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Final keyword. 

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1392358 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz 2012-10-01 14:41:55 +00:00
parent e055dcd21a
commit 57b0a07b9f
1 changed files with 22 additions and 27 deletions
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49
src/main/java/org/apache/commons/math3/stat/regression/MillerUpdatingRegression.java
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@ -175,7 +175,7 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio
if (!this.hasIntercept) {
include(MathArrays.copyOf(x, x.length), 1.0, y);
} else {
double[] tmp = new double[x.length + 1];
final double[] tmp = new double[x.length + 1];
System.arraycopy(x, 0, tmp, 1, x.length);
tmp[0] = 1.0;
include(tmp, 1.0, y);
@ -254,7 +254,7 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio
_w = w;
if (di != 0.0) {
dpi = smartAdd(di, wxi * xi);
double tmp = wxi * xi / di;
final double tmp = wxi * xi / di;
if (FastMath.abs(tmp) > Precision.EPSILON) {
w = (di * w) / dpi;
}
@ -292,16 +292,16 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio
* @return the sum of the a and b
*/
private double smartAdd(double a, double b) {
double _a = FastMath.abs(a);
double _b = FastMath.abs(b);
final double _a = FastMath.abs(a);
final double _b = FastMath.abs(b);
if (_a > _b) {
double eps = _a * Precision.EPSILON;
final double eps = _a * Precision.EPSILON;
if (_b > eps) {
return a + b;
}
return a;
} else {
double eps = _b * Precision.EPSILON;
final double eps = _b * Precision.EPSILON;
if (_a > eps) {
return a + b;
}
@ -380,7 +380,7 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio
if (!this.tol_set) {
tolset();
}
double[] ret = new double[nreq];
final double[] ret = new double[nreq];
boolean rankProblem = false;
for (int i = nreq - 1; i > -1; i--) {
if (Math.sqrt(d[i]) < tol[i]) {
@ -411,9 +411,6 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio
* columns.
*/
private void singcheck() {
double temp;
double y;
double weight;
int pos;
for (int i = 0; i < nvars; i++) {
work_sing[i] = Math.sqrt(d[i]);
@ -422,7 +419,7 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio
// Set elements within R to zero if they are less than tol(col) in
// absolute value after being scaled by the square root of their row
// multiplier
temp = tol[col];
final double temp = tol[col];
pos = col - 1;
for (int row = 0; row < col - 1; row++) {
if (Math.abs(r[pos]) * work_sing[row] < temp) {
@ -443,8 +440,8 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio
x_sing[_xi] = r[_pi];
r[_pi] = 0.0;
}
y = rhs[col];
weight = d[col];
final double y = rhs[col];
final double weight = d[col];
d[col] = 0.0;
rhs[col] = 0.0;
this.include(x_sing, weight, y);
@ -502,10 +499,10 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio
rnk += 1.0;
}
}
double var = rss[nreq - 1] / (nobs - rnk);
double[] rinv = new double[nreq * (nreq - 1) / 2];
final double var = rss[nreq - 1] / (nobs - rnk);
final double[] rinv = new double[nreq * (nreq - 1) / 2];
inverse(rinv, nreq);
double[] covmat = new double[nreq * (nreq + 1) / 2];
final double[] covmat = new double[nreq * (nreq + 1) / 2];
Arrays.fill(covmat, Double.NaN);
int pos2;
int pos1;
@ -552,11 +549,10 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio
int pos1 = -1;
int pos2 = -1;
double total = 0.0;
int start;
Arrays.fill(rinv, Double.NaN);
for (int row = nreq - 1; row > 0; --row) {
if (!this.lindep[row]) {
start = (row - 1) * (nvars + nvars - row) / 2;
final int start = (row - 1) * (nvars + nvars - row) / 2;
for (int col = nreq; col > row; --col) {
pos1 = start;
pos2 = pos;
@ -611,24 +607,23 @@ public class MillerUpdatingRegression implements UpdatingMultipleLinearRegressio
* regressors with each other and the regressand, in lower triangular form
*/
public double[] getPartialCorrelations(int in) {
double[] output = new double[(nvars - in + 1) * (nvars - in) / 2];
int base_pos;
final double[] output = new double[(nvars - in + 1) * (nvars - in) / 2];
int pos;
int pos1;
int pos2;
int rms_off = -in;
int wrk_off = -(in + 1);
double[] rms = new double[nvars - in];
double[] work = new double[nvars - in - 1];
final int rms_off = -in;
final int wrk_off = -(in + 1);
final double[] rms = new double[nvars - in];
final double[] work = new double[nvars - in - 1];
double sumxx;
double sumxy;
double sumyy;
int offXX = (nvars - in) * (nvars - in - 1) / 2;
final int offXX = (nvars - in) * (nvars - in - 1) / 2;
if (in < -1 || in >= nvars) {
return null;
}
int nvm = nvars - 1;
base_pos = r.length - (nvm - in) * (nvm - in + 1) / 2;
final int nvm = nvars - 1;
final int base_pos = r.length - (nvm - in) * (nvm - in + 1) / 2;
if (d[in] > 0.0) {
rms[in + rms_off] = 1.0 / Math.sqrt(d[in]);
}