Beispiel #1
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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
Beispiel #2
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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
Beispiel #3
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        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)
Beispiel #4
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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 = {}