From ea130335dabe2e21bb835aa12813c8fe870565f2 Mon Sep 17 00:00:00 2001
From: Gustav Louw <yellingintothefan@gmail.com>
Date: Sun, 1 Apr 2018 17:01:42 -0700
Subject: [PATCH] const correctness

---
 Tinn.c | 10 +++++-----
 Tinn.h |  7 ++-----
 2 files changed, 7 insertions(+), 10 deletions(-)

diff --git a/Tinn.c b/Tinn.c
index 412980b..af83896 100644
--- a/Tinn.c
+++ b/Tinn.c
@@ -52,8 +52,8 @@ static void backwards(const Tinn t, const float* in, const float* tg, float rate
         // Calculate total error change with respect to output.
         for(int j = 0; j < t.nops; j++)
         {
-            float a = pderr(t.o[j], tg[j]);
-            float b = pdact(t.o[j]);
+            const float a = pderr(t.o[j], tg[j]);
+            const float b = pdact(t.o[j]);
             sum += a * b * t.x[j * t.nhid + i];
             // Correct weights in hidden to output layer.
             t.x[j * t.nhid + i] -= rate * a * b * t.h[i];
@@ -125,7 +125,7 @@ Tinn xtbuild(int nips, int nhid, int nops)
 
 void xtsave(const Tinn t, const char* path)
 {
-    FILE* file = fopen(path, "w");
+    FILE* const file = fopen(path, "w");
     // Header.
     fprintf(file, "%d %d %d\n", t.nips, t.nhid, t.nops);
     // Biases and weights.
@@ -136,14 +136,14 @@ void xtsave(const Tinn t, const char* path)
 
 Tinn xtload(const char* path)
 {
-    FILE* file = fopen(path, "r");
+    FILE* const file = fopen(path, "r");
     int nips = 0;
     int nhid = 0;
     int nops = 0;
     // Header.
     fscanf(file, "%d %d %d\n", &nips, &nhid, &nops);
     // A new tinn is returned.
-    Tinn t = xtbuild(nips, nhid, nips);
+    const Tinn t = xtbuild(nips, nhid, nips);
     // Biases and weights.
     for(int i = 0; i < t.nb; i++) fscanf(file, "%f\n", &t.b[i]);
     for(int i = 0; i < t.nw; i++) fscanf(file, "%f\n", &t.w[i]);
diff --git a/Tinn.h b/Tinn.h
index 250edc5..610fac1 100644
--- a/Tinn.h
+++ b/Tinn.h
@@ -8,11 +8,8 @@ typedef struct
     float* h; // Hidden layer.
     float* o; // Output layer.
 
-    // Number of biases - always two - Tinn only supports a single hidden layer.
-    int nb;
-
-    // Number of weights.
-    int nw;
+    int nb; // Number of biases - always two - Tinn only supports a single hidden layer.
+    int nw; // Number of weights.
 
     int nips; // Number of inputs.
     int nhid; // Number of hidden neurons.