Java Code Examples for org.apache.commons.math3.exception.util.LocalizedFormats#NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS
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org.apache.commons.math3.exception.util.LocalizedFormats#NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS .
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Example 1
| Source File: MillerUpdatingRegression.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Adds multiple observations to the model.
* @param x observations on the regressors
* @param y observations on the regressand
* @throws ModelSpecificationException if {@code x} is not rectangular, does not match
* the length of {@code y} or does not contain sufficient data to estimate the model
*/
public void addObservations(double[][] x, double[] y) {
if ((x == null) || (y == null) || (x.length != y.length)) {
throw new ModelSpecificationException(
LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
(x == null) ? 0 : x.length,
(y == null) ? 0 : y.length);
}
if (x.length == 0) { // Must be no y data either
throw new ModelSpecificationException(
LocalizedFormats.NO_DATA);
}
if (x[0].length + 1 > x.length) {
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
x.length, x[0].length);
}
for (int i = 0; i < x.length; i++) {
addObservation(x[i], y[i]);
}
}
Example 2
| Source File: SimpleRegression.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Adds a series of observations to the regression model. The lengths of
* x and y must be the same and x must be rectangular.
*
* @param x a series of observations on the independent variables
* @param y a series of observations on the dependent variable
* The length of x and y must be the same
* @throws ModelSpecificationException if {@code x} is not rectangular, does not match
* the length of {@code y} or does not contain sufficient data to estimate the model
*/
public void addObservations(final double[][] x,final double[] y) throws ModelSpecificationException {
if ((x == null) || (y == null) || (x.length != y.length)) {
throw new ModelSpecificationException(
LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
(x == null) ? 0 : x.length,
(y == null) ? 0 : y.length);
}
boolean obsOk=true;
for( int i = 0 ; i < x.length; i++){
if( x[i] == null || x[i].length == 0 ){
obsOk = false;
}
}
if( !obsOk ){
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
0, 1);
}
for( int i = 0 ; i < x.length ; i++){
addData( x[i][0], y[i] );
}
}
Example 3
| Source File: MillerUpdatingRegression.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Adds multiple observations to the model
* @param x observations on the regressors
* @param y observations on the regressand
* @throws ModelSpecificationException if {@code x} is not rectangular, does not match
* the length of {@code y} or does not contain sufficient data to estimate the model
*/
public void addObservations(double[][] x, double[] y) {
if ((x == null) || (y == null) || (x.length != y.length)) {
throw new ModelSpecificationException(
LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
(x == null) ? 0 : x.length,
(y == null) ? 0 : y.length);
}
if (x.length == 0) { // Must be no y data either
throw new ModelSpecificationException(
LocalizedFormats.NO_DATA);
}
if (x[0].length + 1 > x.length) {
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
x.length, x[0].length);
}
for (int i = 0; i < x.length; i++) {
this.addObservation(x[i], y[i]);
}
return;
}
Example 4
| Source File: SimpleRegression.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Adds a series of observations to the regression model. The lengths of
* x and y must be the same and x must be rectangular.
*
* @param x a series of observations on the independent variables
* @param y a series of observations on the dependent variable
* The length of x and y must be the same
* @throws ModelSpecificationException if {@code x} is not rectangular, does not match
* the length of {@code y} or does not contain sufficient data to estimate the model
*/
public void addObservations(final double[][] x,final double[] y) {
if ((x == null) || (y == null) || (x.length != y.length)) {
throw new ModelSpecificationException(
LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
(x == null) ? 0 : x.length,
(y == null) ? 0 : y.length);
}
boolean obsOk=true;
for( int i = 0 ; i < x.length; i++){
if( x[i] == null || x[i].length == 0 ){
obsOk = false;
}
}
if( !obsOk ){
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
0, 1);
}
for( int i = 0 ; i < x.length ; i++){
addData( x[i][0], y[i] );
}
return;
}
Example 5
| Source File: MillerUpdatingRegression.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Adds multiple observations to the model.
* @param x observations on the regressors
* @param y observations on the regressand
* @throws ModelSpecificationException if {@code x} is not rectangular, does not match
* the length of {@code y} or does not contain sufficient data to estimate the model
*/
public void addObservations(double[][] x, double[] y) throws ModelSpecificationException {
if ((x == null) || (y == null) || (x.length != y.length)) {
throw new ModelSpecificationException(
LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
(x == null) ? 0 : x.length,
(y == null) ? 0 : y.length);
}
if (x.length == 0) { // Must be no y data either
throw new ModelSpecificationException(
LocalizedFormats.NO_DATA);
}
if (x[0].length + 1 > x.length) {
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
x.length, x[0].length);
}
for (int i = 0; i < x.length; i++) {
addObservation(x[i], y[i]);
}
}
Example 6
| Source File: MillerUpdatingRegression.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Adds multiple observations to the model.
* @param x observations on the regressors
* @param y observations on the regressand
* @throws ModelSpecificationException if {@code x} is not rectangular, does not match
* the length of {@code y} or does not contain sufficient data to estimate the model
*/
public void addObservations(double[][] x, double[] y) throws ModelSpecificationException {
if ((x == null) || (y == null) || (x.length != y.length)) {
throw new ModelSpecificationException(
LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
(x == null) ? 0 : x.length,
(y == null) ? 0 : y.length);
}
if (x.length == 0) { // Must be no y data either
throw new ModelSpecificationException(
LocalizedFormats.NO_DATA);
}
if (x[0].length + 1 > x.length) {
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
x.length, x[0].length);
}
for (int i = 0; i < x.length; i++) {
addObservation(x[i], y[i]);
}
}
Example 7
| Source File: SimpleRegression.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Adds a series of observations to the regression model. The lengths of
* x and y must be the same and x must be rectangular.
*
* @param x a series of observations on the independent variables
* @param y a series of observations on the dependent variable
* The length of x and y must be the same
* @throws ModelSpecificationException if {@code x} is not rectangular, does not match
* the length of {@code y} or does not contain sufficient data to estimate the model
*/
public void addObservations(final double[][] x,final double[] y) {
if ((x == null) || (y == null) || (x.length != y.length)) {
throw new ModelSpecificationException(
LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
(x == null) ? 0 : x.length,
(y == null) ? 0 : y.length);
}
boolean obsOk=true;
for( int i = 0 ; i < x.length; i++){
if( x[i] == null || x[i].length == 0 ){
obsOk = false;
}
}
if( !obsOk ){
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
0, 1);
}
for( int i = 0 ; i < x.length ; i++){
addData( x[i][0], y[i] );
}
}
Example 8
| Source File: MillerUpdatingRegression.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Adds multiple observations to the model.
* @param x observations on the regressors
* @param y observations on the regressand
* @throws ModelSpecificationException if {@code x} is not rectangular, does not match
* the length of {@code y} or does not contain sufficient data to estimate the model
*/
public void addObservations(double[][] x, double[] y) {
if ((x == null) || (y == null) || (x.length != y.length)) {
throw new ModelSpecificationException(
LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
(x == null) ? 0 : x.length,
(y == null) ? 0 : y.length);
}
if (x.length == 0) { // Must be no y data either
throw new ModelSpecificationException(
LocalizedFormats.NO_DATA);
}
if (x[0].length + 1 > x.length) {
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
x.length, x[0].length);
}
for (int i = 0; i < x.length; i++) {
addObservation(x[i], y[i]);
}
}
Example 9
| Source File: SimpleRegression.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Adds a series of observations to the regression model. The lengths of
* x and y must be the same and x must be rectangular.
*
* @param x a series of observations on the independent variables
* @param y a series of observations on the dependent variable
* The length of x and y must be the same
* @throws ModelSpecificationException if {@code x} is not rectangular, does not match
* the length of {@code y} or does not contain sufficient data to estimate the model
*/
public void addObservations(final double[][] x,final double[] y) {
if ((x == null) || (y == null) || (x.length != y.length)) {
throw new ModelSpecificationException(
LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
(x == null) ? 0 : x.length,
(y == null) ? 0 : y.length);
}
boolean obsOk=true;
for( int i = 0 ; i < x.length; i++){
if( x[i] == null || x[i].length == 0 ){
obsOk = false;
}
}
if( !obsOk ){
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
0, 1);
}
for( int i = 0 ; i < x.length ; i++){
addData( x[i][0], y[i] );
}
}
Example 10
| Source File: MillerUpdatingRegression.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Adds multiple observations to the model.
* @param x observations on the regressors
* @param y observations on the regressand
* @throws ModelSpecificationException if {@code x} is not rectangular, does not match
* the length of {@code y} or does not contain sufficient data to estimate the model
*/
public void addObservations(double[][] x, double[] y) throws ModelSpecificationException {
if ((x == null) || (y == null) || (x.length != y.length)) {
throw new ModelSpecificationException(
LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
(x == null) ? 0 : x.length,
(y == null) ? 0 : y.length);
}
if (x.length == 0) { // Must be no y data either
throw new ModelSpecificationException(
LocalizedFormats.NO_DATA);
}
if (x[0].length + 1 > x.length) {
throw new ModelSpecificationException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
x.length, x[0].length);
}
for (int i = 0; i < x.length; i++) {
addObservation(x[i], y[i]);
}
}
Example 11
| 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);
}
tolset();
singcheck();
double[] beta = this.regcf(numberOfRegressors);
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 12
| Source File: AbstractMultipleLinearRegression.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Validates sample data. Checks that
* <ul><li>Neither x nor y is null or empty;</li>
* <li>The length (i.e. number of rows) of x equals the length of y</li>
* <li>x has at least one more row than it has columns (i.e. there is
* sufficient data to estimate regression coefficients for each of the
* columns in x plus an intercept.</li>
* </ul>
*
* @param x the [n,k] array representing the x data
* @param y the [n,1] array representing the y data
* @throws NullArgumentException if {@code x} or {@code y} is null
* @throws DimensionMismatchException if {@code x} and {@code y} do not
* have the same length
* @throws NoDataException if {@code x} or {@code y} are zero-length
* @throws MathIllegalArgumentException if the number of rows of {@code x}
* is not larger than the number of columns + 1
*/
protected void validateSampleData(double[][] x, double[] y) {
if ((x == null) || (y == null)) {
throw new NullArgumentException();
}
if (x.length != y.length) {
throw new DimensionMismatchException(y.length, x.length);
}
if (x.length == 0) { // Must be no y data either
throw new NoDataException();
}
if (x[0].length + 1 > x.length) {
throw new MathIllegalArgumentException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
x.length, x[0].length);
}
}
Example 13
| Source File: AbstractMultipleLinearRegression.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Validates sample data. Checks that
* <ul><li>Neither x nor y is null or empty;</li>
* <li>The length (i.e. number of rows) of x equals the length of y</li>
* <li>x has at least one more row than it has columns (i.e. there is
* sufficient data to estimate regression coefficients for each of the
* columns in x plus an intercept.</li>
* </ul>
*
* @param x the [n,k] array representing the x data
* @param y the [n,1] array representing the y data
* @throws NullArgumentException if {@code x} or {@code y} is null
* @throws DimensionMismatchException if {@code x} and {@code y} do not
* have the same length
* @throws NoDataException if {@code x} or {@code y} are zero-length
* @throws MathIllegalArgumentException if the number of rows of {@code x}
* is not larger than the number of columns + 1
*/
protected void validateSampleData(double[][] x, double[] y) {
if ((x == null) || (y == null)) {
throw new NullArgumentException();
}
if (x.length != y.length) {
throw new DimensionMismatchException(y.length, x.length);
}
if (x.length == 0) { // Must be no y data either
throw new NoDataException();
}
if (x[0].length + 1 > x.length) {
throw new MathIllegalArgumentException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
x.length, x[0].length);
}
}
Example 14
| 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);
}
tolset();
singcheck();
double[] beta = this.regcf(numberOfRegressors);
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 15
| Source File: AbstractMultipleLinearRegression.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Validates sample data. Checks that
* <ul><li>Neither x nor y is null or empty;</li>
* <li>The length (i.e. number of rows) of x equals the length of y</li>
* <li>x has at least one more row than it has columns (i.e. there is
* sufficient data to estimate regression coefficients for each of the
* columns in x plus an intercept.</li>
* </ul>
*
* @param x the [n,k] array representing the x data
* @param y the [n,1] array representing the y data
* @throws NullArgumentException if {@code x} or {@code y} is null
* @throws DimensionMismatchException if {@code x} and {@code y} do not
* have the same length
* @throws NoDataException if {@code x} or {@code y} are zero-length
* @throws MathIllegalArgumentException if the number of rows of {@code x}
* is not larger than the number of columns + 1
*/
protected void validateSampleData(double[][] x, double[] y) throws MathIllegalArgumentException {
if ((x == null) || (y == null)) {
throw new NullArgumentException();
}
if (x.length != y.length) {
throw new DimensionMismatchException(y.length, x.length);
}
if (x.length == 0) { // Must be no y data either
throw new NoDataException();
}
if (x[0].length + 1 > x.length) {
throw new MathIllegalArgumentException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
x.length, x[0].length);
}
}
Example 16
| 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);
}
tolset();
singcheck();
double[] beta = this.regcf(numberOfRegressors);
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 17
| 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);
}
tolset();
singcheck();
double[] beta = this.regcf(numberOfRegressors);
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 18
| 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);
}
tolset();
singcheck();
double[] beta = this.regcf(numberOfRegressors);
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 19
| Source File: AbstractMultipleLinearRegression.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Validates sample data. Checks that
* <ul><li>Neither x nor y is null or empty;</li>
* <li>The length (i.e. number of rows) of x equals the length of y</li>
* <li>x has at least one more row than it has columns (i.e. there is
* sufficient data to estimate regression coefficients for each of the
* columns in x plus an intercept.</li>
* </ul>
*
* @param x the [n,k] array representing the x data
* @param y the [n,1] array representing the y data
* @throws NullArgumentException if {@code x} or {@code y} is null
* @throws DimensionMismatchException if {@code x} and {@code y} do not
* have the same length
* @throws NoDataException if {@code x} or {@code y} are zero-length
* @throws MathIllegalArgumentException if the number of rows of {@code x}
* is not larger than the number of columns + 1
*/
protected void validateSampleData(double[][] x, double[] y) throws MathIllegalArgumentException {
if ((x == null) || (y == null)) {
throw new NullArgumentException();
}
if (x.length != y.length) {
throw new DimensionMismatchException(y.length, x.length);
}
if (x.length == 0) { // Must be no y data either
throw new NoDataException();
}
if (x[0].length + 1 > x.length) {
throw new MathIllegalArgumentException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
x.length, x[0].length);
}
}
Example 20
| Source File: AbstractMultipleLinearRegression.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* Validates sample data. Checks that
* <ul><li>Neither x nor y is null or empty;</li>
* <li>The length (i.e. number of rows) of x equals the length of y</li>
* <li>x has at least one more row than it has columns (i.e. there is
* sufficient data to estimate regression coefficients for each of the
* columns in x plus an intercept.</li>
* </ul>
*
* @param x the [n,k] array representing the x data
* @param y the [n,1] array representing the y data
* @throws NullArgumentException if {@code x} or {@code y} is null
* @throws DimensionMismatchException if {@code x} and {@code y} do not
* have the same length
* @throws NoDataException if {@code x} or {@code y} are zero-length
* @throws MathIllegalArgumentException if the number of rows of {@code x}
* is not larger than the number of columns + 1
*/
protected void validateSampleData(double[][] x, double[] y) throws MathIllegalArgumentException {
if ((x == null) || (y == null)) {
throw new NullArgumentException();
}
if (x.length != y.length) {
throw new DimensionMismatchException(y.length, x.length);
}
if (x.length == 0) { // Must be no y data either
throw new NoDataException();
}
if (x[0].length + 1 > x.length) {
throw new MathIllegalArgumentException(
LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
x.length, x[0].length);
}
}
