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