Sách Deep Learning cơ bản

50 Chương 3. Linear regression
import matplotlib.pyplot as plt
#numOfPoint = 30
#noise = np.random.normal(0,1,numOfPoint).reshape(-1,1)
#x = np.linspace(30, 100, numOfPoint).reshape(-1,1)
#N = x.shape[0]
#y = 15*x + 8 + 20*noise
#plt.scatter(x, y)
data = pd.read_csv('data_linear.csv').values
N = data.shape[0]
x = data[:, 0].reshape(-1, 1)
y = data[:, 1].reshape(-1, 1)
plt.scatter(x, y)
plt.xlabel('mét vuông')
plt.ylabel('giá')
x = np.hstack((np.ones((N, 1)), x))
w = np.array([0.,1.]).reshape(-1,1)
numOfIteration = 100
cost = np.zeros((numOfIteration,1))
learning_rate = 0.000001
for i in range(1, numOfIteration):
r = np.dot(x, w) - y
cost[i] = 0.5*np.sum(r*r)
w[0] -= learning_rate*np.sum(r)
# correct the shape dimension
w[1] -= learning_rate*np.sum(np.multiply(r, x[:,1].reshape(-1,1)))
print(cost[i])
predict = np.dot(x, w)
plt.plot((x[0][1], x[N-1][1]),(predict[0], predict[N-1]), 'r')
plt.show()
x1 = 50
y1 = w[0] + w[1] * x1
print('Giá nhà cho 50m^2 là : ', y1)
3.6 Bài tập
1. Thực hiện các phép nhân ma trận sau:
(a)
(b)
⎛ ⎞
a
⎝b⎠ ∗ ( d e f )
c
⎛ ⎞
( )
d
a b c ∗ ⎝e⎠
f