In [5]:

    

import tensorflow as tf

x = tf.constant([2,2,3],dtype=tf.float32)
y= tf.constant([9,5,6],dtype=tf.float32)

with tf.name_scope("mean_x"):
    x_mean=0
    y_mean=0
    for i in range(3):
        x_mean=tf.div(tf.add(x[i],x_mean),3)
        y_mean=tf.div(tf.add(x[i],x_mean),3)


    

In [6]:

    

#variancex=pow((x[i]-meanx),2)+variancex
        
with tf.name_scope("variancex"):
    variancex=0
    for i in range(3):
        a=tf.subtract(x[i],x_mean)
        b=tf.pow(a,2)
        variancex=tf.add(b,variancex)


    

In [7]:

    

#$$covariance = sum((x(i) - mean(x)) * (y(i) - mean(y)))$$
with tf.name_scope("covariance"):
    covariancex=0
    for i in range(3):
        covariancex=tf.add(tf.multiply(tf.subtract(x[i],x_mean),tf.subtract(y[i],y_mean)),covariancex)


    

In [8]:

    

#$$c = covariance(x,y)/variance(x)$$
with tf.name_scope("c"):
    c=tf.div(covariancex,variancex)


    

In [74]:

    

#$$m = mean(y) -c* mean(x)$$
with tf.name_scope("m"):
    m=tf.subtract(y_mean,tf.multiply(c,x_mean))
with tf.Session() as sess:
    writer = tf.summary.FileWriter("/tmp/tboard/output1", sess.graph)
    print(sess.run(x_mean))
    print(sess.run(y_mean))
    print(sess.run(variancex))
    print(sess.run(covariancex))
    print(sess.run(c))
    print(sess.run(m))
    writer.close()


    

    




1.25926
1.41975
3.64609
9.9561
2.73062
-2.01881

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