Hi.
I try to reproduce the best fitting plot from
http://www.astroml.org/book_figures/chapter4/fig_GMM_1D.html on my data.
I have sample data, in stead of histogram data.
I have the following code, but get an error because the dimension of my
data is 2.
Could anybody help me with this?
My data looks like this:
1 0
2 0
3 0
4 0
5 0.0411
.....
So in the end I would like to generate a plot with on the xaxis the sample
numbers 1...48, and on the y axis the values.
This plot contains the optimal number gaussian curves from my data
Thanks,
Anna
_____________________________________________________________________
from matplotlib import pyplot as plt
import numpy as np
from sklearn.mixture import GMM
# Create 2D data (first column experiment number, second column values)
vals =
np.array([0,0,0,0,0.0411156712759954,0.0225396968388254,0,0,0,0,0,0.022138290459246,0.0425464266883576,0.0508169878359294,0.0567903916825419,0.0563399686823538,0.0681317778725727,0.0512462144596381,0.0527074396631896,0.0295066808329116,
0.0432790264504283,0.0426921517766538,0.048179363737769,0.0449641383805438,0.0558272813262573,0.0423384372441531,0.0506407929564441,0.0621835447230296,0.0411792604054337,0.0339433473847646,0.0252872771399729,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0156058321829883])
sample = np.arange(vals.size)
sample += 1
X = np.vstack((sample,vals)).T
# fit models with 1-10 components
N = np.arange(1, 11)
models = [None for i in range(len(N))]
for i in range(len(N)):
models[i] = GMM(N[i]).fit(X)
# compute the AIC and the BIC
AIC = [m.aic(X) for m in models]
BIC = [m.bic(X) for m in models]
# Find the best fitting with lowest AIC
M_best = models[np.argmin(AIC)]
x = sample
logprob, responsibilities = M_best.eval(x)
And here i get the error: ValueError: The shape of X is not compatible
with self
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