Package org.encog.ml.data.basic

Examples of org.encog.ml.data.basic.BasicMLDataSet

143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
   * Process the array.
   * @param data The array to process.
   * @return A neural data set that contains the time-series.
   */
  public final MLDataSet process(final double[] data) {
    final MLDataSet result = new BasicMLDataSet();

    final int totalWindowSize = this.inputWindow + this.predictWindow;
    final int stopPoint = data.length - totalWindowSize;

    for (int i = 0; i < stopPoint; i++) {
      final MLData inputData
        = new BasicMLData(this.inputWindow);
      final MLData idealData
        = new BasicMLData(this.predictWindow);

      int index = i;

      // handle input window
      for (int j = 0; j < this.inputWindow; j++) {
        inputData.setData(j, data[index++]);
      }

      // handle predict window
      for (int j = 0; j < this.predictWindow; j++) {
        idealData.setData(j, data[index++]);
      }

      final MLDataPair pair = new BasicMLDataPair(inputData,
          idealData);
      result.add(pair);
    }

    return result;
  }
View Full Code Here
198
199
200
201
202
203
204
205
206
207
208
209
     * @param data The data.
     * @return The data set.
     */
    public MLDataSet process(double[][] data)
    {
        MLDataSet result = new BasicMLDataSet();
        for(double[] doubles : data)
        {
            result.add(processToPair(doubles));
        }
        return result;
    }
View Full Code Here
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
   * @param idealSize The ideal size, 0 for unsupervised.
   * @return A NeuralDataSet that holds the contents of the CSV file.
   */
  public static MLDataSet loadCSVTOMemory(CSVFormat format,
      String filename, boolean headers, int inputSize, int idealSize) {
    MLDataSet result = new BasicMLDataSet();
    ReadCSV csv = new ReadCSV(filename, headers, format);
    while (csv.next()) {
      MLData input = null;
      MLData ideal = null;
      int index = 0;

      input = new BasicMLData(inputSize);
      for (int i = 0; i < inputSize; i++) {
        double d = csv.getDouble(index++);
        input.setData(i, d);
      }

      if (idealSize > 0) {
        ideal = new BasicMLData(idealSize);
        for (int i = 0; i < idealSize; i++) {
          double d = csv.getDouble(index++);
          ideal.setData(i, d);
        }
      }

      MLDataPair pair = new BasicMLDataPair(input, ideal);
      result.add(pair);
    }

    return result;
  }
View Full Code Here
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
    if (this.analyst == null) {
      throw new EncogError(
          "Can't normalize yet, file has not been analyzed.");
    }
   
    MLDataSet result = new BasicMLDataSet();
   
    int inputCount = this.analyst.determineInputCount();
    int outputCount = this.analyst.determineOutputCount();
    int totalCount = inputCount+outputCount;
   
    boolean headers = this.analyst.getScript().getProperties()
        .getPropertyBoolean(ScriptProperties.SETUP_CONFIG_INPUT_HEADERS);
   
    final CSVFormat format = this.analyst.getScript().determineFormat();

    CSVHeaders analystHeaders = new CSVHeaders(file, headers,
        format);
   
    ReadCSV csv = new ReadCSV(file.toString(), headers, format);
   
    for (final AnalystField field : analyst.getScript().getNormalize()
        .getNormalizedFields()) {
      field.init();
    }

    TimeSeriesUtil series = new TimeSeriesUtil(analyst,true,
        analystHeaders.getHeaders());
   

    try {
      // write file contents
      while (csv.next()) {

        double[] output = AnalystNormalizeCSV.extractFields(
            this.analyst, analystHeaders, csv, totalCount,
            false);

        if (series.getTotalDepth() > 1) {
          output = series.process(output);
        }

        MLDataPair pair = BasicMLDataPair.createPair(inputCount,outputCount);
        for(int i=0;i<inputCount;i++) {
          pair.getInput().setData(i, output[i]);
        }
        for(int i=0;i<outputCount;i++) {
          pair.getIdeal().setData(i, output[i+inputCount]);
        }
        result.add(pair);
      }
      return result;
    } finally {
      if (csv != null) {
        try {
View Full Code Here
79
80
81
82
83
84
85
86
87
88
89
    setExpectInputHeaders(headers);
    setInputFormat(format);

    setAnalyzed(true);
    this.analyst = theAnalyst;
    this.data = new BasicMLDataSet();
    resetStatus();
    int recordCount = 0;

    final int outputLength = this.analyst.determineTotalColumns();
    final ReadCSV csv = new ReadCSV(this.getInputFilename().toString(),
View Full Code Here
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
  @Override
  public Object read(final InputStream is) {

    final EncogReadHelper in = new EncogReadHelper(is);
    EncogFileSection section;
    final BasicMLDataSet samples = new BasicMLDataSet();
    Map<String, String> networkParams = null;
    PNNKernelType kernel = null;
    PNNOutputMode outmodel = null;
    int inputCount = 0;
    int outputCount = 0;
    double error = 0;
    double[] sigma = null;

    while ((section = in.readNextSection()) != null) {
      if (section.getSectionName().equals("PNN")
          && section.getSubSectionName().equals("PARAMS")) {
        networkParams = section.parseParams();
      }
      if (section.getSectionName().equals("PNN")
          && section.getSubSectionName().equals("NETWORK")) {
        final Map<String, String> params = section.parseParams();
        inputCount = EncogFileSection.parseInt(params,
            PersistConst.INPUT_COUNT);
        outputCount = EncogFileSection.parseInt(params,
            PersistConst.OUTPUT_COUNT);
        kernel = PersistBasicPNN.stringToKernel(params
            .get(PersistConst.KERNEL));
        outmodel = PersistBasicPNN.stringToOutputMode(params
            .get(PersistBasicPNN.PROPERTY_outputMode));
        error = EncogFileSection
            .parseDouble(params, PersistConst.ERROR);
        sigma = section.parseDoubleArray(params,
            PersistConst.SIGMA);
      }
      if (section.getSectionName().equals("PNN")
          && section.getSubSectionName().equals("SAMPLES")) {
        for (final String line : section.getLines()) {
          final List<String> cols = EncogFileSection
              .splitColumns(line);
          int index = 0;
          final MLData inputData = new BasicMLData(inputCount);
          for (int i = 0; i < inputCount; i++) {
            inputData.setData(i,
                CSVFormat.EG_FORMAT.parse(cols.get(index++)));
          }
          final MLData idealData = new BasicMLData(outputCount);
          idealData.setData(0,CSVFormat.EG_FORMAT.parse(cols.get(index++)));
          final MLDataPair pair = new BasicMLDataPair(inputData,idealData);
          samples.add(pair);
        }
      }
    }

    final BasicPNN result = new BasicPNN(kernel, outmodel, inputCount,
View Full Code Here
48
49
50
51
52
53
54
55
56
57
58
  public final File EG_FILENAME = TEMP_DIR.createFile("encogtest.eg");
  public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
 
  private NEATPopulation generate()
  {
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);
   
    CalculateScore score = new TrainingSetScore(trainingSet);
    // train the neural network
    ActivationStep step = new ActivationStep();
    step.setCenter(0.5);
View Full Code Here
89
90
91
92
93
94
95
96
97
98
99
  {
    Assert.assertEquals(10,pop.getPopulationSize());
    Assert.assertEquals(0.2,pop.getSurvivalRate());
   
    // see if the population can actually be used to train
    MLDataSet trainingSet = new BasicMLDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);   
    CalculateScore score = new TrainingSetScore(trainingSet);
    EvolutionaryAlgorithm train = NEATUtil.constructNEATTrainer(pop, score);
    train.iteration();

  }
View Full Code Here
42
43
44
45
46
47
48
49
50
51
52
{
  private static double FAKE_DATA[][] = { { 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0 } };

  public void testNPE() throws Exception
  {
    final CalculateScore score = new TrainingSetScore(new BasicMLDataSet(FAKE_DATA, FAKE_DATA));

    // create a new random population and train it
    NEATPopulation pop = new NEATPopulation(FAKE_DATA[0].length, 1, 50);
    pop.reset();
    EvolutionaryAlgorithm training1 = NEATUtil.constructNEATTrainer(pop, score);
View Full Code Here
73
74
75
76
77
78
79
80
81
82
83
    new PersistNEATPopulation().save(serialized3, training3.getPopulation());
  }

  public void testSaveRead() throws Exception
  {
    final CalculateScore score = new TrainingSetScore(new BasicMLDataSet(FAKE_DATA, FAKE_DATA));
    NEATPopulation pop = new NEATPopulation(FAKE_DATA[0].length, 1, 50);
    pop.reset();
    // create a new random population and train it
    EvolutionaryAlgorithm training1 = NEATUtil.constructNEATTrainer(pop, score);
    training1.iteration();
View Full Code Here
TOP

Related Classes of org.encog.ml.data.basic.BasicMLDataSet

  • com.wiieditor.neuralnetwork.NeuralNetworkWrapper
  • org.encog.app.analyst.csv.AnalystClusterCSV
  • org.encog.app.analyst.util.AnalystUtility
  • org.encog.EncogError
  • org.encog.examples.clustering.kmeans.SimpleKMeans
  • org.encog.examples.neural.activation.CustomActivation
  • org.encog.examples.neural.adaline.AdalineDigits
  • org.encog.examples.neural.benchmark.AverageBenchmark
  • org.encog.examples.neural.benchmark.BinaryVsMemory
  • org.encog.examples.neural.benchmark.FahlmanEncoder
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.