def setUp(self):
# from error logs:
self.all_points = np.array([[0.89, 2.87, 1.22],
[2.12000000001, 0.0, 1.5],
[5.83999999999, 2.5499998, 1.37999999],
[5.83999999999, 4.6399997, 1.57000001],
[0.100000000001, 3.0, 1.48],
[0.100000000001, 4.20000000002, 1.5],
[2.879999999999, 3.33999999999, 0.75],
[5.759999999998, 3.46, 1.0],
[3.54, 6.8700001, 1.139999]])
self.anchors = self.all_points[1:, :]
self.xhat = [0.8947106, 2.87644499, 1.22189597]
distances = [
1.1530589508606381, 1.6797066823110891, 1.1658051261713582,
0.42021624764667165, 1.1196561208517539, 0.44373006779135082,
1.3051970883541162, 2.6628083049963136
]
[M, d] = self.anchors.shape
N = M + 1
self.edm = np.zeros((N, N))
self.edm[1:, 1:] = get_edm(self.anchors)
self.edm[0, 1:] = np.array(distances)**2.0
self.edm[1:, 0] = np.array(distances)**2.0
weights = [10.0, 1.0, 10.0, 10.0, 10.0, 10.0, 10.0, 1.0]
self.weights = np.zeros((N, N))
self.weights[1:, 1:] = 10.0
self.weights[0, 1:] = weights
self.weights[1:, 0] = weights
def completion_dwmds(edm, X0, W=None, tol=1e-10, sweeps=100):
""" Complete an denoise EDM using dwMDS.
The idea here is to simply run reconstruct_dwmds for a few iterations,
yieding a position estimate, which can in turn be used
to get a completed and denoised edm.
:param edm: noisy matrix (NxN)
:param X0: starting points (Nxd)
:param W: optional weight matrix.
:param tol: Stopping criterion of iterative algorithm.
:param sweeps: Maximum number of sweeps.
"""
from .algorithms import reconstruct_dwmds
Xhat, costs = reconstruct_dwmds(edm, X0, W, n=1, tol=tol, sweeps=sweeps)
return get_edm(Xhat)
def create_edm(self):
from pylocus.basics import get_edm
self.edm = get_edm(self.points)