Linear Regression with Python numpy
linear-regression, numpy, python
Solution
As explained in the other answer `linalg.solve` expects a full rank matrix. This is because it tries to solve a matrix equation rather than do linear regression which should work for all ranks.
There are a few methods for linear regression. The simplest one I would suggest is the standard least squares method. Just use `numpy.linalg.lstsq` instead. The documentation including an example is here.
Problem
I'm trying to make a simple linear regression function but continue to encounter a numpy.linalg.linalg.LinAlgError: Singular matrix error Existing function (with debug prints): ``` def makeLLS(inputData, targetData): print "In makeLLS:" print " Shape inputData:",inputData.shape print " Shape targetData:",targetData.shape term1 = np.dot(inputData.T, inputData) term2 = np.dot(inputData.T, targetData) print " Shape term1:",term1.shape print " Shape term2:",term2.shape #print term1 #print term2 result = np.linalg.solve(term1, term2) return result ``` The output to the console with my test data is: ``` In makeLLS: Shape trainInput1: (773, 10) Shape trainTargetData: (773, 1) Shape term1: (10, 10) Shape term2: (10, 1) ``` Then it errors on the linalg.solve line. This is a textbook linear regression function and I can't seem to figure out why it's failing. What is the singular matrix error?