typos

2020-01-02 11:07:07 +01:00
parent 5456959e12
commit 011f787528
8 changed files with 18 additions and 12 deletions
@@ -781,7 +781,7 @@ Note that the function _multivariate_ returns also the covariance discussed abov
 !bc pycod 
 import numpy as np
 import pandas as pd
-import matplotlib as plt
+import matplotlib.pyplot as plt
 from IPython.display import display
 n = 10000
 mean = (-1, 2)
@@ -844,7 +844,8 @@ print(np.cov(X_centered.T))
 Note that the way we define the covariance matrix here has a factor $n-1$ instead of $n$.
 Our own code here is not very elegant and asks for improvements. 
 !bc pycod
-# extract the relevant columns from the centered design matrix of dim n x 2                                                      x = X_centered[:,0]
+# extract the relevant columns from the centered design matrix of dim n x 2
+x = X_centered[:,0]
 y = X_centered[:,1]
 Cov = np.zeros((2,2))
 Cov[0,1] = np.sum(x.T@y)/(n-1.0)