本文整理汇总了Java中edu.stanford.nlp.math.ArrayMath.countNonZero方法的典型用法代码示例。如果您正苦于以下问题：Java ArrayMath.countNonZero方法的具体用法？Java ArrayMath.countNonZero怎么用？Java ArrayMath.countNonZero使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类edu.stanford.nlp.math.ArrayMath的用法示例。
在下文中一共展示了ArrayMath.countNonZero方法的2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: minimize
import edu.stanford.nlp.math.ArrayMath; //导入方法依赖的package包/类
public double[] minimize(DiffFunction function, double[] initial, double l1weight, double tol, int m) {
OptimizerState state = new OptimizerState(function, initial, m, l1weight, quiet);
if (!quiet) {
System.err.printf("Optimizing function of %d variables with OWL-QN parameters:\n", state.dim);
System.err.printf(" l1 regularization weight: %f.\n", l1weight);
System.err.printf(" L-BFGS memory parameter (m): %d\n", m);
System.err.printf(" Convergence tolerance: %f\n\n", tol);
System.err.printf("Iter n:\tnew_value\tdf\t(conv_crit)\tline_search\n");
System.err.printf("Iter 0:\t%.4e\t\t(***********)\t", state.value);
}
StringBuilder buf = new StringBuilder();
termCrit.getValue(state, buf);
for(int i=0; i<maxIters; i++){
buf.setLength(0);
state.updateDir();
state.backTrackingLineSearch();
double termCritVal = termCrit.getValue(state, buf);
if (!quiet) {
int numnonzero = ArrayMath.countNonZero(state.newX);
System.err.printf("Iter %4d:\t%.4e\t%d", state.iter, state.value, numnonzero);
System.err.print("\t"+ buf.toString());
if (printer!=null)
printer.printWeights();
}
//mheilman: I added this check because OWLQN was failing without it sometimes
//for large L1 penalties and few features...
//This checks that the parameters changed in the last iteration.
//If they didn't, then OWL-QN will try to divide by zero when approximating the Hessian.
//The ro values end up 0 when the line search ends up trying a newX that equals X (or newGrad and grad).
//That causes the differences stored in sList and yList to be zero, which eventually causes
//values in roList to be zero. Below, ro values appear in the denominator, and they would
//cause the program to crash in the mapDirByInverseHessian() method if any were zero.
//This only appears to happen once the parameters have already converged.
//I suspect that numerical loss of precision is the cause.
if(arrayEquals(state.x, state.newX)){
System.err.println("Warning: Stopping OWL-QN since there was no change in the parameters in the last iteration. This probably means convergence has been reached.");
break;
}
if (termCritVal < tol)
break;
state.shift();
}
if (!quiet) {
System.err.println();
System.err.printf("Finished with optimization. %d/%d non-zero weights.\n",
ArrayMath.countNonZero(state.newX), state.newX.length);
}
return state.newX;
}
示例2: minimize
import edu.stanford.nlp.math.ArrayMath; //导入方法依赖的package包/类
public double[] minimize(DiffFunction function, double[] initial, double l1weight, double tol, int m) {
OptimizerState state = new OptimizerState(function, initial, m, l1weight, quiet);
if (!quiet) {
System.err.printf("Optimizing function of %d variables with OWL-QN parameters:\n", state.dim);
System.err.printf(" l1 regularization weight: %f.\n", l1weight);
System.err.printf(" L-BFGS memory parameter (m): %d\n", m);
System.err.printf(" Convergence tolerance: %f\n\n", tol);
System.err.printf("Iter n:\tnew_value\tdf\t(conv_crit)\tline_search\n");
System.err.printf("Iter 0:\t%.4e\t\t(***********)\t", state.value);
}
StringBuilder buf = new StringBuilder();
termCrit.getValue(state, buf);
for (int i = 0; i < maxIters; i++) {
buf.setLength(0);
state.updateDir();
state.backTrackingLineSearch();
double termCritVal = termCrit.getValue(state, buf);
if (!quiet) {
int numnonzero = ArrayMath.countNonZero(state.newX);
System.err.printf("Iter %4d:\t%.4e\t%d", state.iter, state.value, numnonzero);
System.err.print("\t" + buf.toString());
}
if (termCritVal < tol) {
break;
}
state.shift();
}
if (!quiet) {
System.err.println();
System.err.printf("Finished with optimization. %d/%d non-zero weights.\n",
ArrayMath.countNonZero(state.newX), state.newX.length);
//System.err.println(Arrays.toString(state.newX));
}
return state.newX;
}