# scikit-learn/scikit-learn

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 """ ================================== Demonstration of sampling from HMM ================================== This script shows how to sample points from a Hiden Markov Model (HMM): we use a 4-components with specified mean and covariance. The plot show the sequence of observations generated with the transitions between them. We can see that, as specified by our transition matrix, there are no transition between component 1 and 3. """ import numpy as np import matplotlib.pyplot as plt from sklearn import hmm ############################################################## # Prepare parameters for a 3-components HMM # Initial population probability start_prob = np.array([0.6, 0.3, 0.1, 0.0]) # The transition matrix, note that there are no transitions possible # between component 1 and 4 trans_mat = np.array([[0.7, 0.2, 0.0, 0.1], [0.3, 0.5, 0.2, 0.0], [0.0, 0.3, 0.5, 0.2], [0.2, 0.0, 0.2, 0.6]]) # The means of each component means = np.array([[0.0, 0.0], [0.0, 11.0], [9.0, 10.0], [11.0, -1.0], ]) # The covariance of each component covars = .5 * np.tile(np.identity(2), (4, 1, 1)) # Build an HMM instance and set parameters model = hmm.GaussianHMM(4, "full", start_prob, trans_mat, random_state=42) # Instead of fitting it from the data, we directly set the estimated # parameters, the means and covariance of the components model.means_ = means model.covars_ = covars ############################################################### # Generate samples X, Z = model.sample(500) # Plot the sampled data plt.plot(X[:, 0], X[:, 1], "-o", label="observations", ms=6, mfc="orange", alpha=0.7) # Indicate the component numbers for i, m in enumerate(means): plt.text(m[0], m[1], 'Component %i' % (i + 1), size=17, horizontalalignment='center', bbox=dict(alpha=.7, facecolor='w')) plt.legend(loc='best') plt.show()
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