Apache/commons-math
1
0
Fork 0
mirror of https://github.com/apache/commons-math.git synced 2025-02-06 18:18:56 +00:00
Code Issues Packages Projects Releases Wiki Activity

Tab and trailing space removal

Browse Source 
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1060125 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Sebastian Bazley 2011-01-17 23:02:05 +00:00
parent b11082e6d2
commit 1148874baa
1 changed files with 35 additions and 35 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

70
src/test/java/org/apache/commons/math/stat/StatUtilsTest.java
View File

@ -424,46 +424,46 @@ public final class StatUtilsTest extends TestCase {
}
/**
* Run the test with the values 50 and 100 and assume standardized values
*/
/**
* Run the test with the values 50 and 100 and assume standardized values
*/
public void testNormalize1() {
double sample[] = { 50, 100 };
double expectedSample[] = { -25 / Math.sqrt(1250), 25 / Math.sqrt(1250) };
double[] out = StatUtils.normalize(sample);
for (int i = 0; i < out.length; i++) {
assertEquals(out[i], expectedSample[i]);
}
public void testNormalize1() {
double sample[] = { 50, 100 };
double expectedSample[] = { -25 / Math.sqrt(1250), 25 / Math.sqrt(1250) };
double[] out = StatUtils.normalize(sample);
for (int i = 0; i < out.length; i++) {
assertEquals(out[i], expectedSample[i]);
}
}
}
/**
* Run with 77 random values, assuming that the outcome has a mean of 0 and a standard deviation of 1 with a
* precision of 1E-10.
*/
/**
* Run with 77 random values, assuming that the outcome has a mean of 0 and a standard deviation of 1 with a
* precision of 1E-10.
*/
public void testNormalize2() {
// create an sample with 77 values
int length = 77;
double sample[] = new double[length];
for (int i = 0; i < length; i++) {
sample[i] = Math.random();
}
// normalize this sample
double standardizedSample[] = StatUtils.normalize(sample);
public void testNormalize2() {
// create an sample with 77 values
int length = 77;
double sample[] = new double[length];
for (int i = 0; i < length; i++) {
sample[i] = Math.random();
}
// normalize this sample
double standardizedSample[] = StatUtils.normalize(sample);
DescriptiveStatistics stats = new DescriptiveStatistics();
// Add the data from the array
for (int i = 0; i < length; i++) {
stats.addValue(standardizedSample[i]);
}
// the calculations do have a limited precision
double distance = 1E-10;
// check the mean an standard deviation
assertEquals(0.0, stats.getMean(), distance);
assertEquals(1.0, stats.getStandardDeviation(), distance);
DescriptiveStatistics stats = new DescriptiveStatistics();
// Add the data from the array
for (int i = 0; i < length; i++) {
stats.addValue(standardizedSample[i]);
}
// the calculations do have a limited precision
double distance = 1E-10;
// check the mean an standard deviation
assertEquals(0.0, stats.getMean(), distance);
assertEquals(1.0, stats.getStandardDeviation(), distance);
}
}
}