Merge branch 'add-genann' into 'master'

[genann] add new case study

See merge request pub/open-source-case-studies!12

Makefile

@@ -56,6 +56,7 @@ TARGETS=\
   cerberus \
   chrony \
   debie1 \
+  genann \
   gzip124 \
   hiredis \
   icpc \

README.md

@@ -127,6 +127,7 @@ when available. We also summarize the license of each directory below.
 - `debie1`: distribution and use authorized by Patria Aviation Oy,
             Space Systems Finland Ltd. and Tidorum Ltd, see `README.txt`
             and `terms_of_use-2014-05.pdf`
+- `genann`: Zlib, see `LICENSE`
 - `gzip124`: GPL
 - `hiredis`: Redis license (BSD-style), see `COPYING`
 - `icpc`: Unlicense

frama-c @ 4adb46e0

-Subproject commit 086dbf6774409ce5457bbc1ddb232e1f97714b23
+Subproject commit 4adb46e09b8f6bd9809fa98cf1bbc7731c8eea87

genann/.frama-c/GNUmakefile

+# Makefile template for Frama-C/Eva case studies.
+# For details and usage information, see the Frama-C User Manual.
+
+### Prologue. Do not modify this block. #######################################
+-include path.mk
+FRAMAC ?= frama-c
+include $(shell $(FRAMAC)-config -print-share-path)/analysis-scripts/prologue.mk
+###############################################################################
+
+# Edit below as needed. Suggested flags are optional.
+
+MACHDEP = gcc_x86_64
+
+# The source code uses the __FILE__ macro; to obtain stable oracles, we use
+# a specific GCC option based on the directory containing this Makefile.
+mkfile_path := $(abspath $(firstword $(MAKEFILE_LIST))/../..)
+
+## Preprocessing flags (for -cpp-extra-args)
+CPPFLAGS    += \
+  -ffile-prefix-map=$(mkfile_path)=.
+
+## General flags
+FCFLAGS     += \
+  -add-symbolic-path=..:. \
+  -kernel-warn-key annot:missing-spec=abort \
+  -kernel-warn-key typing:implicit-function-declaration=abort \
+
+## Eva-specific flags
+EVAFLAGS    += \
+  -eva-warn-key builtins:missing-spec=abort \
+  -eva-slevel 9 \
+
+## GUI-only flags
+FCGUIFLAGS += \
+
+## Analysis targets (suffixed with .eva)
+TARGETS = genann.eva
+
+### Each target <t>.eva needs a rule <t>.parse with source files as prerequisites
+genann.parse: \
+  ../genann.c \
+  ../test.c \
+
+### Epilogue. Do not modify this block. #######################################
+include $(shell $(FRAMAC)-config -print-share-path)/analysis-scripts/epilogue.mk
+###############################################################################
+
+# optional, for OSCS
+-include ../../Makefile.common

genann/.frama-c/genann.eva/alarms.csv

+directory	file	line	function	property kind	status	property
+.	genann.c	115	genann_init	signed_overflow	Unknown	(int)((int)(inputs + 1) * hidden) + (int)((int)((int)(hidden_layers - 1) * (int)(hidden + 1)) * hidden) ≤ 2147483647
+.	genann.c	115	genann_init	signed_overflow	Unknown	(int)(inputs + 1) * hidden ≤ 2147483647
+.	genann.c	115	genann_init	signed_overflow	Unknown	inputs + 1 ≤ 2147483647
+.	genann.c	115	genann_init	signed_overflow	Unknown	(int)((int)(hidden_layers - 1) * (int)(hidden + 1)) * hidden ≤ 2147483647
+.	genann.c	115	genann_init	signed_overflow	Unknown	(int)(hidden_layers - 1) * (int)(hidden + 1) ≤ 2147483647
+.	genann.c	115	genann_init	signed_overflow	Unknown	hidden + 1 ≤ 2147483647
+.	genann.c	116	genann_init	signed_overflow	Unknown	tmp_0 * outputs ≤ 2147483647
+.	genann.c	116	genann_init	signed_overflow	Unknown	inputs + 1 ≤ 2147483647
+.	genann.c	117	genann_init	signed_overflow	Unknown	hidden_weights + output_weights ≤ 2147483647
+.	genann.c	119	genann_init	signed_overflow	Unknown	(int)(inputs + (int)(hidden * hidden_layers)) + outputs ≤ 2147483647
+.	genann.c	119	genann_init	signed_overflow	Unknown	inputs + (int)(hidden * hidden_layers) ≤ 2147483647
+.	genann.c	119	genann_init	signed_overflow	Unknown	hidden * hidden_layers ≤ 2147483647
+.	genann.c	122	genann_init	signed_overflow	Unknown	(int)(total_weights + total_neurons) + (int)(total_neurons - inputs) ≤ 2147483647
+.	genann.c	122	genann_init	signed_overflow	Unknown	total_weights + total_neurons ≤ 2147483647
+.	genann.c	126	genann_init	mem_access	Unknown	\valid(&ret->inputs)
+.	genann.c	127	genann_init	mem_access	Unknown	\valid(&ret->hidden_layers)
+.	genann.c	128	genann_init	mem_access	Unknown	\valid(&ret->hidden)
+.	genann.c	129	genann_init	mem_access	Unknown	\valid(&ret->outputs)
+.	genann.c	131	genann_init	mem_access	Unknown	\valid(&ret->total_weights)
+.	genann.c	132	genann_init	mem_access	Unknown	\valid(&ret->total_neurons)
+.	genann.c	135	genann_init	mem_access	Unknown	\valid(&ret->weight)
+.	genann.c	136	genann_init	mem_access	Unknown	\valid(&ret->output)
+.	genann.c	136	genann_init	mem_access	Unknown	\valid_read(&ret->total_weights)
+.	genann.c	136	genann_init	mem_access	Unknown	\valid_read(&ret->weight)
+.	genann.c	137	genann_init	mem_access	Unknown	\valid(&ret->delta)
+.	genann.c	137	genann_init	mem_access	Unknown	\valid_read(&ret->output)
+.	genann.c	137	genann_init	mem_access	Unknown	\valid_read(&ret->total_neurons)
+.	genann.c	141	genann_init	mem_access	Unknown	\valid(&ret->activation_hidden)
+.	genann.c	142	genann_init	mem_access	Unknown	\valid(&ret->activation_output)
+.	genann.c	164	genann_read	mem_access	Unknown	\valid_read(&ann->total_weights)
+.	genann.c	166	fscanf_va_2	precondition	Unknown	\valid(param0)
+.	genann.c	166	genann_read	mem_access	Unknown	\valid_read(&ann->weight)
+.	genann.c	166	genann_read	precondition of fscanf_va_2	Unknown	\valid(param0)
+.	genann.c	180	genann_copy	mem_access	Unknown	\valid_read(&ann->inputs)
+.	genann.c	180	genann_copy	mem_access	Unknown	\valid_read(&ann->total_neurons)
+.	genann.c	180	genann_copy	mem_access	Unknown	\valid_read(&ann->total_weights)
+.	genann.c	197	genann_randomize	mem_access	Unknown	\valid_read(&ann->total_weights)
+.	genann.c	200	genann_randomize	mem_access	Unknown	\valid(ann->weight + i)
+.	genann.c	200	genann_randomize	mem_access	Unknown	\valid_read(&ann->weight)
+.	genann.c	212	genann_run	mem_access	Unknown	\valid_read(&ann->weight)
+.	genann.c	397	genann_write	mem_access	Unknown	\valid_read(&ann->outputs)
+.	genann.c	401	genann_write	initialization	Unknown	\initialized(ann->weight + i)
+.	test.c	37	basic	mem_access	Unknown	\valid_read(&ann->total_weights)
+.	test.c	71	xor	mem_access	Unknown	\valid(&ann->activation_hidden)
+.	test.c	201	persist	precondition of fclose	Unknown	valid_stream: \valid(stream)
+.	test.c	206	persist	precondition of fclose	Unknown	valid_stream: \valid(stream)
+.	test.c	208	persist	mem_access	Unknown	\valid_read(&second->inputs)
+.	test.c	209	persist	mem_access	Unknown	\valid_read(&second->hidden_layers)
+.	test.c	210	persist	mem_access	Unknown	\valid_read(&second->hidden)
+.	test.c	211	persist	mem_access	Unknown	\valid_read(&second->outputs)
+.	test.c	212	persist	mem_access	Unknown	\valid_read(&second->total_weights)
+.	test.c	216	persist	signed_overflow	Unknown	ltests + 1 ≤ 2147483647
+.	test.c	216	persist	initialization	Unknown	\initialized(first->weight + i)
+.	test.c	216	persist	initialization	Unknown	\initialized(second->weight + i)
+.	test.c	216	persist	is_nan_or_infinite	Unknown	\is_finite(*(second->weight + i))
+.	test.c	216	persist	mem_access	Unknown	\valid_read(&second->weight)
+.	test.c	216	persist	mem_access	Unknown	\valid_read(second->weight + i)
+.	test.c	216	persist	signed_overflow	Unknown	lfails + 1 ≤ 2147483647
+.	test.c	229	copy	signed_overflow	Unknown	ltests + 1 ≤ 2147483647
+.	test.c	229	copy	mem_access	Unknown	\valid_read(&second->inputs)
+.	test.c	230	copy	signed_overflow	Unknown	ltests + 1 ≤ 2147483647
+.	test.c	231	copy	signed_overflow	Unknown	ltests + 1 ≤ 2147483647
+.	test.c	232	copy	signed_overflow	Unknown	ltests + 1 ≤ 2147483647
+.	test.c	233	copy	signed_overflow	Unknown	ltests + 1 ≤ 2147483647
+.	test.c	237	copy	signed_overflow	Unknown	ltests + 1 ≤ 2147483647
+.	test.c	237	copy	initialization	Unknown	\initialized(first->weight + i)
+.	test.c	237	copy	initialization	Unknown	\initialized(second->weight + i)
+.	test.c	237	copy	signed_overflow	Unknown	lfails + 1 ≤ 2147483647
+.	test.c	251	sigmoid	signed_overflow	Unknown	ltests + 1 ≤ 2147483647
+.	test.c	251	sigmoid	signed_overflow	Unknown	lfails + 1 ≤ 2147483647
+.	test.c	270	main	signed_overflow	Unknown	(int)(ltests - ts_6) - (int)(lfails - fs_6) ≤ 2147483647
+.	test.c	270	main	signed_overflow	Unknown	-2147483648 ≤ (int)(ltests - ts_6) - (int)(lfails - fs_6)
+.	test.c	271	main	signed_overflow	Unknown	(int)(ltests - ts_7) - (int)(lfails - fs_7) ≤ 2147483647
+.	test.c	271	main	signed_overflow	Unknown	-2147483648 ≤ (int)(ltests - ts_7) - (int)(lfails - fs_7)
+FRAMAC_SHARE/libc	stdio.h	120	fclose	precondition	Unknown	valid_stream: \valid(stream)

genann/.frama-c/genann.eva/metrics.log

+[metrics] Eva coverage statistics
+=======================
+Syntactically reachable functions = 25 (out of 25)
+Semantically reached functions = 24
+Coverage estimation = 96.0% 
+
+Unreached functions (1) =
+  <genann.c>: genann_act_linear;
+[metrics] References to non-analyzed functions
+------------------------------------
+Function genann_train references genann_act_linear (at genann.c:292)
+Function genann_train references genann_act_linear (at genann.c:293)
+[metrics] Statements analyzed by Eva
+--------------------------
+1042 stmts in analyzed functions, 947 stmts analyzed (90.9%)
+backprop: 18 stmts out of 18 (100.0%)
+genann_act_hidden_indirect: 2 stmts out of 2 (100.0%)
+genann_act_output_indirect: 2 stmts out of 2 (100.0%)
+genann_act_threshold: 2 stmts out of 2 (100.0%)
+genann_copy: 12 stmts out of 12 (100.0%)
+genann_free: 2 stmts out of 2 (100.0%)
+genann_init: 48 stmts out of 48 (100.0%)
+genann_init_sigmoid_lookup: 10 stmts out of 10 (100.0%)
+genann_randomize: 10 stmts out of 10 (100.0%)
+genann_read: 30 stmts out of 30 (100.0%)
+genann_write: 9 stmts out of 9 (100.0%)
+main: 116 stmts out of 116 (100.0%)
+persist: 45 stmts out of 45 (100.0%)
+sigmoid: 21 stmts out of 21 (100.0%)
+train_and: 71 stmts out of 71 (100.0%)
+train_or: 72 stmts out of 72 (100.0%)
+train_xor: 71 stmts out of 71 (100.0%)
+basic: 78 stmts out of 80 (97.5%)
+genann_train: 132 stmts out of 152 (86.8%)
+genann_act_sigmoid_cached: 22 stmts out of 26 (84.6%)
+genann_run: 91 stmts out of 118 (77.1%)
+copy: 31 stmts out of 41 (75.6%)
+xor: 47 stmts out of 73 (64.4%)
+genann_act_sigmoid: 5 stmts out of 11 (45.5%)

genann/.frama-c/genann.parse/metrics.log

+[metrics] Defined functions (25)
+======================
+ backprop (1 call); basic (1 call); copy (1 call);
+ genann_act_hidden_indirect (2 calls);
+ genann_act_linear (address taken) (0 call);
+ genann_act_output_indirect (address taken) (2 calls);
+ genann_act_sigmoid (3 calls);
+ genann_act_sigmoid_cached (address taken) (2 calls);
+ genann_act_threshold (address taken) (0 call); genann_copy (1 call);
+ genann_free (11 calls); genann_init (9 calls);
+ genann_init_sigmoid_lookup (1 call); genann_randomize (2 calls);
+ genann_read (1 call); genann_run (47 calls); genann_train (4 calls);
+ genann_write (1 call); main (0 call); persist (1 call); sigmoid (1 call);
+ train_and (1 call); train_or (1 call); train_xor (1 call); xor (1 call); 
+
+Specified-only functions (0)
+============================
+ 
+
+Undefined and unspecified functions (0)
+=======================================
+ 
+
+'Extern' global variables (0)
+=============================
+ 
+
+Potential entry points (1)
+==========================
+ main; 
+
+Global metrics
+============== 
+Sloc = 1043
+Decision point = 101
+Global variables = 6
+If = 101
+Loop = 32
+Goto = 17
+Assignment = 477
+Exit point = 25
+Function = 25
+Function call = 224
+Pointer dereferencing = 309
+Cyclomatic complexity = 126

genann/.frama-c/genann.parse/warnings.log

+test.c:44:[kernel:parser:decimal-float] warning: Floating-point constant 0.001 is not represented exactly. Will use 0x1.0624dd2f1a9fcp-10.
+(warn-once: no further messages from category 'parser:decimal-float' will be emitted)

genann/.frama-c/path.mk

+../../path.mk
\ No newline at end of file

genann/LICENSE

+zlib License
+
+Copyright (C) 2015-2018 Lewis Van Winkle
+
+This software is provided 'as-is', without any express or implied
+warranty. In no event will the authors be held liable for any damages
+arising from the use of this software.
+
+Permission is granted to anyone to use this software for any purpose,
+including commercial applications, and to alter it and redistribute it
+freely, subject to the following restrictions:
+
+1. The origin of this software must not be misrepresented; you must not
+   claim that you wrote the original software. If you use this software
+   in a product, an acknowledgement in the product documentation would be
+   appreciated but is not required.
+2. Altered source versions must be plainly marked as such, and must not be
+   misrepresented as being the original software.
+3. This notice may not be removed or altered from any source distribution.
+

genann/Makefile

+CFLAGS = -Wall -Wshadow -O3 -g -march=native
+LDLIBS = -lm
+
+all: check example1 example2 example3 example4
+
+sigmoid: CFLAGS += -Dgenann_act=genann_act_sigmoid_cached
+sigmoid: all
+
+threshold: CFLAGS += -Dgenann_act=genann_act_threshold
+threshold: all
+
+linear: CFLAGS += -Dgenann_act=genann_act_linear
+linear: all
+
+test: test.o genann.o
+
+check: test
+	./$^
+
+example1: example1.o genann.o
+
+example2: example2.o genann.o
+
+example3: example3.o genann.o
+
+example4: example4.o genann.o
+
+
+clean:
+	$(RM) *.o
+	$(RM) test example1 example2 example3 example4 *.exe
+	$(RM) persist.txt
+
+.PHONY: sigmoid threshold linear clean

genann/README.md

+[![Build Status](https://travis-ci.org/codeplea/genann.svg?branch=master)](https://travis-ci.org/codeplea/genann)
+
+<img alt="Genann logo" src="https://codeplea.com/public/content/genann_logo.png" align="right" />
+
+# Genann
+
+Genann is a minimal, well-tested library for training and using feedforward
+artificial neural networks (ANN) in C. Its primary focus is on being simple,
+fast, reliable, and hackable. It achieves this by providing only the necessary
+functions and little extra.
+
+## Features
+
+- **C99 with no dependencies**.
+- Contained in a single source code and header file.
+- Simple.
+- Fast and thread-safe.
+- Easily extendible.
+- Implements backpropagation training.
+- *Compatible with alternative training methods* (classic optimization, genetic algorithms, etc)
+- Includes examples and test suite.
+- Released under the zlib license - free for nearly any use.
+
+## Building
+
+Genann is self-contained in two files: `genann.c` and `genann.h`. To use Genann, simply add those two files to your project.
+
+## Example Code
+
+Four example programs are included with the source code.
+
+- [`example1.c`](./example1.c) - Trains an ANN on the XOR function using backpropagation.
+- [`example2.c`](./example2.c) - Trains an ANN on the XOR function using random search.
+- [`example3.c`](./example3.c) - Loads and runs an ANN from a file.
+- [`example4.c`](./example4.c) - Trains an ANN on the [IRIS data-set](https://archive.ics.uci.edu/ml/datasets/Iris) using backpropagation.
+
+## Quick Example
+
+We create an ANN taking 2 inputs, having 1 layer of 3 hidden neurons, and
+providing 2 outputs. It has the following structure:
+
+![NN Example Structure](./doc/e1.png)
+
+We then train it on a set of labeled data using backpropagation and ask it to
+predict on a test data point:
+
+```C
+#include "genann.h"
+
+/* Not shown, loading your training and test data. */
+double **training_data_input, **training_data_output, **test_data_input;
+
+/* New network with 2 inputs,
+ * 1 hidden layer of 3 neurons each,
+ * and 2 outputs. */
+genann *ann = genann_init(2, 1, 3, 2);
+
+/* Learn on the training set. */
+for (i = 0; i < 300; ++i) {
+    for (j = 0; j < 100; ++j)
+        genann_train(ann, training_data_input[j], training_data_output[j], 0.1);
+}
+
+/* Run the network and see what it predicts. */
+double const *prediction = genann_run(ann, test_data_input[0]);
+printf("Output for the first test data point is: %f, %f\n", prediction[0], prediction[1]);
+
+genann_free(ann);
+```
+
+This example is to show API usage, it is not showing good machine learning
+techniques. In a real application you would likely want to learn on the test
+data in a random order. You would also want to monitor the learning to prevent
+over-fitting.
+
+
+## Usage
+
+### Creating and Freeing ANNs
+```C
+genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
+genann *genann_copy(genann const *ann);
+void genann_free(genann *ann);
+```
+
+Creating a new ANN is done with the `genann_init()` function. Its arguments
+are the number of inputs, the number of hidden layers, the number of neurons in
+each hidden layer, and the number of outputs. It returns a `genann` struct pointer.
+
+Calling `genann_copy()` will create a deep-copy of an existing `genann` struct.
+
+Call `genann_free()` when you're finished with an ANN returned by `genann_init()`.
+
+
+### Training ANNs
+```C
+void genann_train(genann const *ann, double const *inputs,
+        double const *desired_outputs, double learning_rate);
+```
+
+`genann_train()` will preform one update using standard backpropogation. It
+should be called by passing in an array of inputs, an array of expected outputs,
+and a learning rate. See *example1.c* for an example of learning with
+backpropogation.
+
+A primary design goal of Genann was to store all the network weights in one
+contigious block of memory. This makes it easy and efficient to train the
+network weights using direct-search numeric optimization algorthims,
+such as [Hill Climbing](https://en.wikipedia.org/wiki/Hill_climbing),
+[the Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm), [Simulated
+Annealing](https://en.wikipedia.org/wiki/Simulated_annealing), etc.
+These methods can be used by searching on the ANN's weights directly.
+Every `genann` struct contains the members `int total_weights;` and
+`double *weight;`.  `*weight` points to an array of `total_weights`
+size which contains all weights used by the ANN. See *example2.c* for
+an example of training using random hill climbing search.
+
+### Saving and Loading ANNs
+
+```C
+genann *genann_read(FILE *in);
+void genann_write(genann const *ann, FILE *out);
+```
+ 
+Genann provides the `genann_read()` and `genann_write()` functions for loading or saving an ANN in a text-based format.
+
+### Evaluating
+
+```C
+double const *genann_run(genann const *ann, double const *inputs);
+```
+
+Call `genann_run()` on a trained ANN to run a feed-forward pass on a given set of inputs. `genann_run()`
+will provide a pointer to the array of predicted outputs (of `ann->outputs` length).
+
+
+## Hints
+
+- All functions start with `genann_`.
+- The code is simple. Dig in and change things.
+
+## Extra Resources
+
+The [comp.ai.neural-nets
+FAQ](http://www.faqs.org/faqs/ai-faq/neural-nets/part1/) is an excellent
+resource for an introduction to artificial neural networks.
+
+If you need an even smaller neural network library, check out the excellent single-hidden-layer library [tinn](https://github.com/glouw/tinn).
+
+If you're looking for a heavier, more opinionated neural network library in C,
+I recommend the [FANN library](http://leenissen.dk/fann/wp/). Another
+good library is Peter van Rossum's [Lightweight Neural
+Network](http://lwneuralnet.sourceforge.net/), which despite its name, is
+heavier and has more features than Genann.

genann/doc/e1.dot

+digraph G {
+    rankdir=LR;
+
+    {i1 i2} -> {h1 h2 h3} -> {o1 o2};
+    i1, i2, h1, h2, h3, o1, o2 [shape=circle; label="";];
+
+    input -> hidden -> output [style=invis;];
+    input, hidden, output [shape=plaintext;];
+}

genann/example/iris.data

+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica

genann/example/iris.names

+1. Title: Iris Plants Database
+	Updated Sept 21 by C.Blake - Added discrepency information
+
+2. Sources:
+     (a) Creator: R.A. Fisher
+     (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
+     (c) Date: July, 1988
+
+3. Past Usage:
+   - Publications: too many to mention!!!  Here are a few.
+   1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
+      Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
+      to Mathematical Statistics" (John Wiley, NY, 1950).
+   2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
+      (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
+   3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
+      Structure and Classification Rule for Recognition in Partially Exposed
+      Environments".  IEEE Transactions on Pattern Analysis and Machine
+      Intelligence, Vol. PAMI-2, No. 1, 67-71.
+      -- Results:
+         -- very low misclassification rates (0% for the setosa class)
+   4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE 
+      Transactions on Information Theory, May 1972, 431-433.
+      -- Results:
+         -- very low misclassification rates again
+   5. See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II
+      conceptual clustering system finds 3 classes in the data.
+
+4. Relevant Information:
+   --- This is perhaps the best known database to be found in the pattern
+       recognition literature.  Fisher's paper is a classic in the field
+       and is referenced frequently to this day.  (See Duda & Hart, for
+       example.)  The data set contains 3 classes of 50 instances each,
+       where each class refers to a type of iris plant.  One class is
+       linearly separable from the other 2; the latter are NOT linearly
+       separable from each other.
+   --- Predicted attribute: class of iris plant.
+   --- This is an exceedingly simple domain.
+   --- This data differs from the data presented in Fishers article
+	(identified by Steve Chadwick,  spchadwick@espeedaz.net )
+	The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
+	where the error is in the fourth feature.
+	The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
+	where the errors are in the second and third features.  
+
+5. Number of Instances: 150 (50 in each of three classes)
+
+6. Number of Attributes: 4 numeric, predictive attributes and the class
+
+7. Attribute Information:
+   1. sepal length in cm
+   2. sepal width in cm
+   3. petal length in cm
+   4. petal width in cm
+   5. class: 
+      -- Iris Setosa
+      -- Iris Versicolour
+      -- Iris Virginica
+
+8. Missing Attribute Values: None
+
+Summary Statistics:
+	         Min  Max   Mean    SD   Class Correlation
+   sepal length: 4.3  7.9   5.84  0.83    0.7826   
+    sepal width: 2.0  4.4   3.05  0.43   -0.4194
+   petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)
+    petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)
+
+9. Class Distribution: 33.3% for each of 3 classes.

genann/example/xor.ann

+2 1 2 1 -1.777 -5.734 -6.029 -4.460 -3.261 -3.172 2.444 -6.581 5.826