本文整理汇总了Java中cc.mallet.util.StatFunctions类的典型用法代码示例。如果您正苦于以下问题：Java StatFunctions类的具体用法？Java StatFunctions怎么用？Java StatFunctions使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
StatFunctions类属于cc.mallet.util包，在下文中一共展示了StatFunctions类的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: pValues
import cc.mallet.util.StatFunctions; //导入依赖的package包/类
public double[] pValues() {
double[] values = new double[dimension];
for (int index=0; index < dimension; index++) {
double standardError = Math.sqrt(meanSquaredError *
xTransposeXInverse[(dimension * index) + index]);
values[index] = 2 * (1.0 - StatFunctions.pt(Math.abs(parameters[index] / standardError),
degreesOfFreedom));
}
return values;
}
示例2: printSummary
import cc.mallet.util.StatFunctions; //导入依赖的package包/类
/** Print a summary of the regression, similar to summary(lm(...)) in R */
public void printSummary() {
double standardError, tPercentile;
System.out.println("\tparam\tStd.Err\tt value\tPr(>|t|)");
System.out.print("(Int)\t");
System.out.print(formatter.format(parameters[interceptIndex]) + "\t");
standardError =
Math.sqrt(meanSquaredError *
xTransposeXInverse[(dimension * interceptIndex) + interceptIndex]);
System.out.print(formatter.format(standardError) + "\t");
System.out.print(formatter.format(parameters[interceptIndex] / standardError) + "\t");
tPercentile =
2 * (1.0 - StatFunctions.pt(Math.abs(parameters[interceptIndex] / standardError),
degreesOfFreedom));
System.out.println(formatter.format(tPercentile) + " " +
significanceStars(tPercentile));
for (int index=0; index < dimension - 1; index++) {
System.out.print(trainingData.getDataAlphabet().lookupObject(index) + "\t");
System.out.print(formatter.format(parameters[index]) + "\t");
standardError =
Math.sqrt(meanSquaredError *
xTransposeXInverse[(dimension * index) + index]);
System.out.print(formatter.format(standardError) + "\t");
System.out.print(formatter.format(parameters[index] / standardError) + "\t");
tPercentile =
2 * (1.0 - StatFunctions.pt(Math.abs(parameters[index] / standardError),
degreesOfFreedom));
System.out.println(formatter.format(tPercentile) + " " +
significanceStars(tPercentile));
}
System.out.println();
System.out.println("SSE: " + formatter.format(sumSquaredError) +
" DF: " + degreesOfFreedom);
System.out.println("R^2: " +
formatter.format(sumSquaredModel / (sumSquaredError + sumSquaredModel)));
}
示例3: calculateWeights
import cc.mallet.util.StatFunctions; //导入依赖的package包/类
/**
* Calculates feature weights for features in the given instance list.
*/
private static double[] calculateWeights(InstanceList instanceList) {
int numFeatures = instanceList.getAlphabet().size();
double[] weights = new double[numFeatures];
double[] truePositives = new double[numFeatures];
double[] falsePositives = new double[numFeatures];
double numPos = 0;
double numNeg = 0;
Label posLabel = null;
for (Instance instance : instanceList) {
if (posLabel == null)
posLabel = (Label) instance.getTarget();
boolean isPos = false;
if (posLabel.equals(instance.getTarget())) {
isPos = true;
numPos++;
} else {
numNeg++;
}
FeatureVector fv = (FeatureVector) instance.getData();
for (int index : fv.getIndices()) {
if (isPos)
truePositives[index]++;
else
falsePositives[index]++;
}
}
for (int i = 0; i < numFeatures; i++) {
double tpr = 0.5;
if (numPos > 0) {
tpr = Math.max(Math.min(BNS_MAX_RATE, truePositives[i] / numPos), BNS_MIN_RATE);
}
double fpr = 0.5;
if (numNeg > 0) {
fpr = Math.max(Math.min(BNS_MAX_RATE, falsePositives[i] / numNeg), BNS_MIN_RATE);
}
weights[i] =
Math.abs(StatFunctions.qnorm(tpr, false) - StatFunctions.qnorm(fpr, false));
}
return weights;
}