Java Code Examples for org.apache.commons.math.exception.util.LocalizedFormats#TOO_MANY_REGRESSORS
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org.apache.commons.math.exception.util.LocalizedFormats#TOO_MANY_REGRESSORS .
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Example 1
| Source File: MillerUpdatingRegression.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* The regcf method conducts the linear regression and extracts the
* parameter vector. Notice that the algorithm can do subset regression
* with no alteration.
*
* @param nreq how many of the regressors to include (either in canonical
* order, or in the current reordered state)
* @return an array with the estimated slope coefficients
*/
private double[] regcf(int nreq) {
int nextr;
if (nreq < 1) {
throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
}
if (nreq > this.nvars) {
throw new ModelSpecificationException(
LocalizedFormats.TOO_MANY_REGRESSORS, nreq, this.nvars);
}
if (!this.tol_set) {
tolset();
}
double[] ret = new double[nreq];
boolean rankProblem = false;
for (int i = nreq - 1; i > -1; i--) {
if (Math.sqrt(d[i]) < tol[i]) {
ret[i] = 0.0;
d[i] = 0.0;
rankProblem = true;
} else {
ret[i] = rhs[i];
nextr = i * (nvars + nvars - i - 1) / 2;
for (int j = i + 1; j < nreq; j++) {
ret[i] = smartAdd(ret[i], -r[nextr] * ret[j]);
++nextr;
}
}
}
if (rankProblem) {
for (int i = 0; i < nreq; i++) {
if (this.lindep[i]) {
ret[i] = Double.NaN;
}
}
}
return ret;
}
Example 2
| Source File: MillerUpdatingRegression.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* The regcf method conducts the linear regression and extracts the
* parameter vector. Notice that the algorithm can do subset regression
* with no alteration.
*
* @param nreq how many of the regressors to include (either in canonical
* order, or in the current reordered state)
* @return an array with the estimated slope coefficients
*/
private double[] regcf(int nreq) {
int nextr;
if (nreq < 1) {
throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
}
if (nreq > this.nvars) {
throw new ModelSpecificationException(
LocalizedFormats.TOO_MANY_REGRESSORS, nreq, this.nvars);
}
if (!this.tol_set) {
tolset();
}
double[] ret = new double[nreq];
boolean rankProblem = false;
for (int i = nreq - 1; i > -1; i--) {
if (Math.sqrt(d[i]) < tol[i]) {
ret[i] = 0.0;
d[i] = 0.0;
rankProblem = true;
} else {
ret[i] = rhs[i];
nextr = i * (nvars + nvars - i - 1) / 2;
for (int j = i + 1; j < nreq; j++) {
ret[i] = smartAdd(ret[i], -r[nextr] * ret[j]);
++nextr;
}
}
}
if (rankProblem) {
for (int i = 0; i < nreq; i++) {
if (this.lindep[i]) {
ret[i] = Double.NaN;
}
}
}
return ret;
}
Example 3
| Source File: MillerUpdatingRegression.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Conducts a regression on the data in the model, using a subset of regressors.
*
* @param numberOfRegressors many of the regressors to include (either in canonical
* order, or in the current reordered state)
* @return RegressionResults the structure holding all regression results
* @exception ModelSpecificationException - thrown if number of observations is
* less than the number of variables or number of regressors requested
* is greater than the regressors in the model
*/
public RegressionResults regress(int numberOfRegressors) throws ModelSpecificationException {
if (this.nobs <= numberOfRegressors) {
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
this.nobs, numberOfRegressors);
}
if( numberOfRegressors > this.nvars ){
throw new ModelSpecificationException(
LocalizedFormats.TOO_MANY_REGRESSORS, numberOfRegressors, this.nvars);
}
this.tolset();
this.singcheck();
double[] beta = this.regcf(numberOfRegressors);
this.ss();
double[] cov = this.cov(numberOfRegressors);
int rnk = 0;
for (int i = 0; i < this.lindep.length; i++) {
if (!this.lindep[i]) {
++rnk;
}
}
boolean needsReorder = false;
for (int i = 0; i < numberOfRegressors; i++) {
if (this.vorder[i] != i) {
needsReorder = true;
break;
}
}
if (!needsReorder) {
return new RegressionResults(
beta, new double[][]{cov}, true, this.nobs, rnk,
this.sumy, this.sumsqy, this.sserr, this.hasIntercept, false);
} else {
double[] betaNew = new double[beta.length];
double[] covNew = new double[cov.length];
int[] newIndices = new int[beta.length];
for (int i = 0; i < nvars; i++) {
for (int j = 0; j < numberOfRegressors; j++) {
if (this.vorder[j] == i) {
betaNew[i] = beta[ j];
newIndices[i] = j;
}
}
}
int idx1 = 0;
int idx2;
int _i;
int _j;
for (int i = 0; i < beta.length; i++) {
_i = newIndices[i];
for (int j = 0; j <= i; j++, idx1++) {
_j = newIndices[j];
if (_i > _j) {
idx2 = _i * (_i + 1) / 2 + _j;
} else {
idx2 = _j * (_j + 1) / 2 + _i;
}
covNew[idx1] = cov[idx2];
}
}
return new RegressionResults(
betaNew, new double[][]{covNew}, true, this.nobs, rnk,
this.sumy, this.sumsqy, this.sserr, this.hasIntercept, false);
}
}
Example 4
| Source File: MillerUpdatingRegression.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Conducts a regression on the data in the model, using a subset of regressors.
*
* @param numberOfRegressors many of the regressors to include (either in canonical
* order, or in the current reordered state)
* @return RegressionResults the structure holding all regression results
* @exception ModelSpecificationException - thrown if number of observations is
* less than the number of variables or number of regressors requested
* is greater than the regressors in the model
*/
public RegressionResults regress(int numberOfRegressors) throws ModelSpecificationException {
if (this.nobs <= numberOfRegressors) {
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
this.nobs, numberOfRegressors);
}
if( numberOfRegressors > this.nvars ){
throw new ModelSpecificationException(
LocalizedFormats.TOO_MANY_REGRESSORS, numberOfRegressors, this.nvars);
}
this.tolset();
this.singcheck();
double[] beta = this.regcf(numberOfRegressors);
this.ss();
double[] cov = this.cov(numberOfRegressors);
int rnk = 0;
for (int i = 0; i < this.lindep.length; i++) {
if (!this.lindep[i]) {
++rnk;
}
}
boolean needsReorder = false;
for (int i = 0; i < numberOfRegressors; i++) {
if (this.vorder[i] != i) {
needsReorder = true;
break;
}
}
if (!needsReorder) {
return new RegressionResults(
beta, new double[][]{cov}, true, this.nobs, rnk,
this.sumy, this.sumsqy, this.sserr, this.hasIntercept, false);
} else {
double[] betaNew = new double[beta.length];
double[] covNew = new double[cov.length];
int[] newIndices = new int[beta.length];
for (int i = 0; i < nvars; i++) {
for (int j = 0; j < numberOfRegressors; j++) {
if (this.vorder[j] == i) {
betaNew[i] = beta[ j];
newIndices[i] = j;
}
}
}
int idx1 = 0;
int idx2;
int _i;
int _j;
for (int i = 0; i < beta.length; i++) {
_i = newIndices[i];
for (int j = 0; j <= i; j++, idx1++) {
_j = newIndices[j];
if (_i > _j) {
idx2 = _i * (_i + 1) / 2 + _j;
} else {
idx2 = _j * (_j + 1) / 2 + _i;
}
covNew[idx1] = cov[idx2];
}
}
return new RegressionResults(
betaNew, new double[][]{covNew}, true, this.nobs, rnk,
this.sumy, this.sumsqy, this.sserr, this.hasIntercept, false);
}
}
