From f9fc4d26ec5fca9ee175c8a6fbcdd0fa36f10947 Mon Sep 17 00:00:00 2001 From: sanine Date: Thu, 16 Nov 2023 14:50:00 -0600 Subject: clear out js files --- src/mind/README.md | 37 ------------------------------------- 1 file changed, 37 deletions(-) delete mode 100644 src/mind/README.md (limited to 'src/mind/README.md') diff --git a/src/mind/README.md b/src/mind/README.md deleted file mode 100644 index 1ece125..0000000 --- a/src/mind/README.md +++ /dev/null @@ -1,37 +0,0 @@ -mind -==== - -This module is used to create arbitrary stateful neural networks. - -The only export is the following function: - -``` -network(input_count, internal_count, output_count, weight_max = 4 : number) -``` - -This function returns an object that represents a neural network with `input_count` input neurons, -`internal_count` internal (and stateful) neurons, and `output_count` output neurons. `max_weight` determines -the maximum absolute value allowed for connection weights. - -A network object has two methods: - -``` -connect(source, sink, weight : number) -``` - -This method returns a new network object that is an exact copy of the original, except with a new -connection between the neuron indexed by `source` and `sink`, with weight `weight`. Neuron indices -are zero-indexed, and span all neurons, first the inputs, then the internal neurons, and finally the outputs. -An error will be thrown if `source` is in the range of output neurons or if `sink` is in the range of input -neurons. - - -``` -compute(inputs, state : array[number]) -``` - -This method returns a tuple `[output, newState]`, where `output` is an array of `output_count` values -corresponding to the output neuron's computed values, and `newState` is the new state of the internal neurons. - -`input` must be an array of numbers with length equal to `input_count`, and `state` must be an array of numbers -with length equal to `internal_count` or an error will be raised. -- cgit v1.2.1