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import numpy as N
import pylab as P
from scipy.sandbox.pyem import GM
#------------------------------
# Hyper parameters:
# - K: number of clusters
# - d: dimension
k = 3
d = 2
#-------------------------------------------------------
# Values for weights, mean and (diagonal) variances
# - the weights are an array of rank 1
# - mean is expected to be rank 2 with one row for one component
# - variances are also expteced to be rank 2. For diagonal, one row
# is one diagonal, for full, the first d rows are the first variance,
# etc... In this case, the variance matrix should be k*d rows and d
# colums
w = N.array([0.2, 0.45, 0.35])
mu = N.array([[4.1, 3], [1, 5], [-2, -3]])
va = N.array([[1, 1.5], [3, 4], [2, 3.5]])
#-----------------------------------------
# First method: directly from parameters:
# Both methods are equivalents.
gm = GM.fromvalues(w, mu, va)
#-------------------------------------
# Second method to build a GM instance:
gm = GM(d, k, mode = 'diag')
# The set_params checks that w, mu, and va corresponds to k, d and m
gm.set_param(w, mu, va)
# Once set_params is called, both methods are equivalent. The 2d
# method is useful when using a GM object for learning (where
# the learner class will set the params), whereas the first one
# is useful when there is a need to quickly sample a model
# from existing values, without a need to give the hyper parameters
# Create a Gaussian Mixture from the parameters, and sample
# 1000 items from it (one row = one 2 dimension sample)
data = gm.sample(1000)
# Plot the samples
P.plot(data[:, 0], data[:, 1], '.')
# Plot the ellipsoids of confidence with a level a 75 %
gm.plot(level = 0.75)
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