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import numpy as np
importmatplotlib.pyplot as plt
fromscipy.stats import pearsonr
np.random.seed(42)
x=np.random.randn(15)
y=x+np.random.randn(15)
plt.scatter(x,y)
plt.plot(np.unique(x),np.poly1d(np.polyfit(x,y,1))(np.unique(x)))
plt.xlabel('x')
plt.ylabel('y')
plt.show()
corr,_=pearsonr(x,y)
print('pearsons correlation:%.3f'%corr)


fromsklearn.metrics.pairwise import cosine_similarity
cos_sin=cosine_similarity(x.reshape(1,-1),y.reshape(1,-1))
print('cosine similarity:%.3f'%cos_sin)

OUTPUT:
cosine similarity:0.773

#jaccard_score
fromsklearn.metrics import Jaccard score
a=[1,1,1,0]
b=[1,1,0,1]
jacc=Jaccard score(a,b)
print("Jaccard score:%.3f"%jacc)


OUTPUT:
Jaccard_score:0.500


#Euclidean Distance

fromscipy.spatial import distance
dat=distance.euclidean(x,y)
print("Euclidean distance:%.3f"%dat)


OUTPUT:
Euclidean distance:3.273


#manhattan Distance

dst=distance.cityblock(x,y)
print("Manhattan Distance:%.3f"%dst)