no.uib.cipr.matrix.Vector.dot()方法的使用及代码示例
本文整理了Java中no.uib.cipr.matrix.Vector.dot()方法的一些代码示例，展示了Vector.dot()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。Vector.dot()方法的具体详情如下：
包路径：no.uib.cipr.matrix.Vector
类名称：Vector
方法名：dot
Vector.dot介绍
[英]xT*y
[中]xT*y
代码示例
代码示例来源：origin: algorithmfoundry/Foundry
/**
* Inner Vector product between two Vectors
*
*
* @param other
* the Vector with which to compute the dot product with this,
* must be the same dimension as this
* @return dot product, (0,\infty)
*/
public double dotProduct(
final AbstractMTJVector other)
{
this.assertSameDimensionality(other);
return this.internalVector.dot( other.internalVector );
}代码示例来源：origin: openimaj/openimaj
private Vector project(Vector v, Vector u){
return u.copy().scale((v.dot(u) / u.dot(u)));
}代码示例来源：origin: algorithmfoundry/Foundry
/**
* Inner Vector product between two Vectors
*
*
* @param other
* the Vector with which to compute the dot product with this,
* must be the same dimension as this
* @return dot product, (0,\infty)
*/
public double dotProduct(
final AbstractMTJVector other)
{
this.assertSameDimensionality(other);
return this.internalVector.dot( other.internalVector );
}代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-common-core
/**
* Inner Vector product between two Vectors
*
*
* @param other
* the Vector with which to compute the dot product with this,
* must be the same dimension as this
* @return dot product, (0,\infty)
*/
public double dotProduct(
final AbstractMTJVector other)
{
this.assertSameDimensionality(other);
return this.internalVector.dot( other.internalVector );
}代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
/**
* Computes standard deviation for given instance, without transforming target
* back into original space.
*/
protected double computeStdDev(Instance inst, Vector k) throws Exception {
double kappa = m_actualKernel.eval(-1, -1, inst) + m_deltaSquared;
double s = m_L.mult(k, new DenseVector(k.size())).dot(k);
double sigma = m_delta;
if (kappa > s) {
sigma = Math.sqrt(kappa - s);
}
return sigma;
}代码示例来源：origin: Waikato/weka-trunk
/**
* Computes standard deviation for given instance, without transforming target
* back into original space.
*/
protected double computeStdDev(Instance inst, Vector k) throws Exception {
double kappa = m_actualKernel.eval(-1, -1, inst) + m_deltaSquared;
double s = m_L.mult(k, new DenseVector(k.size())).dot(k);
double sigma = m_delta;
if (kappa > s) {
sigma = Math.sqrt(kappa - s);
}
return sigma;
}代码示例来源：origin: Waikato/weka-trunk
/**
* Returns natural logarithm of density estimate for given value based on
* given instance.
*
* @param inst the instance to make the prediction for.
* @param value the value to make the prediction for.
* @return the natural logarithm of the density estimate
* @exception Exception if the density cannot be computed
*/
@Override
public double logDensity(Instance inst, double value) throws Exception {
inst = filterInstance(inst);
// Build K vector (and Kappa)
Vector k = new DenseVector(m_NumTrain);
for (int i = 0; i < m_NumTrain; i++) {
k.set(i, m_weights[i] * m_actualKernel.eval(-1, i, inst));
}
double estimate = k.dot(m_t) + m_avg_target;
double sigma = computeStdDev(inst, k);
// transform to GP space
value = value * m_Alin + m_Blin;
// center around estimate
value = value - estimate;
double z = -Math.log(sigma * Math.sqrt(2 * Math.PI)) - value * value
/ (2.0 * sigma * sigma);
return z + Math.log(m_Alin);
}代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
/**
* Returns natural logarithm of density estimate for given value based on
* given instance.
*
* @param inst the instance to make the prediction for.
* @param value the value to make the prediction for.
* @return the natural logarithm of the density estimate
* @exception Exception if the density cannot be computed
*/
@Override
public double logDensity(Instance inst, double value) throws Exception {
inst = filterInstance(inst);
// Build K vector (and Kappa)
Vector k = new DenseVector(m_NumTrain);
for (int i = 0; i < m_NumTrain; i++) {
k.set(i, m_weights[i] * m_actualKernel.eval(-1, i, inst));
}
double estimate = k.dot(m_t) + m_avg_target;
double sigma = computeStdDev(inst, k);
// transform to GP space
value = value * m_Alin + m_Blin;
// center around estimate
value = value - estimate;
double z = -Math.log(sigma * Math.sqrt(2 * Math.PI)) - value * value
/ (2.0 * sigma * sigma);
return z + Math.log(m_Alin);
}代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
/**
* Returns the log of the density value for the given vector.
*
* @param valuePassed input vector
* @return log density based on given distribution
*/
@Override
public double logDensity(double[] valuePassed) {
// calculate mean subtractions
Vector x = new DenseVector(valuePassed);
return lnconstant - 0.5 * x.dot(covarianceInverse.mult(x.add(-1.0, mean), new DenseVector(x.size())));
}代码示例来源：origin: openimaj/openimaj
@Override
public IndependentPair<double[], PerceptronClass> generate() {
double decide = Math.signum(rng.nextDouble() - 0.5);
if(decide == 0) decide = 1;
PerceptronClass dec = PerceptronClass.fromSign(decide);
while(true){
double[] randomPoint = new double[this.dims];
for (int i = 0; i < randomPoint.length; i++) {
randomPoint[i] = nextRandomValue();
}
Vector v = new DenseVector(randomPoint);
v.add(-1, origin);
Vector d = direction.copy();
double dot = v.dot(d);
double sgn = Math.signum(dot);
if(sgn == 0) sgn = 1;
if(rng.nextDouble() <this.error){
sgn = -sgn;
}
PerceptronClass sgnClass = PerceptronClass.fromSign(sgn);
if(sgnClass.equals(dec)) {
return IndependentPair.pair(randomPoint, sgnClass);
}
}
}代码示例来源：origin: Waikato/weka-trunk
/**
* Returns the log of the density value for the given vector.
*
* @param valuePassed input vector
* @return log density based on given distribution
*/
@Override
public double logDensity(double[] valuePassed) {
// calculate mean subtractions
Vector x = new DenseVector(valuePassed);
return lnconstant - 0.5 * x.dot(covarianceInverse.mult(x.add(-1.0, mean), new DenseVector(x.size())));
}代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
/**
* Classifies a given instance.
*
* @param inst the instance to be classified
* @return the classification
* @throws Exception if instance could not be classified successfully
*/
@Override
public double classifyInstance(Instance inst) throws Exception {
// Filter instance
inst = filterInstance(inst);
// Build K vector
Vector k = new DenseVector(m_NumTrain);
for (int i = 0; i < m_NumTrain; i++) {
k.set(i, m_weights[i] * m_actualKernel.eval(-1, i, inst));
}
double result = (k.dot(m_t) + m_avg_target - m_Blin) / m_Alin;
return result;
}代码示例来源：origin: Waikato/weka-trunk
/**
* Classifies a given instance.
*
* @param inst the instance to be classified
* @return the classification
* @throws Exception if instance could not be classified successfully
*/
@Override
public double classifyInstance(Instance inst) throws Exception {
// Filter instance
inst = filterInstance(inst);
// Build K vector
Vector k = new DenseVector(m_NumTrain);
for (int i = 0; i < m_NumTrain; i++) {
k.set(i, m_weights[i] * m_actualKernel.eval(-1, i, inst));
}
double result = (k.dot(m_t) + m_avg_target - m_Blin) / m_Alin;
return result;
}代码示例来源：origin: nz.ac.waikato.cms.weka/discriminantAnalysis
/**
* Output class "probabilities". These need to be calibrated.
*/
public double[] distributionForInstance(Instance inst) throws Exception {
// Filter instance
m_RemoveUseless.input(inst);
inst = m_RemoveUseless.output();
// Convert instance to matrix
Vector instM = new DenseVector(inst.numAttributes() - 1);
int index = 0;
for (int i = 0; i < inst.numAttributes(); i++) {
if (i != m_Data.classIndex()) {
instM.set(index++, inst.value(i));
}
}
// Pipe output through sigmoid
double[] dist = new double[2];
dist[1] = 1/(1 + Math.exp(instM.dot(m_Weights) - m_Threshold));
dist[0] = 1 - dist[1];
return dist;
}代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable
/**
* Computes a prediction interval for the given instance and confidence level.
*
* @param inst the instance to make the prediction for
* @param confidenceLevel the percentage of cases the interval should cover
* @return a 1*2 array that contains the boundaries of the interval
* @throws Exception if interval could not be estimated successfully
*/
@Override
public double[][] predictIntervals(Instance inst, double confidenceLevel)
throws Exception {
inst = filterInstance(inst);
// Build K vector (and Kappa)
Vector k = new DenseVector(m_NumTrain);
for (int i = 0; i < m_NumTrain; i++) {
k.set(i, m_weights[i] * m_actualKernel.eval(-1, i, inst));
}
double estimate = k.dot(m_t) + m_avg_target;
double sigma = computeStdDev(inst, k);
confidenceLevel = 1.0 - ((1.0 - confidenceLevel) / 2.0);
double z = Statistics.normalInverse(confidenceLevel);
double[][] interval = new double[1][2];
interval[0][0] = estimate - z * sigma;
interval[0][1] = estimate + z * sigma;
interval[0][0] = (interval[0][0] - m_Blin) / m_Alin;
interval[0][1] = (interval[0][1] - m_Blin) / m_Alin;
return interval;
}代码示例来源：origin: Waikato/weka-trunk
/**
* Computes a prediction interval for the given instance and confidence level.
*
* @param inst the instance to make the prediction for
* @param confidenceLevel the percentage of cases the interval should cover
* @return a 1*2 array that contains the boundaries of the interval
* @throws Exception if interval could not be estimated successfully
*/
@Override
public double[][] predictIntervals(Instance inst, double confidenceLevel)
throws Exception {
inst = filterInstance(inst);
// Build K vector (and Kappa)
Vector k = new DenseVector(m_NumTrain);
for (int i = 0; i < m_NumTrain; i++) {
k.set(i, m_weights[i] * m_actualKernel.eval(-1, i, inst));
}
double estimate = k.dot(m_t) + m_avg_target;
double sigma = computeStdDev(inst, k);
confidenceLevel = 1.0 - ((1.0 - confidenceLevel) / 2.0);
double z = Statistics.normalInverse(confidenceLevel);
double[][] interval = new double[1][2];
interval[0][0] = estimate - z * sigma;
interval[0][1] = estimate + z * sigma;
interval[0][0] = (interval[0][0] - m_Blin) / m_Alin;
interval[0][1] = (interval[0][1] - m_Blin) / m_Alin;
return interval;
}代码示例来源：origin: fommil/matrix-toolkits-java
public Vector solve(Matrix A, Vector b, Vector x)
throws IterativeSolverNotConvergedException {
checkSizes(A, b, x);
double alpha = 0, beta = 0, rho = 0, rho_1 = 0;
A.multAdd(-1, x, r.set(b));
for (iter.setFirst(); !iter.converged(r, x); iter.next()) {
M.apply(r, z);
rho = r.dot(z);
if (iter.isFirst())
p.set(z);
else {
beta = rho / rho_1;
p.scale(beta).add(z);
}
A.mult(p, q);
alpha = rho / p.dot(q);
x.add(alpha, p);
r.add(-alpha, q);
rho_1 = rho;
}
return x;
}代码示例来源：origin: com.googlecode.matrix-toolkits-java/mtj
public Vector solve(Matrix A, Vector b, Vector x)
throws IterativeSolverNotConvergedException {
checkSizes(A, b, x);
double alpha = 0, beta = 0, rho = 0, rho_1 = 0;
A.multAdd(-1, x, r.set(b));
for (iter.setFirst(); !iter.converged(r, x); iter.next()) {
M.apply(r, z);
rho = r.dot(z);
if (iter.isFirst())
p.set(z);
else {
beta = rho / rho_1;
p.scale(beta).add(z);
}
A.mult(p, q);
alpha = rho / p.dot(q);
x.add(alpha, p);
r.add(-alpha, q);
rho_1 = rho;
}
return x;
}代码示例来源：origin: com.googlecode.matrix-toolkits-java/mtj
M.apply(r, z);
M.transApply(rtilde, ztilde);
rho_1 = z.dot(rtilde);
A.transMult(ptilde, qtilde);
alpha = rho_1 / ptilde.dot(q);
x.add(alpha, p);
r.add(-alpha, q);代码示例来源：origin: fommil/matrix-toolkits-java
rho_1 = rtilde.dot(r);
alpha = rho_1 / rtilde.dot(vhat);
q.set(-alpha, vhat).add(u);内容来源于网络，如有侵权，请联系作者删除！
