In [1]:

    

import numpy as np
import tensorflow as tf
a = tf.constant([34.3, 33.0, 21.5, 4.0, 5.8, 27.5], shape=[1,6], name='a')
b = tf.constant([43.3, 22.0, 21.5, 5.0, 6.7, 45.5], shape=[1,6], name='b')
with tf.name_scope("mean_A"):
    mean_a=tf.reduce_mean(a)
ses1=tf.Session()
print(ses1.run(mean_a))
with tf.name_scope("mean_B"):
    mean_b=tf.reduce_mean(b)
ses2=tf.Session()
print(ses2.run(mean_b))
with tf.name_scope("variance"):
    d=tf.subtract(a,mean_a)
sess=tf.Session()
print(sess.run(d))

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


    

    




21.0167
24.0
[[ 13.28333092  11.98333168   0.48333168 -17.01666832 -15.21666813
    6.48333168]]
883.428

In [2]:

    

with tf.name_scope("covariance"):
    g=tf.subtract(b,mean_b)
sess=tf.Session()
g=tf.multiply(d,g)
cov_xy=tf.reduce_sum(g)
cov=tf.divide(cov_xy,5)
print(sess.run(cov))


    

    




191.43

In [3]:

    

with tf.name_scope("value_of_m"):
    c=tf.divide(cov,variance)
    print(sess.run(c))

with tf.name_scope("value_of_c"):
    i=tf.multiply(c,mean_a)
    k=tf.subtract(mean_b,i)
print(sess.run(k))

with tf.Session() as sess:
    writer = tf.summary.FileWriter("/tmp/tboard/output1", sess.graph)
    writer.close()


    

    




0.21669
19.4459

In [ ]:

    




    

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