add: print all model values after solving simple linear regression

example.py

@@ -9,6 +9,9 @@ from src import centrality
 from src import plot
 from src import fitting
 
+# TODO: implement this AIC:
+# https://www.statlect.com/fundamentals-of-statistics/linear-regression-model-selection-criteria
+
 
 def merfish():
     """

src/fitting.py

@@ -21,7 +21,7 @@ def fit_piece_wise_linear(d, C, M=1000):
     """
     n = len(d) 
 
-    model = gp.Model()
+    model = gp.Model("Piece-wise Linear Model")
     m = model.addVar(vtype=GRB.CONTINUOUS, name="m") # Slope before breakpoint
 
     c0 = model.addVar(vtype=GRB.CONTINUOUS, name="c0") # y-intercept
@@ -64,13 +64,17 @@ def fit_linear_regression(d, C):
     tuple: Optimized slope (beta) and intercept (alpha).
     """
     n = len(d) 
-    model = gp.Model()
+    model = gp.Model("Simple Linear Regression")
 
     alpha = model.addVar(vtype=GRB.CONTINUOUS, name="alpha")
     beta = model.addVar(vtype=GRB.CONTINUOUS, name="beta")
 
     model.setObjective(gp.quicksum((C[i] - alpha - beta * d[i])**2 for i in range(n)), GRB.MINIMIZE)
     model.optimize()
+
+    for v in model.getVars():
+        print(f"{v.VarName} {v.X:g}")
+
     return beta.X, alpha.X