From 6ca8a31143f087f3bc470d39eb3c00156443802a Mon Sep 17 00:00:00 2001
From: 3gg <3gg@shellblade.net>
Date: Thu, 23 Nov 2023 08:38:59 -0800
Subject: Formatting.

---
 src/lib/include/neuralnet/matrix.h    | 15 ++++++++++-----
 src/lib/include/neuralnet/neuralnet.h |  8 +++++---
 src/lib/include/neuralnet/train.h     | 20 +++++++++-----------
 3 files changed, 24 insertions(+), 19 deletions(-)

(limited to 'src/lib/include')

diff --git a/src/lib/include/neuralnet/matrix.h b/src/lib/include/neuralnet/matrix.h
index 0cb40cf..b7281bf 100644
--- a/src/lib/include/neuralnet/matrix.h
+++ b/src/lib/include/neuralnet/matrix.h
@@ -33,7 +33,8 @@ void nnMatrixToArray(const nnMatrix* in, R* out);
 void nnMatrixRowToArray(const nnMatrix* in, int row, R* out);
 
 /// Copy a column from a source to a target matrix.
-void nnMatrixCopyCol(const nnMatrix* in, nnMatrix* out, int col_in, int col_out);
+void nnMatrixCopyCol(
+    const nnMatrix* in, nnMatrix* out, int col_in, int col_out);
 
 /// Mutable borrow of a matrix.
 nnMatrix nnMatrixBorrow(nnMatrix* in);
@@ -56,20 +57,24 @@ void nnMatrixMul(const nnMatrix* left, const nnMatrix* right, nnMatrix* out);
 ///
 /// This function multiples two matrices row-by-row instead of row-by-column.
 /// nnMatrixMul(A, B, O) == nnMatrixMulRows(A, B^T, O).
-void nnMatrixMulRows(const nnMatrix* left, const nnMatrix* right, nnMatrix* out);
+void nnMatrixMulRows(
+    const nnMatrix* left, const nnMatrix* right, nnMatrix* out);
 
 /// Matrix multiply-add.
 ///
 /// out = left + (right * scale)
-void nnMatrixMulAdd(const nnMatrix* left, const nnMatrix* right, R scale, nnMatrix* out);
+void nnMatrixMulAdd(
+    const nnMatrix* left, const nnMatrix* right, R scale, nnMatrix* out);
 
 /// Matrix multiply-subtract.
 ///
 /// out = left - (right * scale)
-void nnMatrixMulSub(const nnMatrix* left, const nnMatrix* right, R scale, nnMatrix* out);
+void nnMatrixMulSub(
+    const nnMatrix* left, const nnMatrix* right, R scale, nnMatrix* out);
 
 /// Hadamard product of two matrices.
-void nnMatrixMulPairs(const nnMatrix* left, const nnMatrix* right, nnMatrix* out);
+void nnMatrixMulPairs(
+    const nnMatrix* left, const nnMatrix* right, nnMatrix* out);
 
 /// Add two matrices.
 void nnMatrixAdd(const nnMatrix* left, const nnMatrix* right, nnMatrix* out);
diff --git a/src/lib/include/neuralnet/neuralnet.h b/src/lib/include/neuralnet/neuralnet.h
index 1cf1c53..05c9406 100644
--- a/src/lib/include/neuralnet/neuralnet.h
+++ b/src/lib/include/neuralnet/neuralnet.h
@@ -5,7 +5,7 @@
 typedef struct nnMatrix nnMatrix;
 
 typedef struct nnNeuralNetwork nnNeuralNetwork;
-typedef struct nnQueryObject nnQueryObject;
+typedef struct nnQueryObject   nnQueryObject;
 
 /// Neuron activation.
 typedef enum nnActivation {
@@ -15,7 +15,8 @@ typedef enum nnActivation {
 } nnActivation;
 
 /// Create a network.
-nnNeuralNetwork* nnMakeNet(int num_layers, const int* layer_sizes, const nnActivation* activations);
+nnNeuralNetwork* nnMakeNet(
+    int num_layers, const int* layer_sizes, const nnActivation* activations);
 
 /// Delete the network and free its internal memory.
 void nnDeleteNet(nnNeuralNetwork**);
@@ -36,7 +37,8 @@ void nnSetBiases(nnNeuralNetwork*, const R* biases);
 void nnQuery(const nnNeuralNetwork*, nnQueryObject*, const nnMatrix* input);
 
 /// Query the network, array version.
-void nnQueryArray(const nnNeuralNetwork*, nnQueryObject*, const R* input, R* output);
+void nnQueryArray(
+    const nnNeuralNetwork*, nnQueryObject*, const R* input, R* output);
 
 /// Create a query object.
 ///
diff --git a/src/lib/include/neuralnet/train.h b/src/lib/include/neuralnet/train.h
index 79f8e7b..6d811c2 100644
--- a/src/lib/include/neuralnet/train.h
+++ b/src/lib/include/neuralnet/train.h
@@ -14,18 +14,18 @@ typedef struct nnMatrix nnMatrix;
 /// activation with many inputs. Thus, a (0,1) initialization is really
 /// (0,scale), for example.
 typedef enum nnWeightInitStrategy {
-  nnWeightInit01,      // (0,1) range.
-  nnWeightInit11,      // (-1,+1) range.
-  nnWeightInitNormal,  // Normal distribution.
+  nnWeightInit01,     // (0,1) range.
+  nnWeightInit11,     // (-1,+1) range.
+  nnWeightInitNormal, // Normal distribution.
 } nnWeightInitStrategy;
 
 /// Network training parameters.
 typedef struct nnTrainingParams {
-  R learning_rate;
-  int max_iterations;
-  uint64_t seed;
+  R                    learning_rate;
+  int                  max_iterations;
+  uint64_t             seed;
   nnWeightInitStrategy weight_init;
-  bool debug;
+  bool                 debug;
 } nnTrainingParams;
 
 /// Train the network.
@@ -36,7 +36,5 @@ typedef struct nnTrainingParams {
 /// |targets| is a matrix of targets, one row per target and as many columns as
 /// the target's dimension.
 void nnTrain(
-  nnNeuralNetwork*,
-  const nnMatrix* inputs,
-  const nnMatrix* targets,
-  const nnTrainingParams*);
+    nnNeuralNetwork*, const nnMatrix* inputs, const nnMatrix* targets,
+    const nnTrainingParams*);
-- 
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