def test_SPD_graph_cv(self):
expected = [
[0, 0.230,0.380,0.390,0, 0, 0, 0, 0, 0],
[0.603,0, 0.209,0, 0.188,0, 0, 0, 0, 0],
[0.366,0.133,0, 0, 0.501,0, 0, 0, 0, 0],
[0.414,0, 0.119,0, 0.383,0, 0, 0, 0.084,0],
[0.002,0.062,0.482,0.454,0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0.921,0.079,0, 0],
[0, 0, 0, 0, 0.006,0.584,0, 0, 0.088,0.322],
[0, 0, 0, 0, 0, 0.286,0, 0, 0.288,0.426],
[0, 0, 0, 0, 0, 0.052,0.541,0.254,0, 0.153],
[0, 0, 0, 0, 0, 0, 0.458,0.408,0.134,0]
]
G = sparse_regularized_graph(self.pts, positive=True)
assert_array_almost_equal(G.matrix('dense'), expected, decimal=3)
def test_SPD_graph(self):
expected = [
[0, 0.216,0.380,0.404,0, 0, 0, 0, 0, 0],
[0.676,0, 0.123,0, 0.202,0, 0, 0, 0, 0],
[0.377,0.140,0, 0, 0.483,0, 0, 0, 0, 0],
[0.506,0, 0, 0, 0.441,0, 0, 0, 0.053,0],
[0.017,0.065,0.454,0.464,0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0.907,0.093,0, 0],
[0, 0, 0, 0, 0, 0.575,0, 0, 0.117,0.308],
[0, 0, 0, 0, 0, 0.295,0, 0, 0.319,0.386],
[0, 0, 0, 0, 0, 0.010,0.599,0.274,0, 0.117],
[0, 0, 0, 0, 0, 0, 0.440,0.386,0.174,0]
]
G = sparse_regularized_graph(self.pts, positive=True, sparsity_param=0.002)
assert_array_almost_equal(G.matrix('dense'), expected, decimal=3)
def test_L1_knn_graph(self):
expected = [
[0, 0.286,0.352,0.362,0, 0, 0, 0, 0, 0],
[0.637,0, 0.209,0, 0.153,0, 0, 0, 0, 0],
[0.446,0.133,0, 0, 0.421,0, 0, 0, 0, 0],
[0.493,0, 0, 0, 0.507,0, 0, 0, 0, 0],
[0, 0, 0.535,0.465,0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0.924,0, 0.076,0],
[0, 0, 0, 0, 0, 0.603,0, 0, 0.136,0.261],
[0, 0, 0, 0, 0, 0, 0, 0, 0.454,0.546],
[0, 0, 0, 0, 0, 0.138,0.520,0, 0, 0.343],
[0, 0, 0, 0, 0, 0, 0.441,0.395,0.164,0]
]
G = sparse_regularized_graph(self.pts, sparsity_param=0.005, kmax=3)
assert_array_almost_equal(G.matrix('dense'), expected, decimal=3)
def test_L1_graph(self):
expected = [
[0, 0.286,0.352,0.362,0, 0, 0, 0, 0, 0],
[0.637,0, 0.209,0, 0.153,0, 0, 0, 0, 0],
[0.446,0.133,0, 0, 0.421,0, 0, 0, 0, 0],
[0.493,0, 0, 0, 0.507,0, 0, 0, 0, 0],
[0, 0.062,0.493,0.444,0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0.906,0.055,0.039,0],
[0, 0, 0, 0, 0, 0.603,0, 0, 0.136,0.261],
[0, 0, 0, 0, 0, 0.172,0, 0, 0.332,0.496],
[0, 0, 0, 0, 0, 0.007,0.576,0.278,0, 0.139],
[0, 0, 0, 0, 0, 0, 0.441,0.395,0.164,0]
]
G = sparse_regularized_graph(self.pts, sparsity_param=0.005)
assert_array_almost_equal(G.matrix('dense'), expected, decimal=3)
def test_L1_graph_cv(self):
expected = [
[0, 0.231,0.372,0.397,0, 0, 0, 0, 0, 0],
[0.670,0, 0.205,0, 0.124,0, 0, 0, 0, 0],
[0.437,0.138,0, 0.012,0.413,0, 0, 0, 0, 0],
[0.503,0, 0, 0, 0.497,0, 0, 0, 0, 0],
[0, 0.053,0.509,0.438,0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0.914,0.061,0.025,0],
[0, 0, 0, 0, 0, 0.597,0, 0, 0.139,0.264],
[0, 0, 0, 0, 0, 0.311,0, 0, 0.391,0.297],
[0, 0, 0, 0, 0, 0.043,0.544,0.310,0, 0.103],
[0, 0, 0, 0, 0, 0, 0.428,0.399,0.173,0]
]
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=ConvergenceWarning)
G = sparse_regularized_graph(self.pts)
assert_array_almost_equal(G.matrix('dense'), expected, decimal=3)