Non gaussian

generate_data.py

@@ -1,9 +1,19 @@
 import numpy as np
+import matplotlib.pyplot as plt
 
 # Fixing random state for reproducibility
 np.random.seed(19680801)
 
 mu, sigma = 100, 15
 x_gaussian = mu + sigma * np.random.randn(10000)
 
-np.savetxt('file_data.csv', x_gaussian, delimiter=',')
+mu, sigma = 100, 5
+x_chisqrd = mu + sigma * (np.random.randm(10,10000)**2).sum(0)
+
+
+
+
+
+
+np.savetxt('file_data_gaussian.csv', x_gaussian, delimiter=',')
+np.savetxt('file_data_chisqrd.csv', x_chisqrd, delimiter=',')

plot.py

@@ -2,16 +2,18 @@
 import matplotlib.pyplot as plt
 
 # Data are loaded from a file
-x = np.loadtxt('file_data.csv')
-
+gaussian = np.loadtxt('file_data_gaussian.csv')
+chisqrd = np.loadtxt('file_data_chisqrd.csv')
 # the histogram of the data
-n, bins, patches = plt.hist(x, 50, density=True, facecolor='r, alpha=0.74)
+n, bins, patches = plt.hist(gaussian, 50, density=True, facecolor='r', alpha=0.74)
+n, bins, patches = plt.hist(chisqrd, 50, density=True, facecolor='b', alpha=0.25)
 
- plt.xlabel('Smarts')
- plt.ylabel('Probability')
- plt.title('Histogram of IQ')
- plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
- plt.xlim(40, 160)
- plt.ylim(0, 0.03)
- plt.grid(True)
- plt.show()
+plt.xlabel('Smarts')
+plt.ylabel('Probability')
+plt.title('Histogram of IQ')
+plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
+plt.text(80, .020, r'$\mu=100,\ \sigma=5$')
+plt.xlim(40, 250)
+plt.ylim(0, 0.03)
+plt.grid(True)
+plt.show()