PLOT_BOUND(1)

NAME
plot_bound - arguments for 1 and 2 dimension classifier plots

SYNOPSIS
There is no program plot_bound. These are the arguments available for all of the decision region and profile plot programs.

plot_bound [ -pathexp directory ] [ -fparam file ]
[ -fregion file ] [ -fprofile file ]
[ -tregion string ] [ -tprofile string ]
[ -first_dim n ] [ -second_dim n ]
[ -other_vars x[,x]... ] [ -autoscale ]
[ -xmin x ] [ -xmax x ] [ -xstep x ] [ -ymin x ]
[ -ymax x] [ -ystep x ] [ -pymin x ] [ -pymax x ]
[ -pystep x ] [ -region ] [ -npoints n ] [ -scatter ]
[ -internals ] [ -internals_scale x ]
[ -internals_level n ] [ -profile ] [ -histogram ]
[ -fdata full_path ] [ -fdesc full_path ]
[ -ninputs n ] [ -noutputs n ] [ -npatterns n ]
[ -color ] [ -misses ] [ -all ] [ -distance x ]
[ -map ] [ -adjust_priors ] [ -priors_list x[,x]... ]
[ -uniform_priors ] [ -no_graphics ] [ -no_norm ]
[ -mac [ -fmacdots file ] [ -fmacscatter file ]
[ -fmaclines file ] [ -fmachisto file ]]
[ -mac_wireframe -mac_wireframe_output n ] [ -fmacwireframe file ]]

EXAMPLE
mlp -epochs 100 -train -create \
-pathdata $LNKHOME/data/class -finput vowel.train \
-fdescribe vowel.defaults -npatterns 338 -fparam mlp.param

mlp_plot_bound -autoscale -fparam mlp.param \
-ninputs 2 -noutputs 10 -npatterns 338 -region -scatter \
-internals -color -all \
-fdata $LNKHOME/data/class/vowel.train \
-fdesc $LNKHOME/data/class/vowel.defaults

DESCRIPTION
The plot_bound programs generate 1D or 2D plots for classifiers and clusterers in the LNKnet pattern classification package. Each classifier and clusterer has its own plot program. If both 1D and 2D plots are requested, only the 2D plots will be displayed.

There are three 2D plots which can be overlaid in one plot display. There is a decision region plot which shows the classifications made by a trained classifier, a scatter plot of input data and an internals plot of classifier parameters. The format of the internals plot depends on the particular classifier or clusterer.

There are two 1D plots which can be overlaid in one plot. There is profile plot which shows classifier outputs for a range of generated inputs and a histogram of input data. Because they do not have continuous outputs, the 1D plots not available for the nearest neighbor style classifiers.

OPTIONS

--  Prints a list of all flags and arguments.

-pathexp directory
    Directory where fparam, fregion is and fprofile should be stored.

-fparam file
    Parameter file for classifier being plotted

-fregion file
    File where the decision region, scatter, and internals plots will be stored.

-fprofile file
    File where the profile and histogram plots will be stored.

-tregion string
    Title for the plot stored in fregion.

-tprofile string
    Title for the plot stored in fprofile.

-first_dim n
    One input dimension which will be swept over to generate the decision region plot. This is also the dimension used for the profile plot. A number from 0 to the number of classifier inputs minus one.

-second_dim n
    The other input dimension which will be swept over to generate the decision region plot. A number from 0 to the number of classifier inputs minus one.

-other_vars x[,x]...
    List of values for all dimensions when generating the decision region and profile plots. The values for first_dim and second_dim will be ignored. The default is to set all other dimensions to 0.

-autoscale
    If this flag is set, the data in fdata will be used to determine the axis limits of the plot. Otherwise, the limits given below are used.

-xmin x
    If autoscale is not set, the lower limit of the x axis for the region and profile plots.

-xmax x
    If autoscale is not set, the upper limit of the x axis for the region and profile plots.

-xstep x
    If autoscale is not set, the space between tic marks on the x axis for the region and profile plots.

-ymin x
    If autoscale is not set, the lower limit of the y axis for the region plot.

-ymax x
    If autoscale is not set, the upper limit of the y axis for the region plot.

-ystep x
    If autoscale is not set, the space between tic marks on the y axis for the region plot.

-pymin x
    If autoscale is not set, the lower limit of the y axis for the profile plot.

-pymax x
    If autoscale is not set, the upper limit of the y axis for the profile plot.

-pystep x
    If autoscale is not set, the space between tic marks on the y axis for the profile plot.

-region
    Display an decision region plot for the given parameter file. Display the class assigned to points while varying the first_dim input from xmin to xmax and the second_dim input from ymin to ymax. The other input values are held constant at 0 or to the values in other_vars.

-npoints n
    The number of points sampled in each dimension when region is set. A total of n*n points will be sampled.

-scatter
    Display a plot showing the positions of the data patterns in fdata. The values for the dimensions first_dim and second_dim are shown.

-internals
    Display a plot showing internal parameters for the classifier model in fparam.

-internals_scale x
    A scaling factor for shrinking or enlarging the representations of the internal parameters.

-internals_level n
    A level for how detailed the internals and black and white scatter plot should be.

    1   Internals plot elements are drawn with no associated text. Black and white plots use lippic symbols to identify the class of the patterns.

    2   For black and white plot, changes plot symbols from lippic standard symbols to alphabetic characters.

    3   Label internals plot elements with the class label or node number.

-profile
    Display a plot showing each output level for the classifier in fparam while sampling npoints patterns along the first_dim. The values of all other dimensions are held at 0 or to the values specified by other_vars during this sweep.

-histogram
    Display a plot showing a histogram of the data. All of the bins are along first_dim. Those patterns which are correctly classified by fparam are drawn above the histogram baseline. Those patterns which are incorrectly classifier are drawn below the histogram baseline.

-fdata full_path
    The full path to the data file with the data for the scatter and histogram plots.

-fdesc full_path
    The full path to the data base description file where the class labels and input feature labels for decision region, profile, scatter, and histogram plots are stored.

-ninputs n
    The number of inputs to read from the data file. This is independent of normalization or feature selection parameters found in the parameter file.

-noutputs n
    The number of output dimensions in the data file.

-npatterns n
    The maximum number of patterns to read from the data file.

-color
    Display the decision regions, scatter plot data patterns, profile plot outputs, and histogram bars in colors corresponding to each class. If the parameter file being read is for a clustering algorithm, the plot will be in black and white.

-misses
    In the color scatter plot, display the missed patterns with a gray border. For black and white scatter plots, display the missed patterns using the plot symbols and the correct patterns are drawn as tiny dots.

-all
    In the scatter and histogram plots, display all of the data projected onto the two selected dimensions.

-distance x
    If all is not set, display those patterns which are within a distance x of the first_dim, second_dim plane (scatter plots) or of the line along first_dim (histogram plots)

-map
    For generating 2 dimensional plots of the results of clustering on mapping data.

-adjust_priors
    While generating decision region and profile plots, and while doing classifications for scatter and histogram plots, scale continuous classifier outputs to simulate having trained on data with uniform a priori class probabilities or using the probabilities given in priors_list.

-priors_list x[,x]...
    If uniform_priors is not set, this is the list of class probabilities to be used while adjusting a priori class probabilities during either training or testing. This must be a comma delimited list of noutputs floating point numbers. The "probabilities" are normalized to sum to one before being used by the classifier, so they can be any floating point number over 0.

-uniform_priors
    When adjust_priors is set, let each class have equal probability. When this flag is set, priors_list is ignored.

-no_graphics
    Create a plot file, but do not display it.

-no_norm
    Display plots using data before normalization. Because internals are created after normalization, they cannot be displayed when no_norm is set.

-mac
    If this flag is set, data from the decision region plot, scatter plot, and profile plot will be stored in the files fmacdots, fmacscatter, fmaclines, and fmachisto. These files can be used to generate plots on a MacIntosh or other PC using a spread-sheet graphing program such as Excel or DeltaGraph.

-fmacdots file
    This is an ASCII file containing the x and y coordinates of points along the borders between classes in the decision region plot. Each line contains one point.

-fmacscatter file
    This is an ASCII file with the first and second dimensions of the patterns in the scatter plot data file. The number of lines in the file is one more than the maximum number of patterns in a class. The first line has the class labels for the input classes and labels the columns of the rest of the file. Each line has a pair of x and y values for a pattern in each class. The patterns for class 0 are found in the first two columns in fmacscatter, the patterns for class 1 are found in the next two, and so one. When a class runs out of patterns, tabs are inserted in its column.

-fmaclines file
    This is an ASCII file with the x and y coordinates of the lines in the profile plot. Each line has a value for the input dimension being plotted followed by the output values for each class.

-fmachisto file
    This is an ASCII file containing the number of patterns in each of the npoints histogram bins. The bin number is the first entry in the line. The number of patterns per class follows. When a profile plot is also done, correctly classified pattterns are displayed separately from incorrectly classified patterns. In the file, the bin counts for the correct patterns come first. The counts for the incorrect bins follow, giving 2*npoints lines in the file. The counts for the incorrect bins are given as negative numbers.

-mac_wireframe
    If this flag is set, output data from the decision region plot will be stored in fmacwireframe. This file can be used to generate a wireframe plot of the class outputs on a MacIntosh or other PC using a spread-sheet graphing program such as Excel or DeltaGraph.

-mac_wireframe_output n
    This flag controls the amount of data printed to acwireframe.

    -1  Print the maximum output at each point.

    0 - noutputs-1
        Print the outputs for the given class at each point.

-fmacwireframe file
    This is an ASCII file containing npoints*npoints output values. The output values are those found while sampling the input space to generate a decision region plot. There are npoints lines with npoints entries per line.

The following plot_bound programs have additional flags:

    rbf_plot_bound, irbf_plot_bound: -exhspread x
        Extra factor multiplied into the Gaussian cluster variances for testing.

    bintree_plot_bound: -prune_tree -max_nodes n
        Prune tree to at most n nodes before testing the classifier for the decision region plot.

    knn: -k n
        Use the classes of the n nearest neighbors to determine the class of patterns in the decision region plot.

    histogram: -use_gauss
        Use a Gaussian classifier to assign classes to patterns outside all histograms. The default is to label those patterns "unknown"

    parzen: -spread x -linear
        spread is an extra factor multiplied multiplied into kernel function variances for testing. linear causes the kernel functions to be rectangular pulse functions. The default is to use Gaussian kernel functions.

INTERNALS
This is a descriptions of the parameters displayed by the internals plots for each classifier or clusterer. In all cases, when the classifier has more than two input features, the internals plot is drawn using data from only those two features being plotted. No attempt is made to find the intersection of the planes, shapes or points shown by the internals with the plane being plotted

mlp This classifier shows the lines defined by the weights and offsets of the first hidden layer.

rbf irbf nc_class kmeans em_clus random
    These classifiers and clusterers show the basis function clusters used as their inputs. The basis functions are shown as ellipses drawn on the one standard deviation equipotential line of the cluster.

gauss gmix parzen
    These classifiers draw one ellipse for each included gaussian. The ellipses are centered on the mean for each gaussian. The line is drawn along the one standard deviation equipotential line of the gaussian. When the -linear flag is set for parzen, boxes are drawn with sides length two standard deviations.

knn cknn lvq
    These classifiers draw the location of each stored pattern or center as a small square.

histogram
    This classifier shows the lines that make the edges of the histogram bins.

bintree
    The lines marking the borders between decision nodes of the binary tree are drawn.

hyper lead_clus
    The borders of the hyperspheres or clusters are drawn.

artmap
    The edges of each artmap node are drawn.

HISTORY
First written 4 November 1992 by Linda Kukolich
Modified 5 July 1993 by Linda Kukolich
Modified 23 June 1994, added fdesc, took out -labels, documented extra arguments
Modified 24 Jan 1997, added descriptions of internals plots.

BUGS
Send bug reports to Linda Kukolich (kukolich@sst.ll.mit.edu)