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java.lang.Object de.grogra.math.Newton
public class Newton
This class contains some numeric algorithms based on the Newton algorithm.
Constructor Summary | |
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Newton()
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Method Summary | |
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static void |
findExtremum(ObjectToDouble<double[]> function,
double[] x,
double[] prec,
int maxSteps,
double[] eps,
double relax)
Finds a local extremum by looking for a root of an approximation of the derivative of function . |
static void |
findRoot(ObjectToObject<double[],double[]> function,
double[] x,
double[] prec,
int maxSteps,
double[] eps,
double relax)
Finds a root of function . |
static void |
fit(ObjectToDouble<double[][]> function,
DataTable data,
double[] params,
double[] prec)
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static void |
fit(ObjectToDouble<double[][]> function,
DataTable data,
double[] params,
double[] prec,
int maxSteps,
double[] eps,
double relax)
Fits function to data . |
static void |
fitParameters(ObjectToDouble<double[]> function,
DataTable data,
double[] params,
double[] prec)
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static void |
fitParameters(ObjectToDouble<double[]> function,
DataTable data,
double[] params,
double[] prec,
int maxSteps,
double[] eps,
double relax)
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static DataTable |
toDataTable(Dataset set)
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Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public Newton()
Method Detail |
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public static void findExtremum(ObjectToDouble<double[]> function, double[] x, double[] prec, int maxSteps, double[] eps, double relax)
function
.
The approximation is obtained by difference quotients, the
delta being given by eps
.
function
- the functionx
- the start value and (after method invocation completes normally)
the final valueprec
- the precision to achievemaxSteps
- maximal number of iterationseps
- the delta to approximate differential quotientsrelax
- relaxation factor for stepfindRoot(de.grogra.xl.lang.ObjectToObject, double[], double[], int, double[], double)
public static void findRoot(ObjectToObject<double[],double[]> function, double[] x, double[] prec, int maxSteps, double[] eps, double relax)
function
. Starting at x
,
the derivative of function
is approximated by difference
quotients (using eps
as delta) to compute a new
x
according to Newton's method, using relax
as factor which reduces the x
-movement computed by
Newton's method. If the movement is less than prec
in all
dimensions, this method returns normally. Otherwise, after
maxSteps
an ArithmeticException
is thrown.
All arrays have to be of the same length.
function
- the functionx
- the start value and (after method invocation completes normally)
the final valueprec
- the precision to achievemaxSteps
- maximal number of iterationseps
- the delta to approximate differential quotientsrelax
- relaxation factor for steppublic static void fit(ObjectToDouble<double[][]> function, DataTable data, double[] params, double[] prec)
public static void fit(ObjectToDouble<double[][]> function, DataTable data, double[] params, double[] prec, int maxSteps, double[] eps, double relax)
function
to data
. function
gets an array as input whose first component contains the
x
-vector and whose second component contains the
parameter vector. data
contains for each row a data set
consisting of the x
-vector and (as last entry)
the function value.
function
- the functiondata
- the data tableparams
- the start value for the parameter vector and
(after method invocation completes normally) the final valueprec
- the precision to achievemaxSteps
- maximal number of iterationseps
- the delta to approximate differential quotientsrelax
- relaxation factor for stepfindExtremum(de.grogra.xl.lang.ObjectToDouble, double[], double[], int, double[], double)
,
findRoot(de.grogra.xl.lang.ObjectToObject, double[], double[], int, double[], double)
public static void fitParameters(ObjectToDouble<double[]> function, DataTable data, double[] params, double[] prec)
public static void fitParameters(ObjectToDouble<double[]> function, DataTable data, double[] params, double[] prec, int maxSteps, double[] eps, double relax)
public static DataTable toDataTable(Dataset set)
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