fixed typos
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@580751 13f79535-47bb-0310-9956-ffa450edef68
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@ -34,7 +34,7 @@ import java.io.Serializable;
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* parameters can be retrieved through the {@link
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* EstimationProblem#getAllParameters
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* EstimationProblem.getAllParameters} method if the measurements are
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* independant of the problem, or directly if they are implemented as
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* independent of the problem, or directly if they are implemented as
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* inner classes of the problem.</p>
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*
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* <p>The instances for which the <code>ignored</code> flag is set
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@ -38,7 +38,7 @@ import java.io.Serializable;
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* variables are set only once the last step has been handled.</p>
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* <p>This is useful for example if the main loop of the user
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* application should remain independant from the integration process
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* application should remain independent from the integration process
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* or if one needs to mimic the behaviour of an analytical model
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* despite a numerical model is used (i.e. one needs the ability to
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* get the model value at any time or to navigate through the
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@ -52,7 +52,7 @@ public interface FirstOrderDifferentialEquations {
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public int getDimension();
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/** Get the current time derivative of the state vector.
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* @param t current value of the independant <I>time</I> variable
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* @param t current value of the independent <I>time</I> variable
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* @param y array containing the current value of the state vector
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* @param yDot placeholder array where to put the time derivative of the state vector
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* @throws DerivativeException this exception is propagated to the caller if the
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@ -53,7 +53,7 @@ public interface SecondOrderDifferentialEquations {
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public int getDimension();
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/** Get the current time derivative of the state vector.
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* @param t current value of the independant <I>time</I> variable
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* @param t current value of the independent <I>time</I> variable
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* @param y array containing the current value of the state vector
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* @param yDot array containing the current value of the first derivative
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* of the state vector
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@ -105,7 +105,7 @@ class SwitchState implements Serializable {
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}
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/** Reinitialize the beginning of the step.
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* @param t0 value of the independant <i>time</i> variable at the
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* @param t0 value of the independent <i>time</i> variable at the
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* beginning of the step
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* @param y0 array containing the current value of the state vector
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* at the beginning of the step
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@ -210,7 +210,7 @@ class SwitchState implements Serializable {
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}
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/** Acknowledge the fact the step has been accepted by the integrator.
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* @param t value of the independant <i>time</i> variable at the
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* @param t value of the independent <i>time</i> variable at the
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* end of the step
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* @param y array containing the current value of the state vector
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* at the end of the step
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@ -240,7 +240,7 @@ class SwitchState implements Serializable {
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}
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/** Let the switching function reset the state if it wants.
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* @param t value of the independant <i>time</i> variable at the
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* @param t value of the independent <i>time</i> variable at the
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* beginning of the next step
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* @param y array were to put the desired state vector at the beginning
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* of the next step
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@ -31,7 +31,7 @@ import java.io.Serializable;
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* the name <em>switching functions</em>.</p>
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*
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* <p>Since events are only problem-dependent and are triggered by the
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* independant <i>time</i> variable and the state vector, they can
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* independent <i>time</i> variable and the state vector, they can
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* occur at virtually any time, unknown in advance. The integrators will
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* take care to avoid sign changes inside the steps, they will reduce
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* the step size when such an event is detected in order to put this
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@ -88,7 +88,7 @@ public interface SwitchingFunction extends Serializable {
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* function must be continuous (at least in its roots neighborhood),
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* as the integrator will need to find its roots to locate the events.</p>
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* @param t current value of the independant <i>time</i> variable
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* @param t current value of the independent <i>time</i> variable
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* @param y array containing the current value of the state vector
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* @return value of the g function
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*/
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@ -122,7 +122,7 @@ public interface SwitchingFunction extends Serializable {
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* will continue.</li>
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* </ul>
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* @param t current value of the independant <i>time</i> variable
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* @param t current value of the independent <i>time</i> variable
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* @param y array containing the current value of the state vector
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* @return indication of what the integrator should do next, this
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* value must be one of {@link #STOP}, {@link #RESET_STATE},
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@ -141,7 +141,7 @@ public interface SwitchingFunction extends Serializable {
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* #RESET_STATE} indicator, this function will never be called, and it is
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* safe to leave its body empty.</p>
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* @param t current value of the independant <i>time</i> variable
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* @param t current value of the independent <i>time</i> variable
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* @param y array containing the current value of the state vector
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* the new state should be put in the same array
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*/
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@ -136,7 +136,7 @@ public class SwitchingFunctionsHandler {
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/** Inform the switching functions that the step has been accepted
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* by the integrator.
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* @param t value of the independant <i>time</i> variable at the
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* @param t value of the independent <i>time</i> variable at the
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* end of the step
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* @param y array containing the current value of the state vector
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* at the end of the step
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@ -161,7 +161,7 @@ public class SwitchingFunctionsHandler {
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}
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/** Let the switching functions reset the state if they want.
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* @param t value of the independant <i>time</i> variable at the
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* @param t value of the independent <i>time</i> variable at the
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* beginning of the next step
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* @param y array were to put the desired state vector at the beginning
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* of the next step
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@ -40,7 +40,7 @@ import org.apache.commons.math.linear.RealMatrixImpl;
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* GaussianRandomGenerator} or {@link UniformRandomGenerator}.</p>
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* <p>Sometimes, the covariance matrix for a given simulation is not
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* strictly positive definite. This means that the correlations are
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* not all independant from each other. In this case, however, the non
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* not all independent from each other. In this case, however, the non
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* strictly positive elements found during the Cholesky decomposition
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* of the covariance matrix should not be negative either, they
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* should be null. Another non-conventional extension handling this case
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@ -141,7 +141,7 @@ implements RandomVectorGenerator {
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}
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/** Get the rank of the covariance matrix.
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* The rank is the number of independant rows in the covariance
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* The rank is the number of independent rows in the covariance
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* matrix, it is also the number of columns of the rectangular
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* matrix of the decomposition.
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* @return rank of the square matrix.
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