perceptron

I'm having sort of an issue trying to figure out how to tune the parameters for my perceptron algorithm so that it performs relatively well on unseen data. I've implemented a verified working perceptron algorithm and I'd like to figure out a method by which I can tune the numbers of iterations and the learning rate of the perceptron. These are the two parameters I'm interested in. I know that the learning rate of the perceptron doesn't affect whether or not the algorithm converges and completes. I'm trying to grasp how to change n. Too fast and it'll swing around a lot, and too low and it'll

For the implementation of single layer neural network, I have two data files. In: 0.832 64.643 0.818 78.843 Out: 0 0 1 0 0 1 The above is the format of 2 data files. The target output is "1" for a particular class that the corresponding input belongs to and "0" for the remaining 2 outputs. The problem is as follows: Your single layer neural network will find A (3 by 2 matrix) and b (3 by 1 vector) in Y = A*X + b where Y is [C1, C2, C3]' and X is [x1, x2]'. To solve the problem above with a neural network, we can re-write the equation as follow: Y = A' * X' where A' = [A b] (3 by 3 matrix) and

I am very new to matplotlib and am working on simple projects to get acquainted with it. I was wondering how I might plot the decision boundary which is the weight vector of the form [w1,w2], which basically separates the two classes lets say C1 and C2, using matplotlib. Is it as simple as plotting a line from (0,0) to the point (w1,w2) (since W is the weight "vector") if so, how do I extend this like in both directions if I need to? Right now all I am doing is : import matplotlib.pyplot as plt plt.plot([0,w1],[0,w2]) plt.show() Thanks in advance. Decision boundary is generally much more

Here is my perceptron implementation in ANSI C: #include <stdio.h> #include <stdlib.h> #include <math.h> float randomFloat() { srand(time(NULL)); float r = (float)rand() / (float)RAND_MAX; return r; } int calculateOutput(float weights[], float x, float y) { float sum = x * weights[0] + y * weights[1]; return (sum >= 0) ? 1 : -1; } int main(int argc, char *argv[]) { // X, Y coordinates of the training set. float x[208], y[208]; // Training set outputs. int outputs[208]; int i = 0; // iterator FILE *fp; if ((fp = fopen("test1.txt", "r")) == NULL) { printf("Cannot open file.\n"); } else { while