学生作业：基于KPCA与SVM的房价预测

帮帮孩子吧，实在是不会python啊，穷且笨

 你们老师出题就给一个标题？

对的，是期末作业

 没提示的吗？

没有，应该是简单的写一下代码，总结一下结果吧

不过好像数据集不能是太久远的

m0_46233534 又来恶心人?能走点正常路?

我是笨，又不是瞎，看不到平台的提醒

做分析有数据文件的哇

数据要求我们自己找

import paddle.fluid as fluid

import os

import paddle

import numpy as np

BUF_SIZE=500

BATCH_SIZE=20

i=0.001

train_reader=paddle.batch(paddle.reader.shuffle(paddle.dataset.uci_housing.train(),buf_size=BUF_SIZE),batch_size=BATCH_SIZE)

test_reader = paddle.batch(

paddle.reader.shuffle(paddle.dataset.uci_housing.test(),

buf_size=BUF_SIZE),

batch_size=BATCH_SIZE)

import matplotlib.pyplot as plt

train_data=paddle.dataset.uci_housing.train();

sampledata=next(train_data())

print(sampledata)

def draw_train_process(iters,train_costs):

title="training cost"

plt.title(title, fontsize=24)

plt.xlabel("iter", fontsize=14)

plt.ylabel("cost", fontsize=14)

plt.plot(iters, train_costs,color='red',label='training cost')

plt.grid()

plt.show()

def draw_infer_result(groud_truths, infer_results):

title='Boston'

plt.title(title, fontsize=24)

x = np.arange(1,20)

y = x

plt.plot(x, y)

plt.xlabel('ground truth', fontsize=14)

plt.ylabel('infer result', fontsize=14)

plt.scatter(groud_truths, infer_results, color='green',label='training cost')

plt.grid()

plt.show()

# def circle():

# if(i<=0.01):

x=fluid.layers.data(name='x',shape=[13],dtype='float32')

y=fluid.layers.data(name='y',shape=[1],dtype='float32')

y_predict=fluid.layers.fc(input=x,size=1,act=None)

cost=fluid.layers.square_error_cost(input=y_predict,label=y)

avg_cost=fluid.layers.mean(cost)

optimizer = fluid.optimizer.SGDOptimizer(learning_rate=0.0051)

opts = optimizer.minimize(avg_cost)

test_program = fluid.default_main_program().clone(for_test=True)

use_cuda=False

place=fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()

exe = fluid.Executor(place)

exe.run(fluid.default_startup_program())

feeder = fluid.DataFeeder(place=place, feed_list=[x, y])

iter=0;

iters=[]

train_costs=[]

EPOCH_NUM=50

model_save_dir = "/home/aistudio/work/fit_a_line.inference.model"

for pass_id in range(EPOCH_NUM):

train_cost = 0

for batch_id, data in enumerate(train_reader()):

train_cost = exe.run(program=fluid.default_main_program(),

feed=feeder.feed(data),

fetch_list=[avg_cost])

if batch_id % 40 == 0:

print("Pass:%d, Cost:%0.5f" % (pass_id, train_cost[0][0]))

iter=iter+BATCH_SIZE

iters.append(iter)

train_costs.append(train_cost[0][0])

test_cost = 0

for batch_id, data in enumerate(test_reader()):

test_cost= exe.run(program=test_program,

feed=feeder.feed(data),

fetch_list=[avg_cost])

print('Test:%d, Cost:%0.5f' % (pass_id, test_cost[0][0]))

if not os.path.exists(model_save_dir):

os.makedirs(model_save_dir)

print ('save models to %s' % (model_save_dir))

fluid.io.save_inference_model(model_save_dir,

['x'],

[y_predict],

exe)

# i+=0.001

# circle()

draw_train_process(iters,train_costs)

infer_exe = fluid.Executor(place)

inference_scope = fluid.core.Scope()

infer_results=[]

groud_truths=[]

with fluid.scope_guard(inference_scope):

[inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(model_save_dir,infer_exe)

infer_reader = paddle.batch(paddle.dataset.uci_housing.test(),batch_size=200)

test_data = next(infer_reader())

test_x = np.array([data[0] for data in test_data]).astype("float32")

test_y= np.array([data[1] for data in test_data]).astype("float32")

print(test_x)

results = infer_exe.run(inference_program, feed={feed_target_names[0]: np.array(test_x)}, fetch_list=fetch_targets)

print(results)

print("infer results and ground truth: (House Price)")

for idx, val in enumerate(zip(results[0], test_y)):

print("%d: infer:%.2f gt:%.2f" % (idx, val[0], val[1]))

infer_results.append(val[0])

groud_truths.append(val[1])

draw_infer_result(groud_truths,infer_results)

数据集在这里下载： https://aistudio.baidu.com/aistudio/datasetdetail/7802

svm实现看这一篇：https://blog.csdn.net/AIHUBEI/article/details/105105688

不好意思啊，我看一直没人说做，就去淘宝请人做了

真的不好意思，我之前想关闭问题的，但是没到48小时

Tao_improvement 耽误您时间了，实在对不起，谢谢您的帮助

我作业已经做完了，您人真的很好，再次谢谢您