partial docs work

virtdata-lib-vectors/src/main/java/io/nosqlbench/virtdata/lib/vectors/dnn/circular/package-info.java

@@ -0,0 +1,25 @@
+/*
+ * Copyright (c) 2024 nosqlbench
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * <P>This package contains an implementation of
+ * {@link io.nosqlbench.virtdata.lib.vectors.dnn.circular.CircularPartitioner},
+ * a space-filling curve which maps ordinals onto 2-d vectors which fall on the unit circle
+ * with increasing density. This allows vector values to get progressive closer together radially
+ * as the arc intervals are divided in half at each level of resolution.</P>
+ *
+ */
+package io.nosqlbench.virtdata.lib.vectors.dnn.circular;

virtdata-lib-vectors/src/main/java/io/nosqlbench/virtdata/lib/vectors/dnn/package-info.java

@@ -15,6 +15,28 @@
  */
 
 /**
- * This is an experimental package based on the DNN or "Das/Direct Nearest Neighbor" method.
+ * <P>This package contains experimental support for new methods for testing vector stores.
+ * projective simulation ... TBD
+ * of vector spaces
+ * within which provably correct KNN relationships can be derived from affine ordinal relationships.
+ * In other words, vectors in some projective  space which are addressable by some ordinal identity
+ * can be constructed with procedural generation methods, and provably correct KNN neighborhoods of
+ * some size can be derived on the fly in a closed form calculation.</P>
+ *
+ * <P>The vector spaces constructed in this way are not intended nor guaranteed to be dimensionally disperse.
+ * They are meant to provide an algebraic basis for exercising vector storage systems with increasing
+ * cardinality of vectors. This means that vector stores can be tested to incrementally higher limits
+ * while their performance and accuracy are both measured.</P>
+ *
+ * <P>Each vector scheme in this method has the following properties:
+ * <UL>
+ *     <LI>All vectors within the space are enumerable. Each increasing ordinal value describes a new and distinct
+ *     vector. The value of this vector is deterministic within the parameters of the space.</LI>
+ * </UL>
+ * </P>
+ *
+ * <P>This work is largely inspired by the DNN or "Das/Direct Nearest Neighbor" method, pioneered by
+ * Shaunak Das at DataStax. Additional implementations and ideas are contributed by the vector performance
+ * team and our testing community.</P>
  */
 package io.nosqlbench.virtdata.lib.vectors.dnn;