/** {@inheritDoc} */
public double[] gradient(double x, double[] parameters) {
final double a = parameters[0];
final double omega = parameters[1];
final double phi = parameters[2];
final double alpha = omega * x + phi;
final double cosAlpha = FastMath.cos(alpha);
final double sinAlpha = FastMath.sin(alpha);
return new double[] {cosAlpha, -a * x * sinAlpha, -a * sinAlpha};
}
/**
* Calculates the Nth generalized harmonic number. See <a
* href="http://mathworld.wolfram.com/HarmonicSeries.html">Harmonic Series</a>.
*
* @param n the term in the series to calculate (must be ≥ 1)
* @param m the exponent; special case m == 1.0 is the harmonic series
* @return the nth generalized harmonic number
*/
private double generalizedHarmonic(final int n, final double m) {
double value = 0;
for (int k = n; k > 0; --k) {
value += 1.0 / FastMath.pow(k, m);
}
return value;
}
public static NullStates getNullState(Context context) {
final VehicleState state = context.getState();
// final Observation obs = context.getObservation();
final BlockStateObservation blockStateObs = state.getBlockStateObservation();
EVehiclePhase phase = state.getJourneyState().getPhase();
if (blockStateObs == null) {
return NullStates.NULL_STATE;
} else {
final boolean hasScheduledTime =
FastMath.abs(state.getBlockStateObservation().getScheduleDeviation()) > 0d;
if (!hasScheduledTime) {
return NullStates.NULL_STATE;
}
if (!state.getBlockStateObservation().isSnapped()
&& ((EVehiclePhase.DEADHEAD_AFTER == phase
&& state.getBlockStateObservation().getScheduleDeviation() == 0d)
|| EVehiclePhase.AT_BASE == phase
|| (EVehiclePhase.DEADHEAD_BEFORE == phase
&& state.getBlockStateObservation().getScheduleDeviation() == 0d)
|| (EVehiclePhase.LAYOVER_BEFORE == phase)
&& state.getBlockStateObservation().getScheduleDeviation() == 0d)) {
return NullStates.NULL_STATE;
}
return NullStates.NON_NULL_STATE;
}
}
/**
* The probability mass function P(X = x) for a Zipf distribution.
*
* @param x the value at which the probability density function is evaluated.
* @return the value of the probability mass function at x
*/
public double probability(final int x) {
if (x <= 0 || x > numberOfElements) {
return 0.0;
}
return (1.0 / FastMath.pow(x, exponent)) / generalizedHarmonic(numberOfElements, exponent);
}
/**
* Get a vector orthogonal to the instance.
*
* <p>There are an infinite number of normalized vectors orthogonal to the instance. This method
* picks up one of them almost arbitrarily. It is useful when one needs to compute a reference
* frame with one of the axes in a predefined direction. The following example shows how to build
* a frame having the k axis aligned with the known vector u :
*
* <pre><code>
* Vector3D k = u.normalize();
* Vector3D i = k.orthogonal();
* Vector3D j = Vector3D.crossProduct(k, i);
* </code></pre>
*
* @return a new normalized vector orthogonal to the instance
* @exception ArithmeticException if the norm of the instance is null
*/
public Vector3D orthogonal() {
double threshold = 0.6 * getNorm();
if (threshold == 0) {
throw MathRuntimeException.createArithmeticException(LocalizedFormats.ZERO_NORM);
}
if ((x >= -threshold) && (x <= threshold)) {
double inverse = 1 / FastMath.sqrt(y * y + z * z);
return new Vector3D(0, inverse * z, -inverse * y);
} else if ((y >= -threshold) && (y <= threshold)) {
double inverse = 1 / FastMath.sqrt(x * x + z * z);
return new Vector3D(-inverse * z, 0, inverse * x);
}
double inverse = 1 / FastMath.sqrt(x * x + y * y);
return new Vector3D(inverse * y, -inverse * x, 0);
}
/**
* Returns the next pseudorandom, Gaussian ("normally") distributed {@code double} value with mean
* {@code 0.0} and standard deviation {@code 1.0} from this random number generator's sequence.
*
* <p>The default implementation uses the <em>Polar Method</em> due to G.E.P. Box, M.E. Muller and
* G. Marsaglia, as described in D. Knuth, <u>The Art of Computer Programming</u>, 3.4.1C.
*
* <p>The algorithm generates a pair of independent random values. One of these is cached for
* reuse, so the full algorithm is not executed on each activation. Implementations that do not
* override this method should make sure to call {@link #clear} to clear the cached value in the
* implementation of {@link #setSeed(long)}.
*
* @return the next pseudorandom, Gaussian ("normally") distributed {@code double} value with mean
* {@code 0.0} and standard deviation {@code 1.0} from this random number generator's sequence
*/
public double nextGaussian() {
if (!Double.isNaN(cachedNormalDeviate)) {
double dev = cachedNormalDeviate;
cachedNormalDeviate = Double.NaN;
return dev;
}
double v1 = 0;
double v2 = 0;
double s = 1;
while (s >= 1) {
v1 = 2 * nextDouble() - 1;
v2 = 2 * nextDouble() - 1;
s = v1 * v1 + v2 * v2;
}
if (s != 0) {
s = FastMath.sqrt(-2 * FastMath.log(s) / s);
}
cachedNormalDeviate = v2 * s;
return v1 * s;
}
/**
* Compute the angular separation between two vectors.
*
* <p>This method computes the angular separation between two vectors using the dot product for
* well separated vectors and the cross product for almost aligned vectors. This allows to have a
* good accuracy in all cases, even for vectors very close to each other.
*
* @param v1 first vector
* @param v2 second vector
* @return angular separation between v1 and v2
* @exception ArithmeticException if either vector has a null norm
*/
public static double angle(Vector3D v1, Vector3D v2) {
double normProduct = v1.getNorm() * v2.getNorm();
if (normProduct == 0) {
throw MathRuntimeException.createArithmeticException(LocalizedFormats.ZERO_NORM);
}
double dot = dotProduct(v1, v2);
double threshold = normProduct * 0.9999;
if ((dot < -threshold) || (dot > threshold)) {
// the vectors are almost aligned, compute using the sine
Vector3D v3 = crossProduct(v1, v2);
if (dot >= 0) {
return FastMath.asin(v3.getNorm() / normProduct);
}
return FastMath.PI - FastMath.asin(v3.getNorm() / normProduct);
}
// the vectors are sufficiently separated to use the cosine
return FastMath.acos(dot / normProduct);
}
private void calculateBlbSum(
double[] sample,
double samplingRate,
double bagExp,
final int numberOfBags,
final int numberOfBootstraps,
Mean perbootstrapTime,
Mean perbagTime) {
bagSums = new double[numberOfBags];
int bag_size = (int) FastMath.ceil(FastMath.pow(sample.length, bagExp));
int[] index = new int[sample.length];
for (int ii = 0; ii < sample.length; ii++) {
index[ii] = ii;
}
int[] origIndex = index.clone();
long bootstrapTime = 0;
Mean actualSum = new Mean();
for (int ii = 0; ii < numberOfBags; ii++) {
SamplingUtilities.KnuthShuffle(index);
double[] sampleBag = new double[bag_size];
for (int jj = 0; jj < bag_size; jj++) {
sampleBag[jj] = sample[index[jj]];
}
BootstrapSum sum =
new BootstrapSum(sampleBag, samplingRate, numberOfBootstraps, sample.length);
bagSums[ii] = sum.Sum();
actualSum.increment(bagSums[ii]);
perbootstrapTime.increment(sum.getMeanTime());
bootstrapTime += sum.getTimes()[sum.getTimes().length - 1];
perbagTime.increment(sum.getTimes()[sum.getTimes().length - 1]);
index = origIndex.clone();
}
meanSum = actualSum.getResult();
totalTime = bootstrapTime;
}
/**
* Get the elevation of the vector.
*
* @return elevation (δ) of the vector, between -π/2 and +π/2
* @see #Vector3D(double, double)
*/
public double getDelta() {
return FastMath.asin(z / getNorm());
}
/**
* Get the azimuth of the vector.
*
* @return azimuth (α) of the vector, between -π and +π
* @see #Vector3D(double, double)
*/
public double getAlpha() {
return FastMath.atan2(y, x);
}
/**
* Get the L<sub>∞</sub> norm for the vector.
*
* @return L<sub>∞</sub> norm for the vector
*/
public double getNormInf() {
return FastMath.max(FastMath.max(FastMath.abs(x), FastMath.abs(y)), FastMath.abs(z));
}
/**
* Get the L<sub>2</sub> norm for the vector.
*
* @return euclidian norm for the vector
*/
public double getNorm() {
return FastMath.sqrt(x * x + y * y + z * z);
}
/**
* Get the L<sub>1</sub> norm for the vector.
*
* @return L<sub>1</sub> norm for the vector
*/
public double getNorm1() {
return FastMath.abs(x) + FastMath.abs(y) + FastMath.abs(z);
}
/**
* Returns a <code>double</code> whose value is <tt>(this<sup>exponent</sup>)</tt>, returning the
* result in reduced form.
*
* @param exponent exponent to which this <code>BigFraction</code> is to be raised.
* @return <tt>this<sup>exponent</sup></tt>.
*/
public double pow(final double exponent) {
return FastMath.pow(numerator.doubleValue(), exponent)
/ FastMath.pow(denominator.doubleValue(), exponent);
}
private void costantiniUnwrap() throws LPException {
final int ny = wrappedPhase.rows - 1; // start from Zero!
final int nx = wrappedPhase.columns - 1; // start from Zero!
if (wrappedPhase.isVector()) throw new IllegalArgumentException("Input must be 2D array");
if (wrappedPhase.rows < 2 || wrappedPhase.columns < 2)
throw new IllegalArgumentException("Size of input must be larger than 2");
// Default weight
DoubleMatrix w1 = DoubleMatrix.ones(ny + 1, 1);
w1.put(0, 0.5);
w1.put(w1.length - 1, 0.5);
DoubleMatrix w2 = DoubleMatrix.ones(1, nx + 1);
w2.put(0, 0.5);
w2.put(w2.length - 1, 0.5);
DoubleMatrix weight = w1.mmul(w2);
DoubleMatrix i, j, I_J, IP1_J, I_JP1;
DoubleMatrix Psi1, Psi2;
DoubleMatrix[] ROWS;
// Compute partial derivative Psi1, eqt (1,3)
i = intRangeDoubleMatrix(0, ny - 1);
j = intRangeDoubleMatrix(0, nx);
ROWS = grid2D(i, j);
I_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1]);
IP1_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0].add(1), ROWS[1]);
Psi1 =
JblasUtils.getMatrixFromIdx(wrappedPhase, IP1_J)
.sub(JblasUtils.getMatrixFromIdx(wrappedPhase, I_J));
Psi1 = UnwrapUtils.wrapDoubleMatrix(Psi1);
// Compute partial derivative Psi2, eqt (2,4)
i = intRangeDoubleMatrix(0, ny);
j = intRangeDoubleMatrix(0, nx - 1);
ROWS = grid2D(i, j);
I_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1]);
I_JP1 = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1].add(1));
Psi2 =
JblasUtils.getMatrixFromIdx(wrappedPhase, I_JP1)
.sub(JblasUtils.getMatrixFromIdx(wrappedPhase, I_J));
Psi2 = UnwrapUtils.wrapDoubleMatrix(Psi2);
// Compute beq
DoubleMatrix beq = DoubleMatrix.zeros(ny, nx);
i = intRangeDoubleMatrix(0, ny - 1);
j = intRangeDoubleMatrix(0, nx - 1);
ROWS = grid2D(i, j);
I_J = JblasUtils.sub2ind(Psi1.rows, ROWS[0], ROWS[1]);
I_JP1 = JblasUtils.sub2ind(Psi1.rows, ROWS[0], ROWS[1].add(1));
beq.addi(JblasUtils.getMatrixFromIdx(Psi1, I_JP1).sub(JblasUtils.getMatrixFromIdx(Psi1, I_J)));
I_J = JblasUtils.sub2ind(Psi2.rows, ROWS[0], ROWS[1]);
I_JP1 = JblasUtils.sub2ind(Psi2.rows, ROWS[0].add(1), ROWS[1]);
beq.subi(JblasUtils.getMatrixFromIdx(Psi2, I_JP1).sub(JblasUtils.getMatrixFromIdx(Psi2, I_J)));
beq.muli(-1 / (2 * Constants._PI));
for (int k = 0; k < beq.length; k++) {
beq.put(k, Math.round(beq.get(k)));
}
beq.reshape(beq.length, 1);
logger.debug("Constraint matrix");
i = intRangeDoubleMatrix(0, ny - 1);
j = intRangeDoubleMatrix(0, nx - 1);
ROWS = grid2D(i, j);
DoubleMatrix ROW_I_J = JblasUtils.sub2ind(i.length, ROWS[0], ROWS[1]);
int nS0 = nx * ny;
// Use by S1p, S1m
DoubleMatrix[] COLS;
COLS = grid2D(i, j);
DoubleMatrix COL_IJ_1 = JblasUtils.sub2ind(i.length, COLS[0], COLS[1]);
COLS = grid2D(i, j.add(1));
DoubleMatrix COL_I_JP1 = JblasUtils.sub2ind(i.length, COLS[0], COLS[1]);
int nS1 = (nx + 1) * (ny);
// SOAPBinding.Use by S2p, S2m
COLS = grid2D(i, j);
DoubleMatrix COL_IJ_2 = JblasUtils.sub2ind(i.length + 1, COLS[0], COLS[1]);
COLS = grid2D(i.add(1), j);
DoubleMatrix COL_IP1_J = JblasUtils.sub2ind(i.length + 1, COLS[0], COLS[1]);
int nS2 = nx * (ny + 1);
// Equality constraint matrix (Aeq)
/*
S1p = + sparse(ROW_I_J, COL_I_JP1,1,nS0,nS1) ...
- sparse(ROW_I_J, COL_IJ_1,1,nS0,nS1);
S1m = -S1p;
S2p = - sparse(ROW_I_J, COL_IP1_J,1,nS0,nS2) ...
+ sparse(ROW_I_J, COL_IJ_2,1,nS0,nS2);
S2m = -S2p;
*/
// ToDo: Aeq matrix should be sparse from it's initialization, look into JblasMatrix factory for
// howto
// ...otherwise even a data set of eg 40x40 pixels will exhaust heap:
// ... dimension of Aeq (equality constraints) matrix for 30x30 input is 1521x6240 matrix
// ... dimension of Aeq ( ) matrix for 50x50 input is 2401x9800
// ... dimension of Aeq ( ) matrix for 512x512 input is 261121x1046528
DoubleMatrix S1p =
JblasUtils.setUpMatrixFromIdx(nS0, nS1, ROW_I_J, COL_I_JP1)
.sub(JblasUtils.setUpMatrixFromIdx(nS0, nS1, ROW_I_J, COL_IJ_1));
DoubleMatrix S1m = S1p.neg();
DoubleMatrix S2p =
JblasUtils.setUpMatrixFromIdx(nS0, nS2, ROW_I_J, COL_IP1_J)
.neg()
.add(JblasUtils.setUpMatrixFromIdx(nS0, nS2, ROW_I_J, COL_IJ_2));
DoubleMatrix S2m = S2p.neg();
DoubleMatrix Aeq =
concatHorizontally(concatHorizontally(S1p, S1m), concatHorizontally(S2p, S2m));
final int nObs = Aeq.columns;
final int nUnkn = Aeq.rows;
DoubleMatrix c1 = JblasUtils.getMatrixFromRange(0, ny, 0, weight.columns, weight);
DoubleMatrix c2 = JblasUtils.getMatrixFromRange(0, weight.rows, 0, nx, weight);
c1.reshape(c1.length, 1);
c2.reshape(c2.length, 1);
DoubleMatrix cost =
DoubleMatrix.concatVertically(
DoubleMatrix.concatVertically(c1, c1), DoubleMatrix.concatVertically(c2, c2));
logger.debug("Minimum network flow resolution");
StopWatch clockLP = new StopWatch();
LinearProgram lp = new LinearProgram(cost.data);
lp.setMinProblem(true);
boolean[] integerBool = new boolean[nObs];
double[] lowerBound = new double[nObs];
double[] upperBound = new double[nObs];
for (int k = 0; k < nUnkn; k++) {
lp.addConstraint(new LinearEqualsConstraint(Aeq.getRow(k).toArray(), beq.get(k), "cost"));
}
for (int k = 0; k < nObs; k++) {
integerBool[k] = true;
lowerBound[k] = 0;
upperBound[k] = 99999;
}
// setup bounds and integer nature
lp.setIsinteger(integerBool);
lp.setUpperbound(upperBound);
lp.setLowerbound(lowerBound);
LinearProgramSolver solver = SolverFactory.newDefault();
// double[] solution;
// solution = solver.solve(lp);
DoubleMatrix solution = new DoubleMatrix(solver.solve(lp));
clockLP.stop();
logger.debug("Total GLPK time: {} [sec]", (double) (clockLP.getElapsedTime()) / 1000);
// Displatch the LP solution
int offset;
int[] idx1p = JblasUtils.intRangeIntArray(0, nS1 - 1);
DoubleMatrix x1p = solution.get(idx1p);
x1p.reshape(ny, nx + 1);
offset = idx1p[nS1 - 1] + 1;
int[] idx1m = JblasUtils.intRangeIntArray(offset, offset + nS1 - 1);
DoubleMatrix x1m = solution.get(idx1m);
x1m.reshape(ny, nx + 1);
offset = idx1m[idx1m.length - 1] + 1;
int[] idx2p = JblasUtils.intRangeIntArray(offset, offset + nS2 - 1);
DoubleMatrix x2p = solution.get(idx2p);
x2p.reshape(ny + 1, nx);
offset = idx2p[idx2p.length - 1] + 1;
int[] idx2m = JblasUtils.intRangeIntArray(offset, offset + nS2 - 1);
DoubleMatrix x2m = solution.get(idx2m);
x2m.reshape(ny + 1, nx);
// Compute the derivative jumps, eqt (20,21)
DoubleMatrix k1 = x1p.sub(x1m);
DoubleMatrix k2 = x2p.sub(x2m);
// (?) Round to integer solution
if (roundK == true) {
for (int idx = 0; idx < k1.length; idx++) {
k1.put(idx, FastMath.floor(k1.get(idx)));
}
for (int idx = 0; idx < k2.length; idx++) {
k2.put(idx, FastMath.floor(k2.get(idx)));
}
}
// Sum the jumps with the wrapped partial derivatives, eqt (10,11)
k1.reshape(ny, nx + 1);
k2.reshape(ny + 1, nx);
k1.addi(Psi1.div(Constants._TWO_PI));
k2.addi(Psi2.div(Constants._TWO_PI));
// Integrate the partial derivatives, eqt (6)
// cumsum() method in JblasTester -> see cumsum_demo() in JblasTester.cumsum_demo()
DoubleMatrix k2_temp = DoubleMatrix.concatHorizontally(DoubleMatrix.zeros(1), k2.getRow(0));
k2_temp = JblasUtils.cumsum(k2_temp, 1);
DoubleMatrix k = DoubleMatrix.concatVertically(k2_temp, k1);
k = JblasUtils.cumsum(k, 1);
// Unwrap - final solution
unwrappedPhase = k.mul(Constants._TWO_PI);
}
/**
* Compute the distance between two vectors according to the L<sub>2</sub> norm.
*
* <p>Calling this method is equivalent to calling: <code>v1.subtract(v2).getNorm()</code> except
* that no intermediate vector is built
*
* @param v1 first vector
* @param v2 second vector
* @return the distance between v1 and v2 according to the L<sub>2</sub> norm
*/
public static double distance(Vector3D v1, Vector3D v2) {
final double dx = v2.x - v1.x;
final double dy = v2.y - v1.y;
final double dz = v2.z - v1.z;
return FastMath.sqrt(dx * dx + dy * dy + dz * dz);
}
public class DenseFeatureMatrix {
int inputSize;
int outputSize;
INDArray features;
INDArray featuresT;
GradientStore gradientStore = new GradientStore();
double l2 = GlobalParameters.l2regularizerLambdaDefault;
double learningRate = GlobalParameters.learningRateDefault;
// adagrad vars
boolean useAdagrad = GlobalParameters.useAdagradDefault;
INDArray adagradQuotient;
double adagradEps = 0.001;
double adagradMax = 10;
// gaussian noise
double noiseVar = GlobalParameters.noiseDevDefault;
double noiseVarSqrt = FastMath.sqrt(noiseVar);;
HashMap<Integer, INDArray> currentNoise = new HashMap<Integer, INDArray>();
// momentum vars
boolean useMomentum = GlobalParameters.useMomentumDefault;
INDArray momentumPrevUpdate;
double momentum = GlobalParameters.momentumDefault;
// adadelta vars
boolean useAdadelta = GlobalParameters.useAdadeltaDefault;
INDArray adadeltaRMSGradient;
INDArray adadeltaRMSUpdate;
double adadeltaMomentum = GlobalParameters.adadeltaMomentumDefault;
double adadeltaEps = GlobalParameters.adadeltaEpsDefault;
// commit
int commitMethod = GlobalParameters.commitMethodDefault;
public DenseFeatureMatrix(
int inputSize, int outputSize, boolean useAdagrad, boolean useMomentum, boolean useAdadelta) {
if (inputSize == 1) {
throw new RuntimeException("input size = 1: use vector instead");
}
this.inputSize = inputSize;
this.outputSize = outputSize;
this.useAdadelta = useAdadelta;
this.useAdagrad = useAdagrad;
this.useMomentum = useMomentum;
if (useAdagrad) {
adagradQuotient = Nd4j.zeros(inputSize, outputSize);
adagradQuotient.addi(adagradEps);
}
if (useMomentum) {
momentumPrevUpdate = Nd4j.zeros(inputSize, outputSize);
}
if (useAdadelta) {
adadeltaRMSGradient = Nd4j.zeros(inputSize, outputSize);
adadeltaRMSUpdate = Nd4j.zeros(inputSize, outputSize);
}
}
public DenseFeatureMatrix(int inputSize, int outputSize) {
if (inputSize == 1) {
throw new RuntimeException("input size = 1: use vector instead");
}
this.inputSize = inputSize;
this.outputSize = outputSize;
if (useAdagrad) {
adagradQuotient = Nd4j.zeros(inputSize, outputSize);
adagradQuotient.addi(adagradEps);
}
if (useMomentum) {
momentumPrevUpdate = Nd4j.zeros(inputSize, outputSize);
}
if (useAdadelta) {
adadeltaRMSGradient = Nd4j.zeros(inputSize, outputSize);
adadeltaRMSUpdate = Nd4j.zeros(inputSize, outputSize);
}
}
public void initialize(double[][] vals) {
features = Nd4j.create(vals);
featuresT = features.transpose();
}
public void initializeUniform(double min, double max) {
double[][] featuresMatrixStub = new double[inputSize][outputSize];
RandomUtils.initializeRandomMatrix(featuresMatrixStub, min, max, 1);
features = Nd4j.create(featuresMatrixStub);
featuresT = features.transpose();
}
public void normalizedInitializationHtan(int fanin, int fanout) {
double max = Math.sqrt(6.0d / (fanout + fanin));
double[][] featuresMatrixStub = new double[inputSize][outputSize];
RandomUtils.initializeRandomMatrix(featuresMatrixStub, -max, max, 1);
features = Nd4j.create(featuresMatrixStub);
featuresT = features.transpose();
}
public void normalizedInitializationSigmoid(int fanin, int fanout) {
double max = 4 * Math.sqrt(6.0d / (fanin + fanout));
double[][] featuresMatrixStub = new double[inputSize][outputSize];
RandomUtils.initializeRandomMatrix(featuresMatrixStub, -max, max, 1);
features = Nd4j.create(featuresMatrixStub);
featuresT = features.transpose();
}
public INDArray getWeights() {
return features;
}
public INDArray getTranspose() {
return featuresT;
}
public void storeGradients(int processId, INDArray gradient) {
gradientStore.addGradient(processId, gradient);
}
public void storeInputsAndOutputs(int id, INDArray x, INDArray yGrad) {
gradientStore.addInputAndOutput(id, x, yGrad);
}
public void checkinGradients(int id) {
gradientStore.computeGradientAndAdd(id);
}
public void update() {
INDArray gradient = null;
if (commitMethod == 0) {
gradient = gradientStore.getGradientAvg();
} else {
gradient = gradientStore.getGradientSum();
}
if (gradient == null) return;
INDArray gradientL2 = gradient.sub(features.mul(l2));
if (useAdagrad) {
getAdagradGradient(gradientL2);
features.addi(gradientL2.mul(learningRate));
} else if (useMomentum) {
getMomentumGradient(gradientL2);
features.addi(gradientL2.mul(learningRate));
} else if (useAdadelta) {
getAdadeltaGradient(gradientL2);
features.addi(gradientL2);
} else {
features.addi(gradientL2.mul(learningRate));
}
capValues(GlobalParameters.maxVal);
featuresT = features.transpose();
gradientStore.init();
}
protected void getAdagradGradient(INDArray gradient) {
adagradQuotient.addi(gradient.mul(gradient));
for (int i = 0; i < inputSize; i++) {
for (int j = 0; j < outputSize; j++) {
double adagradQ = adagradQuotient.getDouble(i, j);
if (adagradMax < adagradQ) {
adagradQuotient.putScalar(new int[] {i, j}, adagradMax);
adagradQ = adagradMax;
}
gradient.putScalar(new int[] {i, j}, gradient.getDouble(i, j) / Math.sqrt(adagradQ));
}
}
}
protected void getAdadeltaGradient(INDArray gradient) {
adadeltaRMSGradient =
adadeltaRMSGradient
.mul(adadeltaMomentum)
.add(gradient.mul(gradient).mul(1 - adadeltaMomentum));
gradient.muli(
Transforms.sqrt(adadeltaRMSUpdate.add(adadeltaEps))
.div(Transforms.sqrt(adadeltaRMSGradient.add(adadeltaEps))));
adadeltaRMSUpdate.mul(adadeltaMomentum).add(gradient.mul(gradient).mul(1 - adadeltaMomentum));
}
protected void getMomentumGradient(INDArray gradient) {
INDArray momemtumUpdate = momentumPrevUpdate.mul(momentum);
gradient.addi(momemtumUpdate);
momentumPrevUpdate = gradient.dup();
}
public INDArray genGaussianNoise(int id) {
if (!currentNoise.containsKey(id)) {
INDArray zeroMean = Nd4j.zeros(inputSize, outputSize);
currentNoise.put(id, Sampling.normal(RandomUtils.getRandomGenerator(id), zeroMean, noiseVar));
} else {
RealDistribution reals =
new NormalDistribution(
RandomUtils.getRandomGenerator(id),
0,
noiseVarSqrt,
NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
INDArrayUtils.shiftLeft(
currentNoise.get(id),
inputSize,
outputSize,
RandomUtils.getRandomGenerator(id).nextInt(inputSize * outputSize),
reals.sample());
}
// currentNoise = Sampling.normal(RandomUtils.getRandomGenerator(id), zeroMean, noiseVar);
return currentNoise.get(id);
}
public void capValues(double max) {
INDArray linear = features.linearView();
for (int i = 0; i < linear.size(0); i++) {
linear.putScalar(i, Math.max(-max, Math.min(max, linear.getDouble(i))));
}
}
public void save(PrintStream out) {
out.println(inputSize);
out.println(outputSize);
out.println(useAdagrad);
out.println(adagradEps);
out.println(adagradMax);
out.println(noiseVar);
out.println(useMomentum);
out.println(momentum);
out.println(useAdadelta);
out.println(adadeltaEps);
out.println(adadeltaMomentum);
saveMatrix(features, out);
if (useAdagrad) {
saveMatrix(adagradQuotient, out);
}
if (useMomentum) {
saveMatrix(momentumPrevUpdate, out);
}
if (useAdadelta) {
saveMatrix(adadeltaRMSGradient, out);
saveMatrix(adadeltaRMSUpdate, out);
}
}
public void saveMatrix(INDArray matrix, PrintStream out) {
for (int row = 0; row < inputSize; row++) {
for (int col = 0; col < outputSize; col++) {
double val = matrix.getDouble(row, col);
if (col < outputSize - 1) {
out.print(val + " ");
} else {
out.println(val);
}
}
}
}
public static DenseFeatureMatrix load(BufferedReader in) {
try {
int inputSize = Integer.parseInt(in.readLine());
int outputSize = Integer.parseInt(in.readLine());
DenseFeatureMatrix matrix = new DenseFeatureMatrix(inputSize, outputSize);
matrix.useAdagrad = Boolean.parseBoolean(in.readLine());
matrix.adagradEps = Double.parseDouble(in.readLine());
matrix.adagradMax = Double.parseDouble(in.readLine());
matrix.noiseVar = Double.parseDouble(in.readLine());
matrix.useMomentum = Boolean.parseBoolean(in.readLine());
matrix.momentum = Double.parseDouble(in.readLine());
matrix.useAdadelta = Boolean.parseBoolean(in.readLine());
matrix.adadeltaEps = Double.parseDouble(in.readLine());
matrix.adadeltaMomentum = Double.parseDouble(in.readLine());
matrix.features = loadMatrix(in, inputSize, outputSize);
if (matrix.useAdagrad) {
matrix.adagradQuotient = loadMatrix(in, inputSize, outputSize);
}
if (matrix.useMomentum) {
matrix.momentumPrevUpdate = loadMatrix(in, inputSize, outputSize);
}
if (matrix.useAdadelta) {
matrix.adadeltaRMSGradient = loadMatrix(in, inputSize, outputSize);
matrix.adadeltaRMSUpdate = loadMatrix(in, inputSize, outputSize);
}
matrix.featuresT = matrix.features.transpose();
return matrix;
} catch (Exception e) {
throw new RuntimeException(e);
}
}
public static INDArray loadMatrix(BufferedReader in, int inputSize, int outputSize)
throws IOException {
INDArray matrix = Nd4j.create(inputSize, outputSize);
for (int row = 0; row < inputSize; row++) {
String[] vals = in.readLine().split("\\s+");
for (int col = 0; col < outputSize; col++) {
matrix.putScalar(new int[] {row, col}, Double.parseDouble(vals[col]));
}
}
return matrix;
}
public void setL2(double l2) {
this.l2 = l2;
}
public static void main(String[] args) {
DenseFeatureMatrix matrix = new DenseFeatureMatrix(1, 5);
matrix.initializeUniform(-0.1, 0.1);
matrix.save(IOUtils.getPrintStream("/tmp/file"));
DenseFeatureMatrix.load(IOUtils.getReader("/tmp/file")).save(System.err);
}
public void normalize() {
features.divi(features.sum(0).getDouble(0) * inputSize);
featuresT = features.transpose();
}
}
/** {@inheritDoc} */
public double value(double x, double[] parameters) {
final double a = parameters[0];
final double omega = parameters[1];
final double phi = parameters[2];
return a * FastMath.cos(omega * x + phi);
}
public static void main(String[] args) {
TreeMap<Double, List<int[]>> voiceLeadingMap = new TreeMap<Double, List<int[]>>();
// List<int[]> vl = voiceLeading2Voices();
// List<int[]> vl = voiceLeading3Voices();
// System.out.println(vl.size());
// for (int[] is : vl) {
// System.out.print("[" );
// for (int i : is) {
// System.out.print(i + ",");
// }
// System.out.print("]" );
// System.out.println();
// }
// for (int[] is : vl) {
// double vlDegree = Utilities.round(analyseVoiceLeading(is),3);
// List<int[]> ch = voiceLeadingMap.get(vlDegree);
// if(ch == null){
// voiceLeadingMap.put(vlDegree, ch = new ArrayList<int[]>());
// }
// ch.add(is);
//
// }
//
// Set<Double> keys = voiceLeadingMap.keySet();
// for (Double key : keys) {
// List<int[]> ch = voiceLeadingMap.get(key);
//// System.out.println();
// System.out.println("size group: " + ch.size());
// for (int[] is : ch) {
// System.out.print(" vl : " + key);
// System.out.print("[" );
// for (int i : is) {
// System.out.print(i + ",");
// }
// System.out.print("]" );
//// System.out.println();
// }
// System.out.println();
//
// }
double[] firstChord = {60.0, 50.0, 60.0};
double[] secondChord = {60.0, 55.0, 0.0};
double sum = 0;
for (int i = 0; i < firstChord.length; i++) {
if (firstChord[i] != 0 && secondChord[i] != 0) {
sum += FastMath.abs(firstChord[i] - secondChord[i]);
}
}
// double smoothnes = MathUtils.distance1(vec1,vec2);
System.out.println("smoothness: " + sum);
Note[] chord1 = new Note[4];
chord1[0] = new Note(60, 1.0);
chord1[1] = new Note(60, 1.0);
chord1[2] = new Note(60, 1.0);
chord1[3] = new Note(60, 1.0);
Note[] chord2 = new Note[4];
chord2[0] = new Note(60, 1.0);
chord2[1] = new Note(70, 1.0);
chord2[2] = new Note(70, 1.0);
chord2[3] = new Note(60, 1.0);
// Calculates the L2 (Euclidean) distance between two points.
double euclideanDistance = euclideanDistance(chord1, chord2);
System.out.println("euclideanDistance: " + euclideanDistance);
// Smoothness: Calculates the L1 (sum of abs) distance between two points.
double smoothness = taxiCab(chord1, chord2);
System.out.println("smoothness: " + smoothness);
// Calculates the L infinite (max of abs) distance between two points.
double lInfDistance = infiniteDistance(chord1, chord2);
System.out.println("lInfDistance: " + lInfDistance);
}
/**
* Compute the distance between two vectors according to the L<sub>1</sub> norm.
*
* <p>Calling this method is equivalent to calling: <code>v1.subtract(v2).getNorm1()</code> except
* that no intermediate vector is built
*
* @param v1 first vector
* @param v2 second vector
* @return the distance between v1 and v2 according to the L<sub>1</sub> norm
*/
public static double distance1(Vector3D v1, Vector3D v2) {
final double dx = FastMath.abs(v2.x - v1.x);
final double dy = FastMath.abs(v2.y - v1.y);
final double dz = FastMath.abs(v2.z - v1.z);
return dx + dy + dz;
}
/**
* Create a fraction given the double value and either the maximum error allowed or the maximum
* number of denominator digits.
*
* <p>NOTE: This constructor is called with EITHER - a valid epsilon value and the maxDenominator
* set to Integer.MAX_VALUE (that way the maxDenominator has no effect). OR - a valid
* maxDenominator value and the epsilon value set to zero (that way epsilon only has effect if
* there is an exact match before the maxDenominator value is reached).
*
* <p>It has been done this way so that the same code can be (re)used for both scenarios. However
* this could be confusing to users if it were part of the public API and this constructor should
* therefore remain PRIVATE. See JIRA issue ticket MATH-181 for more details:
*
* <p>https://issues.apache.org/jira/browse/MATH-181
*
* @param value the double value to convert to a fraction.
* @param epsilon maximum error allowed. The resulting fraction is within <code>epsilon</code> of
* <code>value</code>, in absolute terms.
* @param maxDenominator maximum denominator value allowed.
* @param maxIterations maximum number of convergents.
* @throws FractionConversionException if the continued fraction failed to converge.
*/
private BigFraction(
final double value, final double epsilon, final int maxDenominator, int maxIterations)
throws FractionConversionException {
long overflow = Integer.MAX_VALUE;
double r0 = value;
long a0 = (long) FastMath.floor(r0);
if (a0 > overflow) {
throw new FractionConversionException(value, a0, 1l);
}
// check for (almost) integer arguments, which should not go
// to iterations.
if (FastMath.abs(a0 - value) < epsilon) {
numerator = BigInteger.valueOf(a0);
denominator = BigInteger.ONE;
return;
}
long p0 = 1;
long q0 = 0;
long p1 = a0;
long q1 = 1;
long p2 = 0;
long q2 = 1;
int n = 0;
boolean stop = false;
do {
++n;
final double r1 = 1.0 / (r0 - a0);
final long a1 = (long) FastMath.floor(r1);
p2 = (a1 * p1) + p0;
q2 = (a1 * q1) + q0;
if ((p2 > overflow) || (q2 > overflow)) {
throw new FractionConversionException(value, p2, q2);
}
final double convergent = (double) p2 / (double) q2;
if ((n < maxIterations)
&& (FastMath.abs(convergent - value) > epsilon)
&& (q2 < maxDenominator)) {
p0 = p1;
p1 = p2;
q0 = q1;
q1 = q2;
a0 = a1;
r0 = r1;
} else {
stop = true;
}
} while (!stop);
if (n >= maxIterations) {
throw new FractionConversionException(value, maxIterations);
}
if (q2 < maxDenominator) {
numerator = BigInteger.valueOf(p2);
denominator = BigInteger.valueOf(q2);
} else {
numerator = BigInteger.valueOf(p1);
denominator = BigInteger.valueOf(q1);
}
}
/**
* Compute the distance between two vectors according to the L<sub>∞</sub> norm.
*
* <p>Calling this method is equivalent to calling: <code>v1.subtract(v2).getNormInf()</code>
* except that no intermediate vector is built
*
* @param v1 first vector
* @param v2 second vector
* @return the distance between v1 and v2 according to the L<sub>∞</sub> norm
*/
public static double distanceInf(Vector3D v1, Vector3D v2) {
final double dx = FastMath.abs(v2.x - v1.x);
final double dy = FastMath.abs(v2.y - v1.y);
final double dz = FastMath.abs(v2.z - v1.z);
return FastMath.max(FastMath.max(dx, dy), dz);
}
/** {@inheritDoc} */
@Override
public void integrate(final ExpandableStatefulODE equations, final double t)
throws MathIllegalStateException, MathIllegalArgumentException {
sanityChecks(equations, t);
setEquations(equations);
final boolean forward = t > equations.getTime();
// create some internal working arrays
final double[] y0 = equations.getCompleteState();
final double[] y = y0.clone();
final int stages = c.length + 1;
final double[][] yDotK = new double[stages][y.length];
final double[] yTmp = y0.clone();
final double[] yDotTmp = new double[y.length];
// set up an interpolator sharing the integrator arrays
final RungeKuttaStepInterpolator interpolator = (RungeKuttaStepInterpolator) prototype.copy();
interpolator.reinitialize(
this, yTmp, yDotK, forward, equations.getPrimaryMapper(), equations.getSecondaryMappers());
interpolator.storeTime(equations.getTime());
// set up integration control objects
stepStart = equations.getTime();
double hNew = 0;
boolean firstTime = true;
initIntegration(equations.getTime(), y0, t);
// main integration loop
isLastStep = false;
do {
interpolator.shift();
// iterate over step size, ensuring local normalized error is smaller than 1
double error = 10;
while (error >= 1.0) {
if (firstTime || !fsal) {
// first stage
computeDerivatives(stepStart, y, yDotK[0]);
}
if (firstTime) {
final double[] scale = new double[mainSetDimension];
if (vecAbsoluteTolerance == null) {
for (int i = 0; i < scale.length; ++i) {
scale[i] = scalAbsoluteTolerance + scalRelativeTolerance * FastMath.abs(y[i]);
}
} else {
for (int i = 0; i < scale.length; ++i) {
scale[i] = vecAbsoluteTolerance[i] + vecRelativeTolerance[i] * FastMath.abs(y[i]);
}
}
hNew = initializeStep(forward, getOrder(), scale, stepStart, y, yDotK[0], yTmp, yDotK[1]);
firstTime = false;
}
stepSize = hNew;
if (forward) {
if (stepStart + stepSize >= t) {
stepSize = t - stepStart;
}
} else {
if (stepStart + stepSize <= t) {
stepSize = t - stepStart;
}
}
// next stages
for (int k = 1; k < stages; ++k) {
for (int j = 0; j < y0.length; ++j) {
double sum = a[k - 1][0] * yDotK[0][j];
for (int l = 1; l < k; ++l) {
sum += a[k - 1][l] * yDotK[l][j];
}
yTmp[j] = y[j] + stepSize * sum;
}
computeDerivatives(stepStart + c[k - 1] * stepSize, yTmp, yDotK[k]);
}
// estimate the state at the end of the step
for (int j = 0; j < y0.length; ++j) {
double sum = b[0] * yDotK[0][j];
for (int l = 1; l < stages; ++l) {
sum += b[l] * yDotK[l][j];
}
yTmp[j] = y[j] + stepSize * sum;
}
// estimate the error at the end of the step
error = estimateError(yDotK, y, yTmp, stepSize);
if (error >= 1.0) {
// reject the step and attempt to reduce error by stepsize control
final double factor =
FastMath.min(
maxGrowth, FastMath.max(minReduction, safety * FastMath.pow(error, exp)));
hNew = filterStep(stepSize * factor, forward, false);
}
}
// local error is small enough: accept the step, trigger events and step handlers
interpolator.storeTime(stepStart + stepSize);
System.arraycopy(yTmp, 0, y, 0, y0.length);
System.arraycopy(yDotK[stages - 1], 0, yDotTmp, 0, y0.length);
stepStart = acceptStep(interpolator, y, yDotTmp, t);
System.arraycopy(y, 0, yTmp, 0, y.length);
if (!isLastStep) {
// prepare next step
interpolator.storeTime(stepStart);
if (fsal) {
// save the last evaluation for the next step
System.arraycopy(yDotTmp, 0, yDotK[0], 0, y0.length);
}
// stepsize control for next step
final double factor =
FastMath.min(maxGrowth, FastMath.max(minReduction, safety * FastMath.pow(error, exp)));
final double scaledH = stepSize * factor;
final double nextT = stepStart + scaledH;
final boolean nextIsLast = forward ? (nextT >= t) : (nextT <= t);
hNew = filterStep(scaledH, forward, nextIsLast);
final double filteredNextT = stepStart + hNew;
final boolean filteredNextIsLast = forward ? (filteredNextT >= t) : (filteredNextT <= t);
if (filteredNextIsLast) {
hNew = t - stepStart;
}
}
} while (!isLastStep);
// dispatch results
equations.setTime(stepStart);
equations.setCompleteState(y);
resetInternalState();
}
/**
* Simple constructor. Build a vector from its azimuthal coordinates
*
* @param alpha azimuth (α) around Z (0 is +X, π/2 is +Y, π is -X and 3π/2 is -Y)
* @param delta elevation (δ) above (XY) plane, from -π/2 to +π/2
* @see #getAlpha()
* @see #getDelta()
*/
public Vector3D(double alpha, double delta) {
double cosDelta = FastMath.cos(delta);
this.x = FastMath.cos(alpha) * cosDelta;
this.y = FastMath.sin(alpha) * cosDelta;
this.z = FastMath.sin(delta);
}
/** {@inheritDoc} */
protected final double doSolve() {
// Get initial solution
double x0 = getMin();
double x1 = getMax();
double f0 = computeObjectiveValue(x0);
double f1 = computeObjectiveValue(x1);
// If one of the bounds is the exact root, return it. Since these are
// not under-approximations or over-approximations, we can return them
// regardless of the allowed solutions.
if (f0 == 0.0) {
return x0;
}
if (f1 == 0.0) {
return x1;
}
// Verify bracketing of initial solution.
verifyBracketing(x0, x1);
// Get accuracies.
final double ftol = getFunctionValueAccuracy();
final double atol = getAbsoluteAccuracy();
final double rtol = getRelativeAccuracy();
// Keep track of inverted intervals, meaning that the left bound is
// larger than the right bound.
boolean inverted = false;
// Keep finding better approximations.
while (true) {
// Calculate the next approximation.
final double x = x1 - ((f1 * (x1 - x0)) / (f1 - f0));
final double fx = computeObjectiveValue(x);
// If the new approximation is the exact root, return it. Since
// this is not an under-approximation or an over-approximation,
// we can return it regardless of the allowed solutions.
if (fx == 0.0) {
return x;
}
// Update the bounds with the new approximation.
if (f1 * fx < 0) {
// The value of x1 has switched to the other bound, thus inverting
// the interval.
x0 = x1;
f0 = f1;
inverted = !inverted;
} else {
switch (method) {
case ILLINOIS:
f0 *= 0.5;
break;
case PEGASUS:
f0 *= f1 / (f1 + fx);
break;
case REGULA_FALSI:
// Nothing.
break;
default:
// Should never happen.
throw new MathInternalError();
}
}
// Update from [x0, x1] to [x0, x].
x1 = x;
f1 = fx;
// If the function value of the last approximation is too small,
// given the function value accuracy, then we can't get closer to
// the root than we already are.
if (FastMath.abs(f1) <= ftol) {
switch (allowed) {
case ANY_SIDE:
return x1;
case LEFT_SIDE:
if (inverted) {
return x1;
}
break;
case RIGHT_SIDE:
if (!inverted) {
return x1;
}
break;
case BELOW_SIDE:
if (f1 <= 0) {
return x1;
}
break;
case ABOVE_SIDE:
if (f1 >= 0) {
return x1;
}
break;
default:
throw new MathInternalError();
}
}
// If the current interval is within the given accuracies, we
// are satisfied with the current approximation.
if (FastMath.abs(x1 - x0) < FastMath.max(rtol * FastMath.abs(x1), atol)) {
switch (allowed) {
case ANY_SIDE:
return x1;
case LEFT_SIDE:
return inverted ? x1 : x0;
case RIGHT_SIDE:
return inverted ? x0 : x1;
case BELOW_SIDE:
return (f1 <= 0) ? x1 : x0;
case ABOVE_SIDE:
return (f1 >= 0) ? x1 : x0;
default:
throw new MathInternalError();
}
}
}
}
