def test_simple_calculate_diagonals(self):
query = np.arange(13)
series = np.arange(23)
m = 4
distance_matrix1 = np.arange(200.).reshape((10, 20))
distance_matrix2 = np.ones((10, 20), dtype=np.float)
distance_matrix3 = np.full((10, 20), np.nan, dtype=np.float)
distance_matrix4 = np.full((10, 20), 5., dtype=np.float)
summed_matrix = distance_matrix1 + distance_matrix2 + distance_matrix4
calc = AnytimeCalculator(m, series, query)
calc.add_generator(0, MockGenerator(distance_matrix1))
calc.add_generator(0, MockGenerator(distance_matrix2))
calc.add_generator(0, MockGenerator(distance_matrix3))
calc.add_generator(0, MockGenerator(distance_matrix4))
consumer1 = DistanceMatrix()
consumer2 = DistanceMatrix()
consumer3 = SummingConsumer()
calc.add_consumer([0], consumer1)
calc.add_consumer([3], consumer2)
calc.add_consumer([0, 1, 3], consumer3)
calc.calculate_diagonals()
npt.assert_equal(consumer1.distance_matrix, distance_matrix1)
npt.assert_equal(consumer2.distance_matrix, distance_matrix4)
npt.assert_equal(consumer3.distance_matrix, summed_matrix)
def test_simple_calculate_self_join_diagonals(self):
series = np.arange(23)
m = 4
buffer = 2
# This test verifies that no values below the main diagonal are used.
distance_matrix = np.triu(
np.arange(1., 401.).reshape((20, 20)), buffer + 1)
calc = AnytimeCalculator(m, series, trivial_match_buffer=buffer)
calc.add_generator(0, MockGenerator(distance_matrix))
consumer = DistanceMatrix()
calc.add_consumer([0], consumer)
npt.assert_array_less(np.full(17, buffer), calc._diagonal_calc_order)
calc.calculate_diagonals()
expected = distance_matrix + distance_matrix.T
expected[expected == 0] = np.nan
npt.assert_equal(consumer.distance_matrix, expected)
def test_simple_calculate_diagonals_partial(self):
query = np.arange(13)
series = np.arange(23)
m = 4
distance_matrix1 = np.arange(200.).reshape((10, 20))
distance_matrix2 = np.ones((10, 20), dtype=np.float)
distance_matrix3 = np.full((10, 20), np.nan, dtype=np.float)
distance_matrix4 = np.full((10, 20), 5., dtype=np.float)
summed_matrix = distance_matrix1 + distance_matrix2 + distance_matrix4
max_diag = min(len(query) - m + 1,
len(series) - m + 1) # Maximum length of a diagonal
calc = AnytimeCalculator(m, series, query)
calc.add_generator(0, MockGenerator(distance_matrix1))
calc.add_generator(0, MockGenerator(distance_matrix2))
calc.add_generator(0, MockGenerator(distance_matrix3))
calc.add_generator(0, MockGenerator(distance_matrix4))
consumer1 = DistanceMatrix()
consumer2 = DistanceMatrix()
consumer3 = SummingConsumer()
calc.add_consumer([0], consumer1)
calc.add_consumer([3], consumer2)
calc.add_consumer([0, 1, 3], consumer3)
calc.calculate_diagonals(partial=20)
npt.assert_(20 <= np.count_nonzero(
~np.isnan(consumer1.distance_matrix) < 20 + max_diag))
npt.assert_(20 <= np.count_nonzero(
~np.isnan(consumer2.distance_matrix) < 20 + max_diag))
npt.assert_(20 <= np.count_nonzero(
~np.isnan(consumer3.distance_matrix) < 20 + max_diag))
# For 20 items, at least 3 diagonals are calculated
for diagonal in calc._diagonal_calc_order[:3]:
diag_indices = diag_indices_of(distance_matrix1, diagonal)
npt.assert_equal(consumer1.distance_matrix[diag_indices],
distance_matrix1[diag_indices])
calc.calculate_diagonals(partial=.8)
npt.assert_(160 <= np.count_nonzero(
~np.isnan(consumer1.distance_matrix) < 160 + max_diag))
npt.assert_(160 <= np.count_nonzero(
~np.isnan(consumer2.distance_matrix) < 160 + max_diag))
npt.assert_(160 <= np.count_nonzero(
~np.isnan(consumer3.distance_matrix) < 160 + max_diag))
calc.calculate_diagonals()
npt.assert_(
200 == np.count_nonzero(~np.isnan(consumer1.distance_matrix)))
npt.assert_(
200 == np.count_nonzero(~np.isnan(consumer2.distance_matrix)))
npt.assert_(
200 == np.count_nonzero(~np.isnan(consumer3.distance_matrix)))
npt.assert_equal(consumer1.distance_matrix, distance_matrix1)
npt.assert_equal(consumer2.distance_matrix, distance_matrix4)
npt.assert_equal(consumer3.distance_matrix, summed_matrix)
import numpy as np
from src.series import Series
m = 10
# Create some random data
np.random.seed(123)
data = np.array(np.random.rand(100))
# Calculate the matrix profile
calculator = AnytimeCalculator(m, [data])
generator = calculator.add_generator(
0, ZNormEuclidean(noise_std=0.)) # Generator 0 works on channel 0
consumer = calculator.add_consumer([0], MatrixProfileLR())
calculator.calculate_diagonals()
# The matrix profile
left_matrix_profile = consumer.matrix_profile_left
profile_index_left = consumer.profile_index_left
class TestSeries(TestCase):
def test_it_should_open_the_result_file_as_appending(self):
mock = mock_open()
with patch('builtins.open', mock):
series = Series('key', [m], 1000, './test')
mock.assert_called_once_with('./test/10.txt', 'a')