weka.core.Utils.log2()方法的使用及代码示例

x33g5p2x  于2022-02-01 转载在 其他  
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本文整理了Java中weka.core.Utils.log2()方法的一些代码示例，展示了Utils.log2()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。Utils.log2()方法的具体详情如下：
包路径：weka.core.Utils
类名称：Utils
方法名：log2

Utils.log2介绍

[英]The natural logarithm of 2.
[中]2的自然对数。

代码示例

代码示例来源：origin: Waikato/weka-trunk

/**
 * Returns c*log2(c) for a given integer value c.
 * 
 * @param c an integer value
 * @return c*log2(c) (but is careful to return 0 if c is 0)
 */
public static/* @pure@ */double xlogx(int c) {
 if (c == 0) {
  return 0.0;
 }
 return c * Utils.log2(c);
}

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

/**
 * Returns coding cost for split (used in rule learner).
 */
@Override
public final double codingCost() {
 return Utils.log2(m_index);
}

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

/**
 * Returns c*log2(c) for a given integer value c.
 * 
 * @param c an integer value
 * @return c*log2(c) (but is careful to return 0 if c is 0)
 */
public static/* @pure@ */double xlogx(int c) {
 if (c == 0) {
  return 0.0;
 }
 return c * Utils.log2(c);
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Returns coding cost for split (used in rule learner).
 */
@Override
public final double codingCost() {
 return Utils.log2(m_index);
}

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

@Override
public double getMetricRange(Map<String, WeightMass> preDist) {
 int numClasses = preDist.size();
 if (numClasses < 2) {
  numClasses = 2;
 }
 return Utils.log2(numClasses);
}

代码示例来源：origin: Waikato/weka-trunk

@Override
public double getMetricRange(Map<String, WeightMass> preDist) {
 int numClasses = preDist.size();
 if (numClasses < 2) {
  numClasses = 2;
 }
 return Utils.log2(numClasses);
}

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

/**
 * Subset description length: <br>
 * S(t,k,p) = -k*log2(p)-(n-k)log2(1-p)
 * 
 * Details see Quilan: "MDL and categorical theories (Continued)",ML95
 * 
 * @param t the number of elements in a known set
 * @param k the number of elements in a subset
 * @param p the expected proportion of subset known by recipient
 * @return the subset description length
 */
public static double subsetDL(double t, double k, double p) {
 double rt = Utils.gr(p, 0.0) ? (-k * Utils.log2(p)) : 0.0;
 rt -= (t - k) * Utils.log2(1 - p);
 return rt;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Subset description length: <br>
 * S(t,k,p) = -k*log2(p)-(n-k)log2(1-p)
 * 
 * Details see Quilan: "MDL and categorical theories (Continued)",ML95
 * 
 * @param t the number of elements in a known set
 * @param k the number of elements in a subset
 * @param p the expected proportion of subset known by recipient
 * @return the subset description length
 */
public static double subsetDL(double t, double k, double p) {
 double rt = Utils.gr(p, 0.0) ? (-k * Utils.log2(p)) : 0.0;
 rt -= (t - k) * Utils.log2(1 - p);
 return rt;
}

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

/**
 * Function used to calculate the entropy of given vector of values entropy
 * = (1/sum)*{-sigma[i=1..P](Xi*log2(Xi)) + sum*log2(sum)} where P is the
 * length of the vector
 * 
 * @param value the given vector of values
 * @param sum the sum of the given values. It's provided just for
 *          efficiency.
 * @return the entropy
 */
protected double entropy(double[] value, double sum) {
 if (Utils.smOrEq(sum, 1.0E-6)) {
  return 0;
 }
 double entropy = 0;
 for (int i = 0; i < value.length; i++) {
  if (!Utils.eq(value[i], 0)) {
   entropy -= value[i] * Utils.log2(value[i]);
  }
 }
 entropy += sum * Utils.log2(sum);
 entropy /= sum;
 return entropy;
}

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

/**
 * calculates the probability using a binomial distribution. If the support of
 * the premise is too large this distribution is approximated by a normal
 * distribution.
 * 
 * @param accuracy the accuracy value
 * @param ruleCount the support of the whole rule
 * @param premiseCount the support of the premise
 * @return the probability value
 */
public static final double binomialDistribution(double accuracy,
 double ruleCount, double premiseCount) {
 double mu, sigma;
 if (premiseCount < MAX_N) {
  return Math
   .pow(
    2,
    (Utils.log2(Math.pow(accuracy, ruleCount))
     + Utils.log2(Math.pow((1.0 - accuracy), (premiseCount - ruleCount))) + PriorEstimation
     .logbinomialCoefficient((int) premiseCount, (int) ruleCount)));
 } else {
  mu = premiseCount * accuracy;
  sigma = Math.sqrt((premiseCount * (1.0 - accuracy)) * accuracy);
  return Statistics.normalProbability(((ruleCount + 0.5) - mu)
   / (sigma * Math.sqrt(2)));
 }
}

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

/**
 * The description length of the theory for a given rule. Computed as:<br>
 * 0.5* [||k||+ S(t, k, k/t)]<br>
 * where k is the number of antecedents of the rule; t is the total possible
 * antecedents that could appear in a rule; ||K|| is the universal prior for k
 * , log2*(k) and S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) is the subset encoding
 * length.
 * <p>
 * 
 * Details see Quilan: "MDL and categorical theories (Continued)",ML95
 * 
 * @param index the index of the given rule (assuming correct)
 * @return the theory DL, weighted if weight != 1.0
 */
public double theoryDL(int index) {
 double k = m_Ruleset.get(index).size();
 if (k == 0) {
  return 0.0;
 }
 double tdl = Utils.log2(k);
 if (k > 1) {
  tdl += 2.0 * Utils.log2(tdl); // of log2 star
 }
 tdl += subsetDL(m_Total, k, k / m_Total);
 // System.out.println("!!!theory: "+MDL_THEORY_WEIGHT * REDUNDANCY_FACTOR *
 // tdl);
 return MDL_THEORY_WEIGHT * REDUNDANCY_FACTOR * tdl;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * The description length of the theory for a given rule. Computed as:<br>
 * 0.5* [||k||+ S(t, k, k/t)]<br>
 * where k is the number of antecedents of the rule; t is the total possible
 * antecedents that could appear in a rule; ||K|| is the universal prior for k
 * , log2*(k) and S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) is the subset encoding
 * length.
 * <p>
 * 
 * Details see Quilan: "MDL and categorical theories (Continued)",ML95
 * 
 * @param index the index of the given rule (assuming correct)
 * @return the theory DL, weighted if weight != 1.0
 */
public double theoryDL(int index) {
 double k = m_Ruleset.get(index).size();
 if (k == 0) {
  return 0.0;
 }
 double tdl = Utils.log2(k);
 if (k > 1) {
  tdl += 2.0 * Utils.log2(tdl); // of log2 star
 }
 tdl += subsetDL(m_Total, k, k / m_Total);
 // System.out.println("!!!theory: "+MDL_THEORY_WEIGHT * REDUNDANCY_FACTOR *
 // tdl);
 return MDL_THEORY_WEIGHT * REDUNDANCY_FACTOR * tdl;
}

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

double addend = Utils.log2(distr) - max
  + Utils.log2((Math.pow(2, i) - 1))
  + logbinomialCoefficient(m_instances.numAttributes(), i);
 sum = sum + Math.pow(2, addend);
double addend = Utils.log2((Math.pow(2, i) - 1)) - max
 + logbinomialCoefficient(m_instances.numAttributes(), i);
sum = sum + Math.pow(2, addend);

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

/**
 * Computes the entropy of a dataset.
 * 
 * @param data the data for which entropy is to be computed
 * @return the entropy of the data's class distribution
 * @throws Exception if computation fails
 */
private double computeEntropy(Instances data) throws Exception {
 double [] classCounts = new double[data.numClasses()];
 Enumeration instEnum = data.enumerateInstances();
 while (instEnum.hasMoreElements()) {
  Instance inst = (Instance) instEnum.nextElement();
  classCounts[(int) inst.classValue()]++;
 }
 double entropy = 0;
 for (int j = 0; j < data.numClasses(); j++) {
  if (classCounts[j] > 0) {
   entropy -= classCounts[j] * Utils.log2(classCounts[j]);
  }
 }
 entropy /= (double) data.numInstances();
 return entropy + Utils.log2(data.numInstances());
}

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

after = Utils.log2(numCutPoints) + distAfter + instAfter;

代码示例来源：origin: Waikato/weka-trunk

after = Utils.log2(numCutPoints) + distAfter + instAfter;

代码示例来源：origin: Waikato/weka-trunk

accurate[x] * (Utils.log2(p / t) - Utils.log2(defAcRt));

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

accurate[x] * (Utils.log2(p / t) - Utils.log2(defAcRt));

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

double totalBits = Utils.log2(cover + uncover + 1.0); // how many data?
double coverBits, uncoverBits; // What's the error?
double expErr; // Expected FP or FN

代码示例来源：origin: Waikato/weka-trunk

double totalBits = Utils.log2(cover + uncover + 1.0); // how many data?
double coverBits, uncoverBits; // What's the error?
double expErr; // Expected FP or FN

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