def obtainCompressedCSV(readingsNormalized):
""" Compresses teh sensor data """
T_in = dtensor(readingsNormalized.reshape(necessary, 3, 3))
T_out, fit, itr, _ = als(T_in, 3)
final_Compressed = "\n".join(
[",".join([str(k) for k in n]) for n in T_out.U[0]] +
[",".join([str(k) for k in n]) for n in T_out.U[1]] +
[",".join([str(k) for k in n])
for n in T_out.U[2]] + [",".join([str(n) for n in T_out.lmbda])])
return final_Compressed
def cp_cpt(sparse_data, rank, W):
time_s = time.time()
est_data = sparse_data.copy()
dshape = np.shape(est_data)
SD = dtensor(sparse_data.copy())
U = []
P, fit, itr, arr = cp.als(SD, rank)
loc_data = P.totensor()
est_data = W * est_data + (W == False) * loc_data
time_e = time.time()
print('-' * 8 + 'cp' + '-' * 8)
print('exec_time:' + str(time_e - time_s) + 's')
return est_data
index_movies.append(movie_list.index(m[0]))
index_genres.append(genre_list.index(m[1]))
index_tags.append(tag_list.index(m[3]))
for i in range(len(index_tags)):
mat[index_movies[i]][index_tags[i]][index_genres[i]] = 1
arr = mat
return (arr, movie_list, movie_timestamp)
tensor, m, seeds = load()
T = dtensor(tensor) # SCIKIT TENSOR REPRESENTATION
### CP-DECOMPOSITION ###
P, fit, itr, exectimes = cp.als(T, 5, init='random')
### FACTOR MATRICES GENERATED ###
Factor1 = P.U[0]
Factor2 = P.U[1]
Factor3 = P.U[2]
UF = ktensor.totensor(P)
UF1 = dtensor.unfold(UF, 0)
#print(UF1.shape)
U, s, V = linalg.svd(UF1)
U = U[:, :10]
#print(U.shape)
D_T = np.transpose(U)
movie_movie = np.dot(U, D_T)
for i in range(len(index_tags)):
mat[index_movies[i]][index_tags[i]][index_genres[i]] = 1
arr = mat
tensor,m, user_movies = arr,movie_list,movie_timestamp
irrel = []
while(True):
m = movie_list
#user_movies = list(user_movies.keys())
m, user_movies = movie_list,movie_timestamp
#user_movies = list(user_movies.keys())
T = dtensor(tensor) # SCIKIT TENSOR REPRESENTATION
seeds = movie_timestamp
### CP-DECOMPOSITION ###
P, fit, itr, exectimes = cp.als(T, 5, init = 'nvecs')
### FACTOR MATRICES GENERATED ###
Factor1 = P.U[0]
DP = ktensor.totensor(P)
UF = dtensor.unfold(DP,0)
U, s, V = linalg.svd(UF)
U = U[:,:10]
#print(U.shape)
D_T = np.transpose(U)
movie_movie = np.dot(U, D_T)
seeds = sorted(seeds.items(), key=operator.itemgetter(1), reverse=True)
seeds_weight = {}