# Read 2 arrays x,y containing floating point values

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In [2]:

import numpy as np
import pylab as pl
x=np.array([1,2,3,8,5,6,7,8],dtype=np.float32)
y=np.array([1,2,3,4,5,6,7,8],dtype=np.float32)
print("x : ",x)
print("y : ",y)

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x :  [ 1.  2.  3.  8.  5.  6.  7.  8.]
y :  [ 1.  2.  3.  4.  5.  6.  7.  8.]

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# Calculate mean of x & y

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In [3]:

mean1=np.mean(x)
mean2=np.mean(y)
print("Mean of x : ",mean1)
print("Mean of y : ",mean2)

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Mean of x :  5.0
Mean of y :  4.5

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# Calculate variance for x variance(x)=sum((x-mean(x))^2)

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In [7]:

var=0
for i in np.nditer(x.T):
var+=np.sum(np.square(i-mean1))
variace=var/x.size
print("variance : ",variace)

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variance :  6.5

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In [ ]:

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# Calculate covariance of x & y covariance = sum((x(i) - mean(x)) * (y(i) - mean(y)))

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In [8]:

if(x.size!=y.size):
print('Array size is different ')
else:
covar=0.0
for i in range(0,len(x)):
covar+=((x[i]-mean1)*(y[i]-mean2))
covariance=covar/(len(x)-1)
print("covariance : ",covariance)

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covariance :  5.71428571429

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# Calculate value of m m = covariance(x,y)/variance(x)\$\$

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In [9]:

m=covariance/variace
print("value m : ",m)

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value m :  0.879120879121

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# Calculate value of c cc = mean(y) -m* mean(x)

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In [10]:

cc=mean2-m*mean1
print("value c : ", cc)

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value c :  0.104395604396

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# Plot graph for actual values against predicted value

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In [11]:

pl.plot(x,y)
pl.title("Graph")
pl.show()

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# Calculate root mean square error

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In [12]:

rmse=np.sqrt(np.mean(np.square(x - y)))
print("Root mean square Error : ",rmse)

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Root mean square Error :  1.41421

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In [ ]:

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