Sebastian Raschka - 1 month ago 19x

Python Question

I could really use a tip to help me plotting a decision boundary to separate to classes of data. I created some sample data (from a Gaussian distribution) via Python NumPy. In this case, every data point is a 2D coordinate, i.e., a 1 column vector consisting of 2 rows. E.g.,

`[ 1`

2 ]

Let's assume I have 2 classes, class1 and class2, and I created 100 data points for class1 and 100 data points for class2 via the code below (assigned to the variables x1_samples and x2_samples).

`mu_vec1 = np.array([0,0])`

cov_mat1 = np.array([[2,0],[0,2]])

x1_samples = np.random.multivariate_normal(mu_vec1, cov_mat1, 100)

mu_vec1 = mu_vec1.reshape(1,2).T # to 1-col vector

mu_vec2 = np.array([1,2])

cov_mat2 = np.array([[1,0],[0,1]])

x2_samples = np.random.multivariate_normal(mu_vec2, cov_mat2, 100)

mu_vec2 = mu_vec2.reshape(1,2).T

When I plot the data points for each class, it would look like this:

Now, I came up with an equation for an decision boundary to separate both classes and would like to add it to the plot. However, I am not really sure how I can plot this function:

`def decision_boundary(x_vec, mu_vec1, mu_vec2):`

g1 = (x_vec-mu_vec1).T.dot((x_vec-mu_vec1))

g2 = 2*( (x_vec-mu_vec2).T.dot((x_vec-mu_vec2)) )

return g1 - g2

I would really appreciate any help!

EDIT:

Intuitively (If I did my math right) I would expect the decision boundary to look somewhat like this red line when I plot the function...

Answer

Those were some great suggestions, thanks a lot for your help! I ended up solving the equation analytically and this is the solution I ended up with (I just want to post it for future reference:

And the code can be found here

EDIT:

I also have a convenience function for plotting decision regions for classifiers that implement a `fit`

and `predict`

method, e.g., the classifiers in scikit-learn, which is useful if the solution cannot be found analytically. A more detailed description how it works can be found here.

Source (Stackoverflow)

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