blentea1

from sklearn.metrics.pairwise import cosine_similarity

t1 = np.array([[1, 1, 1]])
t2 = np.array([[2, 0, 1]])
cosine_similarity(t1,t2)​

SVD = TruncatedSVD(n_components=n)
matrix = SVD.fit_transform(tea_user_recipe)
matrix.shape

import numpy as np

t1 = np.array([1, 1, 1])
t2 = np.array([2, 0, 1])

from numpy import dot
from numpy.linalg import norm

def cos_sim(A, B):
	return dot(A, B)/(norm(A)*norm(B)
    
cos_sim(t1, t2) #0.775