io.nosqlbench.virtdata.library.curves4.continuous.int_double.EnumeratedAutoDocsInfo Maven / Gradle / Ivy
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Statistical sampling library for use in virtdata libraries, based
on apache commons math 4
// This file is auto-generated.
package io.nosqlbench.virtdata.library.curves4.continuous.int_double;
import io.nosqlbench.nb.annotations.Service;
import io.nosqlbench.virtdata.api.annotations.Category;
import io.nosqlbench.virtdata.api.processors.DocCtorData;
import io.nosqlbench.virtdata.api.processors.DocForFuncCtor;
import io.nosqlbench.virtdata.api.processors.DocFuncData;
import java.lang.String;
import java.util.ArrayList;
import java.util.LinkedHashMap;
import java.util.List;
@Service(
value = DocFuncData.class,
selector = "io.nosqlbench.virtdata.library.curves4.continuous.int_double.Enumerated"
)
public class EnumeratedAutoDocsInfo implements DocFuncData {
public String getClassName() {
return "Enumerated";
}
public String getPackageName() {
return "io.nosqlbench.virtdata.library.curves4.continuous.int_double";
}
public String getClassJavadoc() {
return "Creates a probability density given the values and optional weights provided, in \"value:weight value:weight ...\" form.\n"
+ "The weight can be elided for any value to use the default weight of 1.0d.\n"
+ "\n"
+ "@see Commons JavaDoc: EnumeratedRealDistribution\n"
+ "\n"
+ "\n";
}
public String getInType() {
return "int";
}
public String getOutType() {
return "double";
}
public Category[] getCategories() {
return new Category[] { Category.distributions };
}
public List getCtors() {
return new ArrayList() {{
add(new DocForFuncCtor("Enumerated", "",
new LinkedHashMap() {{
put("data","java.lang.String");
put("mods","java.lang.String[]...");
}},
new ArrayList>() {{
add(new ArrayList() {{
add("Enumerated('1 2 3 4 5 6')");
add("a fair six-sided die roll");
add("[1-10]/");
}});
add(new ArrayList() {{
add("Enumerated('1:2.0 2 3 4 5 6')");
add("an unfair six-sided die roll, where 1 has probability mass 2.0, and everything else has only 1.0");
}});
}}
));
}};
}
}