new

梯度下降/梯度下降解决面积问题 tensor.py

@@ -1,25 +0,0 @@
-import  numpy as np
-import math
-from    matplotlib import pyplot as plt
-import  torch
-
-def f(r):
-    g = torch.tan(r)+9*torch.tan(3/4*math.pi-r)
-    return g
-def drawfig(f):
-
-    x = torch.linspace(1.1/4*math.pi,0.99/2*math.pi,100)
-    y = f(x)
-    plt.plot(x,y)
-    plt.show()
-
-r = torch.tensor([1.16/4*np.pi], requires_grad=True)
-optimizer = torch.optim.Adam([r], lr=1e-4)
-for step in range(10000):
-    pred = f(r)
-    optimizer.zero_grad()
-    pred.backward()
-    optimizer.step()
-    if step % 200 == 0:
-        print (f'step {step}: r = {r.tolist()}, f(r) = {pred.item()}')
-drawfig(f)

梯度下降/梯度下降解决面积问题.py

@@ -1,39 +0,0 @@
-import math
-import numpy as np
-import matplotlib.pyplot as plt
-
-def f(r):
-    g = np.tan(r)+9*np.tan(3/4*np.pi-r)
-    return g
-
-def drawfig(f):
-
-    x = np.linspace(1.1/4*np.pi,0.99/2*np.pi,100)
-    y = f(x)
-    # print(y)
-    plt.plot(x,y)
-    plt.show()
-
-def grad(r):
-    g = 1.0/(np.cos(r)**2) - 9.0/(np.cos(3/4*np.pi-r)**2)
-    return g
-
-def gradientDescent(f,r0, eta, nstep):
-    r_history = np.zeros(nstep+1)
-    f_history = np.zeros(nstep+1)
-    r_history[0] = r0
-    f_history[0] = f(r0)
-    r = r0
-    for i in range(1,nstep+1):
-        r = r - eta * grad(r)
-        r_history[i] = r
-        f_history[i] = f(r)
-        print(f"r is :{r}   f(r) is {f(r)}")
-    return r_history,f_history
-# print(f(1/3*np.pi))
-drawfig(f)
-x,y = gradientDescent(f, 1.12/4*np.pi, 0.0001*np.pi, 10000)
-plt.plot(x,y)
-plt.show()
-
-