python(房价预测)
因为自身水平还未达到，有待进步，所以不会做这题。
以下为网络上找的代码。
# 计算赤峰面积和房价之间的关系
import numpy as np
import matplotlib.pyplot as plt
data = []
for i in range(300):
area = np.random.uniform(60, 100)
eps2 = np.random.uniform(60, 62)
eps3 = np.random.uniform(200., 700.)
price = eps2 * area + eps3
data.append([area, price])
data = np.array(data)
area = data[:, 0]
price = data[:, 1]
plt.title("Area-Price")
plt.scatter(area, price, s=10)
plt.xlabel("area")
plt.ylabel("price")
plt.show()
loss_list = []
def mse(b, w, data):
TotalError = 0
for i in range(0, len(data)):
x = data[i, 0]
y = data[i, 1]
TotalError += (y - (w * x + b)) ** 2
return TotalError / float(len(data))
def gradient_update(b, w, data, lr):
b_gradient = 0
w_gradient = 0
size = float(len(data))
for i in range(0, len(data)):
x = data[i, 0]
y = data[i, 1]
b_gradient += (2 / size) * ((w * x + b) - y)
w_gradient += (2 / size) * x * ((w * x + b) - y)
b -= lr * b_gradient
w -= lr * w_gradient
return [b, w]
def gradient_descent(data, b, w, lr, num_iterations):
for num in range(num_iterations):
b, w = gradient_update(b, w, data, lr)
loss = mse(b, w, data)
loss_list.append(loss)
print('iteration:[%s] | loss:[%s] | w:[%s] | b:[%s]' % (num, loss, w, b))
return [b, w]
def main():
lr = 0.00001
initial_b = np.random.randn(1)
initial_w = np.random.randn(1)
num_iterations = 100
[b, w] = gradient_descent(data, initial_b, initial_w, lr, num_iterations)
loss = mse(b, w, data)
print('Final loss:[%s] | w:[%s] | b:[%s]' % (loss, w, b))
plt.title("Loss Function")
plt.plot(np.arange(0, 100), loss_list)
plt.xlabel('Interation')
plt.ylabel('Loss Value')
plt.show()
y2 = w * area + b
print(w * 100 + b)
plt.title("Fit the line graph")
plt.scatter(area, price, label='Original Data', s=10)
plt.plot(area, y2, color='Red', label='Fitting Line', linewidth=2)
plt.xlabel('m_j')
plt.ylabel('j_g')
plt.legend()
plt.show()
main()