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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