Hi,
I am facing some problem with the "BayesianGaussianMixture" function, but I
do not know if it is because of my poor knowledge on this type of
statistics or if it is something related to the algorithm. I have set of
data of around 1000 to 4000 observation (every feature is a spectrum of
around 200 point) so in the end I have n_samples = ~1000 and n_features =
~20. The good things is that I am getting the same results of KMeans
however the "predict_proba" has value only of 0 or 1.
I have wrote a small function to simulate my problem with random data that
is reported below. The first 1/2 of the array has the point with a positive
slope while the second 1/2 has a negative slope, so the cross in the
middle. What I have seen is that for a small number of features I obtain
good probability, but if the number of features increases (say 50) than the
probability become only 0 or 1.
Can someone help me in interpret this result?
Here is the code I wrote with the generated random number, I'll generally
run it with ncomponent=2 and nfeatures=5 or 10 or 50 or 100. I am not sure
if it will work in every case is not very highly tested. I have also
attached as a file!
##########################################################################
import numpy as
np
from sklearn.mixture import GaussianMixture,
BayesianGaussianMixture
import matplotlib.pyplot as
plt
def test_bgm(ncomponent,
nfeatures):
temp =
np.random.randn(500,nfeatures)
temp = temp + np.arange(-1,1,
2.0/nfeatures)
temp1 =
np.random.randn(400,nfeatures)
temp1 = temp1 + np.arange(1,-1,
(-2.0/nfeatures))
X = np.vstack((temp,
temp1))
bgm =
BayesianGaussianMixture(ncomponent,degrees_of_freedom_prior=nfeatures*2).fit(X)
bgm_proba =
bgm.predict_proba(X)
bgm_labels =
bgm.predict(X)
plt.figure(-1)
plt.imshow(bgm_labels.reshape(30,-1), origin='lower',
interpolatio='none')
plt.colorbar()
for i in
np.arange(0,ncomponent):
plt.figure(i)
plt.imshow(bgm_proba[:,i].reshape(30,-1), origin='lower',
interpolatio='none')
plt.colorbar()
plt.show()
##############################################################################
Thank you in advance
Tommaso
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import numpy as np
from sklearn.mixture import GaussianMixture, BayesianGaussianMixture
import matplotlib.pyplot as plt
def test_bgm(ncomponent, nfeatures):
temp = np.random.randn(500,nfeatures)
temp = temp + np.arange(-1,1, 2.0/nfeatures)
temp1 = np.random.randn(400,nfeatures)
temp1 = temp1 + np.arange(1,-1, (-2.0/nfeatures))
X = np.vstack((temp, temp1))
bgm = BayesianGaussianMixture(ncomponent,degrees_of_freedom_prior=nfeatures*2).fit(X)
bgm_proba = bgm.predict_proba(X)
bgm_labels = bgm.predict(X)
plt.figure(-1)
plt.imshow(bgm_labels.reshape(30,-1), origin='lower', interpolatio='none')
plt.colorbar()
for i in np.arange(0,ncomponent):
plt.figure(i)
plt.imshow(bgm_proba[:,i].reshape(30,-1), origin='lower', interpolatio='none')
plt.colorbar()
plt.show()
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