Commit ebea5451 authored by Feng Xue's avatar Feng Xue

Merge branch 'master' of prosjekt.nsd.uib.no:nesstar/nesstar-rest-api

Conflicts:
	src/main/java/com/nesstar/rest/resources/CorrelationResource.java
	src/main/java/com/nesstar/rest/resources/RegressionResource.java
parents 8343274b f7ea3ddd
......@@ -8,7 +8,7 @@ Getting Started
1. Run `mvn package` to build everything
1. Copy the `config.yaml.example` to `config.yaml` and edit it to add your own server URI and logon credentials.
1. Run `java -jar target/nesstar_rest_api-0.3.0.jar server config.yaml`
1. Run `java -jar target/nesstar_rest_api-0.4.0.jar server config.yaml`
Note: The name of the JAR file may change. See the `target` folder.
If you want to require users of your API to log in, delete the username and password lines from the config file.
......@@ -18,7 +18,7 @@ The server will start up on port 8080 by default.
The port can be changed by adding a paramater to the java command like
this:
java -Ddw.http.port=3000 -jar target/nesstar_rest_api-0.3.0.jar server config.yaml
java -Ddw.http.port=3000 -jar target/nesstar_rest_api-0.4.0.jar server config.yaml
Alernatively. A port can be set in the config.yaml file by adding a section like
this:
......
......@@ -7,7 +7,7 @@
<name>Nesstar REST API</name>
<groupId>com.nesstar</groupId>
<artifactId>nesstar_rest_api</artifactId>
<version>0.3.0</version>
<version>0.4.0</version>
<organization>
<name>NSD</name>
......
......@@ -63,6 +63,7 @@ public final class CorrelationResource extends AbstractResource {
classVariables.setMissingDeletionType(classVariables.getMissingDeletionType());
} catch (NullPointerException e) {
//ignore
}
CorrelationResult correlationResult = performCorrelation(classVariables);
ETag etag = ETag.generateEtagForCorrelation(classVariables, request);
......
......@@ -72,6 +72,7 @@ public final class RegressionResource extends AbstractResource {
classVariables.setMissingDeletionType(classVariables.getMissingDeletionType());
} catch (NullPointerException e) {
//ignore
}
LinearRegressionResult linearRegressionResult = performRegression(classVariables);
......
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