#%%

from matplotlib import pyplot as plt

from mxnet import autograd, nd

import random

#%%

num_inputs = 1

num_examples = 100

true_w = 1.56

true_b = 1.24

features = nd.arange(0,10,0.1).reshape((-1, 1))

labels = true_w * features + true_b

labels += nd.random.normal(scale=0.2, shape=labels.shape)

features[0], labels[0]

#%%

# 本函数已保存在d2lzh包中方便以后使用

def data_iter(batch_size, features, labels):

    num_examples = len(features)

    indices = list(range(num_examples))

    random.shuffle(indices)  # 样本的读取顺序是随机的

    for i in range(0, num_examples, batch_size):

        j = nd.array(indices[i: min(i + batch_size, num_examples)])

        yield features.take(j), labels.take(j)  # take函数根据索引返回对应元素

#%%

batch_size = 10

for X, y in data_iter(batch_size, features, labels):

    print(X, y)

    break

#%%

w = nd.random.normal(scale=0.01, shape=(num_inputs, 1))

b = nd.zeros(shape=(1,))

#%%

w.attach_grad()

b.attach_grad()

#%%

def linreg(X, w, b):  # 本函数已保存在d2lzh包中方便以后使用

    return nd.dot(X, w) + b

#%%

def squared_loss(y_hat, y):  # 本函数已保存在d2lzh包中方便以后使用

    return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2

#%%

def sgd(params, lr, batch_size):  # 本函数已保存在d2lzh包中方便以后使用

    for param in params:

        param[:] = param - lr * param.grad / batch_size

#%%

lr = 0.05

num_epochs = 20

net = linreg

loss = squared_loss

for epoch in range(num_epochs):  # 训练模型一共需要num_epochs个迭代周期

    # 在每一个迭代周期中，会使用训练数据集中所有样本一次（假设样本数能够被批量大小整除）。X

    # 和y分别是小批量样本的特征和标签

    for X, y in data_iter(batch_size, features, labels):

        with autograd.record():

            l = loss(net(X, w, b), y)  # l是有关小批量X和y的损失

        l.backward()  # 小批量的损失对模型参数求梯度

        sgd([w, b], lr, batch_size)  # 使用小批量随机梯度下降迭代模型参数

    train_l = loss(net(features, w, b), labels)

    print('epoch %d, loss %f' % (epoch + 1, train_l.mean().asnumpy()))

#%%

true_w, w

#%%

true_b, b

#%%

plt.scatter(features.asnumpy(), labels.asnumpy(), 1)

labels1 = linreg(features,w,b)

plt.scatter(features.asnumpy(), labels1.asnumpy(), 1)

plt.show()