hsidky / dmaps Goto Github PK

Hi, in the README.md, it's mentioned that the package is able to re-weight the kernel for biased input. I wonder how to use that feature since it's not mentioned in the README.

I also can't find the "example folder" that's mentioned in the README. The link to the blog that's mentioned in other issues is also down. Also, is the reweighting scheme the same as the umbrella integrated dmap paper?

Hello,

I'm trying to use your C++ implementation of Diffusion Maps with some of my data, which is really big (37k x 60k matrix). In readme you select the kernel bandwith as 3. How can I properly select a kernel bandwith on my data?

Best!
Davi

Hello and thank you for providing access to your library!
I have been playing around with python version of the library, trying to figure out how it works and repeating the basic example; It seems to be that I am doing something wrong, but I cannnot figure out what as the resulting diffusion map is definately not right. I will be gratefull if you could explain where I am mistaken.
Thank you!

Here is the code:

import dmaps
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

length_phi = 12
length_Z = 12
sigma = 0.1
m = 10000

phi = length_phi * np.random.rand(m)
xi = np.random.rand(m)
Z = length_Z * np.random.rand(m)
X = 1./6 * (phi + sigmaxi) * np.sin(phi)
Y = 1./6 * (phi + sigmaxi) * np.cos(phi)

swiss_roll = np.array([X, Y, Z]).transpose()
print(swiss_roll.shape)

dist = dmaps.DistanceMatrix(swiss_roll)
dist.compute(metric=dmaps.metrics.euclidean)
dist.save('distMetr.jpeg')

diffMap = dmaps.DiffusionMap(dist)
diffMap.set_kernel_bandwidth(3)
diffMap.compute(3)

v = diffMap.get_eigenvectors()
w = diffMap.get_eigenvalues()

plt.rcParams["figure.figsize"] = (8, 12)
fig = plt.figure()
Axes3D
ax = fig.add_subplot(211, projection='3d')
ax.scatter(swiss_roll[:, 0], swiss_roll[:, 1], swiss_roll[:, 2], c=swiss_roll[:, 1], cmap=plt.cm.get_cmap("Spectral"))
ax.set_title("Original data")

ax = fig.add_subplot(212)
arr0 = ax.scatter(v[:, 1]/v[:, 0], v[:, 2]/v[:, 0], c=swiss_roll[:, 1], cmap=plt.cm.get_cmap("Spectral"))
plt.xlabel('$\Psi_2$')
plt.ylabel('$\Psi_3$')
plt.title('Projected data')
plt.show()