Why my perceptron doesn't train well and produces bad results on test data?
Two points about the whole thing


You did not test yet. The point behind the training process is to make machine able to learn from the data conditioned on the ability of generalizing this to predicting samples which it has not seen before. Otherwise, the good training is actually overfitting. So here you trained on X and you need to create new samples and check the result to really call it testing (this is an introductory explanation).
Machine Learning is about features a lot! Playing with features and cleaning, modifying and filtering them is a key point. In your example, the last dimension of your 3d data is always 1. Does it distinguish anything? (in my course you get a complete explanation of this in the Lecture 2). So that feature (dimenstion, element of the vector) can/should be removed. To better understanding, imagine your 3d spread of the data. The z axis is always 1 which means the topology of points is what you see in x-y plain. So use only that one.