mhjensen
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
from numpy import *
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from numpy.random import randint, randn
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from time import time
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import matplotlib.mlab as mlab
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import matplotlib.pyplot as plt
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# Returns mean of bootstrap samples
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def stat(data):
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return mean(data)
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# Bootstrap algorithm
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def bootstrap(data, statistic, R):
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t = zeros(R); n = len(data); inds = arange(n); t0 = time()
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# non-parametric bootstrap
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for i in range(R):
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t[i] = statistic(data[randint(0,n,n)])
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# analysis
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print("Runtime: %g sec" % (time()-t0)); print("Bootstrap Statistics :")
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print("original bias std. error")
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print("%8g %8g %14g %15g" % (statistic(data), std(data),mean(t),std(t)))
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return t
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mu, sigma = 100, 15
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datapoints = 10000
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x = mu + sigma*random.randn(datapoints)
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# bootstrap returns the data sample
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t = bootstrap(x, stat, datapoints)
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# the histogram of the bootstrapped data
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n, binsboot, patches = plt.hist(t, 50, facecolor='red', alpha=0.75)
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# add a 'best fit' line
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#y = mlab.normpdf( binsboot, mean(t), std(t))
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#lt = plt.plot(binsboot, y, 'r--', linewidth=1)
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plt.xlabel('Smarts')
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plt.ylabel('Probability')
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plt.axis([95, 105, 0, 10.0])
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plt.grid(True)
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plt.show()
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