import tensorflow as tf

import numpy as np

import xlrd

import matplotlib.pyplot as plt

DATA_FILE = 'fire_theft.xls'

# 1.read from data file

book=xlrd.open_workbook(DATA_FILE,encoding_override="utf-8")

sheet=book.sheet_by_index(0)

data=np.asarray([sheet.row_values(i) for i in range(1,sheet.nrows)])

n_samples=sheet.nrows-1

# 2.creat placeholders for input x(number of file) and label Y(number of theft)

X=tf.placeholder(tf.float32,name='X')

Y=tf.placeholder(tf.float32,name='Y')

# 3.creat weight and bias ,init to 0

w=tf.Variable(0.0,name='weights')

b=tf.Variable(0.0,name='bias')

# 4.build model to predict Y

Y_predicted = X* w +b

# 5.use square error as the lose function

loss=tf.square(Y-Y_predicted,name='loss')

# 6.using gradient descent with learning rate 0.01 to minimize loss

optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)

with tf.Session() as sess:

    # 7.init necessary variables (w and b)

    sess.run(tf.global_variables_initializer())

    writer=tf.summary.FileWriter('./my_graph/linear_reg',sess.graph)

    # 8.train the model 100 times

    for i in range(100):

        total_loss =0

        for x,y in data:

            #session runs train_op and fetch values of loss

            _,l=sess.run([optimizer,loss],feed_dict={X:x,Y:y})

            total_loss +=l

        print('Epoch {0}:{1}'.format(i,total_loss/n_samples))

    # close the writer

    writer.close()

    # 9.output the value of w and b

    w_value,b_value=sess.run([w,b])

# plot the result

X,Y=data.T[0],data.T[1]

plt.plot(X,Y,'bo',label='Real data')

plt.plot(X,X*w_value+b_value,'r',label='Predected data')

plt.legend()

plt.show()