svmlightinterface.html

SVM_light是一种非常流行的支持向量机的java接口

<FONT color="green">141</FONT>       * the Java runtime process.<a name="line.141"></a>
<FONT color="green">142</FONT>       */<a name="line.142"></a>
<FONT color="green">143</FONT>      public native double classifyNative(FeatureVector doc);<a name="line.143"></a>
<FONT color="green">144</FONT>    <a name="line.144"></a>
<FONT color="green">145</FONT>      public TrainingParameters getTrainingParameters() {<a name="line.145"></a>
<FONT color="green">146</FONT>        return m_tp;<a name="line.146"></a>
<FONT color="green">147</FONT>      }<a name="line.147"></a>
<FONT color="green">148</FONT>    <a name="line.148"></a>
<FONT color="green">149</FONT>      private int partition(int[] dims, double[] vals, int low, int high) {<a name="line.149"></a>
<FONT color="green">150</FONT>        Object pivot;<a name="line.150"></a>
<FONT color="green">151</FONT>        double pivotprim = 0;<a name="line.151"></a>
<FONT color="green">152</FONT>        int i = low - 1;<a name="line.152"></a>
<FONT color="green">153</FONT>        int j = high + 1;<a name="line.153"></a>
<FONT color="green">154</FONT>        pivotprim = dims[(low + high) / 2];<a name="line.154"></a>
<FONT color="green">155</FONT>        while (i &lt; j) {<a name="line.155"></a>
<FONT color="green">156</FONT>          i++;<a name="line.156"></a>
<FONT color="green">157</FONT>          while (dims[i] &lt; pivotprim)<a name="line.157"></a>
<FONT color="green">158</FONT>            i++;<a name="line.158"></a>
<FONT color="green">159</FONT>          j--;<a name="line.159"></a>
<FONT color="green">160</FONT>          while (dims[j] &gt; pivotprim)<a name="line.160"></a>
<FONT color="green">161</FONT>            j--;<a name="line.161"></a>
<FONT color="green">162</FONT>          if (i &lt; j) {<a name="line.162"></a>
<FONT color="green">163</FONT>            int tmp = dims[i];<a name="line.163"></a>
<FONT color="green">164</FONT>            dims[i] = dims[j];<a name="line.164"></a>
<FONT color="green">165</FONT>            dims[j] = tmp;<a name="line.165"></a>
<FONT color="green">166</FONT>            double tmpd = vals[i];<a name="line.166"></a>
<FONT color="green">167</FONT>            vals[i] = vals[j];<a name="line.167"></a>
<FONT color="green">168</FONT>            vals[j] = tmpd;<a name="line.168"></a>
<FONT color="green">169</FONT>          }<a name="line.169"></a>
<FONT color="green">170</FONT>        }<a name="line.170"></a>
<FONT color="green">171</FONT>        return j;<a name="line.171"></a>
<FONT color="green">172</FONT>      }<a name="line.172"></a>
<FONT color="green">173</FONT>    <a name="line.173"></a>
<FONT color="green">174</FONT>      private void quicksort(int[] dims, double[] vals, int low, int high) {<a name="line.174"></a>
<FONT color="green">175</FONT>        if (low &gt;= high)<a name="line.175"></a>
<FONT color="green">176</FONT>          return;<a name="line.176"></a>
<FONT color="green">177</FONT>        int p = partition(dims, vals, low, high);<a name="line.177"></a>
<FONT color="green">178</FONT>        quicksort(dims, vals, low, p);<a name="line.178"></a>
<FONT color="green">179</FONT>        quicksort(dims, vals, p + 1, high);<a name="line.179"></a>
<FONT color="green">180</FONT>      }<a name="line.180"></a>
<FONT color="green">181</FONT>    <a name="line.181"></a>
<FONT color="green">182</FONT>      private void sort(FeatureVector[] trainingData) {<a name="line.182"></a>
<FONT color="green">183</FONT>        for (int i = 0; i &lt; trainingData.length; i++) {<a name="line.183"></a>
<FONT color="green">184</FONT>          if (trainingData[i] != null)<a name="line.184"></a>
<FONT color="green">185</FONT>            quicksort(trainingData[i].m_dims, trainingData[i].m_vals, 0,<a name="line.185"></a>
<FONT color="green">186</FONT>                trainingData[i].size() - 1);<a name="line.186"></a>
<FONT color="green">187</FONT>        }<a name="line.187"></a>
<FONT color="green">188</FONT>      }<a name="line.188"></a>
<FONT color="green">189</FONT>    <a name="line.189"></a>
<FONT color="green">190</FONT>      private native SVMLightModel trainmodel(LabeledFeatureVector[] traindata,<a name="line.190"></a>
<FONT color="green">191</FONT>          TrainingParameters p);<a name="line.191"></a>
<FONT color="green">192</FONT>    <a name="line.192"></a>
<FONT color="green">193</FONT>      public SVMLightModel trainModel(LabeledFeatureVector[] trainingData) {<a name="line.193"></a>
<FONT color="green">194</FONT>        this.m_tp = new TrainingParameters();<a name="line.194"></a>
<FONT color="green">195</FONT>        if (SORT_INPUT_VECTORS) {<a name="line.195"></a>
<FONT color="green">196</FONT>          sort(trainingData);<a name="line.196"></a>
<FONT color="green">197</FONT>        }<a name="line.197"></a>
<FONT color="green">198</FONT>        return trainmodel(trainingData, m_tp);<a name="line.198"></a>
<FONT color="green">199</FONT>      }<a name="line.199"></a>
<FONT color="green">200</FONT>    <a name="line.200"></a>
<FONT color="green">201</FONT>      public SVMLightModel trainModel(LabeledFeatureVector[] trainingData,<a name="line.201"></a>
<FONT color="green">202</FONT>          String[] argv) {<a name="line.202"></a>
<FONT color="green">203</FONT>        this.m_tp = new TrainingParameters(argv);<a name="line.203"></a>
<FONT color="green">204</FONT>        if (SORT_INPUT_VECTORS) {<a name="line.204"></a>
<FONT color="green">205</FONT>          sort(trainingData);<a name="line.205"></a>
<FONT color="green">206</FONT>        }<a name="line.206"></a>
<FONT color="green">207</FONT>        return trainmodel(trainingData, m_tp);<a name="line.207"></a>
<FONT color="green">208</FONT>      }<a name="line.208"></a>
<FONT color="green">209</FONT>    <a name="line.209"></a>
<FONT color="green">210</FONT>      public SVMLightModel trainModel(LabeledFeatureVector[] trainingData,<a name="line.210"></a>
<FONT color="green">211</FONT>          TrainingParameters tp) {<a name="line.211"></a>
<FONT color="green">212</FONT>        this.m_tp = tp;<a name="line.212"></a>
<FONT color="green">213</FONT>        if (SORT_INPUT_VECTORS) {<a name="line.213"></a>
<FONT color="green">214</FONT>          sort(trainingData);<a name="line.214"></a>
<FONT color="green">215</FONT>        }<a name="line.215"></a>
<FONT color="green">216</FONT>        return trainmodel(trainingData, m_tp);<a name="line.216"></a>
<FONT color="green">217</FONT>      }<a name="line.217"></a>
<FONT color="green">218</FONT>    }<a name="line.218"></a>




























































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