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Update 'algorithm/optimization/gradient.py'

This commit is contained in:
TerenceLiu 2023-02-20 17:51:39 +08:00
parent 0a8eb87534
commit b35805d96b
1 changed files with 38 additions and 2 deletions
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38
algorithm/optimization/gradient.py
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@ -347,6 +347,30 @@ class gd2d_compete(object):
self.button_plot.on_click(self.plot) self.button_plot.on_click(self.plot)
display(self.plot_output) display(self.plot_output)
def compute_default(self, *args):
with self.compute_output:
x0 = np.array(self.wg_x0.value.split(","), dtype=float)
xn = x0
x1 = symbols("x1")
x2 = symbols("x2")
expr = sympify(self.wg_expr.value)
self.xn_list, self.df_list = [], []
for n in tqdm(range(0, self.wg_max_iter.value)):
gradient = np.array([diff(expr, x1).subs(x1, xn[0]).subs(x2, xn[1]),
diff(expr, x2).subs(x1, xn[0]).subs(x2, xn[1])], dtype=float)
self.xn_list.append(xn)
self.df_list.append(gradient)
if np.linalg.norm(gradient, ord=2) < self.wg_epsilon.value:
clear_output(wait=True)
print("Found solution of {} after".format(expr), n, "iterations")
print("x* = [{}, {}]".format(xn[0], xn[1]))
return None
xn = xn - self.wg_lr.value * gradient
clear_output(wait=True)
display("Exceeded maximum iterations. No solution found.")
return None
def compute(self, *args): def compute(self, *args):
with self.compute_output: with self.compute_output:
expr = sympify(self.wg_expr.value) expr = sympify(self.wg_expr.value)
@ -358,6 +382,7 @@ class gd2d_compete(object):
self.xn_p1_list.append(x0) self.xn_p1_list.append(x0)
direction_p0 = np.array(self.wg_direction_p0.value.split(","), dtype=float) direction_p0 = np.array(self.wg_direction_p0.value.split(","), dtype=float)
direction_p1 = np.array(self.wg_direction_p1.value.split(","), dtype=float) direction_p1 = np.array(self.wg_direction_p1.value.split(","), dtype=float)
self.timer = self.timer + 1 self.timer = self.timer + 1
# calcualte next point position # calcualte next point position
x0_p0 = self.xn_p0_list[self.timer-1] + self.wg_lr.value * direction_p0 x0_p0 = self.xn_p0_list[self.timer-1] + self.wg_lr.value * direction_p0
@ -365,9 +390,20 @@ class gd2d_compete(object):
self.xn_p0_list.append(x0_p0) self.xn_p0_list.append(x0_p0)
self.xn_p1_list.append(x0_p1) self.xn_p1_list.append(x0_p1)
f = lambdify((x1, x2), expr, "numpy") f = lambdify((x1, x2), expr, "numpy")
f_xn = f(np.array([x0_p0, x0_p1])[:, 0], np.array([x0_p0, x0_p1])[:, 1]) f_xn = f(np.array([x0_p0, x0_p1])[:, 0], np.array([x0_p0, x0_p1])[:, 1])
self.expr = expr
self.x0_p0 = x0_p0
self.x0_p1 = x0_p1
gradient_p0 = np.array([diff(expr, x1).subs(x1, x0_p0[0]).subs(x2, x0_p0[1]),
diff(expr, x2).subs(x1, x0_p0[0]).subs(x2, x0_p0[1])], dtype=float)
gradient_p1 = np.array([diff(expr, x1).subs(x1, x0_p1[0]).subs(x2, x0_p1[1]),
diff(expr, x2).subs(x1, x0_p1[0]).subs(x2, x0_p1[1])], dtype=float)
clear_output(wait=True) clear_output(wait=True)
print("a1({}): {}, a2({}): {}".format(self.timer, np.append(x0_p0, f_xn[0]), self.timer,np.append(x0_p1, f_xn[1]))) print("coordinate： a1({}): {}, a2({}): {}".format(self.timer, np.append(x0_p0, f_xn[0]), self.timer,np.append(x0_p1, f_xn[1])))
print("gradient: a1({}): {}, a2({}): {}".format(self.timer, gradient_p0, self.timer,gradient_p1))
def plot(self, *args): def plot(self, *args):
with self.plot_output: with self.plot_output: