fix doc

Author: sth4nth

chapter05/mlpClass.m

@@ -1,9 +1,9 @@
-function [model, L] = mlpClass(X,y,k,lambda)
-% Train a multilayer perceptron neural network for classification with backpropagation
+function [model, L] = mlpClass(X, y, k, lambda)
+% Train a multilayer perceptron neural network for multiclass classification with backpropagation
 % logistic activation function is used.
 % Input:
 %   X: d x n data matrix
-%   Y: p x n response matrix
+%   y: 1 x n label vector
 %   k: T x 1 vector to specify number of hidden nodes in each layer
 %   lambda: regularization parameter
 % Ouput:

chapter05/mlpReg.m

@@ -1,9 +1,9 @@
-function [model, L] = mlpReg(X,y,k,lambda)
+function [model, L] = mlpReg(X, y, k, lambda)
 % Train a multilayer perceptron neural network for regression with backpropagation
 % tanh activation function is used
 % Input:
 %   X: d x n data matrix
-%   y: p x n response matrix
+%   y: 1 x n real value response vector
 %   k: T x 1 vector to specify number of hidden nodes in each layer
 %   lambda: regularization parameter
 % Ouput:

demo/ch05/mlp_demo.m

@@ -26,7 +26,7 @@
 lambda = 1e-2;
 [model, llh] = mlpClass(X,y,h,lambda);
 [t,p] = mlpClassPred(model,X);
+plot(llh);
 figure;
 plotClass(X,t);
 figure;
-plot(llh);