def main(): data = numpy.float_( [[0, 0, 0, 0, 0], [ 1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000 ], [ 7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000 ], [ 2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000 ], [ 5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000 ], [ 2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000 ]]) assignments, means, counts = kmedoids(Matrix(data), 4) # clusters the data in 4 groups. print("assignments", list(assignments)) print("means", list(means)) print("counts", list(counts))
def test_insert_1d(): matrix = Matrix() id1 = matrix.insert(numpy.float_([1.0, 2.0])) assert len(matrix) == 1 assert id1 == 0 id2 = matrix.insert(numpy.float_([2.0, 3.0])) assert len(matrix) == 2 assert id2 == 1
def test_insert_2d(): matrix = Matrix() ids = list(matrix.insert(numpy.float_([[1], [2], [3], [4]]))) assert len(matrix) == 4 assert ids == [0, 1, 2, 3] ids = list(matrix.insert(numpy.float_([[1], [2], [3], [4]]))) assert len(matrix) == 8 assert ids == [4, 5, 6, 7]
def main(): data = numpy.float_([ [0, 0, 0, 0, 0], [1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000], [7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000], [2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000], [5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000], [2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000] ]) matrix = Matrix(data) print(len(matrix)) print(matrix(1, 3))
def main(): data = numpy.float_([ [0, 0, 0, 0, 0], [1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000], [7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000], [2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000], [5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000], [2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000] ]) assignments, exemplars, counts = AffProp()(Matrix(data)) print('assignments', list(assignments)) print('exemplars', list(exemplars)) print('counts', list(counts))
def main(): data = numpy.float_([ [0, 0, 0, 0, 0], [1.74120000000000, 4.07812000000000, -0.0927036000000, 41.7888000000000, 41.7888000000000], [7.75309000000000, 16.2466000000000, 3.03956000000000, 186.074000000000, 186.074000000000], [2.85493000000000, 3.25380000000000, 2.50559000000000, 68.5184000000000, 68.5184000000000], [5.81414000000000, 8.14015000000000, 3.22950000000000, 139.539000000000, 139.539000000000], [2.57927000000000, 2.63399000000000, 2.46802000000000, 61.9026000000000, 61.9026000000000] ]) distance_matrix = Matrix(data) [assignments, seeds, counts] = dbscan(distance_matrix, 64.0, 1) print(list(assignments)) print(list(seeds)) print(list(counts))
def test_create_1d(): data = numpy.float_([1.0, 2.0]) matrix = Matrix(data) assert len(matrix) == 1
def test_create_empty(): matrix = Matrix() assert len(matrix) == 0
def test_create_2d(): matrix = Matrix(numpy.float_([[1], [2], [3]])) assert len(matrix) == 3