update README

README.md

@@ -7,14 +7,15 @@ $$\sum_{i=0}^n (z_i - y_i)^2 + \lambda \sum_{i=0}^n (\delta ^p z)_i ^2  $$
 
 where y are the datapoints, z is the smoothed function, and $\delta^2
 z$ is the pth derivative of $z_i$, which is evaluated numerically.
-A penalty is imposed on nonsmooth functions, with higher values of \lambda increasing the penalty and leading to a smoother output.
+A penalty is imposed on nonsmooth functions, with higher values of $\lambda$ increasing the penalty and leading to a smoother output.
 
 The smoothed output can be obtained by solving the linear system
 $$x = (W + \lambda * D^T D )^{-1} W y $$
 Where W is the weight matrix (Identity matrix in practice) and D is the difference matrix 
 (See [`difference_matrix`](src/whittaker_smoother.rs) for its construction).
 
 ### Examples
+Here we see the wood dataset smoothed whith both order 2 and 3.
 ![wood_2](img/whittaker_on_wood_lambda_20000_order_2.png)
 ![wood_3](img/whittaker_on_wood_lambda_20000_order_3.png)