. </dl>
    884. 

  884. </li>
    885. 

  885. </ul>
    886. 

  886. <a name="setTermCriteria-org.opencv.core.TermCriteria-">
    887. 

  887. <!--   -->
    888. 

  888. </a>
    889. 

  889. <ul class="blockList">
    890. 

  890. <li class="blockList">
    891. 

  891. <h4>setTermCriteria</h4>
    892. 

  892. <pre>public&nbsp;void&nbsp;setTermCriteria(<a href="../../../org/opencv/core/TermCriteria.html" title="class in org.opencv.core">TermCriteria</a>&nbsp;val)</pre>
    893. 

  893. <div class="block">getTermCriteria SEE: getTermCriteria</div>
    894. 

  894. <dl>
    895. 

  895. <dt><span class="paramLabel">Parameters:</span></dt>
    896. 

  896. <dd><code>val</code> - automatically generated</dd>
    897. 

  897. </dl>
    898. 

  898. </li>
    899. 

  899. </ul>
    900. 

  900. <a name="trainE-org.opencv.core.Mat-org.opencv.core.Mat-">
    901. 

  901. <!--   -->
    902. 

  902. </a>
    903. 

  903. <ul class="blockList">
    904. 

  904. <li class="blockList">
    905. 

  905. <h4>trainE</h4>
    906. 

  906. <pre>public&nbsp;boolean&nbsp;trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    907. 

  907.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0)</pre>
    908. 

  908. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    909. 

  909. 

  910.      This variation starts with Expectation step. You need to provide initial means \(a_k\) of
    911. 

  911.      mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
    912. 

  912.      \(S_k\) of mixture components.</div>
    913. 

  913. <dl>
    914. 

  914. <dt><span class="paramLabel">Parameters:</span></dt>
    915. 

  915. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    916. 

  916.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    917. 

  917.          it will be converted to the inner matrix of such type for the further computing.</dd>
    918. 

  918. <dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
    919. 

  919.          \(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
    920. 

  920.          converted to the inner matrix of such type for the further computing.
    921. 

  921.          covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
    922. 

  922.          do not have CV_64F type they will be converted to the inner matrices of such type for the
    923. 

  923.          further computing.
    924. 

  924.          floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.
    925. 

  925.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
    926. 

  926.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    927. 

  927.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
    928. 

  928.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    929. 

  929.          CV_64FC1 type.</dd>
    930. 

  930. <dt><span class="returnLabel">Returns:</span></dt>
    931. 

  931. <dd>automatically generated</dd>
    932. 

  932. </dl>
    933. 

  933. </li>
    934. 

  934. </ul>
    935. 

  935. <a name="trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
    936. 

  936. <!--   -->
    937. 

  937. </a>
    938. 

  938. <ul class="blockList">
    939. 

  939. <li class="blockList">
    940. 

  940. <h4>trainE</h4>
    941. 

  941. <pre>public&nbsp;boolean&nbsp;trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    942. 

  942.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0,
    943. 

  943.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;covs0)</pre>
    944. 

  944. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    945. 

  945. 

  946.      This variation starts with Expectation step. You need to provide initial means \(a_k\) of
    947. 

  947.      mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
    948. 

  948.      \(S_k\) of mixture components.</div>
    949. 

  949. <dl>
    950. 

  950. <dt><span class="paramLabel">Parameters:</span></dt>
    951. 

  951. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    952. 

  952.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    953. 

  953.          it will be converted to the inner matrix of such type for the further computing.</dd>
    954. 

  954. <dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
    955. 

  955.          \(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
    956. 

  956.          converted to the inner matrix of such type for the further computing.</dd>
    957. 

  957. <dd><code>covs0</code> - The vector of initial covariance matrices \(S_k\) of mixture components. Each of
    958. 

  958.          covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
    959. 

  959.          do not have CV_64F type they will be converted to the inner matrices of such type for the
    960. 

  960.          further computing.
    961. 

  961.          floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.
    962. 

  962.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
    963. 

  963.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    964. 

  964.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
    965. 

  965.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    966. 

  966.          CV_64FC1 type.</dd>
    967. 

  967. <dt><span class="returnLabel">Returns:</span></dt>
    968. 

  968. <dd>automatically generated</dd>
    969. 

  969. </dl>
    970. 

  970. </li>
    971. 

  971. </ul>
    972. 

  972. <a name="trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
    973. 

  973. <!--   -->
    974. 

  974. </a>
    975. 

  975. <ul class="blockList">
    976. 

  976. <li class="blockList">
    977. 

  977. <h4>trainE</h4>
    978. 

  978. <pre>public&nbsp;boolean&nbsp;trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    979. 

  979.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0,
    980. 

  980.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;covs0,
    981. 

  981.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;weights0)</pre>
    982. 

  982. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    983. 

  983. 

  984.      This variation starts with Expectation step. You need to provide initial means \(a_k\) of
    985. 

  985.      mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
    986. 

  986.      \(S_k\) of mixture components.</div>
    987. 

  987. <dl>
    988. 

  988. <dt><span class="paramLabel">Parameters:</span></dt>
    989. 

  989. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    990. 

  990.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    991. 

  991.          it will be converted to the inner matrix of such type for the further computing.</dd>
    992. 

  992. <dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
    993. 

  993.          \(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
    994. 

  994.          converted to the inner matrix of such type for the further computing.</dd>
    995. 

  995. <dd><code>covs0</code> - The vector of initial covariance matrices \(S_k\) of mixture components. Each of
    996. 

  996.          covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
    997. 

  997.          do not have CV_64F type they will be converted to the inner matrices of such type for the
    998. 

  998.          further computing.</dd>
    999. 

  999. <dd><code>weights0</code> - Initial weights \(\pi_k\) of mixture components. It should be a one-channel
    1000. 

  1000.          floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.
    1001. 

  1001.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
    1002. 

  1002.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1003. 

  1003.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
    1004. 

  1004.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1005. 

  1005.          CV_64FC1 type.</dd>
    1006. 

  1006. <dt><span class="returnLabel">Returns:</span></dt>
    1007. 

  1007. <dd>automatically generated</dd>
    1008. 

  1008. </dl>
    1009. 

  1009. </li>
    1010. 

  1010. </ul>
    1011. 

  1011. <a name="trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
    1012. 

  1012. <!--   -->
    1013. 

  1013. </a>
    1014. 

  1014. <ul class="blockList">
    1015. 

  1015. <li class="blockList">
    1016. 

  1016. <h4>trainE</h4>
    1017. 

  1017. <pre>public&nbsp;boolean&nbsp;trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    1018. 

  1018.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0,
    1019. 

  1019.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;covs0,
    1020. 

  1020.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;weights0,
    1021. 

  1021.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods)</pre>
    1022. 

  1022. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    1023. 

  1023. 

  1024.      This variation starts with Expectation step. You need to provide initial means \(a_k\) of
    1025. 

  1025.      mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
    1026. 

  1026.      \(S_k\) of mixture components.</div>
    1027. 

  1027. <dl>
    1028. 

  1028. <dt><span class="paramLabel">Parameters:</span></dt>
    1029. 

  1029. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    1030. 

  1030.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    1031. 

  1031.          it will be converted to the inner matrix of such type for the further computing.</dd>
    1032. 

  1032. <dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
    1033. 

  1033.          \(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
    1034. 

  1034.          converted to the inner matrix of such type for the further computing.</dd>
    1035. 

  1035. <dd><code>covs0</code> - The vector of initial covariance matrices \(S_k\) of mixture components. Each of
    1036. 

  1036.          covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
    1037. 

  1037.          do not have CV_64F type they will be converted to the inner matrices of such type for the
    1038. 

  1038.          further computing.</dd>
    1039. 

  1039. <dd><code>weights0</code> - Initial weights \(\pi_k\) of mixture components. It should be a one-channel
    1040. 

  1040.          floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.</dd>
    1041. 

  1041. <dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
    1042. 

  1042.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
    1043. 

  1043.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1044. 

  1044.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
    1045. 

  1045.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1046. 

  1046.          CV_64FC1 type.</dd>
    1047. 

  1047. <dt><span class="returnLabel">Returns:</span></dt>
    1048. 

  1048. <dd>automatically generated</dd>
    1049. 

  1049. </dl>
    1050. 

  1050. </li>
    1051. 

  1051. </ul>
    1052. 

  1052. <a name="trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
    1053. 

  1053. <!--   -->
    1054. 

  1054. </a>
    1055. 

  1055. <ul class="blockList">
    1056. 

  1056. <li class="blockList">
    1057. 

  1057. <h4>trainE</h4>
    1058. 

  1058. <pre>public&nbsp;boolean&nbsp;trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    1059. 

  1059.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0,
    1060. 

  1060.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;covs0,
    1061. 

  1061.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;weights0,
    1062. 

  1062.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    1063. 

  1063.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels)</pre>
    1064. 

  1064. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    1065. 

  1065. 

  1066.      This variation starts with Expectation step. You need to provide initial means \(a_k\) of
    1067. 

  1067.      mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
    1068. 

  1068.      \(S_k\) of mixture components.</div>
    1069. 

  1069. <dl>
    1070. 

  1070. <dt><span class="paramLabel">Parameters:</span></dt>
    1071. 

  1071. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    1072. 

  1072.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    1073. 

  1073.          it will be converted to the inner matrix of such type for the further computing.</dd>
    1074. 

  1074. <dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
    1075. 

  1075.          \(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
    1076. 

  1076.          converted to the inner matrix of such type for the further computing.</dd>
    1077. 

  1077. <dd><code>covs0</code> - The vector of initial covariance matrices \(S_k\) of mixture components. Each of
    1078. 

  1078.          covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
    1079. 

  1079.          do not have CV_64F type they will be converted to the inner matrices of such type for the
    1080. 

  1080.          further computing.</dd>
    1081. 

  1081. <dd><code>weights0</code> - Initial weights \(\pi_k\) of mixture components. It should be a one-channel
    1082. 

  1082.          floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.</dd>
    1083. 

  1083. <dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
    1084. 

  1084.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
    1085. 

  1085. <dd><code>labels</code> - The optional output "class label" for each sample:
    1086. 

  1086.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1087. 

  1087.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
    1088. 

  1088.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1089. 

  1089.          CV_64FC1 type.</dd>
    1090. 

  1090. <dt><span class="returnLabel">Returns:</span></dt>
    1091. 

  1091. <dd>automatically generated</dd>
    1092. 

  1092. </dl>
    1093. 

  1093. </li>
    1094. 

  1094. </ul>
    1095. 

  1095. <a name="trainE-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
    1096. 

  1096. <!--   -->
    1097. 

  1097. </a>
    1098. 

  1098. <ul class="blockList">
    1099. 

  1099. <li class="blockList">
    1100. 

  1100. <h4>trainE</h4>
    1101. 

  1101. <pre>public&nbsp;boolean&nbsp;trainE(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    1102. 

  1102.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;means0,
    1103. 

  1103.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;covs0,
    1104. 

  1104.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;weights0,
    1105. 

  1105.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    1106. 

  1106.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels,
    1107. 

  1107.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs)</pre>
    1108. 

  1108. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    1109. 

  1109. 

  1110.      This variation starts with Expectation step. You need to provide initial means \(a_k\) of
    1111. 

  1111.      mixture components. Optionally you can pass initial weights \(\pi_k\) and covariance matrices
    1112. 

  1112.      \(S_k\) of mixture components.</div>
    1113. 

  1113. <dl>
    1114. 

  1114. <dt><span class="paramLabel">Parameters:</span></dt>
    1115. 

  1115. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    1116. 

  1116.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    1117. 

  1117.          it will be converted to the inner matrix of such type for the further computing.</dd>
    1118. 

  1118. <dd><code>means0</code> - Initial means \(a_k\) of mixture components. It is a one-channel matrix of
    1119. 

  1119.          \(nclusters \times dims\) size. If the matrix does not have CV_64F type it will be
    1120. 

  1120.          converted to the inner matrix of such type for the further computing.</dd>
    1121. 

  1121. <dd><code>covs0</code> - The vector of initial covariance matrices \(S_k\) of mixture components. Each of
    1122. 

  1122.          covariance matrices is a one-channel matrix of \(dims \times dims\) size. If the matrices
    1123. 

  1123.          do not have CV_64F type they will be converted to the inner matrices of such type for the
    1124. 

  1124.          further computing.</dd>
    1125. 

  1125. <dd><code>weights0</code> - Initial weights \(\pi_k\) of mixture components. It should be a one-channel
    1126. 

  1126.          floating-point matrix with \(1 \times nclusters\) or \(nclusters \times 1\) size.</dd>
    1127. 

  1127. <dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
    1128. 

  1128.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
    1129. 

  1129. <dd><code>labels</code> - The optional output "class label" for each sample:
    1130. 

  1130.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1131. 

  1131.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.</dd>
    1132. 

  1132. <dd><code>probs</code> - The optional output matrix that contains posterior probabilities of each Gaussian
    1133. 

  1133.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1134. 

  1134.          CV_64FC1 type.</dd>
    1135. 

  1135. <dt><span class="returnLabel">Returns:</span></dt>
    1136. 

  1136. <dd>automatically generated</dd>
    1137. 

  1137. </dl>
    1138. 

  1138. </li>
    1139. 

  1139. </ul>
    1140. 

  1140. <a name="trainEM-org.opencv.core.Mat-">
    1141. 

  1141. <!--   -->
    1142. 

  1142. </a>
    1143. 

  1143. <ul class="blockList">
    1144. 

  1144. <li class="blockList">
    1145. 

  1145. <h4>trainEM</h4>
    1146. 

  1146. <pre>public&nbsp;boolean&nbsp;trainEM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples)</pre>
    1147. 

  1147. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    1148. 

  1148. 

  1149.      This variation starts with Expectation step. Initial values of the model parameters will be
    1150. 

  1150.      estimated by the k-means algorithm.
    1151. 

  1151. 

  1152.      Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
    1153. 

  1153.      responses (class labels or function values) as input. Instead, it computes the *Maximum
    1154. 

  1154.      Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
    1155. 

  1155.      parameters inside the structure: \(p_{i,k}\) in probs, \(a_k\) in means , \(S_k\) in
    1156. 

  1156.      covs[k], \(\pi_k\) in weights , and optionally computes the output "class label" for each
    1157. 

  1157.      sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most
    1158. 

  1158.      probable mixture component for each sample).
    1159. 

  1159. 

  1160.      The trained model can be used further for prediction, just like any other classifier. The
    1161. 

  1161.      trained model is similar to the NormalBayesClassifier.</div>
    1162. 

  1162. <dl>
    1163. 

  1163. <dt><span class="paramLabel">Parameters:</span></dt>
    1164. 

  1164. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    1165. 

  1165.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    1166. 

  1166.          it will be converted to the inner matrix of such type for the further computing.
    1167. 

  1167.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
    1168. 

  1168.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1169. 

  1169.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
    1170. 

  1170.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1171. 

  1171.          CV_64FC1 type.</dd>
    1172. 

  1172. <dt><span class="returnLabel">Returns:</span></dt>
    1173. 

  1173. <dd>automatically generated</dd>
    1174. 

  1174. </dl>
    1175. 

  1175. </li>
    1176. 

  1176. </ul>
    1177. 

  1177. <a name="trainEM-org.opencv.core.Mat-org.opencv.core.Mat-">
    1178. 

  1178. <!--   -->
    1179. 

  1179. </a>
    1180. 

  1180. <ul class="blockList">
    1181. 

  1181. <li class="blockList">
    1182. 

  1182. <h4>trainEM</h4>
    1183. 

  1183. <pre>public&nbsp;boolean&nbsp;trainEM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    1184. 

  1184.                        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods)</pre>
    1185. 

  1185. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    1186. 

  1186. 

  1187.      This variation starts with Expectation step. Initial values of the model parameters will be
    1188. 

  1188.      estimated by the k-means algorithm.
    1189. 

  1189. 

  1190.      Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
    1191. 

  1191.      responses (class labels or function values) as input. Instead, it computes the *Maximum
    1192. 

  1192.      Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
    1193. 

  1193.      parameters inside the structure: \(p_{i,k}\) in probs, \(a_k\) in means , \(S_k\) in
    1194. 

  1194.      covs[k], \(\pi_k\) in weights , and optionally computes the output "class label" for each
    1195. 

  1195.      sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most
    1196. 

  1196.      probable mixture component for each sample).
    1197. 

  1197. 

  1198.      The trained model can be used further for prediction, just like any other classifier. The
    1199. 

  1199.      trained model is similar to the NormalBayesClassifier.</div>
    1200. 

  1200. <dl>
    1201. 

  1201. <dt><span class="paramLabel">Parameters:</span></dt>
    1202. 

  1202. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    1203. 

  1203.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    1204. 

  1204.          it will be converted to the inner matrix of such type for the further computing.</dd>
    1205. 

  1205. <dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
    1206. 

  1206.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
    1207. 

  1207.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1208. 

  1208.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
    1209. 

  1209.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1210. 

  1210.          CV_64FC1 type.</dd>
    1211. 

  1211. <dt><span class="returnLabel">Returns:</span></dt>
    1212. 

  1212. <dd>automatically generated</dd>
    1213. 

  1213. </dl>
    1214. 

  1214. </li>
    1215. 

  1215. </ul>
    1216. 

  1216. <a name="trainEM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
    1217. 

  1217. <!--   -->
    1218. 

  1218. </a>
    1219. 

  1219. <ul class="blockList">
    1220. 

  1220. <li class="blockList">
    1221. 

  1221. <h4>trainEM</h4>
    1222. 

  1222. <pre>public&nbsp;boolean&nbsp;trainEM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    1223. 

  1223.                        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    1224. 

  1224.                        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels)</pre>
    1225. 

  1225. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    1226. 

  1226. 

  1227.      This variation starts with Expectation step. Initial values of the model parameters will be
    1228. 

  1228.      estimated by the k-means algorithm.
    1229. 

  1229. 

  1230.      Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
    1231. 

  1231.      responses (class labels or function values) as input. Instead, it computes the *Maximum
    1232. 

  1232.      Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
    1233. 

  1233.      parameters inside the structure: \(p_{i,k}\) in probs, \(a_k\) in means , \(S_k\) in
    1234. 

  1234.      covs[k], \(\pi_k\) in weights , and optionally computes the output "class label" for each
    1235. 

  1235.      sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most
    1236. 

  1236.      probable mixture component for each sample).
    1237. 

  1237. 

  1238.      The trained model can be used further for prediction, just like any other classifier. The
    1239. 

  1239.      trained model is similar to the NormalBayesClassifier.</div>
    1240. 

  1240. <dl>
    1241. 

  1241. <dt><span class="paramLabel">Parameters:</span></dt>
    1242. 

  1242. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    1243. 

  1243.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    1244. 

  1244.          it will be converted to the inner matrix of such type for the further computing.</dd>
    1245. 

  1245. <dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
    1246. 

  1246.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
    1247. 

  1247. <dd><code>labels</code> - The optional output "class label" for each sample:
    1248. 

  1248.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1249. 

  1249.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
    1250. 

  1250.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1251. 

  1251.          CV_64FC1 type.</dd>
    1252. 

  1252. <dt><span class="returnLabel">Returns:</span></dt>
    1253. 

  1253. <dd>automatically generated</dd>
    1254. 

  1254. </dl>
    1255. 

  1255. </li>
    1256. 

  1256. </ul>
    1257. 

  1257. <a name="trainEM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
    1258. 

  1258. <!--   -->
    1259. 

  1259. </a>
    1260. 

  1260. <ul class="blockList">
    1261. 

  1261. <li class="blockList">
    1262. 

  1262. <h4>trainEM</h4>
    1263. 

  1263. <pre>public&nbsp;boolean&nbsp;trainEM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    1264. 

  1264.                        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    1265. 

  1265.                        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels,
    1266. 

  1266.                        <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs)</pre>
    1267. 

  1267. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    1268. 

  1268. 

  1269.      This variation starts with Expectation step. Initial values of the model parameters will be
    1270. 

  1270.      estimated by the k-means algorithm.
    1271. 

  1271. 

  1272.      Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
    1273. 

  1273.      responses (class labels or function values) as input. Instead, it computes the *Maximum
    1274. 

  1274.      Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
    1275. 

  1275.      parameters inside the structure: \(p_{i,k}\) in probs, \(a_k\) in means , \(S_k\) in
    1276. 

  1276.      covs[k], \(\pi_k\) in weights , and optionally computes the output "class label" for each
    1277. 

  1277.      sample: \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most
    1278. 

  1278.      probable mixture component for each sample).
    1279. 

  1279. 

  1280.      The trained model can be used further for prediction, just like any other classifier. The
    1281. 

  1281.      trained model is similar to the NormalBayesClassifier.</div>
    1282. 

  1282. <dl>
    1283. 

  1283. <dt><span class="paramLabel">Parameters:</span></dt>
    1284. 

  1284. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    1285. 

  1285.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    1286. 

  1286.          it will be converted to the inner matrix of such type for the further computing.</dd>
    1287. 

  1287. <dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
    1288. 

  1288.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
    1289. 

  1289. <dd><code>labels</code> - The optional output "class label" for each sample:
    1290. 

  1290.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1291. 

  1291.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.</dd>
    1292. 

  1292. <dd><code>probs</code> - The optional output matrix that contains posterior probabilities of each Gaussian
    1293. 

  1293.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1294. 

  1294.          CV_64FC1 type.</dd>
    1295. 

  1295. <dt><span class="returnLabel">Returns:</span></dt>
    1296. 

  1296. <dd>automatically generated</dd>
    1297. 

  1297. </dl>
    1298. 

  1298. </li>
    1299. 

  1299. </ul>
    1300. 

  1300. <a name="trainM-org.opencv.core.Mat-org.opencv.core.Mat-">
    1301. 

  1301. <!--   -->
    1302. 

  1302. </a>
    1303. 

  1303. <ul class="blockList">
    1304. 

  1304. <li class="blockList">
    1305. 

  1305. <h4>trainM</h4>
    1306. 

  1306. <pre>public&nbsp;boolean&nbsp;trainM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    1307. 

  1307.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs0)</pre>
    1308. 

  1308. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    1309. 

  1309. 

  1310.      This variation starts with Maximization step. You need to provide initial probabilities
    1311. 

  1311.      \(p_{i,k}\) to use this option.</div>
    1312. 

  1312. <dl>
    1313. 

  1313. <dt><span class="paramLabel">Parameters:</span></dt>
    1314. 

  1314. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    1315. 

  1315.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    1316. 

  1316.          it will be converted to the inner matrix of such type for the further computing.</dd>
    1317. 

  1317. <dd><code>probs0</code> - the probabilities
    1318. 

  1318.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
    1319. 

  1319.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1320. 

  1320.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
    1321. 

  1321.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1322. 

  1322.          CV_64FC1 type.</dd>
    1323. 

  1323. <dt><span class="returnLabel">Returns:</span></dt>
    1324. 

  1324. <dd>automatically generated</dd>
    1325. 

  1325. </dl>
    1326. 

  1326. </li>
    1327. 

  1327. </ul>
    1328. 

  1328. <a name="trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
    1329. 

  1329. <!--   -->
    1330. 

  1330. </a>
    1331. 

  1331. <ul class="blockList">
    1332. 

  1332. <li class="blockList">
    1333. 

  1333. <h4>trainM</h4>
    1334. 

  1334. <pre>public&nbsp;boolean&nbsp;trainM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    1335. 

  1335.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs0,
    1336. 

  1336.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods)</pre>
    1337. 

  1337. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    1338. 

  1338. 

  1339.      This variation starts with Maximization step. You need to provide initial probabilities
    1340. 

  1340.      \(p_{i,k}\) to use this option.</div>
    1341. 

  1341. <dl>
    1342. 

  1342. <dt><span class="paramLabel">Parameters:</span></dt>
    1343. 

  1343. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    1344. 

  1344.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    1345. 

  1345.          it will be converted to the inner matrix of such type for the further computing.</dd>
    1346. 

  1346. <dd><code>probs0</code> - the probabilities</dd>
    1347. 

  1347. <dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
    1348. 

  1348.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.
    1349. 

  1349.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1350. 

  1350.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
    1351. 

  1351.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1352. 

  1352.          CV_64FC1 type.</dd>
    1353. 

  1353. <dt><span class="returnLabel">Returns:</span></dt>
    1354. 

  1354. <dd>automatically generated</dd>
    1355. 

  1355. </dl>
    1356. 

  1356. </li>
    1357. 

  1357. </ul>
    1358. 

  1358. <a name="trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
    1359. 

  1359. <!--   -->
    1360. 

  1360. </a>
    1361. 

  1361. <ul class="blockList">
    1362. 

  1362. <li class="blockList">
    1363. 

  1363. <h4>trainM</h4>
    1364. 

  1364. <pre>public&nbsp;boolean&nbsp;trainM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    1365. 

  1365.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs0,
    1366. 

  1366.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    1367. 

  1367.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels)</pre>
    1368. 

  1368. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    1369. 

  1369. 

  1370.      This variation starts with Maximization step. You need to provide initial probabilities
    1371. 

  1371.      \(p_{i,k}\) to use this option.</div>
    1372. 

  1372. <dl>
    1373. 

  1373. <dt><span class="paramLabel">Parameters:</span></dt>
    1374. 

  1374. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    1375. 

  1375.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    1376. 

  1376.          it will be converted to the inner matrix of such type for the further computing.</dd>
    1377. 

  1377. <dd><code>probs0</code> - the probabilities</dd>
    1378. 

  1378. <dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
    1379. 

  1379.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
    1380. 

  1380. <dd><code>labels</code> - The optional output "class label" for each sample:
    1381. 

  1381.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1382. 

  1382.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.
    1383. 

  1383.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1384. 

  1384.          CV_64FC1 type.</dd>
    1385. 

  1385. <dt><span class="returnLabel">Returns:</span></dt>
    1386. 

  1386. <dd>automatically generated</dd>
    1387. 

  1387. </dl>
    1388. 

  1388. </li>
    1389. 

  1389. </ul>
    1390. 

  1390. <a name="trainM-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.Mat-">
    1391. 

  1391. <!--   -->
    1392. 

  1392. </a>
    1393. 

  1393. <ul class="blockListLast">
    1394. 

  1394. <li class="blockList">
    1395. 

  1395. <h4>trainM</h4>
    1396. 

  1396. <pre>public&nbsp;boolean&nbsp;trainM(<a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;samples,
    1397. 

  1397.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs0,
    1398. 

  1398.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;logLikelihoods,
    1399. 

  1399.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;labels,
    1400. 

  1400.                       <a href="../../../org/opencv/core/Mat.html" title="class in org.opencv.core">Mat</a>&nbsp;probs)</pre>
    1401. 

  1401. <div class="block">Estimate the Gaussian mixture parameters from a samples set.
    1402. 

  1402. 

  1403.      This variation starts with Maximization step. You need to provide initial probabilities
    1404. 

  1404.      \(p_{i,k}\) to use this option.</div>
    1405. 

  1405. <dl>
    1406. 

  1406. <dt><span class="paramLabel">Parameters:</span></dt>
    1407. 

  1407. <dd><code>samples</code> - Samples from which the Gaussian mixture model will be estimated. It should be a
    1408. 

  1408.          one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
    1409. 

  1409.          it will be converted to the inner matrix of such type for the further computing.</dd>
    1410. 

  1410. <dd><code>probs0</code> - the probabilities</dd>
    1411. 

  1411. <dd><code>logLikelihoods</code> - The optional output matrix that contains a likelihood logarithm value for
    1412. 

  1412.          each sample. It has \(nsamples \times 1\) size and CV_64FC1 type.</dd>
    1413. 

  1413. <dd><code>labels</code> - The optional output "class label" for each sample:
    1414. 

  1414.          \(\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\) (indices of the most probable
    1415. 

  1415.          mixture component for each sample). It has \(nsamples \times 1\) size and CV_32SC1 type.</dd>
    1416. 

  1416. <dd><code>probs</code> - The optional output matrix that contains posterior probabilities of each Gaussian
    1417. 

  1417.          mixture component given the each sample. It has \(nsamples \times nclusters\) size and
    1418. 

  1418.          CV_64FC1 type.</dd>
    1419. 

  1419. <dt><span class="returnLabel">Returns:</span></dt>
    1420. 

  1420. <dd>automatically generated</dd>
    1421. 

  1421. </dl>
    1422. 

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    1501. 

  1501. <p class="legalCopy"><small>Generated on 2023-06-28 12:47:21 / OpenCV 4.8.0</small></p>
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