import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.contrib.learn as skflow
from sklearn.utils import shuffle
import numpy as np
import pandas as pd

df = pd.read_csv("./boston.csv", header=0)
print(df.describe())

f, ax1 = plt.subplots()
plt.figure() # Create a new figure

y = df['MEDV'] #Y为价格

for i in range (1,8):
number = 420 + i
ax1.locator_params(nbins=3)
ax1 = plt.subplot(number)
plt.title(list(df)[i])
ax1.scatter(df[df.columns[i]],y) #Plot a scatter draw of the datapoints
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)


X = tf.placeholder("float", name="X") # create symbolic variables
Y = tf.placeholder("float", name = "Y")


with tf.name_scope("Model"):

w = tf.Variable(tf.random_normal([2], stddev=0.01), name="b0") # create a shared variable
b = tf.Variable(tf.random_normal([2], stddev=0.01), name="b1") # create a shared variable
def model(X, w, b):
return tf.multiply(X, w) + b # We just define the line as X*w + b0

y_model = model(X, w, b)

with tf.name_scope("CostFunction"):
cost = tf.reduce_mean(tf.pow(Y-y_model, 2)) # use sqr error for cost function

train_op = tf.train.AdamOptimizer(0.001).minimize(cost)


sess = tf.Session()
init = tf.initialize_all_variables()
tf.train.write_graph(sess.graph,'./linear2','graph.pbtxt')
cost_op = tf.summary.scalar("loss", cost)
merged = tf.summary.merge_all()
sess.run(init)
writer = tf.summary.FileWriter('./linear2', sess.graph)

xvalues = df[[df.columns[2], df.columns[4]]].values.astype(float)
yvalues = df[df.columns[12]].values.astype(float)
b0temp=b.eval(session=sess)
b1temp=w.eval(session=sess)


for a in range (1,50):
cost1=0.0
for i, j in zip(xvalues, yvalues):
sess.run(train_op, feed_dict={X: i, Y: j})
cost1+=sess.run(cost, feed_dict={X: i, Y: i})/506.00
#writer.add_summary(summary_str, i)
xvalues, yvalues = shuffle (xvalues, yvalues)
print (cost1)
b0temp=b.eval(session=sess)
b1temp=w.eval(session=sess)
print (b0temp)
print (b1temp)