I'm having some issues with the LinearLearner function in learning.py and I would like some help.
I noticed that the function does not seem to have pseudocode, even though in the script it is a standalone algorithm. At first it had some bugs and wouldn't execute, but when I fixed them (#408) it returned a result that confused me.
It does not use an activation function, so it quite probably does regression of sorts. But the given result is way off. This is what I did:
I tested the function on the Iris dataset. First, I converted classes names to numbers. So the targets are 0,1,2. Then I run the LinearLearner on the modified dataset. Every time it returns very large or very small numbers (eg. 1.00974582882851e+128). Even though technically I am asking for classification, it shouldn't produce numbers that off, since classes are converted to numbers in [0, 2].
About the same results are given when I remove one class to make this a binary problem. I even tested this with an activation function, but the numbers were so big that an OverflowError was raised.
Is there something I misunderstood about the function? If it is indeed wrong, since we don't currently have pseudocode, how should we act? Delete it? Try and make it work? Write a warning?
PS: The code I used to test the algorithm is the following:
def test_linear_learner():
iris = DataSet(name="iris")
classes = ["setosa","versicolor","virginica"]
iris.classes_to_numbers()
learner = LinearLearner(iris)
assert learner([5,3,1,0.1]) in range(len(classes))
I'm having some issues with the LinearLearner function in learning.py and I would like some help.
I noticed that the function does not seem to have pseudocode, even though in the script it is a standalone algorithm. At first it had some bugs and wouldn't execute, but when I fixed them (#408) it returned a result that confused me.
It does not use an activation function, so it quite probably does regression of sorts. But the given result is way off. This is what I did:
I tested the function on the Iris dataset. First, I converted classes names to numbers. So the targets are 0,1,2. Then I run the LinearLearner on the modified dataset. Every time it returns very large or very small numbers (eg. 1.00974582882851e+128). Even though technically I am asking for classification, it shouldn't produce numbers that off, since classes are converted to numbers in [0, 2].
About the same results are given when I remove one class to make this a binary problem. I even tested this with an activation function, but the numbers were so big that an OverflowError was raised.
Is there something I misunderstood about the function? If it is indeed wrong, since we don't currently have pseudocode, how should we act? Delete it? Try and make it work? Write a warning?
PS: The code I used to test the algorithm is the following:
def test_linear_learner(): iris = DataSet(name="iris") classes = ["setosa","versicolor","virginica"] iris.classes_to_numbers() learner = LinearLearner(iris) assert learner([5,3,1,0.1]) in range(len(classes))