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<tr class="even"><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html#a8cc497729a854be807655dd1b882ca39">bic</a>(self, ndarray X)</td><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html">tpgmm.TPGMM</a></td><td class="entry"></td></tr>
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<tr><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html#a1e1c7219424eb82570dd6a515abb6dc4">config</a>(self)</td><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html">tpgmm.TPGMM</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html#a4f85685cd9bb342c7dbd59e24c2b46cc">covariances_</a></td><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html">tpgmm.TPGMM</a></td><td class="entry"></td></tr>
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<tr><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html#a02e915f94b3de1ac6d31c302da7b57ba">davies_bouldin_score</a>(self, ndarray X)</td><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html">tpgmm.TPGMM</a></td><td class="entry"></td></tr>
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<tr><td class="entry"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#ae5496eef565af191522a104122d70d75">davies_bouldin_score</a>(self, ndarray X)</td><td class="entry"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html">tpgmm.utils.learning_modules.ClassificationModule</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html#af35fed3e9dc0208f24a89f00f6122417">fit</a>(self, ndarray X)</td><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html">tpgmm.TPGMM</a></td><td class="entry"></td></tr>
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<tr><td class="entry"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1LearningModule.html#aa3908a58e8015050827286ea9e06b4c6">tpgmm::utils::learning_modules::ClassificationModule.fit</a>(self, *args, **kwargs)</td><td class="entry"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1LearningModule.html">tpgmm.utils.learning_modules.LearningModule</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1LearningModule.html#aee9242c3c5e28b607d0dd03a3e5adf72">fit_predict</a>(self, ndarray X)</td><td class="entry"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1LearningModule.html">tpgmm.utils.learning_modules.LearningModule</a></td><td class="entry"></td></tr>
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<tr><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html#a8463d563adb49f0a7f426cbadfaac173">gauss_cdf</a>(self, ndarray X)</td><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html">tpgmm.TPGMM</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html#a94cecc204f6b0776b275b161194491d4">inertia</a>(self, ndarray X)</td><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html">tpgmm.TPGMM</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#ac7b1756be861aec8386daca535466713">inertia</a>(self, ndarray X)</td><td class="entry"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html">tpgmm.utils.learning_modules.ClassificationModule</a></td><td class="entry"></td></tr>
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<tr><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html#a54248b0297f941859f0cc7903ea642c5">means_</a></td><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html">tpgmm.TPGMM</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html#a59e8b02adf78abeab4341a5a35077c6a">predict</a>(self, ndarray X)</td><td class="entry"><a class="el" href="classtpgmm_1_1TPGMM.html">tpgmm.TPGMM</a></td><td class="entry"></td></tr>
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<tr><td class="entry"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1LearningModule.html#a4d727d4da96242cdc30de3062996ec32">tpgmm::utils::learning_modules::ClassificationModule.predict</a>(self, *args, **kwargs)</td><td class="entry"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1LearningModule.html">tpgmm.utils.learning_modules.LearningModule</a></td><td class="entry"></td></tr>

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<tr class="memitem:ab67d18e9b6c678d328a0ec96cc38091e"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtpgmm_1_1TPGMM.html#ab67d18e9b6c678d328a0ec96cc38091e">silhouette_score</a> (self, ndarray X)</td></tr>
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<tr class="memdesc:ab67d18e9b6c678d328a0ec96cc38091e"><td class="mdescLeft">&#160;</td><td class="mdescRight">calculated the silhouette score of the model over the given metric and given data x TODO(Marco Todescato): please review this function if the merge for the silhouette score is correct <a href="classtpgmm_1_1TPGMM.html#ab67d18e9b6c678d328a0ec96cc38091e">More...</a><br /></td></tr>
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<tr class="memdesc:a94cecc204f6b0776b275b161194491d4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sum of squared distances of samples to their closest cluster center. <a href="classtpgmm_1_1TPGMM.html#a94cecc204f6b0776b275b161194491d4">More...</a><br /></td></tr>
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<tr class="separator:a94cecc204f6b0776b275b161194491d4"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<tr class="memitem:a6f2a0ec180d5c5887725c22825299ee8"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtpgmm_1_1TPGMM.html#a6f2a0ec180d5c5887725c22825299ee8">score</a> (self, ndarray X)</td></tr>
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<tr class="memdesc:a6f2a0ec180d5c5887725c22825299ee8"><td class="mdescLeft">&#160;</td><td class="mdescRight">calculate log likelihood score given data <a href="classtpgmm_1_1TPGMM.html#a6f2a0ec180d5c5887725c22825299ee8">More...</a><br /></td></tr>
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<tr class="memdesc:a8cc497729a854be807655dd1b882ca39"><td class="mdescLeft">&#160;</td><td class="mdescRight">calculates the bayesian information criterion as in <a href="classtpgmm_1_1TPGMM.html#a8cc497729a854be807655dd1b882ca39">More...</a><br /></td></tr>
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<tr class="memitem:a02e915f94b3de1ac6d31c302da7b57ba"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtpgmm_1_1TPGMM.html#a02e915f94b3de1ac6d31c302da7b57ba">davies_bouldin_score</a> (self, ndarray X)</td></tr>
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<tr class="memdesc:a02e915f94b3de1ac6d31c302da7b57ba"><td class="mdescLeft">&#160;</td><td class="mdescRight">calculates the davies bouldin score for each frame and averages them <a href="classtpgmm_1_1TPGMM.html#a02e915f94b3de1ac6d31c302da7b57ba">More...</a><br /></td></tr>
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<tr class="memitem:a8463d563adb49f0a7f426cbadfaac173"><td class="memItemLeft" align="right" valign="top">ndarray&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtpgmm_1_1TPGMM.html#a8463d563adb49f0a7f426cbadfaac173">gauss_cdf</a> (self, ndarray X)</td></tr>
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<tr class="memdesc:a8463d563adb49f0a7f426cbadfaac173"><td class="mdescLeft">&#160;</td><td class="mdescRight">calculate the gaussian probability for a given data set. <a href="classtpgmm_1_1TPGMM.html#a8463d563adb49f0a7f426cbadfaac173">More...</a><br /></td></tr>
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def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#abff9dc426bd79aa8a5f663ef117fb903">predict_proba</a> (self, *args, **kwargs)</td></tr>
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<tr class="memdesc:abff9dc426bd79aa8a5f663ef117fb903 inherit pub_methods_classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule"><td class="mdescLeft">&#160;</td><td class="mdescRight">Predict class probabilities for the input data. <br /></td></tr>
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<tr class="memitem:ac7b1756be861aec8386daca535466713 inherit pub_methods_classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#ac7b1756be861aec8386daca535466713">inertia</a> (self, ndarray X)</td></tr>
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<tr class="memdesc:ac7b1756be861aec8386daca535466713 inherit pub_methods_classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule"><td class="mdescLeft">&#160;</td><td class="mdescRight">Calculate the sum of squared distances of samples to their closest cluster center. <a href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#ac7b1756be861aec8386daca535466713">More...</a><br /></td></tr>
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<tr class="memitem:ae5496eef565af191522a104122d70d75 inherit pub_methods_classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule"><td class="memItemLeft" align="right" valign="top">float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#ae5496eef565af191522a104122d70d75">davies_bouldin_score</a> (self, ndarray X)</td></tr>
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<tr class="memdesc:ae5496eef565af191522a104122d70d75 inherit pub_methods_classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule"><td class="mdescLeft">&#160;</td><td class="mdescRight">calculates davies_bouldin_score on given data <a href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#ae5496eef565af191522a104122d70d75">More...</a><br /></td></tr>
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<tr class="memitem:af208639127f0ec879387a0eee9cecbb0 inherit pub_methods_classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule"><td class="memItemLeft" align="right" valign="top">def&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#af208639127f0ec879387a0eee9cecbb0">aic</a> (self, ndarray X)</td></tr>
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<tr class="memdesc:af208639127f0ec879387a0eee9cecbb0 inherit pub_methods_classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule"><td class="mdescLeft">&#160;</td><td class="mdescRight">calculates the Akaike information criterion as in <a href="https://scikit-learn.org/stable/modules/linear_model.html#aic-bic">https://scikit-learn.org/stable/modules/linear_model.html#aic-bic</a> <a href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#af208639127f0ec879387a0eee9cecbb0">More...</a><br /></td></tr>
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<p>Reimplemented from <a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#a6338f01b3f0cf2f3b69a02ea27045e53">tpgmm.utils.learning_modules.ClassificationModule</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#a02e915f94b3de1ac6d31c302da7b57ba">&#9670;&nbsp;</a></span>davies_bouldin_score()</h2>
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<p>calculates the davies bouldin score for each frame and averages them </p>
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TODO(Marco Todescator): is this score correct?</h1>
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<p>Reimplemented from <a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#ae5496eef565af191522a104122d70d75">tpgmm.utils.learning_modules.ClassificationModule</a>.</p>
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<h2 class="memtitle"><span class="permalink"><a href="#a94cecc204f6b0776b275b161194491d4">&#9670;&nbsp;</a></span>inertia()</h2>
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<p>Sum of squared distances of samples to their closest cluster center. </p>
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<p>TODO(Marco Todescato): please review if this is correct</p>
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float average inertia score over all frames </dd></dl>
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<p>Reimplemented from <a class="el" href="classtpgmm_1_1utils_1_1learning__modules_1_1ClassificationModule.html#ac7b1756be861aec8386daca535466713">tpgmm.utils.learning_modules.ClassificationModule</a>.</p>
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