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LICENSE.md Normal file
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GENANN - Minimal C Artificial Neural Network
Copyright (c) 2015, 2016 Lewis Van Winkle
http://CodePlea.com
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.

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CC = gcc
CCFLAGS = -ansi -Wall -Wshadow -O2 -g
LFLAGS = -lm
all: test example1 example2 example3 example4
test: test.o genann.o
$(CC) $(CCFLAGS) -o $@ $^ $(LFLAGS)
./$@
example1: example1.o genann.o
$(CC) $(CCFLAGS) -o $@ $^ $(LFLAGS)
example2: example2.o genann.o
$(CC) $(CCFLAGS) -o $@ $^ $(LFLAGS)
example3: example3.o genann.o
$(CC) $(CCFLAGS) -o $@ $^ $(LFLAGS)
example4: example4.o genann.o
$(CC) $(CCFLAGS) -o $@ $^ $(LFLAGS)
.c.o:
$(CC) -c $(CCFLAGS) $< -o $@
clean:
rm *.o
rm *.exe
rm persist.txt

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#GENANN
GENANN is a very minimal library for training and using feedforward artificial neural
networks (ANN) in C. Its primary focus is on being simple, fast, and hackable. It achieves
this by providing only the necessary functions and little extra.
##Features
- **ANSI C with no dependencies**.
- Contained in a single source code and header file.
- Simple.
- Fast and thread-safe.
- Easily extendible.
- Implements backpropagation training.
- Compatible with training by alternative methods (classic optimization, genetic algorithms, etc)
- Includes examples and test suite.
- Released under the zlib license - free for nearly any use.
##Example Code
Four example programs are included.
- `example1.c` - Trains an ANN on the XOR function using backpropagation.
- `example2.c` - Trains an ANN on the XOR function using random search.
- `example3.c` - Loads and runs an ANN from a file.
- `example4.c` - Trains an ANN on the [IRIS data-set](https://archive.ics.uci.edu/ml/datasets/Iris) using backpropagation.
##Quick Example
Here we create an ANN, train it on a set of labeled data using backpropagation,
ask it to predict on a test data point, and then free it:
```C
#include "genann.h"
/* New network with 5 inputs,
* 2 hidden layer of 10 neurons each,
* and 1 output. */
GENANN *ann = genann_init(5, 2, 10, 1);
/* 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. */
printf("Output for the first test data point is: %f\n", *genann_run(ann, test_data_input[0]));
genann_free(ann);
```
Not that 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. It's 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 output,
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 directly using direct-search numeric optimizion 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).
##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're looking for a heavier, more opinionated neural network library in C,
I highly 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.
##Hints
- All functions start with `genann_`.
- The code is simple. Dig in and change things.

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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

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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.

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

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#include <stdio.h>
#include "genann.h"
int main(int argc, char *argv[])
{
printf("GENANN example 1.\n");
printf("Train a small ANN to the XOR function using backpropagation.\n");
/* Input and expected out data for the XOR function. */
const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
const double output[4] = {0, 1, 1, 0};
int i;
/* New network with 2 inputs,
* 1 hidden layer of 2 neurons,
* and 1 output. */
GENANN *ann = genann_init(2, 1, 2, 1);
/* Train on the four labeled data points many times. */
for (i = 0; i < 300; ++i) {
genann_train(ann, input[0], output + 0, 3);
genann_train(ann, input[1], output + 1, 3);
genann_train(ann, input[2], output + 2, 3);
genann_train(ann, input[3], output + 3, 3);
}
/* Run the network and see what it predicts. */
printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
genann_free(ann);
return 0;
}

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#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include "genann.h"
int main(int argc, char *argv[])
{
printf("GENANN example 2.\n");
printf("Train a small ANN to the XOR function using random search.\n");
/* Input and expected out data for the XOR function. */
const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
const double output[4] = {0, 1, 1, 0};
int i;
/* New network with 2 inputs,
* 1 hidden layer of 2 neurons,
* and 1 output. */
GENANN *ann = genann_init(2, 1, 2, 1);
double err;
double last_err = 1000;
int count = 0;
do {
++count;
if (count % 1000 == 0) {
/* We're stuck, start over. */
genann_randomize(ann);
}
GENANN *save = genann_copy(ann);
/* Take a random guess at the ANN weights. */
for (i = 0; i < ann->total_weights; ++i) {
ann->weight[i] += ((double)rand())/RAND_MAX-0.5;
}
/* See how we did. */
err = 0;
err += pow(*genann_run(ann, input[0]) - output[0], 2.0);
err += pow(*genann_run(ann, input[1]) - output[1], 2.0);
err += pow(*genann_run(ann, input[2]) - output[2], 2.0);
err += pow(*genann_run(ann, input[3]) - output[3], 2.0);
/* Keep these weights if they're an improvement. */
if (err < last_err) {
genann_free(save);
last_err = err;
} else {
genann_free(ann);
ann = save;
}
} while (err > 0.01);
printf("Finished in %d loops.\n", count);
/* Run the network and see what it predicts. */
printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
genann_free(ann);
return 0;
}

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#include <stdio.h>
#include <stdlib.h>
#include "genann.h"
const char *save_name = "example/xor.ann";
int main(int argc, char *argv[])
{
printf("GENANN example 3.\n");
printf("Load a saved ANN to solve the XOR function.\n");
FILE *saved = fopen(save_name, "r");
if (!saved) {
printf("Couldn't open file: %s\n", save_name);
exit(1);
}
GENANN *ann = genann_read(saved);
fclose(saved);
if (!ann) {
printf("Error loading ANN from file.", save_name);
exit(1);
}
/* Input data for the XOR function. */
const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
/* Run the network and see what it predicts. */
printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
genann_free(ann);
return 0;
}

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#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include "genann.h"
/* This example is to illustrate how to use GENANN.
* It is NOT an example of good machine learning techniques.
*/
const char *iris_data = "example/iris.data";
double *input, *class;
int samples;
const char *class_names[] = {"Iris-setosa", "Iris-versicolor", "Iris-virginica"};
void load_data() {
/* Load the iris data-set. */
FILE *in = fopen("example/iris.data", "r");
if (!in) {
printf("Could not open file: %s\n", iris_data);
exit(1);
}
/* Loop through the data to get a count. */
char line[1024];
while (!feof(in) && fgets(line, 1024, in)) {
++samples;
}
fseek(in, 0, SEEK_SET);
printf("Loading %d data points from %s\n", samples, iris_data);
/* Allocate memory for input and output data. */
input = malloc(sizeof(double) * samples * 4);
class = malloc(sizeof(double) * samples * 3);
/* Read the file into our arrays. */
int i, j;
for (i = 0; i < samples; ++i) {
double *p = input + i * 4;
double *c = class + i * 3;
c[0] = c[1] = c[2] = 0.0;
fgets(line, 1024, in);
char *split = strtok(line, ",");
for (j = 0; j < 4; ++j) {
p[j] = atof(split);
split = strtok(0, ",");
}
split[strlen(split)-1] = 0;
if (strcmp(split, class_names[0]) == 0) {c[0] = 1.0;}
else if (strcmp(split, class_names[1]) == 0) {c[1] = 1.0;}
else if (strcmp(split, class_names[2]) == 0) {c[2] = 1.0;}
else {
printf("Unknown class %s.\n", split);
exit(1);
}
/* printf("Data point %d is %f %f %f %f -> %f %f %f\n", i, p[0], p[1], p[2], p[3], c[0], c[1], c[2]); */
}
fclose(in);
}
int main(int argc, char *argv[])
{
printf("GENANN example 4.\n");
printf("Train an ANN on the IRIS dataset using backpropagation.\n");
/* Load the data from file. */
load_data();
/* 4 inputs.
* 1 hidden layer(s) of 4 neurons.
* 3 outputs (1 per class)
*/
GENANN *ann = genann_init(4, 1, 4, 3);
int i, j;
int loops = 5000;
/* Train the network with backpropagation. */
printf("Training for %d loops over data.\n", loops);
for (i = 0; i < loops; ++i) {
for (j = 0; j < samples; ++j) {
genann_train(ann, input + j*4, class + j*3, .01);
}
/* printf("%1.2f ", xor_score(ann)); */
}
int correct = 0;
for (j = 0; j < samples; ++j) {
const double *guess = genann_run(ann, input + j*4);
if (class[j*3+0] == 1.0) {if (guess[0] > guess[1] && guess[0] > guess[2]) ++correct;}
else if (class[j*3+1] == 1.0) {if (guess[1] > guess[0] && guess[1] > guess[2]) ++correct;}
else if (class[j*3+2] == 1.0) {if (guess[2] > guess[0] && guess[2] > guess[1]) ++correct;}
else {printf("Logic error.\n"); exit(1);}
}
printf("%d/%d correct (%0.1f%%).\n", correct, samples, (double)correct / samples * 100.0);
genann_free(ann);
return 0;
}

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/*
* GENANN - Minimal C Artificial Neural Network
*
* Copyright (c) 2015, 2016 Lewis Van Winkle
*
* http://CodePlea.com
*
* 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.
*
*/
#include "genann.h"
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <assert.h>
#include <stdio.h>
#define LOOKUP_SIZE 4096
double genann_act_sigmoid(double a) {
if (a < -45.0) return 0;
if (a > 45.0) return 1;
return 1.0 / (1 + exp(-a));
}
double genann_act_sigmoid_cached(double a) {
/* If you're optimizing for memory usage, just
* delete this entire function and replace references
* of genann_act_sigmoid_cached to genann_act_sigmoid
*/
const double min = -15.0;
const double max = 15.0;
static double interval;
static int initialized = 0;
static double lookup[LOOKUP_SIZE];
/* Calculate entire lookup table on first run. */
if (!initialized) {
interval = (max - min) / LOOKUP_SIZE;
int i;
for (i = 0; i < LOOKUP_SIZE; ++i) {
lookup[i] = genann_act_sigmoid(min + interval * i);
}
/* This is down here to make this thread safe. */
initialized = 1;
}
int i;
i = (int)((a-min)/interval+0.5);
if (i <= 0) return lookup[0];
if (i >= LOOKUP_SIZE) return lookup[LOOKUP_SIZE-1];
return lookup[i];
}
double genann_act_threshold(double a) {
return a > 0;
}
GENANN *genann_init(int inputs, int hidden_layers, int hidden, int outputs) {
if (hidden_layers < 0) return 0;
if (inputs < 1) return 0;
if (outputs < 1) return 0;
if (hidden_layers > 0 && hidden < 1) return 0;
const int hidden_weights = hidden_layers ? (inputs+1) * hidden + (hidden_layers-1) * (hidden+1) * hidden : 0;
const int output_weights = (hidden_layers ? (hidden+1) : (inputs+1)) * outputs;
const int total_weights = (hidden_weights + output_weights);
const int total_neurons = (inputs + hidden * hidden_layers + outputs);
/* Allocate extra size for weights, outputs, and deltas. */
const int size = sizeof(GENANN) + sizeof(double) * (total_weights + total_neurons + (total_neurons - inputs));
GENANN *ret = malloc(size);
if (!ret) return 0;
ret->inputs = inputs;
ret->hidden_layers = hidden_layers;
ret->hidden = hidden;
ret->outputs = outputs;
ret->total_weights = total_weights;
ret->total_neurons = total_neurons;
/* Set pointers. */
ret->weight = (double*)((char*)ret + sizeof(GENANN));
ret->output = ret->weight + ret->total_weights;
ret->delta = ret->output + ret->total_neurons;
genann_randomize(ret);
ret->activation_hidden = genann_act_sigmoid_cached;
ret->activation_output = genann_act_sigmoid_cached;
return ret;
}
GENANN *genann_read(FILE *in) {
int inputs, hidden_layers, hidden, outputs;
fscanf(in, "%d %d %d %d", &inputs, &hidden_layers, &hidden, &outputs);
GENANN *ann = genann_init(inputs, hidden_layers, hidden, outputs);
int i;
for (i = 0; i < ann->total_weights; ++i) {
fscanf(in, " %le", ann->weight + i);
}
return ann;
}
GENANN *genann_copy(GENANN const *ann) {
const int size = sizeof(GENANN) + sizeof(double) * (ann->total_weights + ann->total_neurons + (ann->total_neurons - ann->inputs));
GENANN *ret = malloc(size);
if (!ret) return 0;
memcpy(ret, ann, size);
/* Set pointers. */
ret->weight = (double*)((char*)ret + sizeof(GENANN));
ret->output = ret->weight + ret->total_weights;
ret->delta = ret->output + ret->total_neurons;
return ret;
}
void genann_randomize(GENANN *ann) {
int i;
for (i = 0; i < ann->total_weights; ++i) {
double r = GENANN_RANDOM();
/* Sets weights from -0.5 to 0.5. */
ann->weight[i] = r - 0.5;
}
}
void genann_free(GENANN *ann) {
/* The weight, output, and delta pointers go to the same buffer. */
free(ann);
}
double const *genann_run(GENANN const *ann, double const *inputs) {
double const *w = ann->weight;
double *o = ann->output + ann->inputs;
double const *i = ann->output;
/* Copy the inputs to the scratch area, where we also store each neuron's
* output, for consistency. This way the first layer isn't a special case. */
memcpy(ann->output, inputs, sizeof(double) * ann->inputs);
int h, j, k;
const GENANN_ACTFUN act = ann->activation_hidden;
const GENANN_ACTFUN acto = ann->activation_output;
/* Figure hidden layers, if any. */
for (h = 0; h < ann->hidden_layers; ++h) {
for (j = 0; j < ann->hidden; ++j) {
double sum = 0;
for (k = 0; k < (h == 0 ? ann->inputs : ann->hidden) + 1; ++k) {
if (k == 0) {
sum += *w++ * -1.0;
} else {
sum += *w++ * i[k-1];
}
}
*o++ = act(sum);
}
i += (h == 0 ? ann->inputs : ann->hidden);
}
double const *ret = o;
/* Figure output layer. */
for (j = 0; j < ann->outputs; ++j) {
double sum = 0;
for (k = 0; k < (ann->hidden_layers ? ann->hidden : ann->inputs) + 1; ++k) {
if (k == 0) {
sum += *w++ * -1.0;
} else {
sum += *w++ * i[k-1];
}
}
*o++ = acto(sum);
}
/* Sanity check that we used all weights and wrote all outputs. */
assert(w - ann->weight == ann->total_weights);
assert(o - ann->output == ann->total_neurons);
return ret;
}
void genann_train(GENANN const *ann, double const *inputs, double const *desired_outputs, double learning_rate) {
/* To begin with, we must run the network forward. */
genann_run(ann, inputs);
int h, j, k;
/* First set the output layer deltas. */
{
double const *o = ann->output + ann->inputs + ann->hidden * ann->hidden_layers; /* First output. */
double *d = ann->delta + ann->hidden * ann->hidden_layers; /* First delta. */
double const *t = desired_outputs; /* First desired output. */
/* Set output layer deltas. */
for (j = 0; j < ann->outputs; ++j) {
*d = (*t - *o) * *o * (1.0 - *o);
++o; ++d; ++t;
}
}
/* Set hidden layer deltas, start on last layer and work backwards. */
/* Note that loop is skipped in the case of hidden_layers == 0. */
for (h = ann->hidden_layers - 1; h >= 0; --h) {
/* Find first output and delta in this layer. */
double const *o = ann->output + ann->inputs + (h * ann->hidden);
double *d = ann->delta + (h * ann->hidden);
/* Find first delta in following layer (which may be hidden or output). */
double const * const dd = ann->delta + ((h+1) * ann->hidden);
/* Find first weight in following layer (which may be hidden or output). */
double const * const ww = ann->weight + ((ann->inputs+1) * ann->hidden) + ((ann->hidden+1) * ann->hidden * (h));
for (j = 0; j < ann->hidden; ++j) {
double delta = 0;
for (k = 0; k < (h == ann->hidden_layers-1 ? ann->outputs : ann->hidden); ++k) {
const double forward_delta = dd[k];
const int windex = k * (ann->hidden + 1) + (j + 1);
const double forward_weight = ww[windex];
delta += forward_delta * forward_weight;
}
*d = *o * (1.0-*o) * delta;
++d; ++o;
}
}
/* Train the outputs. */
{
/* Find first output delta. */
double const *d = ann->delta + ann->hidden * ann->hidden_layers; /* First output delta. */
/* Find first weight to first output delta. */
double *w = ann->weight + (ann->hidden_layers
? ((ann->inputs+1) * ann->hidden + (ann->hidden+1) * ann->hidden * (ann->hidden_layers-1))
: (0));
/* Find first output in previous layer. */
double const * const i = ann->output + (ann->hidden_layers
? (ann->inputs + (ann->hidden) * (ann->hidden_layers-1))
: 0);
/* Set output layer deltas. */
for (j = 0; j < ann->outputs; ++j) {
for (k = 0; k < (ann->hidden_layers ? ann->hidden : ann->inputs) + 1; ++k) {
if (k == 0) {
*w++ += *d * learning_rate * -1.0;
} else {
*w++ += *d * learning_rate * i[k-1];
}
}
++d;
}
assert(w - ann->weight == ann->total_weights);
}
/* Train the hidden layers. */
for (h = ann->hidden_layers - 1; h >= 0; --h) {
/* Find first delta in this layer. */
double const *d = ann->delta + (h * ann->hidden);
/* Find first input to this layer. */
double const *i = ann->output + (h
? (ann->inputs + ann->hidden * (h-1))
: 0);
/* Find first weight to this layer. */
double *w = ann->weight + (h
? ((ann->inputs+1) * ann->hidden + (ann->hidden+1) * (ann->hidden) * (h-1))
: 0);
for (j = 0; j < ann->hidden; ++j) {
for (k = 0; k < (h == 0 ? ann->inputs : ann->hidden) + 1; ++k) {
if (k == 0) {
*w++ += *d * learning_rate * -1.0;
} else {
*w++ += *d * learning_rate * i[k-1];
}
}
++d;
}
}
}
void genann_write(GENANN const *ann, FILE *out) {
fprintf(out, "%d %d %d %d", ann->inputs, ann->hidden_layers, ann->hidden, ann->outputs);
int i;
for (i = 0; i < ann->total_weights; ++i) {
fprintf(out, " %.20e", ann->weight[i]);
}
}

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/*
* GENANN - Minimal C Artificial Neural Network
*
* Copyright (c) 2015, 2016 Lewis Van Winkle
*
* http://CodePlea.com
*
* 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.
*
*/
#ifndef __GENANN_H__
#define __GENANN_H__
#include <stdio.h>
#ifndef GENANN_RANDOM
/* We use the following for uniform random numbers between 0 and 1.
* If you have a better function, redefine this macro. */
#define GENANN_RANDOM() (((double)rand())/RAND_MAX)
#endif
typedef double (*GENANN_ACTFUN)(double a);
typedef struct GENANN {
/* How many inputs, outputs, and hidden neurons. */
int inputs, hidden_layers, hidden, outputs;
/* Which activation function to use for hidden neurons. Default: gennann_act_sigmoid_cached*/
GENANN_ACTFUN activation_hidden;
/* Which activation function to use for output. Default: gennann_act_sigmoid_cached*/
GENANN_ACTFUN activation_output;
/* Total number of weights, and size of weights buffer. */
int total_weights;
/* Total number of neurons + inputs and size of output buffer. */
int total_neurons;
/* All weights (total_weights long). */
double *weight;
/* Stores input array and output of each neuron (total_neurons long). */
double *output;
/* Stores delta of each hidden and output neuron (total_neurons - inputs long). */
double *delta;
} GENANN;
/* Creates and returns a new ann. */
GENANN *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
/* Creates ANN from file saved with genann_write. */
GENANN *genann_read(FILE *in);
/* Sets weights randomly. Called by init. */
void genann_randomize(GENANN *ann);
/* Returns a new copy of ann. */
GENANN *genann_copy(GENANN const *ann);
/* Frees the memory used by an ann. */
void genann_free(GENANN *ann);
/* Runs the feedforward algorithm to calculate the ann's output. */
double const *genann_run(GENANN const *ann, double const *inputs);
/* Does a single backprop update. */
void genann_train(GENANN const *ann, double const *inputs, double const *desired_outputs, double learning_rate);
/* Saves the ann. */
void genann_write(GENANN const *ann, FILE *out);
double genann_act_sigmoid(double a);
double genann_act_sigmoid_cached(double a);
double genann_act_threshold(double a);
#endif /*__GENANN_H__*/

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/*
*
* MINCTEST - Minimal C Test Library - 0.1
*
* Copyright (c) 2014, 2015, 2016 Lewis Van Winkle
*
* http://CodePlea.com
*
* 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.
*
*/
/*
* MINCTEST - Minimal testing library for C
*
*
* Example:
*
* void test1() {
* lok('a' == 'a');
* }
*
* void test2() {
* lequal(5, 6);
* lfequal(5.5, 5.6);
* }
*
* int main() {
* lrun("test1", test1);
* lrun("test2", test2);
* lresults();
* return lfails != 0;
* }
*
*
*
* Hints:
* All functions/variables start with the letter 'l'.
*
*/
#ifndef __MINCTEST_H__
#define __MINCTEST_H__
#include <stdio.h>
#include <math.h>
#include <time.h>
/* How far apart can floats be before we consider them unequal. */
#define LTEST_FLOAT_TOLERANCE 0.001
/* Track the number of passes, fails. */
/* NB this is made for all tests to be in one file. */
static int ltests = 0;
static int lfails = 0;
/* Display the test results. */
#define lresults() do {\
if (lfails == 0) {\
printf("ALL TESTS PASSED (%d/%d)\n", ltests, ltests);\
} else {\
printf("SOME TESTS FAILED (%d/%d)\n", ltests-lfails, ltests);\
}\
} while (0)
/* Run a test. Name can be any string to print out, test is the function name to call. */
#define lrun(name, test) do {\
const int ts = ltests;\
const int fs = lfails;\
const clock_t start = clock();\
printf("\t%-14s", name);\
test();\
printf("pass:%2d fail:%2d %4dms\n",\
(ltests-ts)-(lfails-fs), lfails-fs,\
(int)((clock() - start) * 1000 / CLOCKS_PER_SEC));\
} while (0)
/* Assert a true statement. */
#define lok(test) do {\
++ltests;\
if (!(test)) {\
++lfails;\
printf("%s:%d error \n", __FILE__, __LINE__);\
}} while (0)
/* Assert two integers are equal. */
#define lequal(a, b) do {\
++ltests;\
if ((a) != (b)) {\
++lfails;\
printf("%s:%d (%d != %d)\n", __FILE__, __LINE__, (a), (b));\
}} while (0)
/* Assert two floats are equal (Within LTEST_FLOAT_TOLERANCE). */
#define lfequal(a, b) do {\
++ltests;\
if (fabs((double)(a)-(double)(b)) > LTEST_FLOAT_TOLERANCE) {\
++lfails;\
printf("%s:%d (%f != %f)\n", __FILE__, __LINE__, (double)(a), (double)(b));\
}} while (0)
#endif /*__MINCTEST_H__*/

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/*
* GENANN - Minimal C Artificial Neural Network
*
* Copyright (c) 2015, 2016 Lewis Van Winkle
*
* http://CodePlea.com
*
* 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.
*
*/
#include "genann.h"
#include "minctest.h"
#include <stdio.h>
#include <math.h>
#include <stdlib.h>
void basic() {
GENANN *ann = genann_init(1, 0, 0, 1);
lequal(ann->total_weights, 2);
double a;
a = 0;
ann->weight[0] = 0;
ann->weight[1] = 0;
lfequal(0.5, *genann_run(ann, &a));
a = 1;
lfequal(0.5, *genann_run(ann, &a));
a = 11;
lfequal(0.5, *genann_run(ann, &a));
a = 1;
ann->weight[0] = 1;
ann->weight[1] = 1;
lfequal(0.5, *genann_run(ann, &a));
a = 10;
ann->weight[0] = 1;
ann->weight[1] = 1;
lfequal(1.0, *genann_run(ann, &a));
a = -10;
lfequal(0.0, *genann_run(ann, &a));
genann_free(ann);
}
void xor() {
GENANN *ann = genann_init(2, 1, 2, 1);
ann->activation_hidden = genann_act_threshold;
ann->activation_output = genann_act_threshold;
lequal(ann->total_weights, 9);
/* First hidden. */
ann->weight[0] = .5;
ann->weight[1] = 1;
ann->weight[2] = 1;
/* Second hidden. */
ann->weight[3] = 1;
ann->weight[4] = 1;
ann->weight[5] = 1;
/* Output. */
ann->weight[6] = .5;
ann->weight[7] = 1;
ann->weight[8] = -1;
double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
double output[4] = {0, 1, 1, 0};
lfequal(output[0], *genann_run(ann, input[0]));
lfequal(output[1], *genann_run(ann, input[1]));
lfequal(output[2], *genann_run(ann, input[2]));
lfequal(output[3], *genann_run(ann, input[3]));
genann_free(ann);
}
void backprop() {
GENANN *ann = genann_init(1, 0, 0, 1);
double input, output;
input = .5;
output = 1;
double first_try = *genann_run(ann, &input);
genann_train(ann, &input, &output, .5);
double second_try = *genann_run(ann, &input);
lok(fabs(first_try - output) > fabs(second_try - output));
genann_free(ann);
}
void train_and() {
double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
double output[4] = {0, 0, 0, 1};
GENANN *ann = genann_init(2, 0, 0, 1);
int i, j;
for (i = 0; i < 50; ++i) {
for (j = 0; j < 4; ++j) {
genann_train(ann, input[j], output + j, .8);
}
}
ann->activation_output = genann_act_threshold;
lfequal(output[0], *genann_run(ann, input[0]));
lfequal(output[1], *genann_run(ann, input[1]));
lfequal(output[2], *genann_run(ann, input[2]));
lfequal(output[3], *genann_run(ann, input[3]));
genann_free(ann);
}
void train_or() {
double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
double output[4] = {0, 1, 1, 1};
GENANN *ann = genann_init(2, 0, 0, 1);
genann_randomize(ann);
int i, j;
for (i = 0; i < 50; ++i) {
for (j = 0; j < 4; ++j) {
genann_train(ann, input[j], output + j, .8);
}
}
ann->activation_output = genann_act_threshold;
lfequal(output[0], *genann_run(ann, input[0]));
lfequal(output[1], *genann_run(ann, input[1]));
lfequal(output[2], *genann_run(ann, input[2]));
lfequal(output[3], *genann_run(ann, input[3]));
genann_free(ann);
}
void train_xor() {
double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
double output[4] = {0, 1, 1, 0};
GENANN *ann = genann_init(2, 1, 2, 1);
int i, j;
for (i = 0; i < 300; ++i) {
for (j = 0; j < 4; ++j) {
genann_train(ann, input[j], output + j, 3);
}
/* printf("%1.2f ", xor_score(ann)); */
}
ann->activation_output = genann_act_threshold;
lfequal(output[0], *genann_run(ann, input[0]));
lfequal(output[1], *genann_run(ann, input[1]));
lfequal(output[2], *genann_run(ann, input[2]));
lfequal(output[3], *genann_run(ann, input[3]));
genann_free(ann);
}
void persist() {
GENANN *first = genann_init(1000, 5, 50, 10);
FILE *out = fopen("persist.txt", "w");
genann_write(first, out);
fclose(out);
FILE *in = fopen("persist.txt", "r");
GENANN *second = genann_read(in);
fclose(out);
lequal(first->inputs, second->inputs);
lequal(first->hidden_layers, second->hidden_layers);
lequal(first->hidden, second->hidden);
lequal(first->outputs, second->outputs);
lequal(first->total_weights, second->total_weights);
int i;
for (i = 0; i < first->total_weights; ++i) {
lok(first->weight[i] == second->weight[i]);
}
genann_free(first);
genann_free(second);
}
void copy() {
GENANN *first = genann_init(1000, 5, 50, 10);
GENANN *second = genann_copy(first);
lequal(first->inputs, second->inputs);
lequal(first->hidden_layers, second->hidden_layers);
lequal(first->hidden, second->hidden);
lequal(first->outputs, second->outputs);
lequal(first->total_weights, second->total_weights);
int i;
for (i = 0; i < first->total_weights; ++i) {
lfequal(first->weight[i], second->weight[i]);
}
genann_free(first);
genann_free(second);
}
void sigmoid() {
double i = -20;
const double max = 20;
const double d = .0001;
while (i < max) {
lfequal(genann_act_sigmoid(i), genann_act_sigmoid_cached(i));
i += d;
}
}
int main(int argc, char *argv[])
{
printf("GENANN TEST SUITE\n");
srand(100);
lrun("basic", basic);
lrun("xor", xor);
lrun("backprop", backprop);
lrun("train and", train_and);
lrun("train or", train_or);
lrun("train xor", train_xor);
lrun("persist", persist);
lrun("copy", copy);
lrun("sigmoid", sigmoid);
lresults();
return lfails != 0;
}