import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
from matplotlib import style
 
 
#構造數(shù)據(jù)
def get_data(sample_num=10000):
 """
 擬合函數(shù)為
 y = 5*x1 + 7*x2
 :return:
 """
 x1 = np.linspace(0, 9, sample_num)
 x2 = np.linspace(4, 13, sample_num)
 x = np.concatenate(([x1], [x2]), axis=0).T
 y = np.dot(x, np.array([5, 7]).T) 
 return x, y
#梯度下降法
def GD(samples, y, step_size=0.01, max_iter_count=1000):
 """
 :param samples: 樣本
 :param y: 結果value
 :param step_size: 每一接迭代的步長
 :param max_iter_count: 最大的迭代次數(shù)
 :param batch_size: 隨機選取的相對于總樣本的大小
 :return:
 """
 #確定樣本數(shù)量以及變量的個數(shù)初始化theta值
 m, var = samples.shape
 theta = np.zeros(2)
 y = y.flatten()
 #進入循環(huán)內
 print(samples)
 loss = 1
 iter_count = 0
 iter_list=[]
 loss_list=[]
 theta1=[]
 theta2=[]
 #當損失精度大于0.01且迭代此時小于最大迭代次數(shù)時，進行
 while loss > 0.001 and iter_count < max_iter_count:
 loss = 0
 #梯度計算
 theta1.append(theta[0])
 theta2.append(theta[1])
 for i in range(m):
  h = np.dot(theta,samples[i].T) 
 #更新theta的值,需要的參量有：步長，梯度
  for j in range(len(theta)):
  theta[j] = theta[j] - step_size*(1/m)*(h - y[i])*samples[i,j]
 #計算總體的損失精度，等于各個樣本損失精度之和
 for i in range(m):
  h = np.dot(theta.T, samples[i])
  #每組樣本點損失的精度
  every_loss = (1/(var*m))*np.power((h - y[i]), 2)
  loss = loss + every_loss
 
 print("iter_count: ", iter_count, "the loss:", loss)
 
 iter_list.append(iter_count)
 loss_list.append(loss)
 
 iter_count += 1
 plt.plot(iter_list,loss_list)
 plt.xlabel("iter")
 plt.ylabel("loss")
 plt.show()
 return theta1,theta2,theta,loss_list
def painter3D(theta1,theta2,loss):
 style.use('ggplot')
 fig = plt.figure()
 ax1 = fig.add_subplot(111, projection='3d')
 x,y,z = theta1,theta2,loss
 ax1.plot_wireframe(x,y,z, rstride=5, cstride=5)
 ax1.set_xlabel("theta1")
 ax1.set_ylabel("theta2")
 ax1.set_zlabel("loss")
 plt.show()
def predict(x, theta):
 y = np.dot(theta, x.T)
 return y 
if __name__ == '__main__':
 samples, y = get_data()
 theta1,theta2,theta,loss_list = GD(samples, y)
 print(theta) # 會很接近[5, 7] 
 painter3D(theta1,theta2,loss_list)
 predict_y = predict(theta, [7,8])
 print(predict_y)