Cost function for Logistic Regression always infinite [MATLAB]

I am trying to implement a logistic regression solver in MATLAB and i am finding the weights by stochastic gradient descent. I am running into a problem where my data seems to produce an infinite cost, and no matter what happens it never goes down...

Here is my gradient descent function:

function weightVector = logisticWeightsByGradientDescentStochastic(trueClass,features) %% This function attemps to converge on the best set of weights for a logistic regression order 1 %% Input: % trueClass - the training data's vector of true class values % features %% Output: % weightVector - vector of size n+1 (n is number of features) % corresponding to convergent weights %% Get Data Size dataSize = size(features); %% Initial pick for weightVector weightVector = zeros(dataSize(2)+1, 1) %create a zero vector equal to size of number of features plus 1 %% Choose learning Rate learningRate = 0.0001; %% Initial Cost cost = logisticCost(weightVector, features, trueClass) %% Stochastic Gradient Descent costThresh = 0.05 %define cost threshold iterCount = 0; while(cost > costThresh) for m=1:dataSize(1) %for all samples %% test Statement curFeatures = transpose([1.0 features(m,:)]) %% calculate Sigmoid predicted predictedClass = evaluateSigmoid(weightVector , [1.0 features(m,:)] ) %% test Statement truth = trueClass(m) %% Calculate gradient for all features gradient = learningRate .* (trueClass(m) - predictedClass) .* transpose([1.0 features(m,:)]) %% Update weight vector by subtrating gradient from the old one weight vector weightVector = weightVector - gradient %% Re-evaluate Cost with new weight vector cost = logisticCost(weightVector, features, trueClass) if(cost < costThresh) break end iterCount = iterCount + 1 end %for m end %while cost > 0.05 weightVector iterCount end 

And here is my cost function:

function cost = logisticCost(weightVector, features, trueClass) %% Calculates the total cost of applying weightVector to all samples %% for a linear regression model according to %% J(theta) = -(1/m) sum[ trueClass(log(predictedClass) + (1-trueClass)log(predictedClass)] %% Input: % weightVector - vector of n+1 weights where n is number of features % plus 1 % features - matrix of features % trueClass - the training data's true class %% Output: % cost - the total cost dataSize = size(features); %get size of data errorSum = 0.0; %stores sum of errors for m = 1:dataSize(1) %for each row predictedClass = evaluateSigmoid(weightVector, [1.0 features(m,:)]); %evaluate the sigmoid to predict a class for sample m if trueClass(m) == 1 errorSum = errorSum + log(predictedClass); else errorSum = errorSum + log(1 - predictedClass); end end cost = errorSum / (-1 .* dataSize(1)); %multiply by -(1/m) to get cost end 

Both these seem perfectly fine, i cant imagine why my cost function would ALWAYS return infinite.

Here is my training data where the first column is the class (Either 1 or 0) and the next seven columns are the features i am trying to regress on.