Write a program using tensorflow to calculate : \$\$y=mx+c\$\$

### Part 1

1. Read 2 arrays x,y containing floating point values
2. Calculate mean of x & y
3. Calculate variance for x \$\$variance(x)=sum((x-mean(x))^2)\$\$
4. Calculate covariance of x & y \$\$covariance = sum((x(i) - mean(x)) * (y(i) - mean(y)))\$\$
5. Calculate value of m \$\$m = covariance(x,y)/variance(x)\$\$
6. Calculate value of c \$\$c = mean(y) -m* mean(x)\$\$

### Part 2

1. Plot graph for actual values against predicted value
2. Calculate root mean square error.
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In [1]:

## Calculate mean of  X and Y
import tensorflow as tf
x=tf.constant([4,3,5],dtype=tf.float32)
y=tf.constant([8,9,5],dtype=tf.float32)
x_mean=0
y_mean=0
for i in range(0,3):
with tf.name_scope("mean_x"):
with tf.name_scope("mean_y"):
x_mean=tf.div(x_mean,3)
y_mean=tf.div(y_mean,3)

##variance(x)=sum((x−mean(x))2

var_x=0
var_y=0
for i in range(0,3):
with tf.name_scope("variance_X"):
var_x=tf.div(var_x,2)
with tf.name_scope("variance_Y"):
var_y=tf.div(var_y,2)

#covariance=sum((x(i)−mean(x))∗(y(i)−mean(y)))

with tf.name_scope("Covariance"):
covar=0
for i in range(0,3):

#m=covariance(x,y)/variance(x)

with tf.name_scope("value_of_m"):
m=tf.div(covar,var_x)

#c=mean(y)−m∗mean(x)
with tf.name_scope("value_of_c"):
c=tf.subtract(y_mean,tf.multiply(m,x_mean))
with tf.Session() as sess:
print(sess.run(x_mean))
print(sess.run(y_mean))
print(sess.run(var_x))
print(sess.run(var_y))
print(sess.run(covar))
print(sess.run(m))
print(sess.run(c))

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2.14815
2.96296
6.14403
32.9835
20.2798
3.30074
-4.12751

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