Neural Data Analysis Environment

User's Guide

This software has been written and is copyrighted by

Research group on Software Engineering and Computational Intelligence

Pasi Koikkalainen (group leader)

Erkki Häkkinen

Anssi Lensu

Mika Rekkilä

• Contents
• Basics
  • Basics of data management in the NDA
    • Basic data types
    • Naming of data items
    • Commands, switches, scripts and script parameters
    • Variables and parameter substitution
• User's reference
  • Basic operations for managing the name space
    • Managing directories
    • Listing names in the name space
    • Removing name entries
    • Renaming name entries
    • Copying data frames or fields
    • Displaying the contents of data frames and fields
    • Querying the vector lenght of a name entry
    • Setting the contents of a data field
    • Selecting fields into a data frame
    • Selecting classes to a classified data frame
    • Selecting items to frames
    • Attaching a data frame to the end of another
    • Handling classified data frames
    • Creating a data field as a number series
    • Converting fields of a data frame into a string field
    • Conversion routines for time
    • Matrices
      • The matrix structure
      • Frame - Matrix commands
  • Data reorganization
    • Transposing a data frame
    • Joining data frames
    • Indexing through a joining operation
    • Finding unique data records
    • Finding unique indexes from classes
    • Finding the indexes of a classified data from another classified data
    • Merging a grouping and a classified data
    • Set-relational operations for classes
    • Convert a classification into a field
    • Converting SOM classification to location codes
    • Combining classifications
    • Converting sum values into propabilities
    • Binarizing a classification to data fields
    • Picking data records based on a classified data
    • Sorting a data frame
    • Classifying a data frame with a BMU classifier
    • Classifying data records to sequences
    • Concatenating sequences
    • Overlapping sequential data sets
    • Compacting self-similar data
    • Changing field types
  • Basic computing operations
    • Preprocessing data
      • Basic preprocessing
      • Whitening preprocessor command
      • Equalize values within records
      • Treatment for missing data
    • Field evaluator
      • Expressions
      • Field calculator
      • Record selector
      • Classification with expressions
      • Select classes by evaluating a boolean expression
    • Record evaluator
    • Class evaluator
    • Field statistics
      • Basic statistics for fields
      • Histograms for fields
      • Comparison of two data frames
    • Class statistics
      • Basic statistics for classes
      • Class identifiers
      • Identifying classes by values
      • Evaluating the number of items in classes
      • Values for distributions in classes
      • Histograms for classes
    • Fuzzy sets
      • Creating a fuzzy structure
      • Creating fuzzy sets
      • Altering the membership functions of fuzzy sets
      • An example sequence of fuzzy operations
      • Computing fuzzy membership values for a field
    • Calculating distance between data and groups
    • Group membership evaluation
  • Algorithms
    • The Tree-Structured Self-Organizing Map and related structures
      • Training a TS-SOM
      • Classifying data by a TS-SOM
      • Smoothing data in a TS-SOM
      • Creating a field for TS-SOM layer information
      • Storing the indexes of neurons into a data frame
      • Getting and setting data in a TS-SOM layer
      • Computing a U-matrix
      • Spreading the groups of core neurons
      • Mapping data records onto a TS-SOM
      • Generating data according to the grid of a TS-SOM
    • Backpropagation
      • The basic learning rule
      • The basic learning rule with a momentum term
      • Global adaptive training step length
      • The Matlab neural network toolbox style training
      • The Silva & Almeida training
      • The RPROP training
      • The Levenberg-Marquard training
      • Importing a new data to a trained network
      • Visualizing an MLP network
    • Classical scaling and Sammon's mapping
    • Data clustering
      • Hierarchical clustering
      • k-means clustering
      • Fuzzy c-means clustering
  • Image processing
    • File commands
    • Basic image commands
    • Filtering and thresholding
    • Histograms
    • Conversions between images and data frames
    • Fourier commands
    • Commands related to fuzzy-analysis
  • Graphics
    • Creating and initializing graphic structures
      • Creating a graphic structure and connecting a frame to it
      • Managing subgraphs
    • Setting a structure over subgraphs
      • Setting the visualized layer
      • Setting the topology and child links of TS-SOM
      • Creating trajectories
      • Connecting neurons
      • Creating contours
      • The U-matrix presentation
      • Axis for each dimension - x, y and z
      • 3D scatterplotting
    • Setting the graphical contents of subgraphs
      • Setting the locations of neurons in 3D
      • Setting the shape of the neurons
      • Setting the size of neurons
      • Setting the proportional diameters of circular neurons
      • Setting the colors or gray levels of neurons
      • The visibility of the neurons
    • Graphical presentations inside subgraphs
      • Axes in subgraphs
      • Plotting data in subgraphs
      • Bars in subgraphs
      • Range plots in subgraphs
      • Line diagrams in subgraphs
      • Line diagrams based on the values of the field
      • Histograms in subgraphs
      • The curves of the functions
      • Field labels and values as text in subgraphs
      • Record labels in subgraphs
    • Colors and scaling
    • Graphics for groups in a SOM
    • Setting a viewpoint to a graphic image in 3D
    • Displaying an image in a window
    • Displaying graphics
    • Querying the settings of a graphic structure
    • Saving the settings of a graphic structure into a file
    • Graphic frames
      • Creating a graphic frame
      • Adding a graphic structure to a graphic frame
      • Setting the places of graphic structures within graphic frames
    • Commands for working with low level images
      • Generating a low level image from a graphic structure
      • Finding objects from a graphic structure
      • Executing macros according to a selection within a graphic structure
      • Generating a low level image for a graphic frame (tech.)
      • Updating a graphic structure
  • File I/O
    • Loading data structures from a file
    • Saving data structures into a file
    • PostScript printing
    • Executing command files
  • Miscellaneous commands
    • Conditional execution
    • Command echo and messaging
    • Error tolerance
    • Running a UNIX command from the NDA
    • Commands for the Xview graphical user interface
      • Opening a graphic tool
      • Opening a graphical fuzzy tool
      • Updating a graphic window from the command line
  • Undocumented commands
    • Data operations
    • Image operations
• Appendices
  • Quick reference
    • Basic operations for managing the name space
    • Data reorganization
    • Basic computing operations
    • Algorithms
    • Image processing
    • Graphics
    • File I/O
    • Miscellaneous commands
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