载入数据
data = genfromtxt(r"\Delivery.csv", delimiter=',')
观察一下数据
x_data为特征值,y_data为标签值
所以应该设置3个参数θ0,θ1,θ2
lr = 0.0001
# 参数
theta0 = 0
theta1 = 0
theta2 = 0
# 最大迭代次数
epochs = 1000
def compute_error(theta0, theta1, theta2, x_data, y_data):
totalError = 0
for i in range(0, len(x_data)):
totalError += (y_data[i] - (theta1 * x_data[i, 0] + theta2 * x_data[i, 1] + theta0)) ** 2
return totalError / float(len(x_data))
def gradient_descent_runner(x_data, y_data, theta0, theta1, theta2, lr, epochs):
# 计算总数据量
m = float(len(x_data))
for i in range(epochs):
theta0_grad = 0
theta1_grad = 0
theta2_grad = 0
# 计算梯度的总和再求平均
for j in range(0, len(x_data)):
theta0_grad += -(1/m) * (y_data[j] - (theta1 * x_data[j, 0] + theta2*x_data[j, 1] + theta0))
theta1_grad += -(1 / m) * x_data[j, 0] * (y_data[j] - (theta1 * x_data[j, 0] + theta2 * x_data[j, 1] + theta0))
theta2_grad += -(1 / m) * x_data[j, 1] * (y_data[j] - (theta1 * x_data[j, 0] + theta2 * x_data[j, 1] + theta0))
#更新b和k
theta0 = theta0 - (lr*theta0_grad)
theta1 = theta1 - (lr*theta1_grad)
theta2 = theta2 - (lr*theta2_grad)
return theta0, theta1, theta2
theta0, theta1, theta2 = gradient_descent_runner(x_data, y_data, theta0, theta1, theta2, lr, epochs)
画图
ax = plt.figure().add_subplot(111, projection='3d')
ax.scatter(x_data[:, 0], x_data[:, 1], y_data, c='r', marker='o', s=100) # 点为红色三角形
x0 = x_data[:, 0]
x1 = x_data[:, 1]
# 生成网络矩阵
x0, x1 = np.meshgrid(x0, x1)
z = theta0 + x0 * theta1 + theta2
# 画3D图
ax.plot_surface(x0, x1, z)
# 设置坐标轴
ax.set_xlabel('Miles')
ax.set_ylabel('Num of Deliveries')
ax.set_zlabel('Time')
plt.show()
载入数据、切分数据与普通方法无异
创建模型
model = linear_model.LinearRegression()
model.fit(x_data, y_data)
打印出相关信息
# 系数
print('coefficients:', model.coef_)
# 截距
print('intercept:', model.intercept_)
对模型进行测试
x_test = [[10, 45]]
predict = model.predict(x_test)
print('predict:', predict)
ax = plt.figure().add_subplot(111, projection = '3d')
ax.scatter(x_data[:, 0], x_data[:, 1], y_data, c='r', marker='o', s=100)
x0 = x_data[:, 0]
x1 = x_data[:, 1]
# 生成网络矩阵
x0, x1 = np.meshgrid(x0, x1)
z = model.intercept_ + x0 * model.coef_[0] + x1 * model.coef_[1]
# 画3D图
ax.plot_surface(x0, x1, z)
# 设置坐标轴
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()