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<p>SQL functions for support vector machines.
<a href="#details">More...</a></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:acc2d778a8eb48ab775ff9c1dff4a3141"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#acc2d778a8eb48ab775ff9c1dff4a3141">svm_dot</a> (float8[] x, float8[] y)</td></tr>
<tr class="memdesc:acc2d778a8eb48ab775ff9c1dff4a3141"><td class="mdescLeft">&#160;</td><td class="mdescRight">Dot product kernel function. <a href="#acc2d778a8eb48ab775ff9c1dff4a3141">More...</a><br/></td></tr>
<tr class="separator:acc2d778a8eb48ab775ff9c1dff4a3141"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1ac76fdf9623e0a4db47665f2a80be90"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a1ac76fdf9623e0a4db47665f2a80be90">svm_polynomial</a> (float8[] x, float8[] y, float8 degree)</td></tr>
<tr class="memdesc:a1ac76fdf9623e0a4db47665f2a80be90"><td class="mdescLeft">&#160;</td><td class="mdescRight">Polynomial kernel function. <a href="#a1ac76fdf9623e0a4db47665f2a80be90">More...</a><br/></td></tr>
<tr class="separator:a1ac76fdf9623e0a4db47665f2a80be90"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9f2a96e1a241ecc66386a78b110777d3"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a9f2a96e1a241ecc66386a78b110777d3">svm_gaussian</a> (float8[] x, float8[] y, float8 gamma)</td></tr>
<tr class="memdesc:a9f2a96e1a241ecc66386a78b110777d3"><td class="mdescLeft">&#160;</td><td class="mdescRight">Gaussian kernel function. <a href="#a9f2a96e1a241ecc66386a78b110777d3">More...</a><br/></td></tr>
<tr class="separator:a9f2a96e1a241ecc66386a78b110777d3"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2e577a561d057a897aad0e4cd2377195"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a2e577a561d057a897aad0e4cd2377195">svm_predict_sub</a> (int, int, float8[], float8[], float8[], text)</td></tr>
<tr class="separator:a2e577a561d057a897aad0e4cd2377195"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aea2d42c49ed8e5147d376dfbb3715ddd"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#aea2d42c49ed8e5147d376dfbb3715ddd">svm_predict</a> (svm_model_rec svs, float8[] ind, text kernel)</td></tr>
<tr class="separator:aea2d42c49ed8e5147d376dfbb3715ddd"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac7a57455085b8f39c1bfc13c80891b6d"><td class="memItemLeft" align="right" valign="top">svm_model_rec&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#ac7a57455085b8f39c1bfc13c80891b6d">svm_reg_update</a> (svm_model_rec svs, float8[] ind, float8 label, text kernel, float8 eta, float8 nu, float8 slambda)</td></tr>
<tr class="separator:ac7a57455085b8f39c1bfc13c80891b6d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a375157525d296bf8da17dde9746be3cf"><td class="memItemLeft" align="right" valign="top">aggregate svm_model_rec&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a375157525d296bf8da17dde9746be3cf">svm_reg_agg</a> (float8[], float8, text, float8, float8, float8)</td></tr>
<tr class="separator:a375157525d296bf8da17dde9746be3cf"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7dc0f6c84880ae8f021e8c74c8ba38b0"><td class="memItemLeft" align="right" valign="top">svm_model_rec&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a7dc0f6c84880ae8f021e8c74c8ba38b0">svm_cls_update</a> (svm_model_rec svs, float8[] ind, float8 label, text kernel, float8 eta, float8 nu)</td></tr>
<tr class="separator:a7dc0f6c84880ae8f021e8c74c8ba38b0"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9dad49dc805e93dc5b28a6e904b50a9d"><td class="memItemLeft" align="right" valign="top">aggregate svm_model_rec&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a9dad49dc805e93dc5b28a6e904b50a9d">svm_cls_agg</a> (float8[], float8, text, float8, float8)</td></tr>
<tr class="separator:a9dad49dc805e93dc5b28a6e904b50a9d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aeb0acf60b180f961873dd430cd855ff7"><td class="memItemLeft" align="right" valign="top">svm_model_rec&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#aeb0acf60b180f961873dd430cd855ff7">svm_nd_update</a> (svm_model_rec svs, float8[] ind, text kernel, float8 eta, float8 nu)</td></tr>
<tr class="separator:aeb0acf60b180f961873dd430cd855ff7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8121cdffa842a3b902c9c0fd39a1ed25"><td class="memItemLeft" align="right" valign="top">aggregate svm_model_rec&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a8121cdffa842a3b902c9c0fd39a1ed25">svm_nd_agg</a> (float8[], text, float8, float8)</td></tr>
<tr class="separator:a8121cdffa842a3b902c9c0fd39a1ed25"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3983f0ef562835388506026fde4c3d59"><td class="memItemLeft" align="right" valign="top">lsvm_sgd_model_rec&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a3983f0ef562835388506026fde4c3d59">lsvm_sgd_update</a> (lsvm_sgd_model_rec svs, float8[] ind, float8 label, float8 eta, float8 reg)</td></tr>
<tr class="separator:a3983f0ef562835388506026fde4c3d59"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4679e4fed0151d2c456adaae29de9061"><td class="memItemLeft" align="right" valign="top">aggregate lsvm_sgd_model_rec&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a4679e4fed0151d2c456adaae29de9061">lsvm_sgd_agg</a> (float8[], float8, float8, float8)</td></tr>
<tr class="separator:a4679e4fed0151d2c456adaae29de9061"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad2cd899daaa2036b475f71c28cc031ae"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#ad2cd899daaa2036b475f71c28cc031ae">svm_store_model</a> (text model_temp_table, text model_name, text model_table)</td></tr>
<tr class="separator:ad2cd899daaa2036b475f71c28cc031ae"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab54d33f13c0e00faa358e3e3f17c10fb"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#ab54d33f13c0e00faa358e3e3f17c10fb">svm_drop_model</a> (text model_table)</td></tr>
<tr class="memdesc:ab54d33f13c0e00faa358e3e3f17c10fb"><td class="mdescLeft">&#160;</td><td class="mdescRight">Drops all tables pertaining to a model. <a href="#ab54d33f13c0e00faa358e3e3f17c10fb">More...</a><br/></td></tr>
<tr class="separator:ab54d33f13c0e00faa358e3e3f17c10fb"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9916305653d464b23ef0fbd78867a654"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a9916305653d464b23ef0fbd78867a654">svm_predict</a> (text model_table, float8[] ind)</td></tr>
<tr class="memdesc:a9916305653d464b23ef0fbd78867a654"><td class="mdescLeft">&#160;</td><td class="mdescRight">Evaluates a support-vector model on a given data point. <a href="#a9916305653d464b23ef0fbd78867a654">More...</a><br/></td></tr>
<tr class="separator:a9916305653d464b23ef0fbd78867a654"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a883ff4ca340d19a11204b461dd388276"><td class="memItemLeft" align="right" valign="top">set&lt; svm_model_pr &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a883ff4ca340d19a11204b461dd388276">svm_predict_combo</a> (text model_table, float8[] ind)</td></tr>
<tr class="memdesc:a883ff4ca340d19a11204b461dd388276"><td class="mdescLeft">&#160;</td><td class="mdescRight">Evaluates multiple support-vector models on a data point. <a href="#a883ff4ca340d19a11204b461dd388276">More...</a><br/></td></tr>
<tr class="separator:a883ff4ca340d19a11204b461dd388276"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acaf1f4aa3eec5710de5c03e368a4b106"><td class="memItemLeft" align="right" valign="top">set&lt; svm_reg_result &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#acaf1f4aa3eec5710de5c03e368a4b106">svm_regression</a> (text input_table, text model_table, bool parallel, text kernel_func)</td></tr>
<tr class="memdesc:acaf1f4aa3eec5710de5c03e368a4b106"><td class="mdescLeft">&#160;</td><td class="mdescRight">This is the support vector regression function. <a href="#acaf1f4aa3eec5710de5c03e368a4b106">More...</a><br/></td></tr>
<tr class="separator:acaf1f4aa3eec5710de5c03e368a4b106"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac5cb9c20d6620b155ac872576a056f2a"><td class="memItemLeft" align="right" valign="top">set&lt; svm_reg_result &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#ac5cb9c20d6620b155ac872576a056f2a">svm_regression</a> (text input_table, text model_table, bool parallel, text kernel_func, bool verbose, float8 eta, float8 nu, float8 slambda)</td></tr>
<tr class="memdesc:ac5cb9c20d6620b155ac872576a056f2a"><td class="mdescLeft">&#160;</td><td class="mdescRight">This is the support vector regression function. <a href="#ac5cb9c20d6620b155ac872576a056f2a">More...</a><br/></td></tr>
<tr class="separator:ac5cb9c20d6620b155ac872576a056f2a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a20a2c8a905be6e922885e23e9dab0a4c"><td class="memItemLeft" align="right" valign="top">set&lt; svm_cls_result &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a20a2c8a905be6e922885e23e9dab0a4c">svm_classification</a> (text input_table, text model_table, bool parallel, text kernel_func)</td></tr>
<tr class="memdesc:a20a2c8a905be6e922885e23e9dab0a4c"><td class="mdescLeft">&#160;</td><td class="mdescRight">This is the support vector classification function. <a href="#a20a2c8a905be6e922885e23e9dab0a4c">More...</a><br/></td></tr>
<tr class="separator:a20a2c8a905be6e922885e23e9dab0a4c"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad90b6bf3b807f22d37b0e2b1893262f0"><td class="memItemLeft" align="right" valign="top">set&lt; svm_cls_result &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#ad90b6bf3b807f22d37b0e2b1893262f0">svm_classification</a> (text input_table, text model_table, bool parallel, text kernel_func, bool verbose, float8 eta, float8 nu)</td></tr>
<tr class="memdesc:ad90b6bf3b807f22d37b0e2b1893262f0"><td class="mdescLeft">&#160;</td><td class="mdescRight">This is the support vector classification function. <a href="#ad90b6bf3b807f22d37b0e2b1893262f0">More...</a><br/></td></tr>
<tr class="separator:ad90b6bf3b807f22d37b0e2b1893262f0"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3448ea62ab57fe4cf177f5fa6b5db7d3"><td class="memItemLeft" align="right" valign="top">set&lt; svm_nd_result &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a3448ea62ab57fe4cf177f5fa6b5db7d3">svm_novelty_detection</a> (text input_table, text model_table, bool parallel, text kernel_func)</td></tr>
<tr class="memdesc:a3448ea62ab57fe4cf177f5fa6b5db7d3"><td class="mdescLeft">&#160;</td><td class="mdescRight">This is the support vector novelty detection function. <a href="#a3448ea62ab57fe4cf177f5fa6b5db7d3">More...</a><br/></td></tr>
<tr class="separator:a3448ea62ab57fe4cf177f5fa6b5db7d3"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a5bae5335b51e448cd7fb9cb7a54b0bfa"><td class="memItemLeft" align="right" valign="top">set&lt; svm_nd_result &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a5bae5335b51e448cd7fb9cb7a54b0bfa">svm_novelty_detection</a> (text input_table, text model_table, bool parallel, text kernel_func, bool verbose, float8 eta, float8 nu)</td></tr>
<tr class="memdesc:a5bae5335b51e448cd7fb9cb7a54b0bfa"><td class="mdescLeft">&#160;</td><td class="mdescRight">This is the support vector novelty detection function. <a href="#a5bae5335b51e448cd7fb9cb7a54b0bfa">More...</a><br/></td></tr>
<tr class="separator:a5bae5335b51e448cd7fb9cb7a54b0bfa"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a91ac71354e9dec74e25339bf168c2e5b"><td class="memItemLeft" align="right" valign="top">text&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a91ac71354e9dec74e25339bf168c2e5b">svm_predict_batch</a> (text input_table, text data_col, text id_col, text model_table, text output_table, bool parallel)</td></tr>
<tr class="memdesc:a91ac71354e9dec74e25339bf168c2e5b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Scores the data points stored in a table using a learned support-vector model. <a href="#a91ac71354e9dec74e25339bf168c2e5b">More...</a><br/></td></tr>
<tr class="separator:a91ac71354e9dec74e25339bf168c2e5b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1dc40f2607434e3df8f6fa583676a2a4"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a1dc40f2607434e3df8f6fa583676a2a4">__svm_random_ind</a> (int d)</td></tr>
<tr class="separator:a1dc40f2607434e3df8f6fa583676a2a4"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aad5312d437c151842cae525380882755"><td class="memItemLeft" align="right" valign="top">float8[]&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#aad5312d437c151842cae525380882755">__svm_random_ind2</a> (int d)</td></tr>
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<tr class="memitem:adc9fe3f9b4a04dff13004edbaab8240f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#adc9fe3f9b4a04dff13004edbaab8240f">svm_generate_reg_data</a> (text output_table, int num, int dim)</td></tr>
<tr class="separator:adc9fe3f9b4a04dff13004edbaab8240f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae861516ef0c8ad4926061297ff2b2f31"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#ae861516ef0c8ad4926061297ff2b2f31">__svm_target_reg_func</a> (float8[] ind)</td></tr>
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<tr class="memitem:a1cf6d0632772caf6643212a4698d2507"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a1cf6d0632772caf6643212a4698d2507">svm_generate_cls_data</a> (text output_table, int num, int dim)</td></tr>
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<tr class="memitem:ae328001913d6a57598df43e93f18898a"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#ae328001913d6a57598df43e93f18898a">__svm_target_cl_func</a> (float8[] ind)</td></tr>
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<tr class="memitem:a8be25cfa6134cc12bbc6d31c04712c23"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a8be25cfa6134cc12bbc6d31c04712c23">svm_generate_nd_data</a> (text output_table, int num, int dim)</td></tr>
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<tr class="memitem:a78bb07d8f4aee80c3bbc8e8176e512be"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a78bb07d8f4aee80c3bbc8e8176e512be">svm_data_normalization</a> (text input_table)</td></tr>
<tr class="memdesc:a78bb07d8f4aee80c3bbc8e8176e512be"><td class="mdescLeft">&#160;</td><td class="mdescRight">Normalizes the data stored in a table, and save the normalized data in a new table. <a href="#a78bb07d8f4aee80c3bbc8e8176e512be">More...</a><br/></td></tr>
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<tr class="memitem:a75d126981ae4bf2e6641627501f0a2a5"><td class="memItemLeft" align="right" valign="top">set&lt; lsvm_sgd_result &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a75d126981ae4bf2e6641627501f0a2a5">lsvm_classification</a> (text input_table, text model_table, bool parallel)</td></tr>
<tr class="memdesc:a75d126981ae4bf2e6641627501f0a2a5"><td class="mdescLeft">&#160;</td><td class="mdescRight">This is the linear support vector classification function. <a href="#a75d126981ae4bf2e6641627501f0a2a5">More...</a><br/></td></tr>
<tr class="separator:a75d126981ae4bf2e6641627501f0a2a5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a50896def00d0e0950bec3d95b387e6b9"><td class="memItemLeft" align="right" valign="top">set&lt; lsvm_sgd_result &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a50896def00d0e0950bec3d95b387e6b9">lsvm_classification</a> (text input_table, text model_table, bool parallel, bool verbose, float8 eta, float8 reg)</td></tr>
<tr class="memdesc:a50896def00d0e0950bec3d95b387e6b9"><td class="mdescLeft">&#160;</td><td class="mdescRight">This is the linear support vector classification function. <a href="#a50896def00d0e0950bec3d95b387e6b9">More...</a><br/></td></tr>
<tr class="separator:a50896def00d0e0950bec3d95b387e6b9"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1c0a002f50250133c0ef1d3c43c6d338"><td class="memItemLeft" align="right" valign="top">text&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a1c0a002f50250133c0ef1d3c43c6d338">lsvm_predict_batch</a> (text input_table, text data_col, text id_col, text model_table, text output_table, bool parallel)</td></tr>
<tr class="memdesc:a1c0a002f50250133c0ef1d3c43c6d338"><td class="mdescLeft">&#160;</td><td class="mdescRight">Scores the data points stored in a table using a learned linear support-vector model. <a href="#a1c0a002f50250133c0ef1d3c43c6d338">More...</a><br/></td></tr>
<tr class="separator:a1c0a002f50250133c0ef1d3c43c6d338"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a5fe084c8364c0657097410458f8ea1e9"><td class="memItemLeft" align="right" valign="top">float8&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a5fe084c8364c0657097410458f8ea1e9">lsvm_predict</a> (text model_table, float8[] ind)</td></tr>
<tr class="memdesc:a5fe084c8364c0657097410458f8ea1e9"><td class="mdescLeft">&#160;</td><td class="mdescRight">Evaluates a linear support-vector model on a given data point. <a href="#a5fe084c8364c0657097410458f8ea1e9">More...</a><br/></td></tr>
<tr class="separator:a5fe084c8364c0657097410458f8ea1e9"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0ae9c50ca072757ff6493a8bf26dbc9c"><td class="memItemLeft" align="right" valign="top">set&lt; svm_model_pr &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="online__sv_8sql__in.html#a0ae9c50ca072757ff6493a8bf26dbc9c">lsvm_predict_combo</a> (text model_table, float8[] ind)</td></tr>
<tr class="memdesc:a0ae9c50ca072757ff6493a8bf26dbc9c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Evaluates multiple linear support-vector models on a data point. <a href="#a0ae9c50ca072757ff6493a8bf26dbc9c">More...</a><br/></td></tr>
<tr class="separator:a0ae9c50ca072757ff6493a8bf26dbc9c"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><dl class="section see"><dt>See Also</dt><dd>For an introduction to Support vector machines (SVMs) and related kernel methods, see the module description <a class="el" href="group__grp__kernmach.html">Support Vector Machines</a>. </dd></dl>
</div><h2 class="groupheader">Function Documentation</h2>
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<td class="memname">float8 [] __svm_random_ind2 </td>
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<td class="paramname"><em>d</em>)</td><td></td>
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<td class="memname">float8 __svm_target_cl_func </td>
<td>(</td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>ind</em>)</td><td></td>
<td></td>
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<td class="memname">float8 __svm_target_reg_func </td>
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<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>ind</em>)</td><td></td>
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<td class="memname">set&lt;lsvm_sgd_result&gt; lsvm_classification </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>input_table</em>, </td>
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<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>, </td>
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<td class="paramtype">bool&#160;</td>
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<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">input_table</td><td>The name of the table/view with the training data </td></tr>
<tr><td class="paramname">model_table</td><td>The name of the table under which we want to store the learned model </td></tr>
<tr><td class="paramname">parallel</td><td>A flag indicating whether the system should learn multiple models in parallel </td></tr>
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</dd>
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<dl class="section return"><dt>Returns</dt><dd>A summary of the learning process </dd></dl>
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<td class="memname">set&lt;lsvm_sgd_result&gt; lsvm_classification </td>
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<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>reg</em>&#160;</td>
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<td>)</td>
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<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">input_table</td><td>The name of the table/view with the training data </td></tr>
<tr><td class="paramname">model_table</td><td>The name of the table under which we want to store the learned model </td></tr>
<tr><td class="paramname">parallel</td><td>A flag indicating whether the system should learn multiple models in parallel </td></tr>
<tr><td class="paramname">verbose</td><td>Verbosity of reporting </td></tr>
<tr><td class="paramname">eta</td><td>Initial learning rate in (0,1] </td></tr>
<tr><td class="paramname">reg</td><td>Regularization parameter, often chosen by cross-validation </td></tr>
</table>
</dd>
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<dl class="section return"><dt>Returns</dt><dd>A summary of the learning process </dd></dl>
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<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
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<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>ind</em>&#160;</td>
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<td>)</td>
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<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">model_table</td><td>The table storing the learned model \( f \) to be used </td></tr>
<tr><td class="paramname">ind</td><td>The data point \( \boldsymbol x \) </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>This function returns \( f(\boldsymbol x) \) </dd></dl>
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<td class="memname">text lsvm_predict_batch </td>
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<td class="paramtype">text&#160;</td>
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<td class="paramname"><em>data_col</em>, </td>
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<td class="paramtype">text&#160;</td>
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<td>)</td>
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<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">input_table</td><td>Name of table/view containing the data points to be scored </td></tr>
<tr><td class="paramname">data_col</td><td>Name of column in input_table containing the data points </td></tr>
<tr><td class="paramname">id_col</td><td>Name of column in input_table containing the integer identifier of data points </td></tr>
<tr><td class="paramname">model_table</td><td>Name of table where the learned model to be used is stored </td></tr>
<tr><td class="paramname">output_table</td><td>Name of table to store the results </td></tr>
<tr><td class="paramname">parallel</td><td>A flag indicating whether the model to be used was learned in parallel </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>Textual summary of the algorithm run </dd></dl>
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<td class="memname">set&lt;svm_model_pr&gt; lsvm_predict_combo </td>
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<td>)</td>
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<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">model_table</td><td>The table storing the learned models to be used. </td></tr>
<tr><td class="paramname">ind</td><td>The data point \( \boldsymbol x \) </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>This function returns a table, a row for each model. Moreover, the last row contains the average value, over all models.</dd></dl>
<p>The different models are assumed to be named <code><em>model_table</em>0</code>, <code><em>model_table</em>1</code>, .... </p>
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<td class="memname">aggregate lsvm_sgd_model_rec lsvm_sgd_agg </td>
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<td class="paramname">, </td>
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<td class="paramtype">float8&#160;</td>
<td class="paramname">, </td>
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<td class="paramtype">float8&#160;</td>
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<td class="memname">lsvm_sgd_model_rec lsvm_sgd_update </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>ind</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>label</em>, </td>
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<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>eta</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>reg</em>&#160;</td>
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<td class="memname">set&lt;svm_cls_result&gt; svm_classification </td>
<td>(</td>
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<td class="paramname"><em>input_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>parallel</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>kernel_func</em>&#160;</td>
</tr>
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<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">input_table</td><td>The name of the table/view with the training data </td></tr>
<tr><td class="paramname">model_table</td><td>The name of the table under which we want to store the learned model </td></tr>
<tr><td class="paramname">parallel</td><td>A flag indicating whether the system should learn multiple models in parallel </td></tr>
<tr><td class="paramname">kernel_func</td><td>Kernel function </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>A summary of the learning process </dd></dl>
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<td class="memname">set&lt;svm_cls_result&gt; svm_classification </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>input_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>parallel</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>kernel_func</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>verbose</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>eta</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>nu</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">input_table</td><td>The name of the table/view with the training data </td></tr>
<tr><td class="paramname">model_table</td><td>The name of the table under which we want to store the learned model </td></tr>
<tr><td class="paramname">parallel</td><td>A flag indicating whether the system should learn multiple models in parallel </td></tr>
<tr><td class="paramname">kernel_func</td><td>Kernel function </td></tr>
<tr><td class="paramname">verbose</td><td>Verbosity of reporting </td></tr>
<tr><td class="paramname">eta</td><td>Learning rate in (0,1] </td></tr>
<tr><td class="paramname">nu</td><td>Compression parameter in (0,1] associated with the fraction of training data that will become support vectors </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>A summary of the learning process </dd></dl>
</div>
</div>
<a class="anchor" id="a9dad49dc805e93dc5b28a6e904b50a9d"></a>
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<td class="memname">aggregate svm_model_rec svm_cls_agg </td>
<td>(</td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">[], </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname">, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
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<td class="memname">svm_model_rec svm_cls_update </td>
<td>(</td>
<td class="paramtype">svm_model_rec&#160;</td>
<td class="paramname"><em>svs</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>ind</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>label</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>kernel</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>eta</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>nu</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
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<td class="memname">void svm_data_normalization </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>input_table</em>)</td><td></td>
<td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">input_table</td><td>Name of table/view containing the data points to be scored </td></tr>
</table>
</dd>
</dl>
</div>
</div>
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<td class="memname">float8 svm_dot </td>
<td>(</td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>x</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>y</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">x</td><td>The data point \( \boldsymbol x \) </td></tr>
<tr><td class="paramname">y</td><td>The data point \( \boldsymbol y \) </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>Returns dot product of the two data points. </dd></dl>
</div>
</div>
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<td class="memname">void svm_drop_model </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>)</td><td></td>
<td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">model_table</td><td>The table to be dropped. </td></tr>
</table>
</dd>
</dl>
</div>
</div>
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<td class="memname">float8 svm_gaussian </td>
<td>(</td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>x</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>y</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>gamma</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">x</td><td>The data point \( \boldsymbol x \) </td></tr>
<tr><td class="paramname">y</td><td>The data point \( \boldsymbol y \) </td></tr>
<tr><td class="paramname">gamma</td><td>The spread \( \gamma \) </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>Returns \( K(\boldsymbol x,\boldsymbol y)=exp(-\gamma || \boldsymbol x \cdot \boldsymbol y ||^2 ) \) </dd></dl>
</div>
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<td class="memname">void svm_generate_cls_data </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>output_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int&#160;</td>
<td class="paramname"><em>num</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int&#160;</td>
<td class="paramname"><em>dim</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
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<td class="memname">void svm_generate_nd_data </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>output_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int&#160;</td>
<td class="paramname"><em>num</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int&#160;</td>
<td class="paramname"><em>dim</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
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<td class="memname">void svm_generate_reg_data </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>output_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int&#160;</td>
<td class="paramname"><em>num</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int&#160;</td>
<td class="paramname"><em>dim</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
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<td class="memname">aggregate svm_model_rec svm_nd_agg </td>
<td>(</td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">[], </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname">, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
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<td class="memname">svm_model_rec svm_nd_update </td>
<td>(</td>
<td class="paramtype">svm_model_rec&#160;</td>
<td class="paramname"><em>svs</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>ind</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>kernel</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>eta</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>nu</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
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<td class="memname">set&lt;svm_nd_result&gt; svm_novelty_detection </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>input_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>parallel</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>kernel_func</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">input_table</td><td>The name of the table/view with the training data </td></tr>
<tr><td class="paramname">model_table</td><td>The name of the table under which we want to store the learned model </td></tr>
<tr><td class="paramname">parallel</td><td>A flag indicating whether the system should learn multiple models in parallel </td></tr>
<tr><td class="paramname">kernel_func</td><td>Kernel function </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>A summary of the learning process </dd></dl>
</div>
</div>
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<td class="memname">set&lt;svm_nd_result&gt; svm_novelty_detection </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>input_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>parallel</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>kernel_func</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>verbose</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>eta</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>nu</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">input_table</td><td>The name of the table/view with the training data </td></tr>
<tr><td class="paramname">model_table</td><td>The name of the table under which we want to store the learned model </td></tr>
<tr><td class="paramname">parallel</td><td>A flag indicating whether the system should learn multiple models in parallel </td></tr>
<tr><td class="paramname">kernel_func</td><td>Kernel function </td></tr>
<tr><td class="paramname">verbose</td><td>Verbosity of reporting </td></tr>
<tr><td class="paramname">eta</td><td>Learning rate in (0,1] </td></tr>
<tr><td class="paramname">nu</td><td>Compression parameter in (0,1] associated with the fraction of training data that will become support vectors </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>A summary of the learning process </dd></dl>
</div>
</div>
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<td class="memname">float8 svm_polynomial </td>
<td>(</td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>x</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>y</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>degree</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">x</td><td>The data point \( \boldsymbol x \) </td></tr>
<tr><td class="paramname">y</td><td>The data point \( \boldsymbol y \) </td></tr>
<tr><td class="paramname">degree</td><td>The degree \( d \) </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>Returns \( K(\boldsymbol x,\boldsymbol y)=(\boldsymbol x \cdot \boldsymbol y)^d \) </dd></dl>
</div>
</div>
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<td class="memname">float8 svm_predict </td>
<td>(</td>
<td class="paramtype">svm_model_rec&#160;</td>
<td class="paramname"><em>svs</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>ind</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>kernel</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
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<td class="memname">float8 svm_predict </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>ind</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">model_table</td><td>The table storing the learned model \( f \) to be used </td></tr>
<tr><td class="paramname">ind</td><td>The data point \( \boldsymbol x \) </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>This function returns \( f(\boldsymbol x) \) </dd></dl>
</div>
</div>
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<td class="memname">text svm_predict_batch </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>input_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>data_col</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>id_col</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>output_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>parallel</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">input_table</td><td>Name of table/view containing the data points to be scored </td></tr>
<tr><td class="paramname">data_col</td><td>Name of column in input_table containing the data points </td></tr>
<tr><td class="paramname">id_col</td><td>Name of column in input_table containing the integer identifier of data points </td></tr>
<tr><td class="paramname">model_table</td><td>Name of table where the learned model to be used is stored </td></tr>
<tr><td class="paramname">output_table</td><td>Name of table to store the results </td></tr>
<tr><td class="paramname">parallel</td><td>A flag indicating whether the model to be used was learned in parallel </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>Textual summary of the algorithm run </dd></dl>
</div>
</div>
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<td class="memname">set&lt;svm_model_pr&gt; svm_predict_combo </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>ind</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">model_table</td><td>The table storing the learned models to be used. </td></tr>
<tr><td class="paramname">ind</td><td>The data point \( \boldsymbol x \) </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>This function returns a table, a row for each model. Moreover, the last row contains the average value, over all models.</dd></dl>
<p>The different models are assumed to be named <code><em>model_table</em>1</code>, <code><em>model_table</em>2</code>, .... </p>
</div>
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<td class="memname">float8 svm_predict_sub </td>
<td>(</td>
<td class="paramtype">int&#160;</td>
<td class="paramname">, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int&#160;</td>
<td class="paramname">, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">[], </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">[], </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">[], </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname">&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
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<td class="memname">aggregate svm_model_rec svm_reg_agg </td>
<td>(</td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">[], </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname">, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname">&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
<a class="anchor" id="ac7a57455085b8f39c1bfc13c80891b6d"></a>
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<td class="memname">svm_model_rec svm_reg_update </td>
<td>(</td>
<td class="paramtype">svm_model_rec&#160;</td>
<td class="paramname"><em>svs</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8[]&#160;</td>
<td class="paramname"><em>ind</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>label</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>kernel</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>eta</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>nu</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>slambda</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
<a class="anchor" id="acaf1f4aa3eec5710de5c03e368a4b106"></a>
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<td class="memname">set&lt;svm_reg_result&gt; svm_regression </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>input_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>parallel</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>kernel_func</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">input_table</td><td>The name of the table/view with the training data </td></tr>
<tr><td class="paramname">model_table</td><td>The name of the table under which we want to store the learned model </td></tr>
<tr><td class="paramname">parallel</td><td>A flag indicating whether the system should learn multiple models in parallel </td></tr>
<tr><td class="paramname">kernel_func</td><td>Kernel function </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>A summary of the learning process </dd></dl>
</div>
</div>
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<td class="memname">set&lt;svm_reg_result&gt; svm_regression </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>input_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>parallel</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>kernel_func</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool&#160;</td>
<td class="paramname"><em>verbose</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>eta</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>nu</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>slambda</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">input_table</td><td>The name of the table/view with the training data </td></tr>
<tr><td class="paramname">model_table</td><td>The name of the table under which we want to store the learned model </td></tr>
<tr><td class="paramname">parallel</td><td>A flag indicating whether the system should learn multiple models in parallel </td></tr>
<tr><td class="paramname">kernel_func</td><td>Kernel function </td></tr>
<tr><td class="paramname">verbose</td><td>Verbosity of reporting </td></tr>
<tr><td class="paramname">eta</td><td>Learning rate in (0,1] </td></tr>
<tr><td class="paramname">nu</td><td>Compression parameter in (0,1] associated with the fraction of training data that will become support vectors </td></tr>
<tr><td class="paramname">slambda</td><td>Regularisation parameter </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>A summary of the learning process </dd></dl>
</div>
</div>
<a class="anchor" id="ad2cd899daaa2036b475f71c28cc031ae"></a>
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<td class="memname">void svm_store_model </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_temp_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_name</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model_table</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
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