BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

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+1. Title: Car Evaluation Database
+
+2. Sources:
+   (a) Creator: Marko Bohanec
+   (b) Donors: Marko Bohanec   (marko.bohanec@ijs.si)
+               Blaz Zupan      (blaz.zupan@ijs.si)
+   (c) Date: June, 1997
+
+3. Past Usage:
+
+   The hierarchical decision model, from which this dataset is
+   derived, was first presented in 
+
+   M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for
+   multi-attribute decision making. In 8th Intl Workshop on Expert
+   Systems and their Applications, Avignon, France. pages 59-78, 1988.
+
+   Within machine-learning, this dataset was used for the evaluation
+   of HINT (Hierarchy INduction Tool), which was proved to be able to
+   completely reconstruct the original hierarchical model. This,
+   together with a comparison with C4.5, is presented in
+
+   B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by
+   function decomposition. ICML-97, Nashville, TN. 1997 (to appear)
+
+4. Relevant Information Paragraph:
+
+   Car Evaluation Database was derived from a simple hierarchical
+   decision model originally developed for the demonstration of DEX
+   (M. Bohanec, V. Rajkovic: Expert system for decision
+   making. Sistemica 1(1), pp. 145-157, 1990.). The model evaluates
+   cars according to the following concept structure:
+
+   CAR                      car acceptability
+   . PRICE                  overall price
+   . . buying               buying price
+   . . maint                price of the maintenance
+   . TECH                   technical characteristics
+   . . COMFORT              comfort
+   . . . doors              number of doors
+   . . . persons            capacity in terms of persons to carry
+   . . . lug_boot           the size of luggage boot
+   . . safety               estimated safety of the car
+
+   Input attributes are printed in lowercase. Besides the target
+   concept (CAR), the model includes three intermediate concepts:
+   PRICE, TECH, COMFORT. Every concept is in the original model
+   related to its lower level descendants by a set of examples (for
+   these examples sets see http://www-ai.ijs.si/BlazZupan/car.html).
+
+   The Car Evaluation Database contains examples with the structural
+   information removed, i.e., directly relates CAR to the six input
+   attributes: buying, maint, doors, persons, lug_boot, safety.
+
+   Because of known underlying concept structure, this database may be
+   particularly useful for testing constructive induction and
+   structure discovery methods.
+
+5. Number of Instances: 1728
+   (instances completely cover the attribute space)
+
+6. Number of Attributes: 6
+
+7. Attribute Values:
+
+   buying       v-high, high, med, low
+   maint        v-high, high, med, low
+   doors        2, 3, 4, 5-more
+   persons      2, 4, more
+   lug_boot     small, med, big
+   safety       low, med, high
+
+8. Missing Attribute Values: none
+
+9. Class Distribution (number of instances per class)
+
+   class      N          N[%]
+   -----------------------------
+   unacc     1210     (70.023 %) 
+   acc        384     (22.222 %) 
+   good        69     ( 3.993 %) 
+   v-good      65     ( 3.762 %)