From 8666acd6c33c3d408328d9f548175029fb1b5bd2 Mon Sep 17 00:00:00 2001
From: Jaron Kent-Dobias <jaron@kent-dobias.com>
Date: Mon, 10 Feb 2025 19:04:48 -0300
Subject: Modfied code to produce chain of examples

---
 fits.cpp | 24 +++++++++++-------------
 1 file changed, 11 insertions(+), 13 deletions(-)

diff --git a/fits.cpp b/fits.cpp
index 7ee9c64..9696e16 100644
--- a/fits.cpp
+++ b/fits.cpp
@@ -125,7 +125,6 @@ Data generateData(Real(*f)(Real), unsigned M, Real σ, Rng& r) {
 }
 
 int main(int argc, char* argv[]) {
-  unsigned N = 10;
   unsigned M = 10;
   unsigned nBatches = 5;
   Real σ = 0.2;
@@ -134,11 +133,8 @@ int main(int argc, char* argv[]) {
 
   int opt;
 
-  while ((opt = getopt(argc, argv, "N:M:s:S:B:i:")) != -1) {
+  while ((opt = getopt(argc, argv, "M:s:S:B:i:")) != -1) {
     switch (opt) {
-    case 'N':
-      N = (unsigned)atof(optarg);
-      break;
     case 'M':
       M = (unsigned)atof(optarg);
       break;
@@ -170,17 +166,19 @@ int main(int argc, char* argv[]) {
   }
   std::cout << std::endl;
 
-  Vector a₀ = Vector::Zero(N);
-  for (Real& aa : a₀) {
-    aa = r.variate<Real, std::normal_distribution>(0, iniVar);
-  }
+  for (unsigned N = 1; N <= 2 * M; N++) {
+    Vector a₀ = Vector::Zero(N);
+    for (Real& aa : a₀) {
+      aa = r.variate<Real, std::normal_distribution>(0, iniVar);
+    }
 
-  Vector a = stochasticGradientDescent(data, a₀, nBatches, maxSteps);
+    Vector a = stochasticGradientDescent(data, a₀, nBatches, maxSteps);
 
-  for (unsigned i = 0; i < N; i++) {
-    std::cout << a[i] << " ";
+    for (Real ai : a) {
+      std::cout << ai << " ";
+    }
+    std::cout << std::endl;
   }
-  std::cout << std::endl;
 
   return 0;
 }
-- 
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