Create WekaBatteryPredictionExample.java

Author: Cat-Gawr

Java/ML/WekaBatteryPredictionExample.java

@@ -0,0 +1,83 @@
+package ml;
+
+/*
+ * This program is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with this program.  If not, see <http://www.gnu.org/licenses/>.
+**/
+
+import com.google.common.io.Resources;
+import weka.classifiers.Classifier;
+import weka.classifiers.functions.LinearRegression;
+import weka.core.Attribute;
+import weka.core.DenseInstance;
+import weka.core.Instances;
+
+import java.io.*;
+import java.util.ArrayList;
+
+public class WekaBatteryPredictionExample {
+
+    private static Classifier cls;
+
+    static {
+        try {
+            String trainingSetFile = Resources.getResource("training-set.txt").getFile();
+            Instances trainingSet = loadDatasetFromTxt(trainingSetFile);
+
+            cls = new LinearRegression();
+            cls.buildClassifier(trainingSet);
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+    public static double predictBatteryLife(double timeCharged) throws Exception {
+        return cls.classifyInstance(new DenseInstance(1.0, new double[]{timeCharged}));
+    }
+
+    private static Instances loadDatasetFromTxt(String txtFile) throws IOException {
+        ArrayList<Attribute> atts = new ArrayList<>(2);
+        atts.add(new Attribute("time_charged", Attribute.NUMERIC));
+        atts.add(new Attribute("battery_lasted_time", Attribute.NUMERIC));
+        Instances data = new Instances("battery-prediction-training-set", atts, 0);
+        data.setClassIndex(1);
+
+        File file = new File(txtFile);
+        FileReader fr = new FileReader(file);
+        BufferedReader br = new BufferedReader(fr);
+        String line;
+        while((line = br.readLine()) != null){
+            String[] values = line.split(",");
+            double[] newInst = new double[2];
+            newInst[0] = Double.valueOf(values[0]);
+            newInst[1] = Double.valueOf(values[1]);
+
+            data.add(new DenseInstance(1.0, newInst));
+        }
+        br.close();
+        fr.close();
+
+        return data;
+    }
+
+    public static void main(String... args) throws Exception {
+        BufferedReader in = new BufferedReader(new InputStreamReader(System.in));
+        String input;
+        while ((input = in.readLine()) != null && input.length() != 0) {
+            Double timeCharged = Double.valueOf(input);
+            if (timeCharged != null) {
+                System.out.println(predictBatteryLife(timeCharged));
+            }
+        }
+    }
+}