fix: return type hints and added MAX_ITER global variable

gd.py

@@ -5,6 +5,8 @@ from typing import Callable
 import matplotlib.pyplot as plt
 from matplotlib.colors import LogNorm
 
+MAX_ITER = 1e5
+
 
 def symb_grad_2d(f: Function) -> (Function, Function):
     return f.diff(x), f.diff(y)
@@ -21,7 +23,7 @@ def callable_func(f: Function) -> Callable[[float, float], float]:
 
 
 def base_gd(f: Function, init_pt: list[float], lr: float) -> \
-        float:
+        pd.DataFrame:
     df = pd.DataFrame(columns=['x', 'y', 'Cost', 'NormGrad'])
     partialx, partialy = callable_grad_2d(f)
 
@@ -32,16 +34,16 @@ def base_gd(f: Function, init_pt: list[float], lr: float) -> \
 
     f_call = callable_func(f)
 
-    while iter < 1e4:
+    while iter < MAX_ITER:
         if np.linalg.norm(grad) < 1e-6:
             break
         grad = np.array([partialx(x, y), partialy(x, y)])
         df.loc[iter] = [x, y, f_call(x, y), np.linalg.norm(grad)]
 
-        step = -lr * grad
+        step = lr * grad
 
-        x += step[0]
-        y += step[1]
+        x -= step[0]
+        y -= step[1]
 
         iter += 1
 
@@ -49,7 +51,7 @@ def base_gd(f: Function, init_pt: list[float], lr: float) -> \
 
 
 def momentum_gd(f: Function, init_pt: list[float], lr: float, momentum: float)\
-        -> float:
+        -> pd.DataFrame:
     df = pd.DataFrame(columns=['x', 'y', 'Cost', 'NormGrad'])
     partialx, partialy = callable_grad_2d(f)
 
@@ -62,7 +64,7 @@ def momentum_gd(f: Function, init_pt: list[float], lr: float, momentum: float)\
 
     step = np.array([0, 0])
 
-    while iter < 1e4:
+    while iter < MAX_ITER:
         if np.linalg.norm(grad) < 1e-6:
             break
 
@@ -80,7 +82,7 @@ def momentum_gd(f: Function, init_pt: list[float], lr: float, momentum: float)\
 
 
 def nesterov_gd(f: Function, init_pt: list[float], lr: float, momentum: float)\
-        -> float:
+        -> pd.DataFrame:
     df = pd.DataFrame(columns=['x', 'y', 'Cost', 'NormGrad'])
     partialx, partialy = callable_grad_2d(f)
 
@@ -93,7 +95,7 @@ def nesterov_gd(f: Function, init_pt: list[float], lr: float, momentum: float)\
 
     step = np.array([0, 0])
 
-    while iter < 1e4:
+    while iter < MAX_ITER:
         grad = np.array([partialx(x, y), partialy(x, y)])
         if np.linalg.norm(grad) < 1e-6:
             break
@@ -113,7 +115,7 @@ def nesterov_gd(f: Function, init_pt: list[float], lr: float, momentum: float)\
 
 
 def adam_gd(f: Function, init_pt: list[float], lr: float)\
-        -> float:
+        -> pd.DataFrame:
     df = pd.DataFrame(columns=['x', 'y', 'Cost', 'NormGrad'])
     partialx, partialy = callable_grad_2d(f)
 
@@ -130,8 +132,7 @@ def adam_gd(f: Function, init_pt: list[float], lr: float)\
     iter = 0
     grad = np.array([partialx(x, y), partialy(x, y)])
 
-    while iter < 1e4:
-        iter += 1
+    while iter < MAX_ITER:
         grad = np.array([partialx(x, y), partialy(x, y)])
         if np.linalg.norm(grad) < 1e-6:
             break
@@ -163,12 +164,12 @@ def adam_gd(f: Function, init_pt: list[float], lr: float)\
 if __name__ == "__main__":
     x, y = symbols('x y')
 
-    # f: Function = x**2 + 5 * y**2
+    f: Function = x**2 + 7 * y**2
     # f: Function = 1 - exp(-10 * x**2 - y**2)
     # f: Function = x**2 * y - 2 * x * y**3 + 3 * x * y + 4
 
     # Rosenbrock(x, y)
-    # f: Function = (1 - x)**2 + 100 * (y - x**2)**2
+    # f: Function = (1 - x)**2 + 1 * (y - x**2)**2
 
     # Beale(x, y)
     # f: Function = (1.5 - x + x * y)**2 + (2.25 - x + x *
@@ -178,17 +179,15 @@ if __name__ == "__main__":
     # f: Function = (x + 2 * y - 7)**2 + (2 * x + y - 5)**2
 
     # Ackley(x, y)
-    f: Function = -20.0 * exp(-0.2 * sqrt(0.5 * (x**2 + y**2))) - \
-        exp(0.5 * (cos(2 * pi * x) + cos(2 * pi * y))) + exp(1) + 20
+    # f: Function = -20.0 * exp(-0.2 * sqrt(0.5 * (x**2 + y**2))) - \
+    #     exp(0.5 * (cos(2 * pi * x) + cos(2 * pi * y))) + exp(1) + 20
 
     f_call = callable_func(f)
 
     LR = 1e-2
     MOMENTUM = 0.9
 
-    plot_range = (10, 10)
-
-    # init_pt = [1, 1]
+    plot_range = (7, 7)
 
     init_pt = np.array([np.random.randint(-plot_range[0], plot_range[0] + 1),
                         np.random.randint(-plot_range[1], plot_range[1] + 1)])
@@ -212,7 +211,7 @@ if __name__ == "__main__":
     fig = plt.figure(1)
     ax = plt.axes(projection='3d')
     ax.plot_surface(X, Y, Z, cmap='jet', rstride=1,
-                    cstride=1, norm=LogNorm(), alpha=0.2)
+                    cstride=1, norm=LogNorm(), alpha=0.4)
     ax.set_xlabel('x')
     ax.set_ylabel('y')
     ax.set_zlabel('z')