renamed to be more readable

experiements/distributions.py

@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jan 17 20:17:14 2015
+
+@author: rlabbe
+"""
+
+from scipy.stats import t, norm
+import matplotlib.pyplot as plt
+import numpy as np
+import math
+import random
+
+
+df =4
+mu = 10
+std = 2
+
+x = np.linspace(-5, 20, 100)
+               
+plt.plot(x, t.pdf(x, df=df, loc=mu, scale=std), 'r-', lw=5, label='t pdf')
+
+
+x2 = np.linspace(mu-10, mu+10, 100)
+plt.plot(x, norm.pdf(x, mu, std), 'b-', lw=5,  label='gaussian pdf')
+plt.legend()
+plt.figure()
+
+def student_t(df, mu, std): # nu equals number of degrees of freedom
+    x = random.gauss(0, std)
+    y = 2.0*random.gammavariate(0.5*df, 2.0)
+    return x / (math.sqrt(y/df)) + mu
+    
+    
+N = 100000
+ys = [student_t(2.7, 100, 2) for i in range(N)]
+plt.hist(ys, 10000, histtype='step')
+
+ys = [random.gauss(100,2) for i in range(N)]
+plt.hist(ys, 10000, histtype='step',color='r')
+
+plt.show()