本文整理汇总了Java中tberg.murphy.opt.DifferentiableFunction类的典型用法代码示例。如果您正苦于以下问题：Java DifferentiableFunction类的具体用法？Java DifferentiableFunction怎么用？Java DifferentiableFunction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。
DifferentiableFunction类属于tberg.murphy.opt包，在下文中一共展示了DifferentiableFunction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: train
import tberg.murphy.opt.DifferentiableFunction; //导入依赖的package包/类
public CounterInterface<Integer> train(CounterInterface<Integer> initWeights, final LossAugmentedLinearModel<D> model, List<D> data, int iters) {
double[] denseInitWeights = dense(initWeights, numFeatures);
List<DifferentiableFunction> objs = new ArrayList<DifferentiableFunction>();
int numBatches = (int) Math.ceil(data.size() / (double) batchSize);
for (int b=0; b<numBatches; ++b) {
final List<D> batch = data.subList(b*batchSize, Math.min(data.size(), (b+1)*batchSize));
objs.add(new DifferentiableFunction() {
public Pair<Double, double[]> calculate(double[] x) {
CounterInterface<Integer> weights = sparse(x);
model.setWeights(weights);
List<UpdateBundle> ubBatch = model.getLossAugmentedUpdateBundleBatch(batch, 1.0);
double valBatch = 0.0;
CounterInterface<Integer> deltaBatch = new IntCounter();
for (UpdateBundle ub : ubBatch) {
CounterInterface<Integer> delta = new IntCounter();
delta.incrementAll(ub.gold, -1.0);
delta.incrementAll(ub.guess, 1.0);
double val = ub.loss + delta.dotProduct(weights);
if (val > 0.0) {
valBatch += val;
deltaBatch.incrementAll(delta);
}
}
return Pair.makePair(valBatch, dense(deltaBatch, numFeatures));
}
});
}
OnlineMinimizer minimizer = (L1reg? new AdaGradL1Minimizer(stepSize, delta, C, iters) : new AdaGradL2Minimizer(stepSize, delta, C, iters));
return sparse(minimizer.minimize(objs, denseInitWeights, true, null));
}
示例2: gradientTester
import tberg.murphy.opt.DifferentiableFunction; //导入依赖的package包/类
public static void gradientTester(float[][] Wtrans0, float[][] Htrans0, float[][] Xtrans0, float silenceEps) {
int[] loud = null;
if (silenceEps > 0.0) {
Pair<float[][], int[]> filterSilence = filterSilence(Xtrans0, silenceEps);
Xtrans0 = filterSilence.getFirst();
loud = filterSilence.getSecond();
Htrans0 = filterSilence(Htrans0, loud);
}
// W : n x r
// H : r x m
// X : n x m
final int n = Xtrans0[0].length;
final int m = Xtrans0.length;
final int r = Wtrans0.length;
System.out.println("Gradient Test GPU: ");
final Matrix X = Matrix.build(a.transpose(Xtrans0));
float[] flattenedWinit = Matrix.build(a.transpose(Wtrans0)).toArray();
float[] flattenedHinit = Matrix.build(a.transpose(Htrans0)).toArray();
DifferentiableFunction obj = new DifferentiableFunction() {
public Pair<Double, double[]> calculate(double[] xDouble) {
float[] x = a.toFloat(xDouble);
Matrix W = Matrix.build(n, r, Arrays.copyOfRange(x, 0, n * r));
Matrix H = Matrix.build(r, m, Arrays.copyOfRange(x, n * r, n * r + r * m));
// Matrix Htrans = H.transpose();
// Matrix Wtrans = W.transpose();
Matrix WH = W.mmul(H);
// Matrix logX = X.log();
// Matrix logWH = WH.log();
// float c = 1.0f;
// Matrix logXsubLogWHdivWH = logX.sub(logWH).add(Matrix.ones(n,
// m).mul(c)).div(WH);
float beta = 0.5f;
Matrix WHbetaSub1 = WH.pow(beta - 1.0f);
Matrix WHbetaSub2 = WH.pow(beta - 2.0f);
Matrix gH = W.transpose().mmul(WHbetaSub1.sub(X.mul(WHbetaSub2)));
Matrix gW = (WHbetaSub1.sub(X.mul(WHbetaSub2))).mmul(H.transpose());
double val = (1.0f / (beta * (beta - 1.0f))) * (X.pow(beta).norm1()
+ (beta - 1.0f) * WH.pow(beta).norm1() - beta * (X.mul(WH.pow(beta - 1.0f)).norm1()));
// Matrix gW = logXsubLogWHdivWH.mmul(Htrans).mul(-2.0f);
// Matrix gH = Wtrans.mmul(logXsubLogWHdivWH).mul(-2.0f);
// double val = Math.pow(logX.sub(logWH).add(Matrix.ones(n,
// m).mul(c)).norm2(), 2.0);
// Matrix gW = (X.mmul(Htrans)).comb(-2.0f, 2.0f,
// (WH).mmul(Htrans));
// Matrix gH = (Wtrans.mmul(X)).comb(-2.0f, 2.0f,
// Wtrans.mmul(WH));
// double val = Math.pow(X.distance2(WH), 2.0);
Pair<Double, double[]> result = Pair.makePair(val, a.toDouble(a.append(gW.toArray(), gH.toArray())));
CublasUtil.freeAllBut(X);
return result;
}
};
EmpiricalGradientTester.test(obj, a.toDouble(a.append(flattenedWinit, flattenedHinit)), 1e-2, 1.0, 1e-7);
}
示例3: gradientTester
import tberg.murphy.opt.DifferentiableFunction; //导入依赖的package包/类
public static void gradientTester(float[][] Wtrans0, float[][] Htrans0, float[][] Xtrans0, float silenceEps) {
int[] loud = null;
if (silenceEps > 0.0) {
Pair<float[][], int[]> filterSilence = filterSilence(Xtrans0, silenceEps);
Xtrans0 = filterSilence.getFirst();
loud = filterSilence.getSecond();
Htrans0 = filterSilence(Htrans0, loud);
}
// W : n x r
// H : r x m
// X : n x m
final int n = Xtrans0[0].length;
final int m = Xtrans0.length;
final int r = Wtrans0.length;
System.out.println("Gradient Test GPU: ");
final Matrix X = Matrix.build(a.transpose(Xtrans0));
float[] flattenedWinit = Matrix.build(a.transpose(Wtrans0)).toArray();
float[] flattenedHinit = Matrix.build(a.transpose(Htrans0)).toArray();
DifferentiableFunction obj = new DifferentiableFunction() {
public Pair<Double, double[]> calculate(double[] xDouble) {
float[] x = a.toFloat(xDouble);
Matrix W = Matrix.build(n, r, Arrays.copyOfRange(x, 0, n * r));
Matrix H = Matrix.build(r, m, Arrays.copyOfRange(x, n * r, n * r + r * m));
// Matrix Htrans = H.transpose();
// Matrix Wtrans = W.transpose();
Matrix WH = W.mmul(H);
// Matrix logX = X.log();
// Matrix logWH = WH.log();
// float c = 1.0f;
// Matrix logXsubLogWHdivWH = logX.sub(logWH).add(Matrix.ones(n,
// m).mul(c)).div(WH);
float beta = 0.5f;
Matrix WHbetaSub1 = WH.pow(beta - 1.0f);
Matrix WHbetaSub2 = WH.pow(beta - 2.0f);
Matrix gH = W.transpose().mmul(WHbetaSub1.sub(X.mul(WHbetaSub2)));
Matrix gW = (WHbetaSub1.sub(X.mul(WHbetaSub2))).mmul(H.transpose());
double val = (1.0f / (beta * (beta - 1.0f))) * (X.pow(beta).norm1()
+ (beta - 1.0f) * WH.pow(beta).norm1() - beta * (X.mul(WH.pow(beta - 1.0f)).norm1()));
// Matrix gW = logXsubLogWHdivWH.mmul(Htrans).mul(-2.0f);
// Matrix gH = Wtrans.mmul(logXsubLogWHdivWH).mul(-2.0f);
// double val = Math.pow(logX.sub(logWH).add(Matrix.ones(n,
// m).mul(c)).norm2(), 2.0);
// Matrix gW = (X.mmul(Htrans)).comb(-2.0f, 2.0f,
// (WH).mmul(Htrans));
// Matrix gH = (Wtrans.mmul(X)).comb(-2.0f, 2.0f,
// Wtrans.mmul(WH));
// double val = Math.pow(X.distance2(WH), 2.0);
Pair<Double, double[]> result = Pair.makePair(val, a.toDouble(a.append(gW.toArray(), gH.toArray())));
JOCLBlasUtil.freeAllBut(X);
return result;
}
};
EmpiricalGradientTester.test(obj, a.toDouble(a.append(flattenedWinit, flattenedHinit)), 1e-2, 1.0, 1e-7);
}