コード例 #1
0
ファイル: LVQ.java プロジェクト: triguero/Keel3.0
  private void normalizarTest() {

    int i, j, cont = 0, k;
    Instance temp;
    boolean hecho;
    double caja[];
    StringTokenizer tokens;
    boolean nulls[];

    /* Check if dataset corresponding with a classification problem */

    if (Attributes.getOutputNumAttributes() < 1) {
      System.err.println(
          "This dataset haven´t outputs, so it not corresponding to a classification problem.");
      System.exit(-1);
    } else if (Attributes.getOutputNumAttributes() > 1) {
      System.err.println("This dataset have more of one output.");
      System.exit(-1);
    }

    if (Attributes.getOutputAttribute(0).getType() == Attribute.REAL) {
      System.err.println(
          "This dataset have an input attribute with floating values, so it not corresponding to a classification problem.");
      System.exit(-1);
    }

    datosTest = new double[test.getNumInstances()][Attributes.getInputNumAttributes()];
    clasesTest = new int[test.getNumInstances()];
    caja = new double[1];

    for (i = 0; i < test.getNumInstances(); i++) {
      temp = test.getInstance(i);
      nulls = temp.getInputMissingValues();
      datosTest[i] = test.getInstance(i).getAllInputValues();
      for (j = 0; j < nulls.length; j++) if (nulls[j]) datosTest[i][j] = 0.0;
      caja = test.getInstance(i).getAllOutputValues();
      clasesTest[i] = (int) caja[0];
      for (k = 0; k < datosTest[i].length; k++) {
        if (Attributes.getInputAttribute(k).getType() == Attribute.NOMINAL) {
          datosTest[i][k] /= Attributes.getInputAttribute(k).getNominalValuesList().size() - 1;
        } else {
          datosTest[i][k] -= Attributes.getInputAttribute(k).getMinAttribute();
          datosTest[i][k] /=
              Attributes.getInputAttribute(k).getMaxAttribute()
                  - Attributes.getInputAttribute(k).getMinAttribute();
        }
      }
    }
  }
コード例 #2
0
ファイル: LVQ.java プロジェクト: triguero/Keel3.0
  private void normalizarReferencia() throws CheckException {

    int i, j, cont = 0, k;
    Instance temp;
    boolean hecho;
    double caja[];
    StringTokenizer tokens;
    boolean nulls[];

    /*Check if dataset corresponding with a classification problem*/

    if (Attributes.getOutputNumAttributes() < 1) {
      throw new CheckException(
          "This dataset haven´t outputs, so it not corresponding to a classification problem.");
    } else if (Attributes.getOutputNumAttributes() > 1) {
      throw new CheckException("This dataset have more of one output.");
    }

    if (Attributes.getOutputAttribute(0).getType() == Attribute.REAL) {
      throw new CheckException(
          "This dataset have an input attribute with floating values, so it not corresponding to a classification problem.");
    }

    datosReferencia = new double[referencia.getNumInstances()][Attributes.getInputNumAttributes()];
    clasesReferencia = new int[referencia.getNumInstances()];
    caja = new double[1];

    /*Get the number of instances that have a null value*/
    for (i = 0; i < referencia.getNumInstances(); i++) {
      temp = referencia.getInstance(i);
      nulls = temp.getInputMissingValues();
      datosReferencia[i] = referencia.getInstance(i).getAllInputValues();
      for (j = 0; j < nulls.length; j++) if (nulls[j]) datosReferencia[i][j] = 0.0;
      caja = referencia.getInstance(i).getAllOutputValues();
      clasesReferencia[i] = (int) caja[0];
      for (k = 0; k < datosReferencia[i].length; k++) {
        if (Attributes.getInputAttribute(k).getType() == Attribute.NOMINAL) {
          datosReferencia[i][k] /=
              Attributes.getInputAttribute(k).getNominalValuesList().size() - 1;
        } else {
          datosReferencia[i][k] -= Attributes.getInputAttribute(k).getMinAttribute();
          datosReferencia[i][k] /=
              Attributes.getInputAttribute(k).getMaxAttribute()
                  - Attributes.getInputAttribute(k).getMinAttribute();
        }
      }
    }
  }
コード例 #3
0
ファイル: kmeansImpute.java プロジェクト: Navieclipse/KEEL
  /**
   * Computes the distance between two instances (without previous normalization)
   *
   * @param i First instance
   * @param j Second instance
   * @return The Euclidean distance between i and j
   */
  private double distance(Instance i, Instance j) {
    double dist = 0;
    int in = 0;
    int out = 0;

    for (int l = 0; l < nvariables; l++) {
      Attribute a = Attributes.getAttribute(l);

      direccion = a.getDirectionAttribute();
      tipo = a.getType();

      if (direccion == Attribute.INPUT) {
        if (tipo != Attribute.NOMINAL && !i.getInputMissingValues(in)) {
          // real value, apply euclidean distance
          dist +=
              (i.getInputRealValues(in) - j.getInputRealValues(in))
                  * (i.getInputRealValues(in) - j.getInputRealValues(in));
        } else {
          if (!i.getInputMissingValues(in)
              && i.getInputNominalValues(in) != j.getInputNominalValues(in)) dist += 1;
        }
        in++;
      } else {
        if (direccion == Attribute.OUTPUT) {
          if (tipo != Attribute.NOMINAL && !i.getOutputMissingValues(out)) {
            dist +=
                (i.getOutputRealValues(out) - j.getOutputRealValues(out))
                    * (i.getOutputRealValues(out) - j.getOutputRealValues(out));
          } else {
            if (!i.getOutputMissingValues(out)
                && i.getOutputNominalValues(out) != j.getOutputNominalValues(out)) dist += 1;
          }
          out++;
        }
      }
    }
    return dist;
  }
コード例 #4
0
ファイル: LVQ.java プロジェクト: triguero/Keel3.0
  public void ejecutar() {

    int i, j, l, m;
    double alfai;
    int nClases;

    int claseObt;

    boolean marcas[];
    boolean notFound;

    int init;
    int clasSel[];

    int baraje[];

    int pos, tmp;
    String instanciasIN[];
    String instanciasOUT[];

    long tiempo = System.currentTimeMillis();

    /* Getting the number of differents classes */

    nClases = 0;

    for (i = 0; i < clasesTrain.length; i++) if (clasesTrain[i] > nClases) nClases = clasesTrain[i];

    nClases++;

    /* Shuffle the train set */

    baraje = new int[datosTrain.length];

    Randomize.setSeed(semilla);

    for (i = 0; i < datosTrain.length; i++) baraje[i] = i;

    for (i = 0; i < datosTrain.length; i++) {

      pos = Randomize.Randint(i, datosTrain.length - 1);

      tmp = baraje[i];

      baraje[i] = baraje[pos];

      baraje[pos] = tmp;
    }

    /*
     * Inicialization of the flagged instaces vector for a posterior
     * elimination
     */

    marcas = new boolean[datosTrain.length];

    for (i = 0; i < datosTrain.length; i++) marcas[i] = false;

    if (datosTrain.length > 0) {

      // marcas[baraje[0]] = true; //the first instance is included always

      nSel = n_p;
      if (nSel < nClases) nSel = nClases;

    } else {

      System.err.println("Input dataset is empty");

      nSel = 0;
    }
    clasSel = new int[nClases];
    System.out.print("Selecting initial neurons... ");
    // at least, there must be 1 neuron of each class at the beginning
    init = nClases;
    for (i = 0; i < nClases && i < datosTrain.length; i++) {
      pos = Randomize.Randint(0, datosTrain.length - 1);
      tmp = 0;
      while ((clasesTrain[pos] != i || marcas[pos]) && tmp < datosTrain.length) {
        pos = (pos + 1) % datosTrain.length;
        tmp++;
      }
      if (tmp < datosTrain.length) marcas[pos] = true;
      else init--;
      // clasSel[i] = i;
    }
    for (i = init; i < Math.min(nSel, datosTrain.length); i++) {
      tmp = 0;
      pos = Randomize.Randint(0, datosTrain.length - 1);
      while (marcas[pos]) {
        pos = (pos + 1) % datosTrain.length;
        tmp++;
      }
      // if(i<nClases){
      // notFound = true;
      // do{
      // for(j=i-1;j>=0 && notFound;j--){
      // if(clasSel[j] == clasesTrain[pos])
      // notFound = false;
      // }
      // if(!notFound)
      // pos = Randomize.Randint (0, datosTrain.length-1);
      // }while(!notFound);
      // }
      // clasSel[i] = clasesTrain[pos];
      marcas[pos] = true;
      init++;
    }
    nSel = init;
    System.out.println("Initial neurons selected: " + nSel);

    /* Building of the S set from the flags */

    conjS = new double[nSel][datosTrain[0].length];

    clasesS = new int[nSel];

    for (m = 0, l = 0; m < datosTrain.length; m++) {

      if (marcas[m]) { // the instance must be copied to the solution

        for (j = 0; j < datosTrain[0].length; j++) {

          conjS[l][j] = datosTrain[m][j];
        }

        clasesS[l] = clasesTrain[m];

        l++;
      }
    }

    alfai = alpha;
    boolean change = true;
    /* Body of the LVQ algorithm. */

    // Train the network
    for (int it = 0; it < T && change; it++) {
      change = false;
      alpha = alfai;
      for (i = 1; i < datosTrain.length; i++) {
        // search for the nearest neuron to training instance
        pos = NN(nSel, conjS, datosTrain[baraje[i]]);
        // nearest neuron labels correctly the class of training
        // instance?

        if (clasesS[pos] != clasesTrain[baraje[i]]) { // NO - repel
          // the neuron
          for (j = 0; j < conjS[pos].length; j++) {
            conjS[pos][j] = conjS[pos][j] - alpha * (datosTrain[baraje[i]][j] - conjS[pos][j]);
          }
          change = true;
        } else { // YES - migrate the neuron towards the input vector
          for (j = 0; j < conjS[pos].length; j++) {
            conjS[pos][j] = conjS[pos][j] + alpha * (datosTrain[baraje[i]][j] - conjS[pos][j]);
          }
        }
        alpha = nu * alpha;
      }
      // Shuffle again the training partition
      baraje = new int[datosTrain.length];

      for (i = 0; i < datosTrain.length; i++) baraje[i] = i;

      for (i = 0; i < datosTrain.length; i++) {

        pos = Randomize.Randint(i, datosTrain.length - 1);

        tmp = baraje[i];

        baraje[i] = baraje[pos];

        baraje[pos] = tmp;
      }
    }
    System.out.println(
        "LVQ " + relation + " " + (double) (System.currentTimeMillis() - tiempo) / 1000.0 + "s");
    // Classify the train data set
    instanciasIN = new String[datosReferencia.length];
    instanciasOUT = new String[datosReferencia.length];
    for (i = 0; i < datosReferencia.length; i++) {
      /* Classify the instance selected in this iteration */
      Attribute a = Attributes.getOutputAttribute(0);

      int tipo = a.getType();
      claseObt = KNN.evaluacionKNN2(1, conjS, clasesS, datosReferencia[i], nClases);
      if (tipo != Attribute.NOMINAL) {
        instanciasIN[i] = new String(String.valueOf(clasesReferencia[i]));
        instanciasOUT[i] = new String(String.valueOf(claseObt));
      } else {
        instanciasIN[i] = new String(a.getNominalValue(clasesReferencia[i]));
        instanciasOUT[i] = new String(a.getNominalValue(claseObt));
      }
    }

    escribeSalida(
        ficheroSalida[0], instanciasIN, instanciasOUT, entradas, salida, nEntradas, relation);

    // Classify the test data set
    normalizarTest();
    instanciasIN = new String[datosTest.length];
    instanciasOUT = new String[datosTest.length];
    for (i = 0; i < datosTest.length; i++) {
      /* Classify the instance selected in this iteration */
      Attribute a = Attributes.getOutputAttribute(0);

      int tipo = a.getType();

      claseObt = KNN.evaluacionKNN2(1, conjS, clasesS, datosTest[i], nClases);
      if (tipo != Attribute.NOMINAL) {
        instanciasIN[i] = new String(String.valueOf(clasesTest[i]));
        instanciasOUT[i] = new String(String.valueOf(claseObt));
      } else {
        instanciasIN[i] = new String(a.getNominalValue(clasesTest[i]));
        instanciasOUT[i] = new String(a.getNominalValue(claseObt));
      }
    }

    escribeSalida(
        ficheroSalida[1], instanciasIN, instanciasOUT, entradas, salida, nEntradas, relation);

    // Print the network to a file
    printNetworkToFile(ficheroSalida[2], referencia.getHeader());
  }
コード例 #5
0
ファイル: svmRegression.java プロジェクト: Navieclipse/KEEL
  /** Process the training and test files provided in the parameters file to the constructor. */
  public void process() {
    double[] outputs;
    double[] outputs2;
    Instance neighbor;
    double dist, mean;
    int actual;
    int[] N = new int[nneigh];
    double[] Ndist = new double[nneigh];
    boolean allNull;
    svm_problem SVMp = null;
    svm_parameter SVMparam = new svm_parameter();
    svm_model svr = null;
    svm_node SVMn[];
    double[] outputsCandidate = null;
    boolean same = true;
    Vector instancesSelected = new Vector();
    Vector instancesSelected2 = new Vector();

    // SVM PARAMETERS
    SVMparam.C = C;
    SVMparam.cache_size = 10; // 10MB of cache
    SVMparam.degree = degree;
    SVMparam.eps = eps;
    SVMparam.gamma = gamma;
    SVMparam.nr_weight = 0;
    SVMparam.nu = nu;
    SVMparam.p = p;
    SVMparam.shrinking = shrinking;
    SVMparam.probability = 0;
    if (kernelType.compareTo("LINEAR") == 0) {
      SVMparam.kernel_type = svm_parameter.LINEAR;
    } else if (kernelType.compareTo("POLY") == 0) {
      SVMparam.kernel_type = svm_parameter.POLY;
    } else if (kernelType.compareTo("RBF") == 0) {
      SVMparam.kernel_type = svm_parameter.RBF;
    } else if (kernelType.compareTo("SIGMOID") == 0) {
      SVMparam.kernel_type = svm_parameter.SIGMOID;
    }

    SVMparam.svm_type = svm_parameter.EPSILON_SVR;

    try {

      // Load in memory a dataset that contains a classification problem
      IS.readSet(input_train_name, true);
      int in = 0;
      int out = 0;

      ndatos = IS.getNumInstances();
      nvariables = Attributes.getNumAttributes();
      nentradas = Attributes.getInputNumAttributes();
      nsalidas = Attributes.getOutputNumAttributes();

      X = new String[ndatos][2]; // matrix with transformed data

      mostCommon = new String[nvariables];
      SVMp = new svm_problem();
      SVMp.l = ndatos;
      SVMp.y = new double[SVMp.l];
      SVMp.x = new svm_node[SVMp.l][nentradas + 1];
      for (int l = 0; l < SVMp.l; l++) {
        for (int n = 0; n < Attributes.getInputNumAttributes() + 1; n++) {
          SVMp.x[l][n] = new svm_node();
        }
      }

      for (int i = 0; i < ndatos; i++) {
        Instance inst = IS.getInstance(i);

        SVMp.y[i] = inst.getAllOutputValues()[0];
        for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
          SVMp.x[i][n].index = n;
          SVMp.x[i][n].value = inst.getAllInputValues()[n];
          SVMp.y[i] = inst.getAllOutputValues()[0];
        }
        // end of instance
        SVMp.x[i][nentradas].index = -1;
      }
      if (svm.svm_check_parameter(SVMp, SVMparam) != null) {
        System.out.println("SVM parameter error in training:");
        System.out.println(svm.svm_check_parameter(SVMp, SVMparam));
        System.exit(-1);
      }
      // train the SVM
      if (ndatos > 0) {
        svr = svm.svm_train(SVMp, SVMparam);
      }
      for (int i = 0; i < ndatos; i++) {
        Instance inst = IS.getInstance(i);
        X[i][0] = new String(String.valueOf(inst.getAllOutputValues()[0]));
        //			the values used for regression
        SVMn = new svm_node[Attributes.getInputNumAttributes() + 1];
        for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
          SVMn[n] = new svm_node();
          SVMn[n].index = n;
          SVMn[n].value = inst.getAllInputValues()[n];
        }
        SVMn[nentradas] = new svm_node();
        SVMn[nentradas].index = -1;
        // pedict the class
        X[i][1] = new String(String.valueOf((svm.svm_predict(svr, SVMn))));
      }
    } catch (Exception e) {
      System.out.println("Dataset exception = " + e);
      e.printStackTrace();
      System.exit(-1);
    }
    write_results(output_train_name);
    /** ************************************************************************************ */
    try {

      // Load in memory a dataset that contains a classification
      // problem
      IS.readSet(input_test_name, false);
      int in = 0;
      int out = 0;

      ndatos = IS.getNumInstances();
      nvariables = Attributes.getNumAttributes();
      nentradas = Attributes.getInputNumAttributes();
      nsalidas = Attributes.getOutputNumAttributes();

      X = new String[ndatos][2]; // matrix with transformed data
      // data

      mostCommon = new String[nvariables];

      for (int i = 0; i < ndatos; i++) {
        Instance inst = IS.getInstance(i);
        X[i][0] = new String(String.valueOf(inst.getAllOutputValues()[0]));

        SVMn = new svm_node[Attributes.getInputNumAttributes() + 1];
        for (int n = 0; n < Attributes.getInputNumAttributes(); n++) {
          SVMn[n] = new svm_node();
          SVMn[n].index = n;
          SVMn[n].value = inst.getAllInputValues()[n];
        }
        SVMn[nentradas] = new svm_node();
        SVMn[nentradas].index = -1;
        // pedict the class
        X[i][1] = new String(String.valueOf(svm.svm_predict(svr, SVMn)));
      }
    } catch (Exception e) {
      System.out.println("Dataset exception = " + e);
      e.printStackTrace();
      System.exit(-1);
    }
    System.out.println("escribiendo test");
    write_results(output_test_name);
  }
コード例 #6
0
ファイル: CNN_TomekLinks.java プロジェクト: RubelAhmed57/KEEL
  /**
   * This function builds the data matrix for reference data and normalizes inputs values
   *
   * @throws keel.Algorithms.Preprocess.Basic.CheckException Can not be normalized.
   */
  protected void normalizar() throws CheckException {

    int i, j, k;
    Instance temp;
    double caja[];
    StringTokenizer tokens;
    boolean nulls[];

    /*Check if dataset corresponding with a classification problem*/

    if (Attributes.getOutputNumAttributes() < 1) {
      throw new CheckException(
          "This dataset haven?t outputs, so it not corresponding to a classification problem.");
    } else if (Attributes.getOutputNumAttributes() > 1) {
      throw new CheckException("This dataset have more of one output.");
    }

    if (Attributes.getOutputAttribute(0).getType() == Attribute.REAL) {
      throw new CheckException(
          "This dataset have an input attribute with floating values, so it not corresponding to a classification problem.");
    }

    entradas = Attributes.getInputAttributes();
    salida = Attributes.getOutputAttribute(0);
    nEntradas = Attributes.getInputNumAttributes();
    tokens = new StringTokenizer(training.getHeader(), " \n\r");
    tokens.nextToken();
    relation = tokens.nextToken();

    datosTrain = new double[training.getNumInstances()][Attributes.getInputNumAttributes()];
    clasesTrain = new int[training.getNumInstances()];
    caja = new double[1];

    nulosTrain = new boolean[training.getNumInstances()][Attributes.getInputNumAttributes()];
    nominalTrain = new int[training.getNumInstances()][Attributes.getInputNumAttributes()];
    realTrain = new double[training.getNumInstances()][Attributes.getInputNumAttributes()];

    for (i = 0; i < training.getNumInstances(); i++) {
      temp = training.getInstance(i);
      nulls = temp.getInputMissingValues();
      datosTrain[i] = training.getInstance(i).getAllInputValues();
      for (j = 0; j < nulls.length; j++)
        if (nulls[j]) {
          datosTrain[i][j] = 0.0;
          nulosTrain[i][j] = true;
        }
      caja = training.getInstance(i).getAllOutputValues();
      clasesTrain[i] = (int) caja[0];
      for (k = 0; k < datosTrain[i].length; k++) {
        if (Attributes.getInputAttribute(k).getType() == Attribute.NOMINAL) {
          nominalTrain[i][k] = (int) datosTrain[i][k];
          datosTrain[i][k] /= Attributes.getInputAttribute(k).getNominalValuesList().size() - 1;
        } else {
          realTrain[i][k] = datosTrain[i][k];
          datosTrain[i][k] -= Attributes.getInputAttribute(k).getMinAttribute();
          datosTrain[i][k] /=
              Attributes.getInputAttribute(k).getMaxAttribute()
                  - Attributes.getInputAttribute(k).getMinAttribute();
          if (Double.isNaN(datosTrain[i][k])) {
            datosTrain[i][k] = realTrain[i][k];
          }
        }
      }
    }

    datosTest = new double[test.getNumInstances()][Attributes.getInputNumAttributes()];
    clasesTest = new int[test.getNumInstances()];
    caja = new double[1];

    for (i = 0; i < test.getNumInstances(); i++) {
      temp = test.getInstance(i);
      nulls = temp.getInputMissingValues();
      datosTest[i] = test.getInstance(i).getAllInputValues();
      for (j = 0; j < nulls.length; j++)
        if (nulls[j]) {
          datosTest[i][j] = 0.0;
        }
      caja = test.getInstance(i).getAllOutputValues();
      clasesTest[i] = (int) caja[0];
    }
  } // end-method
コード例 #7
0
ファイル: kmeansImpute.java プロジェクト: Navieclipse/KEEL
  /** Process the training and test files provided in the parameters file to the constructor. */
  public void process() {
    // declarations
    double[] outputs;
    double[] outputs2;
    Instance neighbor;
    double dist, mean;
    int actual;
    Randomize rnd = new Randomize();
    Instance ex;
    gCenter kmeans = null;
    int iterations = 0;
    double E;
    double prevE;
    int totalMissing = 0;
    boolean allMissing = true;

    rnd.setSeed(semilla);
    // PROCESS
    try {

      // Load in memory a dataset that contains a classification problem
      IS.readSet(input_train_name, true);
      int in = 0;
      int out = 0;

      ndatos = IS.getNumInstances();
      nvariables = Attributes.getNumAttributes();
      nentradas = Attributes.getInputNumAttributes();
      nsalidas = Attributes.getOutputNumAttributes();

      X = new String[ndatos][nvariables]; // matrix with transformed data
      kmeans = new gCenter(K, ndatos, nvariables);

      timesSeen = new FreqList[nvariables];
      mostCommon = new String[nvariables];

      // first, we choose k 'means' randomly from all
      // instances
      totalMissing = 0;
      for (int i = 0; i < ndatos; i++) {
        Instance inst = IS.getInstance(i);
        if (inst.existsAnyMissingValue()) totalMissing++;
      }
      if (totalMissing == ndatos) allMissing = true;
      else allMissing = false;
      for (int numMeans = 0; numMeans < K; numMeans++) {
        do {
          actual = (int) (ndatos * rnd.Rand());
          ex = IS.getInstance(actual);
        } while (ex.existsAnyMissingValue() && !allMissing);

        kmeans.copyCenter(ex, numMeans);
      }

      // now, iterate adjusting clusters' centers and
      // instances to them
      prevE = 0;
      iterations = 0;
      do {
        for (int i = 0; i < ndatos; i++) {
          Instance inst = IS.getInstance(i);

          kmeans.setClusterOf(inst, i);
        }
        // set new centers
        kmeans.recalculateCenters(IS);
        // compute RMSE
        E = 0;
        for (int i = 0; i < ndatos; i++) {
          Instance inst = IS.getInstance(i);

          E += kmeans.distance(inst, kmeans.getClusterOf(i));
        }
        iterations++;
        // System.out.println(iterations+"\t"+E);
        if (Math.abs(prevE - E) == 0) iterations = maxIter;
        else prevE = E;
      } while (E > minError && iterations < maxIter);
      for (int i = 0; i < ndatos; i++) {
        Instance inst = IS.getInstance(i);

        in = 0;
        out = 0;

        for (int j = 0; j < nvariables; j++) {
          Attribute a = Attributes.getAttribute(j);

          direccion = a.getDirectionAttribute();
          tipo = a.getType();

          if (direccion == Attribute.INPUT) {
            if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in)) {
              X[i][j] = new String(String.valueOf(inst.getInputRealValues(in)));
            } else {
              if (!inst.getInputMissingValues(in)) X[i][j] = inst.getInputNominalValues(in);
              else {
                actual = kmeans.getClusterOf(i);
                X[i][j] = new String(kmeans.valueAt(actual, j));
              }
            }
            in++;
          } else {
            if (direccion == Attribute.OUTPUT) {
              if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
                X[i][j] = new String(String.valueOf(inst.getOutputRealValues(out)));
              } else {
                if (!inst.getOutputMissingValues(out)) X[i][j] = inst.getOutputNominalValues(out);
                else {
                  actual = kmeans.getClusterOf(i);
                  X[i][j] = new String(kmeans.valueAt(actual, j));
                }
              }
              out++;
            }
          }
        }
      }
    } catch (Exception e) {
      System.out.println("Dataset exception = " + e);
      e.printStackTrace();
      System.exit(-1);
    }
    write_results(output_train_name);
    /** ************************************************************************************ */
    // does a test file associated exist?
    if (input_train_name.compareTo(input_test_name) != 0) {
      try {

        // Load in memory a dataset that contains a classification problem
        IStest.readSet(input_test_name, false);
        int in = 0;
        int out = 0;

        ndatos = IStest.getNumInstances();
        nvariables = Attributes.getNumAttributes();
        nentradas = Attributes.getInputNumAttributes();
        nsalidas = Attributes.getOutputNumAttributes();

        for (int i = 0; i < ndatos; i++) {
          Instance inst = IStest.getInstance(i);

          in = 0;
          out = 0;

          for (int j = 0; j < nvariables; j++) {
            Attribute a = Attributes.getAttribute(j);

            direccion = a.getDirectionAttribute();
            tipo = a.getType();

            if (direccion == Attribute.INPUT) {
              if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in)) {
                X[i][j] = new String(String.valueOf(inst.getInputRealValues(in)));
              } else {
                if (!inst.getInputMissingValues(in)) X[i][j] = inst.getInputNominalValues(in);
                else {
                  actual = kmeans.getClusterOf(i);
                  X[i][j] = new String(kmeans.valueAt(actual, j));
                }
              }
              in++;
            } else {
              if (direccion == Attribute.OUTPUT) {
                if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
                  X[i][j] = new String(String.valueOf(inst.getOutputRealValues(out)));
                } else {
                  if (!inst.getOutputMissingValues(out)) X[i][j] = inst.getOutputNominalValues(out);
                  else {
                    actual = kmeans.getClusterOf(i);
                    X[i][j] = new String(kmeans.valueAt(actual, j));
                  }
                }
                out++;
              }
            }
          }
        }
      } catch (Exception e) {
        System.out.println("Dataset exception = " + e);
        e.printStackTrace();
        System.exit(-1);
      }
      write_results(output_test_name);
    }
  }
コード例 #8
0
ファイル: ignore_missing.java プロジェクト: TheMurderer/keel
  /** Process the training and test files provided in the parameters file to the constructor. */
  public void process() {
    try {

      // Load in memory a dataset that contains a classification problem
      IS.readSet(input_train_name, true);
      int in = 0;
      int out = 0;

      ndatos = IS.getNumInstances();
      nvariables = Attributes.getNumAttributes();
      nentradas = Attributes.getInputNumAttributes();
      nsalidas = Attributes.getOutputNumAttributes();

      X = new String[ndatos][nvariables]; // matrix with transformed data
      boolean[] isMissed =
          new boolean[ndatos]; // vector which points out instances with missed data

      try {
        FileWriter file_write = new FileWriter(output_train_name);

        file_write.write(IS.getHeader());

        // now, print the normalized data
        file_write.write("@data\n");
        // file_write.close();
        PrintWriter pw = new PrintWriter(file_write);
        for (int i = 0; i < ndatos; i++) {
          Instance inst = IS.getInstance(i);
          if (!inst.existsAnyMissingValue()) {
            inst.printAsOriginal(pw);
            // file_write.write(inst.toString()); // DOES NOT WRITE BACK NON-DEF DIRECTION
            // ATTRIBUTES!!!!
            file_write.write("\n");
          }
        }
        pw.close();
        file_write.close();
      } catch (IOException e) {
        System.out.println("IO exception = " + e);
        System.exit(-1);
      }

    } catch (Exception e) {
      System.out.println("Dataset exception = " + e);
      e.printStackTrace();
      System.exit(-1);
    }
    // does a test file associated exist?
    if (input_train_name.compareTo(input_test_name) != 0) {
      try {

        // Load in memory a dataset that contains a classification problem
        IS.readSet(input_test_name, false);
        int in = 0;
        int out = 0;

        ndatos = IS.getNumInstances();
        nvariables = Attributes.getNumAttributes();
        nentradas = Attributes.getInputNumAttributes();
        nsalidas = Attributes.getOutputNumAttributes();

        X = new String[ndatos][nvariables]; // matrix with transformed data
        boolean[] isMissed =
            new boolean[ndatos]; // vector which points out instances with missed data

        try {
          FileWriter file_write = new FileWriter(output_test_name);

          file_write.write(IS.getHeader());

          // now, print the normalized data
          file_write.write("@data\n");
          PrintWriter pw = new PrintWriter(file_write);
          for (int i = 0; i < ndatos; i++) {
            Instance inst = IS.getInstance(i);
            if (!inst.existsAnyMissingValue()) {
              inst.printAsOriginal(pw);
              file_write.write("\n");
            }
          }
          pw.close();
          file_write.close();
        } catch (IOException e) {
          System.out.println("IO exception = " + e);
          System.exit(-1);
        }

      } catch (Exception e) {
        System.out.println("Dataset exception = " + e);
        e.printStackTrace();
        System.exit(-1);
      }
    }

    // write_results(); / since there ins't any data transformation, is not needed
  }
コード例 #9
0
  /** Process the training and test files provided in the parameters file to the constructor. */
  public void process() {
    double[] outputs;
    double[] outputs2;
    try {
      FileWriter file_write = new FileWriter(output_train_name);

      try {

        // Load in memory a dataset that contains a classification problem
        IS.readSet(input_train_name, true);
        int in = 0;
        int out = 0;
        int in2 = 0;
        int out2 = 0;
        int lastMissing = -1;
        boolean fin = false;
        boolean stepNext = false;

        ndatos = IS.getNumInstances();
        nvariables = Attributes.getNumAttributes();
        nentradas = Attributes.getInputNumAttributes();
        nsalidas = Attributes.getOutputNumAttributes();

        String[] row = null;
        X = new Vector[ndatos]; // matrix with transformed data
        for (int i = 0; i < ndatos; i++) X[i] = new Vector();

        timesSeen = new FreqList[nvariables];
        mostCommon = new String[nvariables];

        file_write.write(IS.getHeader());

        // now, print the normalized data
        file_write.write("@data\n");

        // now, search for missed data, and replace them with
        // the most common value

        for (int i = 0; i < ndatos; i++) {
          Instance inst = IS.getInstance(i);
          in = 0;
          out = 0;
          row = new String[nvariables];

          for (int j = 0; j < nvariables; j++) {
            Attribute a = Attributes.getAttribute(j);

            direccion = a.getDirectionAttribute();
            tipo = a.getType();

            if (direccion == Attribute.INPUT) {
              if (tipo != Attribute.NOMINAL && !inst.existsAnyMissingValue()) {
                row[j] = new String(String.valueOf(inst.getInputRealValues(in)));
              } else {
                if (!inst.existsAnyMissingValue()) row[j] = inst.getInputNominalValues(in);
                else {
                  // missing data
                  outputs = inst.getAllOutputValues();
                  in2 = 0;
                  out2 = 0;
                  for (int attr = 0; attr < nvariables; attr++) {
                    Attribute b = Attributes.getAttribute(attr);
                    direccion = b.getDirectionAttribute();
                    tipo = b.getType();
                    if (direccion == Attribute.INPUT) {
                      if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in2)) {
                        row[attr] = new String(String.valueOf(inst.getInputRealValues(in2)));
                      } else {
                        if (!inst.getInputMissingValues(in2))
                          row[attr] = inst.getInputNominalValues(in2);
                      }
                      in2++;
                    } else {
                      if (direccion == Attribute.OUTPUT) {
                        if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out2)) {
                          row[attr] = new String(String.valueOf(inst.getOutputRealValues(out2)));
                        } else {
                          if (!inst.getOutputMissingValues(out2))
                            row[attr] = inst.getOutputNominalValues(out2);
                        }
                        out2++;
                      }
                    }
                  }
                  // make frecuencies  for each attribute
                  for (int attr = 0; attr < nvariables; attr++) {
                    Attribute b = Attributes.getAttribute(attr);

                    direccion = b.getDirectionAttribute();
                    tipo = b.getType();
                    if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
                      lastMissing = attr;
                      timesSeen[attr] = new FreqList();
                      for (int m = 0; m < ndatos; m++) {
                        Instance inst2 = IS.getInstance(m);
                        outputs2 = inst2.getAllOutputValues();
                        boolean sameClass = true;
                        // are they same concept instances??
                        for (int k = 0; k < nsalidas && sameClass; k++)
                          if (outputs[k] != outputs2[k]) sameClass = false;
                        if (sameClass) {
                          if (tipo != Attribute.NOMINAL && !inst2.getInputMissingValues(attr)) {
                            timesSeen[attr].AddElement(
                                new String(String.valueOf(inst2.getInputRealValues(attr))));

                          } else {
                            if (!inst2.getInputMissingValues(attr)) {
                              timesSeen[attr].AddElement(inst2.getInputNominalValues(attr));
                            }
                          }
                        }
                      }
                    }
                  }
                  for (int attr = 0; attr < nvariables; attr++) {
                    if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
                      timesSeen[attr].reset();
                    }
                  }
                  fin = false;
                  stepNext = false;
                  while (!fin) {
                    in2 = 0;
                    for (int attr = 0; attr < nvariables && !fin; attr++) {
                      Attribute b = Attributes.getAttribute(attr);

                      direccion = b.getDirectionAttribute();
                      tipo = b.getType();
                      if (direccion == Attribute.INPUT && inst.getInputMissingValues(in2)) {
                        if (stepNext) {
                          timesSeen[attr].iterate();
                          stepNext = false;
                        }
                        if (timesSeen[attr].outOfBounds()) {
                          stepNext = true;
                          if (attr == lastMissing) fin = true;
                          timesSeen[attr].reset();
                        }
                        if (!fin)
                          row[attr] =
                              ((ValueFreq) timesSeen[attr].getCurrent())
                                  .getValue(); // replace missing data
                      }
                      in2++;
                    }
                    if (!fin) {
                      stepNext = true;
                      file_write.write(row[0]);
                      for (int y = 1; y < nvariables; y++) {
                        file_write.write("," + row[y]);
                      }
                      file_write.write("\n");
                      // X[i].addElement(row);
                      // row = (String[])row.clone();
                    }
                  }
                }
              }
              in++;
            } else {
              if (direccion == Attribute.OUTPUT) {
                if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
                  row[j] = new String(String.valueOf(inst.getOutputRealValues(out)));
                } else {
                  if (!inst.getOutputMissingValues(out)) row[j] = inst.getOutputNominalValues(out);
                  else row[j] = new String("?");
                }
                out++;
              }
            }
          }
          if (!inst.existsAnyMissingValue()) {
            file_write.write(row[0]);
            for (int y = 1; y < nvariables; y++) {
              file_write.write("," + row[y]);
            }
            file_write.write("\n");
          }
        }
      } catch (Exception e) {
        System.out.println("Dataset exception = " + e);
        e.printStackTrace();
        System.exit(-1);
      }
      file_write.close();
    } catch (IOException e) {
      System.out.println("IO exception = " + e);
      e.printStackTrace();
      System.exit(-1);
    }

    /** ************************************************************************************ */
    // does a test file associated exist?
    if (input_train_name.compareTo(input_test_name) != 0) {
      try {
        FileWriter file_write = new FileWriter(output_test_name);

        try {

          // Load in memory a dataset that contains a classification problem
          IS.readSet(input_test_name, false);
          int in = 0;
          int out = 0;
          int in2 = 0;
          int out2 = 0;
          int lastMissing = -1;
          boolean fin = false;
          boolean stepNext = false;

          ndatos = IS.getNumInstances();
          nvariables = Attributes.getNumAttributes();
          nentradas = Attributes.getInputNumAttributes();
          nsalidas = Attributes.getOutputNumAttributes();

          String[] row = null;
          X = new Vector[ndatos]; // matrix with transformed data
          for (int i = 0; i < ndatos; i++) X[i] = new Vector();

          timesSeen = new FreqList[nvariables];
          mostCommon = new String[nvariables];

          file_write.write(IS.getHeader());

          // now, print the normalized data
          file_write.write("@data\n");

          // now, search for missed data, and replace them with
          // the most common value

          for (int i = 0; i < ndatos; i++) {
            Instance inst = IS.getInstance(i);
            in = 0;
            out = 0;
            row = new String[nvariables];

            for (int j = 0; j < nvariables; j++) {
              Attribute a = Attributes.getAttribute(j);

              direccion = a.getDirectionAttribute();
              tipo = a.getType();

              if (direccion == Attribute.INPUT) {
                if (tipo != Attribute.NOMINAL && !inst.existsAnyMissingValue()) {
                  row[j] = new String(String.valueOf(inst.getInputRealValues(in)));
                } else {
                  if (!inst.existsAnyMissingValue()) row[j] = inst.getInputNominalValues(in);
                  else {
                    // missing data
                    outputs = inst.getAllOutputValues();
                    in2 = 0;
                    out2 = 0;
                    for (int attr = 0; attr < nvariables; attr++) {
                      Attribute b = Attributes.getAttribute(attr);
                      direccion = b.getDirectionAttribute();
                      tipo = b.getType();
                      if (direccion == Attribute.INPUT) {
                        if (tipo != Attribute.NOMINAL && !inst.getInputMissingValues(in2)) {
                          row[attr] = new String(String.valueOf(inst.getInputRealValues(in2)));
                        } else {
                          if (!inst.getInputMissingValues(in2))
                            row[attr] = inst.getInputNominalValues(in2);
                        }
                        in2++;
                      } else {
                        if (direccion == Attribute.OUTPUT) {
                          if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out2)) {
                            row[attr] = new String(String.valueOf(inst.getOutputRealValues(out2)));
                          } else {
                            if (!inst.getOutputMissingValues(out2))
                              row[attr] = inst.getOutputNominalValues(out2);
                          }
                          out2++;
                        }
                      }
                    }
                    // make frecuencies  for each attribute
                    for (int attr = 0; attr < nvariables; attr++) {
                      Attribute b = Attributes.getAttribute(attr);

                      direccion = b.getDirectionAttribute();
                      tipo = b.getType();
                      if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
                        lastMissing = attr;
                        timesSeen[attr] = new FreqList();
                        for (int m = 0; m < ndatos; m++) {
                          Instance inst2 = IS.getInstance(m);
                          outputs2 = inst2.getAllOutputValues();
                          boolean sameClass = true;
                          // are they same concept instances??
                          for (int k = 0; k < nsalidas && sameClass; k++)
                            if (outputs[k] != outputs2[k]) sameClass = false;
                          if (sameClass) {
                            if (tipo != Attribute.NOMINAL && !inst2.getInputMissingValues(attr)) {
                              timesSeen[attr].AddElement(
                                  new String(String.valueOf(inst2.getInputRealValues(attr))));

                            } else {
                              if (!inst2.getInputMissingValues(attr)) {
                                timesSeen[attr].AddElement(inst2.getInputNominalValues(attr));
                              }
                            }
                          }
                        }
                      }
                    }
                    for (int attr = 0; attr < nvariables; attr++) {
                      if (direccion == Attribute.INPUT && inst.getInputMissingValues(attr)) {
                        timesSeen[attr].reset();
                      }
                    }
                    fin = false;
                    stepNext = false;
                    while (!fin) {
                      in2 = 0;
                      for (int attr = 0; attr < nvariables && !fin; attr++) {
                        Attribute b = Attributes.getAttribute(attr);

                        direccion = b.getDirectionAttribute();
                        tipo = b.getType();
                        if (direccion == Attribute.INPUT && inst.getInputMissingValues(in2)) {
                          if (stepNext) {
                            timesSeen[attr].iterate();
                            stepNext = false;
                          }
                          if (timesSeen[attr].outOfBounds()) {
                            stepNext = true;
                            if (attr == lastMissing) fin = true;
                            timesSeen[attr].reset();
                          }
                          if (!fin)
                            row[attr] =
                                ((ValueFreq) timesSeen[attr].getCurrent())
                                    .getValue(); // replace missing data
                        }
                        in2++;
                      }
                      if (!fin) {
                        stepNext = true;
                        file_write.write(row[0]);
                        for (int y = 1; y < nvariables; y++) {
                          file_write.write("," + row[y]);
                        }
                        file_write.write("\n");
                        // X[i].addElement(row);
                        // row = (String[])row.clone();
                      }
                    }
                  }
                }
                in++;
              } else {
                if (direccion == Attribute.OUTPUT) {
                  if (tipo != Attribute.NOMINAL && !inst.getOutputMissingValues(out)) {
                    row[j] = new String(String.valueOf(inst.getOutputRealValues(out)));
                  } else {
                    if (!inst.getOutputMissingValues(out))
                      row[j] = inst.getOutputNominalValues(out);
                    else row[j] = new String("?");
                  }
                  out++;
                }
              }
            }
            if (!inst.existsAnyMissingValue()) {
              file_write.write(row[0]);
              for (int y = 1; y < nvariables; y++) {
                file_write.write("," + row[y]);
              }
              file_write.write("\n");
            }
          }
        } catch (Exception e) {
          System.out.println("Dataset exception = " + e);
          e.printStackTrace();
          System.exit(-1);
        }
        file_write.close();
      } catch (IOException e) {
        System.out.println("IO exception = " + e);
        e.printStackTrace();
        System.exit(-1);
      }
    }
  }