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5 Commits
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f6c22401d2 |
@@ -11,7 +11,7 @@ functions and little extra.
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## Features
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## Features
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- **ANSI C with no dependencies**.
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- **C99 with no dependencies**.
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- Contained in a single source code and header file.
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- Contained in a single source code and header file.
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- Simple.
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- Simple.
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- Fast and thread-safe.
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- Fast and thread-safe.
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@@ -105,7 +105,7 @@ backpropogation.
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A primary design goal of Genann was to store all the network weights in one
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A primary design goal of Genann was to store all the network weights in one
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contigious block of memory. This makes it easy and efficient to train the
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contigious block of memory. This makes it easy and efficient to train the
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network weights using direct-search numeric optimizion algorthims,
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network weights using direct-search numeric optimization algorthims,
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such as [Hill Climbing](https://en.wikipedia.org/wiki/Hill_climbing),
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such as [Hill Climbing](https://en.wikipedia.org/wiki/Hill_climbing),
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[the Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm), [Simulated
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[the Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm), [Simulated
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Annealing](https://en.wikipedia.org/wiki/Simulated_annealing), etc.
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Annealing](https://en.wikipedia.org/wiki/Simulated_annealing), etc.
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@@ -23,7 +23,7 @@ int main(int argc, char *argv[])
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genann *ann = genann_init(2, 1, 2, 1);
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genann *ann = genann_init(2, 1, 2, 1);
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/* Train on the four labeled data points many times. */
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/* Train on the four labeled data points many times. */
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for (i = 0; i < 300; ++i) {
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for (i = 0; i < 500; ++i) {
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genann_train(ann, input[0], output + 0, 3);
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genann_train(ann, input[0], output + 0, 3);
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genann_train(ann, input[1], output + 1, 3);
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genann_train(ann, input[1], output + 1, 3);
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genann_train(ann, input[2], output + 2, 3);
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genann_train(ann, input[2], output + 2, 3);
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