Transform N-dimensional points to and from a 1-dimensional Hilbert fractal curve index in C# .Net.
The core algorithm is a port to C# of a C program written by John Skilling and published in the journal article "Programming the Hilbert curve", (c) 2004 American Institute of Physics.
The original C# code was written by Paul Anton Chernoch and may be freely used with attribution.
A. The using statements
Reference these namespaces:
using System.Numerics;
using HilbertTransformation;
using Clustering;B. Hilbert Index to HilbertPoint to N-Dimensional coordinates
This is how to convert a distance along the Hilbert curve into a D-dimensional point.
int bits = ???; // Pick so that 2^bits exceeds the largest value in any coordinate.
int dimensions = ???; // Number of dimensions for the point.
var index1 = new BigInteger(...);
var hPoint1 = new HilbertPoint(index1, dimensions, bits);
uint[] coordinates = hPoint.Coordinates;C. Coordinates to Hilbert Index
This is how you transform a point to a HilbertPoint.
var hPoint2 = new HilbertPoint(coordinates, bits);
BigInteger index2 = hPoint2.Index;If one does not need the transformed index but merely want to sort points in Hilbert order, in the most memory-efficient (but 5x slower) way, using an in-place sort:
UnsignedPoint[] points = ...
PointBalancer balancer = null;
HilbertSort.SmallBalancedSort(points, ref balancer);For a compromise between the two ways, this way of sorting uses about half as much memory as the HilbertIndex and is faster than it, but uses more memory than SmallUnbalancedSort:
UnsignedPoint[] points = ...
int bitsPerDimension = ?;
HilbertSort.Sort(points, bitsPerDimension); The UnsignedPoint class is little more than a vector of unsigned coordinates with some extra space to hold pre-computed values that will speed up Euclidean distance calculations. The PointBalancer shifts coordinates such that their median value falls in the middle of the range, which aids in reducing memory usage and speeds up the sort by reducing the number of bits necessary to represent the Hilbert index.
The SmallBalancedSort algorithm starts by sorting every point using a Hilbert transform of one bit per dimension to form large buckets of points sharing the same low-precision Hilbert index. Then it sorts each bucket with progressively more bits of precision until every bucket has a single element, or we reach the maximum precision and are faced with true duplicates. This approach means that the full-precision Hilbert transform is needed for comparatively few points, and at no time do we hold the Hilbert index for all points in memory at the same time. At any given time, we have one-to-two bits per dimension of space in memory per point, as opposed to trebling the data size during the sort as the former method did.
There are really four transformations that occur:
From Transposed back to BigInteger.
The transposed form is a rewrite of the BigInteger in which the high bit of the BigInteger goes to the high bit of the first byte of the transposed array, the next highest bit goes to the high bit of the next transposed byte, etc in a striped fashion. It is only in this rearranged form that the Hilbert transformation can be performed. However, only the BigInteger index and the Hilbert Axes (multi-dimensional coordinate form) are useful to library users in their analysis. The HilbertPoint class provides the interface to these operations.
This transform is most suitable for non-negative integer data. To apply it to floating point numbers, you need to do the following:
Decide how much resolution in bits you require for each coordinate. The more bits of precision you use, the higher the cost of the transformation.
Write methods to perform a two-way mapping from your coordinate system to the non-negative integers. This transform may require shifting and scaling each dimension a different amount in order to yield a desirable distance metric and origin.
Example.
This mapping will have to quantize values. For example, if your numbers range from -10 to +20 and you want to resolve to 0.1 increments, then perform these transformations:
a. translate by +10 (so all numbers are positive)
b. scale by x10 (so all numbers are integers)
c. Since the range is now from zero to 300, the next highest power of two is 512, so choose nine bits of resolution for your HilbertPoints.
In progress.... SLASH, a tool to cluster high-dimensional data using the Hilbert curve.
The class SlashCommand implements a console application that can perform unassisted classification of high-dimensional data. To run the program, execute the bash command slash.sh. You may need to modify this script to point to your mono installation.
"./slash.sh help" will exlpain how to call the program. Study class SlashConfig to understand the attributes found in the yaml configuration file. You will want to set the input and output files, and the name of the id field in your data, if any.
Example output from slash help:
Slash Version 0.1
Usage: 1. slash [help | -h | -help]
slash version
config-file ....... If omitted, assume slash.yaml is the configuration file. Configuration values are either written to this file (for 'define') or read from it, optionally overriding the values for input and output files. input-data-file ... If given, read input records from this file. If a hyphen, read from standard input. If omitted when defining a configuration, assume standard input holds the input records. Otherwise, use the value from the existing configuration file. output-data-file .. If present, write output records to this file. If a hyphen, write to standard input. If a question mark, suppress output. If omitted when defining a configuration, assume writing to standard output. Otherwise, use the value from the existing configuration file.
HELP. The first usage shows this help message.
DEFINE. The second usage creates a new YAML configuration file with the given name but does not perform clustering. The file will have default settings for all properties, except any file names optionally supplied on the command line. The user should edit this file to specify important properties like the names of the id field and category field, and whether there is a header record in the input CSV file.
ASSESS. Assess the clustering tendency of the data. If the data has no clustering tendency, it is fruitless to cluster it. The assessment will draw one of the following conclusions:
a. Unclustered - The data is fairly randomly distributed with no large clusters. b. Singly Clustered - All points not in outlying groups are in a single, large cluster. c. Majority Clustered - Of the points that are not outliers, two thirds or more are in a single, large cluster. d. Weakly Clustered - Two-thirds or more of the points are outliers. e. Moderately Clustered - Between one- and two-thirds of the points are outliers. f. Highly Clustered - Fewer than one-third of the points are outliers.
CLUSTER. The third usage reads a configuration file and the indicated input data file (or standard input), clusters the data and writes the results to the indicated output file (or standard output). If the input data includes clustering categories, a comparison is logged to indicate how similar the new clustering is to the clustering done via some other source or a previous run of SLASH. The original clustering, if present, has no influence over the resulting clustering.
RECLUSTER. The fourth usage reads a configuration file and the indicated input data file (or standard input). It assumes that the records have already been clustered. It begins with the records grouped by this original clustering and continues with a new round of clustering. It writes the results to the indicated output file (or standard output). A comparison between the original categories and the final categories is logged to indicate how different the new clustering is from the original clustering.
VERSION. Print out the program version number.