48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from scipy import stats
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npoints = 20 # number of integer support points of the distribution minus 1
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npointsh = npoints // 2
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npointsf = float(npoints)
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nbound = 4 # bounds for the truncated normal
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normbound = (1 + 1/npointsf) * nbound # actual bounds of truncated normal
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grid = np.arange(-npointsh, npointsh+2,1) # integer grid
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gridlimitsnorm = (grid - 0.5) / npointsh * nbound # bin limits for the truncnorm
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gridlimits = grid - 0.5
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grid = grid[:-1]
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probs = np.diff(stats.truncnorm.cdf(gridlimitsnorm, -normbound, normbound))
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gridint = grid
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rng = np.random.default_rng()
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normdiscrete = stats.rv_discrete(
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values=(gridint, np.round(probs, decimals=7)),
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name='normdiscrete')
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n_sample = 500
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rvs = normdiscrete.rvs(size=n_sample, random_state=rng)
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f, l = np.histogram(rvs,bins=gridlimits)
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sfreq = np.vstack([gridint,f,probs*n_sample]).T
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fs = sfreq[:,1] / float(n_sample)
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ft = sfreq[:,2] / float(n_sample)
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fs = sfreq[:,1].cumsum() / float(n_sample)
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ft = sfreq[:,2].cumsum() / float(n_sample)
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nd_std = np.sqrt(normdiscrete.stats(moments='v'))
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ind = gridint # the x locations for the groups
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width = 0.35 # the width of the bars
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plt.figure()
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plt.subplot(111)
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rects1 = plt.bar(ind, ft, width, color='b')
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rects2 = plt.bar(ind+width, fs, width, color='r')
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normline = plt.plot(ind+width/2.0, stats.norm.cdf(ind+0.5, scale=nd_std),
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color='b')
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plt.ylabel('cdf')
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plt.title('Cumulative Frequency and CDF of normdiscrete')
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plt.xticks(ind+width, ind)
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plt.legend((rects1[0], rects2[0]), ('true', 'sample'))
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plt.show()
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