本文整理了Java中weka.core.Instances.meanOrMode()方法的一些代码示例，展示了Instances.meanOrMode()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Instances.meanOrMode()方法的具体详情如下：
包路径：weka.core.Instances
类名称：Instances
方法名：meanOrMode

Instances.meanOrMode介绍

[英]Returns the mean (mode) for a numeric (nominal) attribute as a floating-point value. Returns 0 if the attribute is neither nominal nor numeric. If all values are missing it returns zero.
[中]以浮点值形式返回数值（标称）属性的平均值（模式）。如果属性既不是标称属性也不是数字属性，则返回0。如果缺少所有值，则返回零。

代码示例

代码示例来源：origin: com.googlecode.obvious/obviousx-weka

@Override
public double meanOrMode(int arg0) {
 return super.meanOrMode(arg0);
}

代码示例来源：origin: com.googlecode.obvious/obviousx-weka

@Override
public double meanOrMode(Attribute att) {
 return super.meanOrMode(att);
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Returns the mean (mode) for a numeric (nominal) attribute as a
 * floating-point value. Returns 0 if the attribute is neither nominal nor
 * numeric. If all values are missing it returns zero.
 * 
 * @param att the attribute
 * @return the mean or the mode
 */
public/* @pure@ */double meanOrMode(Attribute att) {
 return meanOrMode(att.index());
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Returns the mean (mode) for a numeric (nominal) attribute as a
 * floating-point value. Returns 0 if the attribute is neither nominal nor
 * numeric. If all values are missing it returns zero.
 * 
 * @param att the attribute
 * @return the mean or the mode
 */
public/* @pure@ */double meanOrMode(Attribute att) {
 return meanOrMode(att.index());
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

Instance inst;
double r, diff1, diff2, num = 0.0, sx = 0.0, sy = 0.0;
double mx = m_trainInstances.meanOrMode(m_trainInstances.attribute(att1));
double my = m_trainInstances.meanOrMode(m_trainInstances.attribute(att2));

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

m_globalMeansOrModes[i] = m_instances.meanOrMode(i);

代码示例来源：origin: Waikato/weka-trunk

m_globalMeansOrModes[i] = m_instances.meanOrMode(i);

代码示例来源：origin: nz.ac.waikato.cms.weka/multiInstanceLearning

double[] xBar = new double[m_Dimension];
for (int i = 0; i < exi.numAttributes(); i++) {
 xBar[i] = exi.meanOrMode(i);

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Returns the R-squared value for a linear regression model, where sum of
 * squared residuals is already calculated. This works for either a simple or
 * a multiple linear regression model.
 * 
 * @param data (the data set)
 * @param ssr (sum of squared residuals)
 * @return R^2 value
 * @throws Exception if there is a missing class value in data
 */
public static double calculateRSquared(Instances data, double ssr)
 throws Exception {
 // calculate total sum of squares (derivation of y from mean)
 double yMean = data.meanOrMode(data.classIndex());
 double tss = 0.0;
 for (int i = 0; i < data.numInstances(); i++) {
  tss += (data.instance(i).value(data.classIndex()) - yMean)
   * (data.instance(i).value(data.classIndex()) - yMean);
 }
 // calculate R-squared value and return
 double rsq = 1 - (ssr / tss);
 return rsq;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Returns the R-squared value for a linear regression model, where sum of
 * squared residuals is already calculated. This works for either a simple or
 * a multiple linear regression model.
 * 
 * @param data (the data set)
 * @param ssr (sum of squared residuals)
 * @return R^2 value
 * @throws Exception if there is a missing class value in data
 */
public static double calculateRSquared(Instances data, double ssr)
 throws Exception {
 // calculate total sum of squares (derivation of y from mean)
 double yMean = data.meanOrMode(data.classIndex());
 double tss = 0.0;
 for (int i = 0; i < data.numInstances(); i++) {
  tss += (data.instance(i).value(data.classIndex()) - yMean)
   * (data.instance(i).value(data.classIndex()) - yMean);
 }
 // calculate R-squared value and return
 double rsq = 1 - (ssr / tss);
 return rsq;
}

代码示例来源：origin: nz.ac.waikato.cms.weka/multiInstanceLearning

/**
 * 
 * @param ex the given test exemplar
 * @return the classification
 * @throws Exception if the exemplar could not be classified successfully
 */
@Override
public double classifyInstance(Instance ex) throws Exception {
 // Instance ex = new Exemplar(e);
 Instances exi = ex.relationalValue(1);
 double[] n = new double[m_Dimension];
 double[] xBar = new double[m_Dimension];
 for (int i = 0; i < exi.numAttributes(); i++) {
  xBar[i] = exi.meanOrMode(i);
 }
 for (int w = 0, t = 0; w < m_Dimension; w++, t++) {
  // if((t==m_ClassIndex) || (t==m_IdIndex))
  // t++;
  for (int u = 0; u < exi.numInstances(); u++) {
   if (!exi.instance(u).isMissing(t)) {
    n[w] += exi.instance(u).weight();
   }
  }
 }
 double logOdds = likelihoodRatio(n, xBar);
 return (logOdds > m_Cutoff) ? 1 : 0;
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

if (input.attribute(i).isNumeric() &&
    (input.classIndex() != i)) {
 m_Means[i] = input.meanOrMode(i);
 m_StdDevs[i] = Math.sqrt(input.variance(i));

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Signify that this batch of input to the filter is finished. 
 * If the filter requires all instances prior to filtering,
 * output() may now be called to retrieve the filtered instances.
 *
 * @return true             if there are instances pending output
 * @throws IllegalStateException     if no input structure has been defined
 */
public boolean batchFinished() {
 if (getInputFormat() == null)
  throw new IllegalStateException("No input instance format defined");
 if (m_Means == null) {
  Instances input = getInputFormat();
  m_Means = new double[input.numAttributes()];
  for (int i = 0; i < input.numAttributes(); i++) {
   if (input.attribute(i).isNumeric() &&
       (input.classIndex() != i)) {
    m_Means[i] = input.meanOrMode(i);
   }
  }
  // Convert pending input instances
  for (int i = 0; i < input.numInstances(); i++)
   convertInstance(input.instance(i));
 }
 // Free memory
 flushInput();
 m_NewBatch = true;
 return (numPendingOutput() != 0);
}

代码示例来源：origin: Waikato/weka-trunk

if (input.attribute(i).isNumeric() &&
    (input.classIndex() != i)) {
 m_Means[i] = input.meanOrMode(i);
 m_StdDevs[i] = Math.sqrt(input.variance(i));

代码示例来源：origin: Waikato/weka-trunk

/**
 * Signify that this batch of input to the filter is finished. 
 * If the filter requires all instances prior to filtering,
 * output() may now be called to retrieve the filtered instances.
 *
 * @return true             if there are instances pending output
 * @throws IllegalStateException     if no input structure has been defined
 */
public boolean batchFinished() {
 if (getInputFormat() == null)
  throw new IllegalStateException("No input instance format defined");
 if (m_Means == null) {
  Instances input = getInputFormat();
  m_Means = new double[input.numAttributes()];
  for (int i = 0; i < input.numAttributes(); i++) {
   if (input.attribute(i).isNumeric() &&
       (input.classIndex() != i)) {
    m_Means[i] = input.meanOrMode(i);
   }
  }
  // Convert pending input instances
  for (int i = 0; i < input.numInstances(); i++)
   convertInstance(input.instance(i));
 }
 // Free memory
 flushInput();
 m_NewBatch = true;
 return (numPendingOutput() != 0);
}

代码示例来源：origin: nz.ac.waikato.cms.weka/multiInstanceLearning

double[] sSq = new double[m_Dimension];
for (int i = 0; i < exi.numAttributes(); i++) {
 xBar[i] = exi.meanOrMode(i);
 sSq[i] = exi.variance(i);

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

m_stdDevs = new double[m_trainInstances.numAttributes()];
for (int i = 0; i < m_trainInstances.numAttributes(); i++) {
 m_means[i] = m_trainInstances.meanOrMode(i);
 m_stdDevs[i] =
  Math.sqrt(Utils.variance(m_trainInstances.attributeToDoubleArray(i)));

代码示例来源：origin: Waikato/weka-trunk

m_stdDevs = new double[m_trainInstances.numAttributes()];
for (int i = 0; i < m_trainInstances.numAttributes(); i++) {
 m_means[i] = m_trainInstances.meanOrMode(i);
 m_stdDevs[i] =
  Math.sqrt(Utils.variance(m_trainInstances.attributeToDoubleArray(i)));

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

public void testTypical() {
 Instances result = useFilter();
 // Number of attributes and instances shouldn't change
 assertEquals(m_Instances.numAttributes(), result.numAttributes());
 assertEquals(m_Instances.numInstances(),  result.numInstances());
 // Check conversion is OK
 for (int j = 0; j < result.numAttributes(); j++) {
  if (result.attribute(j).isNumeric()) {
 double mean = result.meanOrMode(j);
 assertTrue("Mean should be 0", Utils.eq(mean, 0));
 double stdDev = Math.sqrt(result.variance(j));
 assertTrue("StdDev should be 1 (or 0)", 
     Utils.eq(stdDev, 0) || Utils.eq(stdDev, 1));
  }
 }
}

代码示例来源：origin: Waikato/weka-trunk

public void testTypical() {
 Instances result = useFilter();
 // Number of attributes and instances shouldn't change
 assertEquals(m_Instances.numAttributes(), result.numAttributes());
 assertEquals(m_Instances.numInstances(),  result.numInstances());
 // Check conversion is OK
 for (int j = 0; j < result.numAttributes(); j++) {
  if (result.attribute(j).isNumeric()) {
 double mean = result.meanOrMode(j);
 assertTrue("Mean should be 0", Utils.eq(mean, 0));
 double stdDev = Math.sqrt(result.variance(j));
 assertTrue("StdDev should be 1 (or 0)", 
     Utils.eq(stdDev, 0) || Utils.eq(stdDev, 1));
  }
 }
}