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diff --git a/test/monniaux/genann/genann.h b/test/monniaux/genann/genann.h
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+/*
+ * GENANN - Minimal C Artificial Neural Network
+ *
+ * Copyright (c) 2015-2018 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>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#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
+
+struct genann;
+
+typedef double (*genann_actfun)(const struct genann *ann, 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);
+
+void genann_init_sigmoid_lookup(const genann *ann);
+#ifndef VERIMAG
+double genann_act_sigmoid(const genann *ann, double a);
+double genann_act_sigmoid_cached(const genann *ann, double a);
+double genann_act_threshold(const genann *ann, double a);
+double genann_act_linear(const genann *ann, double a);
+#endif
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /*GENANN_H*/