def do_all_for_me(matr, bit_words_machine):
"""
It takes the matrix and calls all the functions necessary to compress it
Args:
matr: matrix to be compressed
bit_words_machine: machine word bit number
returns:
matr_shape: shape of the matrix that we compress
int_data: list of integers representing the huffman encoding
of the vector data of the csc representation
d_rev_data: dict encoded --> element
row_index: vector of the row indices of the csc representation
cum: vector of the number of elements of each column
expected_c: number of columns in the matrix
min_length_encoded: minimum length of huffman encodings
"""
data, row_index, cum = sparse_huffman.convert_dense_to_csc(matr)
d_data, d_rev_data = huffman_sparse_encoded_dict(data)
data_encoded = encoded_matrix(data, d_data, d_rev_data)
int_data = huffman.convert_bin_to_int(
huffman.make_words_list_to_int(data_encoded, bit_words_machine))
expected_c = len(cum)
matr_shape = matr.shape
min_length_encoded = huffman.min_len_string_encoded(d_rev_data)
return matr_shape, int_data, d_rev_data, row_index, cum, expected_c, min_length_encoded
def test_code_decode_with_zeros_columns(self):
n = 50
m = 10
p = 0.7 #probablità che sia "prunato"
mask = np.random.choice(a=[False, True], size=(n, m), p=[p, 1 - p])
matr = np.random.randint(100, size=(n, m)) * (1 * mask)
matr[:, 3] = 0
matr[:, 6] = 0
data, row_index, cum = sparse_huffman.convert_dense_to_csc(matr)
d_data, d_rev_data, d_row_index, d_rev_row_index, d_cum, d_rev_cum = sparse_huffman.huffman_sparse_encoded_dict(
data, row_index, cum)
data_encoded, row_index_encoded, cum_encoded = sparse_huffman.encoded_matrix(
data, d_data, d_rev_data, row_index, d_row_index, d_rev_row_index,
cum, d_cum, d_rev_cum)
int_data = huffman.convert_bin_to_int(
huffman.make_words_list_to_int(data_encoded,
self.bit_words_machine))
int_row_index = huffman.convert_bin_to_int(
huffman.make_words_list_to_int(row_index_encoded,
self.bit_words_machine))
int_cum = huffman.convert_bin_to_int(
huffman.make_words_list_to_int(cum_encoded,
self.bit_words_machine))
expected_c = len(cum)
dense = sparse_huffman.sparsed_encoded_to_dense(
matr.shape, int_data, int_row_index, int_cum, d_rev_data,
d_rev_row_index, d_rev_cum, self.bit_words_machine, expected_c)
self.assertTrue(np.all(dense == matr))
def __init__(self, *args, **kwargs):
super(SparseHuffmanOnlyDataTest, self).__init__(*args, **kwargs)
n = 500
m = 100
self.input_x = np.random.randint(1000, size=(70, n))
p = 0.7 #probablità che sia "prunato"
mask = np.random.choice(a=[False, True], size=(n, m), p=[p, 1 - p])
self.matr = np.random.randint(500, size=(n, m)) * (1 * mask)
self.data, self.row_index, self.cum = sparse_huffman.convert_dense_to_csc(
self.matr)
self.d_data, self.d_rev_data = sparse_huffman_only_data.huffman_sparse_encoded_dict(
self.data)
data_encoded = sparse_huffman_only_data.encoded_matrix(
self.data, self.d_data, self.d_rev_data)
self.bit_words_machine = 64
self.int_data = huffman.convert_bin_to_int(
huffman.make_words_list_to_int(data_encoded,
self.bit_words_machine))
self.expected_c = len(self.cum)
self.min_length_encoded = huffman.min_len_string_encoded(
self.d_rev_data)