In [22]:

    

import tensorflow as tf

with tf.name_scope("var"):
    with tf.name_scope("mean_x"):
        a=tf.constant([5.0,7.0,20.2,17.32],shape=[1,4],name='a')
        b=tf.constant([7.0,9.0,19.0,18.0],shape=[1,4],name='b')
        x=tf.reduce_mean(a)
        sess=tf.Session()
print("mean X",sess.run(x))



#mean of y

with tf.name_scope("mean_y"):
    y=tf.reduce_mean(b)
    sess=tf.Session()
print("mean Y",sess.run(y))



#variance of x
d=tf.subtract(a,x)
sess=tf.Session()
print(sess.run(d))

e=tf.square(d)
f=tf.reduce_sum(e)
sess=tf.Session()
print("variance",sess.run(f))


#covariance
with tf.name_scope("covariance"):
    g=tf.subtract(b,y)
    sess=tf.Session()
    g=tf.multiply(d,g)
    h=tf.reduce_sum(g)
print("covariance",sess.run(h))



#value of c
with tf.name_scope("value_of_c"):
    j=tf.divide(h,f)
print("value c",sess.run(j))


#m value
with tf.name_scope("value_m"):
    i=tf.multiply(j,x)
    k=tf.subtract(y,i)
print("value m",sess.run(j))


#calculate root mean square error

with tf.name_scope("rmse"):
    l=tf.subtract(b,y)
    m=tf.multiply(l,l)
    n=tf.reduce_sum(m)
    o=tf.divide(n, 4,)
    p=tf.sqrt(o)
    print ("rmse",sess.run(p))
    
    
with tf.Session() as lb:
    Writer =tf.summary.FileWriter("/tmp/tboard/output6",lb.graph)
    Writer.close()


    

    




mean X 12.38
mean Y 13.25
[[-7.38000011 -5.38000011  7.82000065  4.93999958]]
variance 168.965
covariance 137.42
value c 0.813305
value m 0.813305
rmse 5.30919

In [19]:

    

#calculate y using x
import numpy
with tf.Session() as sess:
    x1=sess.run(a)
    y1=sess.run(b)
g_x = numpy.asarray(x1)
g_y = numpy.asarray(y1)



x2=tf.constant([1.0,2.0, 3.0, 4.0, 5.0])
with tf.Session() as sess:
    x3=sess.run(x2)
x4=numpy.asarray(x3)

y2=[0]*5
    
with tf.Session() as sess:
        for i in range(len(sess.run(x2))):
            xx = sess.run(x2[i])
            a=tf.multiply(xx,j)
            b=tf.add(a,k)
            y2[i]=sess.run(b)
print(y2)


    

    




[3.9945836, 4.807889, 5.6211948, 6.4345002, 7.2478056]

In [20]:

    

#graph
import matplotlib.pyplot as plt
plt.plot(g_x,g_y, 'ro', label='Actual values')
plt.plot(x4, y2, label='fitted line')
plt.legend()
plt.show()


    

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