Add examples

jdswinbank · Mar 23, 2014 · 1a2da34 · 1a2da34
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diff --git a/examples/plot_hmm_sampling.py b/examples/plot_hmm_sampling.py
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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.
+
+.. warning::
+
+       The HMM module and its functions will be removed in 0.17
+       as it no longer falls within the project's scope and API.
+
+"""
+print(__doc__)
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from hmmlearn 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()
diff --git a/examples/plot_hmm_stock_analysis.py b/examples/plot_hmm_stock_analysis.py
@@ -0,0 +1,106 @@
+"""
+==========================
+Gaussian HMM of stock data
+==========================
+
+This script shows how to use Gaussian HMM.
+It uses stock price data, which can be obtained from yahoo finance.
+For more information on how to get stock prices with matplotlib, please refer
+to date_demo1.py of matplotlib.
+
+.. warning::
+
+       The HMM module and its functions will be removed in 0.17
+       as it no longer falls within the project's scope and API.
+
+"""
+
+from __future__ import print_function
+
+import datetime
+import numpy as np
+import pylab as pl
+from matplotlib.finance import quotes_historical_yahoo
+from matplotlib.dates import YearLocator, MonthLocator, DateFormatter
+from hmmlearn.hmm import GaussianHMM
+
+
+print(__doc__)
+
+###############################################################################
+# Downloading the data
+date1 = datetime.date(1995, 1, 1)  # start date
+date2 = datetime.date(2012, 1, 6)  # end date
+# get quotes from yahoo finance
+quotes = quotes_historical_yahoo("INTC", date1, date2)
+if len(quotes) == 0:
+    raise SystemExit
+
+# unpack quotes
+dates = np.array([q[0] for q in quotes], dtype=int)
+close_v = np.array([q[2] for q in quotes])
+volume = np.array([q[5] for q in quotes])[1:]
+
+# take diff of close value
+# this makes len(diff) = len(close_t) - 1
+# therefore, others quantity also need to be shifted
+diff = close_v[1:] - close_v[:-1]
+dates = dates[1:]
+close_v = close_v[1:]
+
+# pack diff and volume for training
+X = np.column_stack([diff, volume])
+
+###############################################################################
+# Run Gaussian HMM
+print("fitting to HMM and decoding ...", end='')
+n_components = 5
+
+# make an HMM instance and execute fit
+model = GaussianHMM(n_components, covariance_type="diag", n_iter=1000)
+
+model.fit([X])
+
+# predict the optimal sequence of internal hidden state
+hidden_states = model.predict(X)
+
+print("done\n")
+
+###############################################################################
+# print trained parameters and plot
+print("Transition matrix")
+print(model.transmat_)
+print()
+
+print("means and vars of each hidden state")
+for i in range(n_components):
+    print("%dth hidden state" % i)
+    print("mean = ", model.means_[i])
+    print("var = ", np.diag(model.covars_[i]))
+    print()
+
+years = YearLocator()   # every year
+months = MonthLocator()  # every month
+yearsFmt = DateFormatter('%Y')
+fig = pl.figure()
+ax = fig.add_subplot(111)
+
+for i in range(n_components):
+    # use fancy indexing to plot data in each state
+    idx = (hidden_states == i)
+    ax.plot_date(dates[idx], close_v[idx], 'o', label="%dth hidden state" % i)
+ax.legend()
+
+# format the ticks
+ax.xaxis.set_major_locator(years)
+ax.xaxis.set_major_formatter(yearsFmt)
+ax.xaxis.set_minor_locator(months)
+ax.autoscale_view()
+
+# format the coords message box
+ax.fmt_xdata = DateFormatter('%Y-%m-%d')
+ax.fmt_ydata = lambda x: '$%1.2f' % x
+ax.grid(True)
+
+fig.autofmt_xdate()
+pl.show()