Manual Perceptron example in R - are the results a

I am trying to get a perceptron algorithm for classification working but I think something is missing. This is the decision boundary achieved with logistic regression:

The red dots got into college, after performing better on tests 1 and 2.

This is the data, and this is the code for the logistic regression in R:

dat = read.csv("perceptron.txt", header=F)
colnames(dat) = c("test1","test2","y")
plot(test2 ~ test1, col = as.factor(y), pch = 20, data=dat)
fit = glm(y ~ test1 + test2, family = "binomial", data = dat)
coefs = coef(fit)
(x = c(min(dat[,1])-2,  max(dat[,1])+2))
(y = c((-1/coefs[3]) * (coefs[2] * x + coefs[1])))
lines(x, y)

The code for the "manual" implementation of the perceptron is as follows:

# DATA PRE-PROCESSING:
dat = read.csv("perceptron.txt", header=F)
dat[,1:2] = apply(dat[,1:2], MARGIN = 2, FUN = function(x) scale(x)) # scaling the data
data = data.frame(rep(1,nrow(dat)), dat) # introducing the "bias" column
colnames(data) = c("bias","test1","test2","y")
data$y[data$y==0] = -1 # Turning 0/1 dependent variable into -1/1.
data = as.matrix(data) # Turning data.frame into matrix to avoid mmult problems.

# PERCEPTRON:
set.seed(62416)
no.iter = 1000                           # Number of loops
theta = rnorm(ncol(data) - 1)            # Starting a random vector of coefficients.
theta = theta/sqrt(sum(theta^2))         # Normalizing the vector.
h = theta %*% t(data[,1:3])              # Performing the first f(theta^T X)

for (i in 1:no.iter){                    # We will recalculate 1,000 times
  for (j in 1:nrow(data)){               # Each time we go through each example.
      if(h[j] * data[j, 4] < 0){         # If the hypothesis disagrees with the sign of y,
      theta = theta + (sign(data[j,4]) * data[j, 1:3]) # We + or - the example from theta.
      }
      else
      theta = theta                      # Else we let it be.
  }
  h = theta %*% t(data[,1:3])            # Calculating h() after iteration.
}
theta                                    # Final coefficients
mean(sign(h) == data[,4])                # Accuracy

With this, I get the following coefficients:

     bias     test1     test2 
 9.131054 19.095881 20.736352

and an accuracy of 88%, consistent with that calculated with the glm() logistic regression function: mean(sign(predict(fit))==data[,4]) of 89% - logically, there is no way of linearly classifying all of the points, as it is obvious from the plot above. In fact, iterating only 10 times and plotting the accuracy, a ~90% is reach after just 1 iteration:

Being in line with the training classification performance of logistic regression, it is likely that the code is not conceptually wrong.

QUESTIONS: Is it OK to get coefficients so different from the logistic regression:

(Intercept)       test1       test2 
   1.718449    4.012903    3.743903

This is really more of a CrossValidated question than a StackOverflow question, but I'll go ahead and answer.

Yes, it's normal and expected to get very different coefficients because you can't directly compare the magnitude of the coefficients between these 2 techniques.

With the logit (logistic) model you're using a binomial distribution and logit-link based on a sigmoid cost function. The coefficients are only meaningful in this context. You've also got an intercept term in the logit.

None of this is true for the perceptron model. The interpretation of the coefficients are thus totally different.

Now, that's not saying anything about which model is better. There aren't comparable performance metrics in your question that would allow us to determine that. To determine that you should do cross-validation or at least use a holdout sample.